The Use of High Resolution Satellite Data (Eg IKONOS)

Home , EROS A, EROS B, Orthophoto, QuickBird, Radarsat, RemoteView

The use of high resolution satellite data (IKONOS) in the establishment and maintenance of an urban Geographical Information System.

Masters of Engineering (Surveying and Spatial Information Systems)

Eric W Richards

January 2009 Originality Statement

‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project’s design and conception or in style, presentation and linguistic expression is acknowledged.’

Signed…………………………………………………..

Date……………………………………………………..

i Acknowledgements

For an activity lasting this long it is needless to say there are a number of people who have played a part in its final completion, some of whom may or may not know they played a part.

I must first thank the staff at the University of NSW such as Brian Donnelly and Helve who accommodated my arrivals, stays and departures to and from Canberra. A major vote of appreciation goes to my supervisor Dr John Trinder who has shown significant patience and has always provided timely and well needed feedback and advice by correspondence, particularly after the unfortunate passing of Dr Ewan Masters early in my starting of this study. Next comes all the industry participates who replied to my questionnaire in particular Graham Boler from the Sutherland Shire Council and Chris Comer at ACT Urban Services, also Noel Ward and Neil Fraser at SKM who provided access to their practical experience. Finally the staff at Hobart City Council, Glenorchy City Council, Mackay City Council and at the Tasmanian Governments’ “The LIST” office who cheerfully supplied me with reference data and answered my questions.

The completion of this thesis would not have been possible without the scholarship support of the Cooperative Research Centre for Spatial Information (CRC-SI). I would like to thank in particular Graham Kernich and Michael Ridout for their support. In the same light Dr Clive Fraser of the University of Melbourne who suggested I apply for the CRC-SI scholarship and when he heard of my problems sourcing stereo high resolution commercial satellite imagery supplied the control point data over Hobart and facilitated access to the IKONOS sample imagery kindly supplied by Mr Gene Dial at Space Imaging. The final person in this administrative loop to thank is Ms Elizabeth Milne from the Department of Defence who on finding no previous precedence on allowing a Defence staff member to accept such a scholarship gave me permission.

On a personal front I would also like to thank and acknowledge a range of friends, family and work colleagues such as Dan Carmody, Richard Stanaway, Rae Absolom, Neil

ii Sparks, Mel Clark, Owen Moss, John Gregs, Graham McCloy and my brother Adrian who either allowed me to take time off or gave me quiet encouragement as well as the occasional “Man, are you still doing that?”.

Finally the last paragraph goes to my wife Louise for her love, patience and unbelievable tolerance; no words can ever describe or thank you for this devotion. I promise I will finish the house renovations now that you are no longer a “Masters widow”. But first we are going on a picnic. To Thomas and Lydia, you can now use the Dell.

iii Abstract

The past years has seen the advent of the availability of high resolution commercial satellite imagery. This study shows that whilst high resolution commercial satellite imagery is capable of producing reasonable spatial data both in quality and cost for use in an urban GIS the challenges of supplying this data commercially is not limited to simply the provision of the imagery.

Since a significant amount of work has been done by others to examine and quantify the technical suitability and limitations of high resolution commercial satellite imagery, this study examines the practical limitations and opportunities presented with the arrival of this new spatial data source. In order to do this a number of areas are examined; the historical development of the satellite systems themselves, the business evolution of the owning commercial ventures, Geographical Information Systems (GIS) data and service requirements for a diverse range of spatial data applications and finally the evaluation and comparison of the imagery as a spatial data source.

The study shows that high resolution commercial satellite imagery is capable of providing spatial data and imagery for a variety of uses at different levels of accuracy as well as opening up a new era in the supply and application of metric imagery. From a technical approach high resolution commercial satellite imagery provides remote access, one metre or better resolution, 11 bit imagery and a multispectral capability not previously available from space. Equally as challenging is the process or achievement in making the technical capability a reality in a commercial world requiring a financial return at all levels; from the image vendors to the spatial science professional providing a service to a paying customer. The imagery must be financially viable for all concerned.

iv Contents

Chapter 1 - Introduction………………………………………………………….. 1

1.1 - Background…………….…………………………………………………….. 1 1.2 - The Usability of High Resolution Commercial Satellite Imagery……...... 2 1.3 - What Data and Source are required?...... 4 1.4 - Selection of Collection Method..……………………………………...... 5 1.5 - Objectives of the Research…………..………………………………………. 6 1.6 - Overview of the Thesis………….…………………………………………… 7

Chapter 2 - High Resolution Commercial Satellite Imagery – Development and Characteristics………………………………………………………………... 9

2.1 - Available Satellite Sourced Data (1.0m resolution or better)…....………... 9 2.1.1 - Satellite Imagery Collection Technique……………………...... 9 2.1.2 - Distortions……….…………………………………………………... 12 2.1.2.1 - Observer Distortions………….……………...... 13 2.1.2.2 - Observed Distortions……………………………………… 14 2.1.3 - Accuracy……….……………………………………………………. 17

2.2 - Emergence of Commercial High Resolution Satellite Imagery….…...... 20 2.2.1 - Business Ventures………………………….………………………... 22 2.2.1.1 - ClearView and NextView Contracts………...... 22 2.2.1.2 - Digital Globe……………………………………...... 24 2.2.1.3 - Space Imaging…………………………………………….. 28 2.2.1.4 - OrbImage……….…………………………………………. 30 2.2.1.5 - Geoeye Inc………….……………………………………... 32 2.2.1.6 - ImageSat…………………………………………………... 33 2.2.1.7 - Centre National d'Etudes Spatiales (CNES)……...... 36

2.3 - Satellite Systems…………….………………………………………………... 39 2.3.1 - IKONOS…………………………………………………………….. 39 2.3.2 - EROS A……………………………………………………………... 40 2.3.3 - EROS B…………………………...….…....………………………... 41 2.3.4 - Quickbird…………….……………………………...... 42 2.3.5 - WorldView 1………………………………………………………... 43 2.3.6 - OrbView – 3………………………………………...... 44

2.4 - Terrestrial Based Methods…………………………………………………... 45 2.4.1 - Ground Survey………………………………………………………. 46 2.4.2 - Aerial Photogrammetry……………………………………………... 48 2.4.3 - Airborne Interferometric Synthetic Aperture Radar (InSAR)………. 51 2.4.4 - Light Detection and Ranging (LIDAR)………….………………….. 52

2.5 - Summary……………………………………………………………………... 55

v Chapter 3 - Imagery Applications in a Geographical Information System (GIS)………………………………………………………………………………... 57

3.1 - Introduction – Composition of a Geographical Information System (GIS)……………………………………………………………………………….. . 57 3.1.1 - GIS Data Acquisition………………..………………………………. 60 3.1.2 - GIS Preprocessing…………………………………………………... 62 3.1.3 - GIS Data Management………………………...... 62 3.1.4 - GIS Manipulation and Analysis……………………………………... 63 3.1.5 - Product Generation………………………………………………….. 64

3.2 - Imagery in Data Maintenance…………………………………...... 65

3.3 - Satellite Imagery versus Aerial Photography (Imagery)………………….. 65 3.3.1 - Cost of High Resolution Commercial Satellite Imagery...... 67 3.3.2 - Application of Imagery………………………….…………………... 68 3.3.3 - Processing Tools Required………………………………………….. 70

3.4 - Imagery Applications and Considerations…….……………...... 72 3.4.1 - Local Council Requirements………………………………………... 73 3.4.2 - Emergency Services…………….………………...... 79 3.4.3 - Public Information (Street Directories, Mapping, General Spatial Data, Analytical Applications)…………………….. 82 3.4.4 - Land Use Identification………………..……………………………. 85

3.5 - Summary……………………………………………………………………... 87

Chapter 4 - The Suitability of High Resolution Commercial Satellite Imagery as a Spatial Data Source……………………………………………...... 89

4.1 - Introduction….………………………………………………………………. 89 4.1.1 - Imagery and Study Area………………..…………………………… 90

4.2 - Ground Control Points Comparison…………..……………………………. 96 4.2.1 - Methodology………..……………………………………………….. 96 4.2.2 - Comparison of GPS RTK Coordinate Values to IKONOS Stereo Model Values……………….……………………………….. 96

vi 4.3 - Feature Interpretation and Extraction…………..…………………………. 104 4.3.1 - Development of a Civil NIIRS Rating for Imagery Comparison...... 104 4.3.2 - Factors Affecting the Interpretation of Features in High Resolution Commercial Satellite Imagery…..………………………………..…. 108 4.3.3 - Evaluation of Feature Interpolation and Extraction from IKONOS Imagery………….…………………………………. 111 4.3.3.1 - Urban Trial area at Binya St, Glenorchy, Hobart…….…… 112 4.3.3.2 - Central Business District (CBD) area of Hobart……….…. 126 4.3.3.3 - Summary Observations from Feature Interpolation and Extraction of Study Areas……………….…………… 141

4.4 - Digital Elevation Model (DEM) Creation……………..………...…………. 146 4.4.1 - Derivation of Digital Elevation Models………….…………………. 146 4.4.2 - Comparison of Digital Elevation Models…………..………...... 148

4.5 - Summary……….……………………………………………………...... 154

Chapter 5 - Conclusions and Recommendations………………………………... 157

5.1 - Conclusions………………….……………………………………………….. 157 5.1.1 - Objectives and Strategies…………..………………………………... 157 5.1.2 - Historical Development……………..………………………………. 159 5.1.3 - Business Evolution………………….………………………………. 160 5.1.4 - Geographical Information System (GIS) Requirements and Applications…………………………………………………..... 161 5.1.5 - Evaluation and Comparison of High Resolution Commercial Satellite Imagery as a Spatial Data Source….……………………… 162

5.2 - The Future and Recommendations………………..………………………... 162

References………………………………………………………………………….. 164

Appendix A - Masters Imagery Users Survey – 2005 Appendix B - Hobart Ground Control Points Comparison Appendix C - Compiled Drawings Extracted from IKONOS Stereo Imagery showing Features, Contours and Photo Locations

vii List of Tables Table 2.1 - Resolution Classification of Satellite Imagery………….……………… 9 Table 2.2 - Current and Planned High Resolution Satellite Images………………... 10 Table 2.3 - Summary of Distortions in High Resolution Satellite Imagery………… 15 Table 2.4 - Map Scale Estimates for the existing and proposed satellite data…….... 18 Table 2.5 - Quickbird Imagery Price Changes for 2002……………………………. 26 Table 2.6 - DigitalGlobe ClearView and Next View Contract Awards…………….. 27 Table 2.7 - Satellite Operational Parameters………………………………………... 38 Table 2.8 - Spacings between spot heights or measurements………………………. 47 Table 2.9 - Comparison of Spatial Data Sources………………………………...... 55 Table 3.1 - Cost Comparison of Satellite Imagery………………………………….. 68 Table 3.2 - AUSIMAGE 2007 pricing…………………...... 71 Table 3.3 - Features which can be captured from Quickbird imagery at national (UK) Scales…..………………………………………………………… 84 Table 3.4 - Summary of the classification results for an IKONOS image using maximum likelihood classification………...………..…………………. 87 Table 4.1 - IKONOS Imagery Geometric Collection Parameters………………….. 90 Table 4.2 - IKONOS Imagery Set Details………………………………………….. 91 Table 4.3 - Mean, Minimum and Maximum Component Differences and Vector Distance……………………………………………………. 97 Table 4.4 - Root Mean Square Error of Component and Vector Distance……...... 97 Table 4.5 - Definition of Civil NIIRS ratings…….………………………………… 106 Table 4.6 - Civil NIIRS ratings for imagery over Hobart………….………...... 107 Table 4.7 - Feature Classes identified in IKONOS Imagery………………...... 144

viii List of Figures Figure 2.1 - One single image covers a large area………….………………………. 11 Figure 2.2 - IKONOS image acquisition technique……………….………………... 12 Figure 2.3 - Distortions due to Orbit variations and Earth Shape and Relief………. 15 Figure 2.4 - Effect of Roll, Pitch and Yaw…………………………………………. 16 Figure 2.5 - Difference between the Physical Earth, Tangent Plane, Geoid and Ellipsoid………………………………………………………….. 16 Figure 2.6 - IKONOS Satellite…………………………………………...... 39 Figure 2.7 - IKONOS Imagery - Dalrymple Bay, Queensland, Australia. (May 23, 2005)……………………………………………... 39 Figure 2.8 - EROS A Satellite…………………………………….…...... 40 Figure 2.9 - EROS Imagery - Adelaide Cricket ground, 5th March 2002……...…... 41 Figure 2.10 - EROS B Satellite…………….……………………………………….. 41 Figure 2.11 - EROS Imagery - Circular Quay, Sydney, 17 May 2006………...... 42 Figure 2.12 - Quickbird Satellite……………………………………...... 42 Figure 2.13 - Quickbird Imagery - Singapore, 21 March 2004………………...…... 43 Figure 2.14 - WorldView – 1 Satellite…………….………………………………... 43 Figure 2.15 - WorldView – 1 Imagery - Sydney, 31 December 2007……...... 44 Figure 2.16 - OrbView–3 Satellite…………….……………………………………. 44 Figure 2.17 - Orbview Imagery……………….…………………………………….. 45 Figure 2.18 - Typical Ground Survey Party…………………...... 46 Figure 2.19 - Typical Detail Survey………………………………………………… 47 Figure 2.20 - Aerial Stereo Photography collect…………………...... 49 Figure 2.21 - Concept of IFSAR Mapping…………………………………………. 52 Figure 2.22 - Intermap’s LearJet 36 STAR-3i System……………………………... 52 Figure 2.23 - LIDAR system………………………………………………………... 53 Figure 2.24 - LIDAR feature collection methodology……………………………... 54 Figure 3.1 - Relationship of Data in a GIS…………………………………………. 59 Figure 3.2 - Data flow of a Geographic Information System (GIS)………………... 61 Figure 3.3 - Example of non quantitative reporting…………………...... 69 Figure 3.4 - Inability to identify assets in high resolution satellite imagery…….….. 75

ix Figure 3.5 - Level of detail from aerial photography…….…………………...... 76 Figure 3.6 - Mackay City Council Urban Aerial Photography……………………... 76 Figure 3.7 - Quickbird Stereo Pair over Sydney……………….…...... 78 Figure 3.8 - Example of Sentinel Product……………………...…………………… 81 Figure 3.9 - Complexity of Scenes due to Ground Cover (IKONOS imagery)….…. 86 Figure 4.1 - Extent of IKONOS Study Imagery over Hobart……………….……… 93 Figure 4.2 - Urban area at Binya St, Glenorchy, Hobart…………...... 94 Figure 4.3 - CBD Central business district (CBD) of Hobart………………………. 95 Figure 4.5a - Vector Distance against Ellipsoidal Height…………….…………….. 99 Figure 4.5b - Vector Distance against Easting…………….………………………... 100 Figure 4.5c - Vector Distance against Northing……………………………………. 100 Figure 4.5d - Z (m) against Ellipsoidal Height……….…………………………… 101 Figure 4.5e - Z (m) against Easting……………….………………………………. 101 Figure 4.5f - Z (m) against Northing…………….………………………………... 102 Figure 4.6 - Vector distances between IKONOS stereo model control points and the RTK GPS reference data……………..………………...... 103 Figure 4.7 - Change in roof line, not apparent on IKONOS imagery or feature extraction…………………………………………………… 112 Figure 4.8 - Building Data from Glenorchy City Council………….………………. 113 Figure 4.9 - Features Extracted from IKONOS Imagery…………………………... 113 Figure 4.10 - Stormwater drains and Survey Mark………………….……………… 114 Figure 4.11 - Example of track and steepness……………………………………… 115 Figure 4.12 - Track Detail from Glenorchy City Council…………...... 116 Figure 4.13 - Track Detail extracted from IKONOS stereo imagery…….…………. 116 Figure 4.14 - Example of power line visible only by clearing not lines and poles……………………………………………………………... 117 Figure 4.15 - Example of water main, corresponds with Glenorchy City Council plans…………...... 118 Figure 4.16 - Glenorchy City Council plan detail of water main……….…………... 119 Figure 4.17a - IKONOS 8 bit Imagery……………...... 119 Figure 4.17b - IKONOS 11 bit Imagery……………………………………………. 119

x Figure 4.18 - Example of tops of reservoirs…….…………………………………... 120 Figure 4.19 - Features extracted from 11bit IKONOS Stereo Imagery………….…. 121 Figure 4.20 - Example of roof tops and their roof line………………...... 121 Figure 4.21 - Example of number of power poles and lines that cannot be seen on IKONOS imagery……….…………………………………………… 124 Figure 4.22 - New construction (mobile telephone relay station)…….……...... 125 Figure 4.23 - Example of new construction (children’s’ playground)…….………... 125 Figure 4.24 - Example of detail…………………………………………...... 126 Figure 4.25 - Hobart City Council data……………………………………...... 127 Figure 4.26 - Features extracted from 11bit IKONOS Stereo Imagery……….……. 128 Figure 4.27 - Hobart Midcity Hotel – example of roofline and level of detail extracted possible……………………………………………… 129 Figure 4.28 - Hobart City Council data……………………………………...... 130 Figure 4.29 - Features extracted from 11bit IKONOS Stereo Imagery………...... 130 Figure 4.30 - Example of roof line………….………………………………………. 131 Figure 4.31 - Hobart City Council data……………………………………...... 132 Figure 4.32 - Features extracted from 11bit IKONOS Stereo Imagery…….………. 132 Figure 4.33 - Example of building detail. This multistory car park does not appear as one from a vertical perspective………………...... 133 Figure 4.34 - Hobart City Council data…………………………...... 134 Figure 4.35 - Features extracted from 11bit IKONOS Stereo Imagery…….………. 134 Figure 4.36 - Example of roof line and detail from top of multistory car park...... 135 Figure 4.37 - Hobart City Council data…………………………...... 136 Figure 4.38 - Features extracted from 11bit IKONOS Stereo Imagery…………….. 136 Figure 4.39 - Example of roof top detail and the possibility of to misinterpretation……….…………………………………………….. 137 Figure 4.40 - Example of detail and interpretability…….………...... 138 Figure 4.41 - Example of roof line and detail………….…………………………… 139 Figure 4.42 - Hobart City Council data………….…………………………...... 140 Figure 4.43 - Features extracted from 11bit IKONOS Stereo Imagery………...... 140 Figure 4.44a - Sewerage Manhole……………………………………...... 141

xi Figure 4.44b - Water Service – Size approx 0.15m……………...…………………. 141 Figure 4.44c - IKONOS 8 bit Imagery……………………………………...... 141 Figure 4.44d - IKONOS 8 bit Imagery…………………………………………….. 141 Figure 4.45 - 1:7000 Sullivans Cove Orthophoto……….…………………...... 142 Figure 4.46a - CBD Study Area: 5m IKONOS derived contours and 5m Hobart 12.5m DTM contours……………………………………………….. 149 Figure 4.46b - CBD Study Area: 5m IKONOS derived contours and 5m 100m GDA DTM contours………………………………………………… 149 Figure 4.47a - Urban Study Area: 5m IKONOS derived contours and 5m Hobart 12.5 DTM contours…………………………………...... 149 Figure 4.47b - Urban Study Area: 5m IKONOS derived contours and 5m 100m GDA DTM contours……………………..………………………….. 149 Figure 4.48a - CBD Longitudinal Section – Liverpool St……………...... 150 Figure 4.48b - CBD Longitudinal Section – Macquarie St…………………………. 151 Figure 4.49a - Urban Longitudinal Section – South………………………………... 152 Figure 4.49b - Urban Longitudinal Section – North………………………………... 153 Figure 4.50 - Geoeye-1 Imagery – Cambridge, Massachusetts, 18 October 2008…. 155

xii CHAPTER 1

INTRODUCTION

1.1 Background

In 1987 the Soviet Union made an unexpected decision to allow images taken with the Cosmos KFA-1000, MK-4 and MFK-6 satellites, all with resolutions of approximately five meters, (Steinberg 1998) to be sold on the world-wide market. This was then followed in 1992 by another unexpected decision by the Russian government to allow the sale of imagery with a resolution of two to three metres taken with the KVR-10000 and KFA-3000 satellites. These events could be considered to be the trigger for developments in the area of high resolution commercial satellite imagery (Petrie 1999).

Since then, the last eight years have seen the advent of commercially available high resolution satellite imagery from satellites such as IKONOS, which was launched by Space Imaging from the United States (U.S.) in September 1999 to the more recent Worldview 1 and Geoeye -1 satellites that were launched on the 18 September 2007 and 6 September 2008. In this period of eight years the new generation of imaging satellites have taken the resolution of images from space from the initial quantum leap of barely one metre to systems that promise to deliver resolutions of half a metre or less.

During this time a range of countries as well as the United States of America such as Israel, Russia and Korea have deployed commercial satellites capable of obtaining high resolution images of the order of one to two metres. This has ensured that the market has had competition and variety.

Despite the promises given by the operators of these satellites the revolution in the use of space based imagery anticipated from the availability of high resolution satellite imagery has not occurred. In fact Government organisations (particularly the U.S. government)

1 remain the chief customers of U.S. companies marketing high resolution commercial satellite imagery. The existence for operators of more traditional aircraft based imaging systems such as metric aerial photography and the latest airborne scanners such as the Leica ADS40 continue to provide data with an accuracy, resolution and value that still cannot be achieved from images sourced from satellites.

The competition that still exists from traditional systems has not prevented the growth of the industry. Recent world events such as the September 11, 2001 terrorist attack on New York and Washington in the U.S and the subsequent “War on Terror” leading to the ongoing conflicts in both Iraq and Afghanistan have ensured a need for spaced based imagery. This is because it provides a “no risk” look into the Worlds trouble spots for customers such as the military, aid organisations and the media.

In addition to the world’s troubles, a good global economy over recent years has meant a need for spatial data to assist in new infrastructure projects, particularly in third world nations. After long periods in the late 1980s and 1990s when economic downturn meant little money was available for the mapping of the environment, many countries and organisations relied on older spatial data to fulfil needs. The improved economic times coincided with the launch of the first commercial satellite capable of high resolution imagery (IKONOS in 1999) and readily provided a market and need that could meet for the first time with a system that collected spatial data and produced a product without ever having to visit an area. This was a distinct advantage for infrastructure projects and military planners as not only did it reduce risks to staff, but also removed or reduced the need to deploy expensive survey parties and aircraft to remote locations.

1.2 The Usability of High Resolution Commercial Satellite Imagery

In 1998 Steinberg reported that the French publication, Air and Cosmos Aviation International stated:

2 “By the early years of the next millennium, the number of countries owning earth observation satellites will have doubled… Earth observation is going to enter a new phase this year, with more and more satellites going up, entry into service of the first high-resolution and hyper-spectral instruments as well as deployment of the first private commercial systems and new systems co-financed by government and private industry. This should lead to broad changes of the landscape in this sector of activity over the next 10 years. The world market, which has seen no growth in the last decade, should finally see that long hoped for expansion.”

The advent of high resolution commercial satellite imagery was lauded by the vendors and supporters of such ventures as a revolution in the acquisition of metric imagery and spatial data. It soon became apparent that this was not the case and with all new technologies it had to find itself a place within the spatial data market.

The first years saw a rapid expansion and creation of authorised resellers of the imagery but this was not to last as the realities of the market place started to occur. Recently opened offices closed, as in the case of the Australian Space Imaging office. In answer to these problems modification of licencing agreements and a series of special price offers occurred. These were intended to attract new customers and woo back initial customers lost through the initial restrictive licencing arrangements, delayed imagery and the combination of high costs and lower resolution when compared to aerial photography.

From all this the high resolution commercial satellite imagery market has buoyed and found its place. It is no longer common to hear how good the resolution or what an alternative satellite imagery is, when compared to aerial photography. It is now a case of how it complements and supplements aerial photography in cases such as providing a low cost supplement in intervening years between higher resolution aerial photography surveys in a mapping programme. High resolution commercial satellite imagery is also now being used as a common option in rapid reporting and assessment over isolated areas or disaster zones where it is either too difficult, dangerous or politically unacceptable to deploy aircraft or ground crews.

3 From a beginning that promised everything to a number of years attempting to find a place in the spatial data market, high resolution commercial satellite imagery has filled a niche. This niche is being in between good resolution aerial photography and the requirement to easily obtain reasonable imagery over difficult areas or situations, as well as basic supplementation of aerial photography in a mapping programme.

1.3 What Data and Source are required?

Spatial data is information that defines the geographic location as well as description of features and boundaries on Earth. They can be either natural features, for example hills and rivers, or constructed features such as buildings, pipelines or roads. Their position can be acquired and stored as coordinates and together with their topology (vectors) as well as imagery (raster). The spatial data required and the purpose for what it is intended still dictates the methodology to be used in obtaining the information.

If, for example, imagery is required to monitor and provide base information for town planning or utilities work, aerial photography or imaging are still the preferred solution. If it is not possible to access the required airspace due to political, financial or isolation issues, or persistent cloud cover makes it impossible or economically unviable to use aircraft, high resolution satellite imaging could be the solution. High resolution satellite images provide good planning scale data (1:5 000 to 1:10 000), without the requirement to physically access the site or to maintain a field or aerial survey party on location until atmospheric conditions are favourable.

Spatial data has become indispensable to the modern world. Not only is it used for scientific purposes but also to:

(a) Map the Earth above and below sea level;

4 (b) Prepare navigation charts for air, sea and land ;

(d) Develop and maintain databases of land use and natural resource information;

(e) Determine measures on the size, shape, gravity and magnetic fields of the Earth (geodesy)

(Wolf: 2002: 9)

In today’s world that has taken for granted modern technology such as Global Navigation Satellite Systems (GNSS), Geographic Information Systems (GIS) and mobile phones, spatial data is critical for the success of these technologies. Without reliable and accurate spatial data against which to represent any location based service, for example street mapping showing latest road changes, the sophistication of the technology is worthless.

1.4 Selection of Collection Method

Before commencing the collection of purpose specific spatial data, the following need to be considered (Wolf & Brinker: 1989: 310):

(a) Purpose of the survey or data;

(b) Map or data use (accuracy required);

(d) Output format (such as digital data, orthoimage or hardcopy map);

5 (e) Contour interval or Digital Terrain Model (DTM) post spacing;

(f) Cost;

(g) Equipment and time available to collect data;

(h) Experience and training of staff involved;

These factors will clearly influence the method chosen to collect the data, the most important being cost and accuracy required. An example of how these factors together play a part, is a mapping task located in an undeveloped region of the world. In such a case, availability of funds could allow for a solution based on sophisticated technology making the project simple to achieve for skilled technicians in a short timeframe. A sophisticated solution could also be suitable in the same location if the concern was a shortage of trained and experienced staff as the training liability could be lower due to the automation and less requirement for labour. Though what must also be considered are the benefits of a simple technical solution being labour intensive but providing an opportunity for skilling a larger workforce due to the lower labour and equipment costs.

1.5 Objectives of the Research

This research does not dwell on improving the technical level of the data or imagery sourced from high resolution commercial satellites, but demonstrate its practical application and implementation. This has be done to provide both a background knowledge to existing spatial science professionals on possible use (both in private and public sector) and also highlighting its use by other professions or industry areas such as engineers, local councils or emergency services which could benefit from the services provided by spatial science professionals in this area.

The main objectives of this study are to:

6 (a) Provide an understanding of the imagery collection techniques and methodology of high resolution commercial satellites.

(b) Give an understanding of the way the commercial and government interests have influenced the development and sustainability of high resolution commercial imaging satellites, particularly in the context of U.S. companies and the U.S. government.

(c) Allay practical concerns in the application of using the imagery in regards to cost, equipment (hardware and software) and training when compared to more traditional or terrestrial means such as aerial photography or field survey techniques.

(d) Give an assessment of the imagery’s ability to represent features and phenomena on the ground to a practical level in a variety of applications.

(e) Provide a guide to possible uses and disadvantages of such imagery away from the traditional qualifiers used by the spatial industry such as scale and planimetric accuracy and more to its functionality in fulfilling needs.

1.6 Overview of the Thesis

This thesis has been organised into the following chapters. Chapter Two provides an explanation of high resolution imagery from commercial satellites in regards to how the imagery is collected and factors that need to be considered during the acquisition of the imagery, followed by how these can impact on the accuracy and hence usability of the imagery. Further since collecting satellite imagery is not the only issue but also there is the commercial aspects, Chapter Two then provides an investigation of the business ventures behind the current and future satellite systems, a comparison of the satellite

7 systems and imagery available, and then the infrastructure in regards to hardware, software and training required in order to successfully exploit the imagery at the user or customer level. Chapter Three defines an urban Geographic Information System (GIS) within the context of various applications and some of the potential advantages and limitations of using high resolution commercial satellite imagery within a GIS in organisations with varying requirements (such as local councils and emergency services).

A practical investigation and evaluation of a sample set of high resolution commercial satellite imagery is conducted in Chapter Four in order to quantify the practicalities and versatility of using the imagery since ultimately the purpose of the spatial sciences is to represent and define the shape of the earth and describe complex features upon it. This chapter describes how well the imagery is capable of satisfying this.

Finally Chapter Five provides a summary, conclusion and recommendations for the usability and applicability of high resolution commercial satellite imagery.

8 CHAPTER 2

HIGH RESOLUTION SATELLITE IMAGERY - DEVELOPMENT AND CHARACTERISTICS

2.1 Available Satellite Sourced Data (1.0m resolution or better)

There are a variety of Earth observation satellites in orbit capable of imaging objects on the earth with various degrees of resolution. Hart and McCleave (2002), provide a classification of satellite imagery as shown in Table 2.1.

Classification Resolution Examples Very Low 300m NOAA (Oceanographic and Weather) Low 30m < 300m Landsat MSS Medium 3m < 30m Landsat TM, SPOT High 0.5m < 3m Quickbird, IKONOS Table 2.1 - Resolution Classification of Satellite Imagery

Since 1999 a number of commercial high resolution imaging satellites have been launched or are planned to be launched in the near future as displayed in Table 2.2, with further detail being provided in Section 2.3.

2.1.1 Satellite Imagery Collection Techniques

Most of the high resolution Earth observation satellites referred to above, such as IKONOS and Quickbird, use the “pushbroom” imaging technique which is based on a linear array sensor that collects images by sweeping over the terrain in a similar manner to a broom. The width of the array provides coverage across the satellite track, whilst the motion of the satellite provides coverage along track. This method of collecting is in contrast to “staring” or

9 “spinning” sensors. A “staring” sensor points to an area and instantaneously collects images of the area covered by the array. “Spinning” sensors rotate as they collect data of an area.

Resolution Life Satellite Launch Date Company/Country (m) (Pan) Expectancy IKONOS 24/9/1999 Geoeye (US) 1.0 > 8.5 years EROS A 5/12/2000 ImageSat (Israel) 1.8 10 years Quickbird 18/10/2001 Digitalglobe (US) 0.6 Orbview - 3 26/6/2003 Geoeye (US) 1.0 At least 5 years EROS B 25/4/06 ImageSat (Israel) 0.7 10 years Resurs DK-1 NTs OMZ (Russia) 1.0 3 years 15/06/06 (01-N5) Korean Aerospace 1.0 KOMPSAT-2 28/07/06 Research Institute (KARI) (Korea) IRS Cartosat 2 10/01/07 India 1.0 Worldview 1 18/09/07 Digitalglobe (US) 0.5 7.25 years

GeoEye-1 1st half 2008 Geoeye (US) 0.41 7 years. WorldView -2 01/07/08 Digitalglobe (US) 0.5 7.25 years EROS C 21/03/08 Israel 0.7 10 years Pleiades-1 End of 2009 France 0.7 5 years Pleiades-2 March 2011 France 0.7 5 years Table 2.2 – Current and Planned High Resolution Satellite Images

The “pushbroom” sensor has the advantage that it can scan the terrain with a certain width as the satellite orbits the earth (as opposed to a “staring” sensor which acts as a shutter camera does) and hence collects images of larger areas than an imaging system based on a sensor area array. This technique reduces the need for mosiacing in order to produce images of large areas as shown in Figure 2.1 (DigitalGlobe Website: 2002). Typically the size of the image is limited by the satellite’s onboard electronic storage capability.

10 Figure 2.1 – One single image covers a large area (Source: DigitalGlobe Website: 2008)

In addition, these satellites have highly manoeuvrable bodies or buses, which are sometimes referred to as “agile buses”. As the imaging system does not move independently of the satellite bus, the agility allows the satellite to tip and tilt in order to point the sensors at specific areas. This pointing system is based on reaction wheels that can accelerate and decelerate to change the satellite’s orientation (roll and pitch). In order to prevent any impact of these movements on the imagery there is a “settle time” measured in milliseconds between completion of the motion of the satellite bus and turning on the imaging system to prevent image jitter or blurring (DigitalGlobe Website: 2002).

This high level of maneuverability achieved by the “agile bus” allows the satellites to collect stereo images along their orbit path by pitching the satellite forward and backwards (as in Figure 2.2), a technique that is known as “in-track” acquisition. This is opposed to “cross- track” stereo, where the images in the stereo pair are collected on separate orbits. These orbits are required to occur on different days which may result in unsuitable weather conditions for the second acquisition date or inconsistencies between the two images of the stereo pair. As a result the main advantage of using the “in-track” stereo collect is that it shares many benefits of traditional airborne collection such as timeliness, constant illumination and constant ground conditions (Gonzalez: 1998: 12).

11 Zhou and Li (2000) provide a good description of the example of stereo collection using the agile bus of IKONOS (referred to in more detail in Section 2.3.1). The IKONOS satellite travels at 7 km per second at an altitude of 680 km. For the forward looking image acquisition, it pitches forward (fore-looking) 26° to begin collecting data (imagery) after which it pitches to nadir viewing and collects data over the same ground area. Once this is completed it pitches aft and collects the same area for the third time. Stereo pairs created from forward, nadir and aft-looking ensure high quality collection of imagery as the images are acquired under nearly the same ground and atmospheric conditions, with convergence angles of 26° or 52° (Zhou & Li: 2000: 1104).

Figure 2.2 – IKONOS image acquisition technique (Dial & Grodecki:2003:11)

As with stereo aerial photography, the collection of stereo images from an imaging satellite allows for the creation of digital terrain models and the extraction of features with three dimensional coordinates (x, y and z). Such a capability provides versatility in the use of high resolution satellite imagery, as it allows for the creation three dimensional spatial data without the use of ground control, thus eliminating the need to visit a site to collect spatial data such as digital elevation models (DEM) and cultural features.

2.1.2 Distortions

Toutin (2002) reports that geometric distortions that need to be corrected in high resolution

12 satellite imagery can be placed into two general categories:

(a) Observer Distortions: due to the platform or satellite itself in motion, or the image sensor on board the satellite.

(b) Observed Distortions: due to the Earth’s atmosphere and the shape of the Earth.

Also, as most Geographical Information Systems (GIS) end user applications are represented and conducted in referenced topographic maps coordinate systems, distortions due to the map projection being used needs to be corrected (Toutin: 2002).

2.1.2.1 Observer Distortions

Distortions caused by the platform are mainly related to variations of the satellite’s elliptic orbit around the Earth. The degree to which these variations will affect the image being obtained is a function of the duration of image acquisition as well as the size of image being sourced. Variations of the elliptic movement of the platform have the following impacts on image geometry (Toutin: 2002):

(a) Altitude variations caused by the Earth’s curvature and topography affects the focal length, changing the pixel spacing;

(b) Attitude variations of the roll (x), pitch (y) and yaw (z) axes cause changes in the orientation and shape of high resolution images;

There are distortion due to the imaging sensor (Toutin: 2002):

(a) Calibration parameters such as focal length and the instantaneous field of view (IFOV).

13 (b) Panoramic distortions in combination with the oblique viewing system, Earth curvature and the topographic relief change the ground pixel sampling interval along the column or across track direction of image scan.

2.1.2.2 Observed Distortions

According to Toutin (2002) the Earth’s motion and topographic features can cause the following distortions in satellite images:

(a) The Earth’s rotation generates lateral displacements across track depending on the latitude.

(b) The Earth’s curvature creates variation in the image pixel spacing.

Map projections cause the following deformations (Toutin: 2002):

(a) The approximation of the geoid by the chosen reference ellipsoid.

(b) The projection of the reference ellipsoid onto the plane of the chosen projection (tangent plane).

Work undertaken, as detailed later in Chapter 4, indicates that whilst geometric distortions can occur, there are no significant unpredictable influences in x, y or z directions that could be attributed to Observer or Observed distortions. The only distortion that could be detected was a directional bias in a west-north-west direction, but this was consistent across the entire sample stereo pair.

14 Observer Distortions Impact on image geometry due to variation of the elliptic movement. (a) Altitude variation in conjunction with focal length and the effect of the Earth’s curvature and topography changing the pixel spacing. (b) Attitude variations of the roll, pitch and yaw axes cause changes in the orientation and shape of high resolution images. (c) Velocity variations affect the line spacing or can create line gaps or overlaps in the resultant images. Distortions due to the imaging sensor. (a) Calibration parameters such as focal length and the instantaneous field of view (IFOV). (b) Panoramic distortion in combination with the oblique viewing system, Earth curvature and the topographic relief, changes the ground pixel sampling along the column or direction (across track) of image scan. Observed Distortions Distortions due to Earth’s motion and topographic features. (a) The Earth’s rotation generates lateral displacements across track depending on the latitude. (b) The Earth’s curvature creates variation in the image pixel spacing. (c) The topographic relief generates a parallax in the scanning azimuth. Deformations caused by map projections. (a) The approximation of the geoid by the chosen reference ellipsoid. (b) The projection of the reference ellipsoid onto the tangent plane. Table 2.3 – Summary of Distortions in High Resolution Satellite Imagery

Effect of Earth Curvature

Effect of Topography creates a parallex in the scanning direction Earth

Effect of Satellite Orbit Altitude Change

Figure 2.3 – Distortions due to Orbit variations and Earth Shape and Relief

15 Yaw (z)

Roll (x)

Pitch (y)

Image Satellite Track Roll

Yaw

Pitch Note: (1) Rotation around the x axis is roll (2) Rotation around the y axis is pitch (3) Rotation around the z axis is yaw

Figure 2.4 – Effect of Roll, Pitch and Yaw

Physical Tangent Plane Earth of the chosen map projection

Ellipsoid Geoid

Figure 2.5 – Difference between the Physical Earth, Tangent Plane, Geoid and Ellipsoid

16 2.1.3 Accuracy

To enable imagery to be used for practical purposes such as mapping, infrastructure planning or a Geographical Information Systems (GIS) base, a qualitative accuracy value needs to be able to be determined for it. Gonzalez (1998) by an empirical method, calculated that the imagery resolution required for a specific map scale can be determined by:

GRD = (1/5) x 0.25 mm x Map Scale = 5 x 10-5 x Map Scale Factor (in metre) (2.1)

where GRD = Ground Resolution Distance

In using this formula it is assumed that the smallest feature on a map has a dimension of at least 0.25mm at map scale and that for an object to be identifiable on imagery with medium contrast conditions, it must be imaged by at least one-fifth of the reported image ground sampling distance (GSD) (Gonzalez:1998).

At this point it is worth explaining the difference between spatial resolution and Ground Sampling Distance (GSD). Spatial resolution and GSD when referred to imagery have two different meanings. Spatial resolution is defined as the size of the smallest object or distance that can be resolved in the image (Lillesand et al: 1979). This differs from GSD which is the area of ground covered by one pixel. This means that even though an image may have a GSD of one metre, the smallest object (or spatial resolution) may be greater than one metre (Poon et al: 2006)

An initial investigation using the above expression and based purely on vendor’s data, shows that typical map scales that could be derived from the satellite systems are as shown in Table 2.4.

The estimates in Table 2.4 are based purely on the ground resolution of each of the satellites. In producing data from such imagery standard scales would be used such as 1:10 000, 1:25 000, 1:50 000 and 1:100 000. This means that when outputting such data, the standard scales are derived from the data directly.

17 Resolution Satellite Map Scale (m) (Pan) IKONOS 1.0 1: 20 000 EROS A 1.8 1: 36 000 Quickbird 0.6 1: 12 000 Orbview - 3 1.0 1: 20 000 EROS B 0.7 1: 14 000 Resurs DK-1 1.0 1: 20 000 (01-N5)

KOMPSAT-2 1.0 1: 20 000 IRS Cartosat 2 1.0 1: 20 000 WorldView -1 0.5 1: 10 000 GeoEye-1 0.41 1: 8 200 WorldView -2 0.5 1: 10 000 EROS C 0.7 1: 14 000 Pleiades-1 0.7 1: 14 000 Pleiades-2 0.7 1: 14 000 Table 2.4 Map Scale estimates for the existing and proposed satellite data.

The use of scale to provide an indication of the suitability and accuracy of imagery can cause uncertainty or misunderstanding in its application. From Table 2.4 above the largest scale that could be derived from the current high resolution commercial satellite images is 1:10 000, but typical scales being quoted from various sources range from 1:10 000 to 1:20 000.

The following example using high resolution commercial satellite imagery shows that the final scale achieved is dependent on the outcome or application required. In Vietnam, Hanh & Tuan (2005) reported that by ortho-rectifying Quickbird satellite imagery it can then be used for topographic mapping updates to recognise new features in a map sheet. According to their results, Hanh & Tuan established that:

(a) Quickbird imagery can be used as an alternative to aerial photographs for updating topographic maps;

(b) This method can be applied for the scale 1:5000 and smaller, within permitted

18 errors.

This accuracy was achieved by orthorectifying the satellite image and then overlaying it on the original topographic map to assess the accuracy. They found that changes could be clearly seen in roads, watercourses and buildings and these were updated on the topographic map using the orthorectified Quickbird imagery. Other information that needed to be updated was collected in the field, based on changes which were found on the satellite image as primary information.

From this they determined that in the case of work in Hanoi, Quickbird satellite imagery could be used for topographic map revision with periods of 2 to 3 years between image acquisition, instead of periods of 5 to 6 years when using aerial photographs.

Fraser et al (2002) also determined that by using IKONOS imagery with affine sensor models, planimetric accuracy of 0.3 - 0.6 m and height accuracy of 0.5 - 0.9 m are readily possible with only 3 to 6 ground control points. It is interesting to note that they state that there are some limitations in the suitability of the imagery of 1m resolution because of the variability of image quality from scene to scene. This will limit its application for such tasks as building reconstruction and city modeling. The problems encountered by Fraser et al include the user’s inability to control the date and time of image collection, the specification of favourable sun angles and atmospheric conditions. Areas more specific to their investigation included the identification of all buildings of a certain size and the reconstruction of their form without excessive generalisation.

From this discussion it can be concluded that whilst it is possible to determine the positional accuracy of the imagery, it is more difficult to determine the suitability of the imagery for a specific application in terms of identifying features of interest. This issue will be furthered discussed in Chapter 3.

19 2.2 Emergence of Commercial High Resolution Satellite Imagery

The end of the 1990s saw commercial high resolution satellite imagery becoming available to the wider community. Up until this time the use of imagery taken from such space platforms was available only to government agencies and authorities. The successful launch of such platforms as IKONOS, EROS A and Quickbird saw the start of a new era in space remote sensing as systems now existed that provided images comparable with those available from small scale aerial photography in terms of accuracy and potentially price.

According to Li (1998), high resolution imaging satellites have four advantages. They;

(1) Provide the highest resolution satellite data available to the civilian mapping community. This could then be better than small scale aerial photography.

(2) Comprise an extremely long camera focal length of ten metres for capturing terrain relief information from satellite orbit.

(3) Include fore, nadir and aft-looking linear CCD arrays supplying in-track stereo strips.

(4) Have a base-height (sensor baseline vs. orbit height) ratio of 0.6 and greater, which is similar to that of aerial photographs.

Petrie (1999) indicates that the trigger for developments in the area of commercial high resolution satellite imagery was the unexpected decision by the Soviet Union in 1987 to allow images taken with the Cosmos KFA-1000, MK-4 and MFK-6, with resolutions of approximately 5 metres (Steinberg 1998), to be sold on the world-wide market. This was followed in 1992 by another unexpected decision by the Russian government allowing the sale of imagery with a ground resolution of 2 to 3 m taken with the KVR-1000 and KFA- 3000 cameras (Petrie 1999).

Before the release of the Russian imagery President Carter had, under Presidential Directive 37 in 1978, limited the ground resolution of American space imagery to 10m. The initial Russian decision described above resulted in an easing of this restriction by the Reagan administration. In March 1994 President Clinton then issued Presidential Directive 23 in

20 which the development of commercial satellites capable of producing imagery down to the 1m ground resolution was permitted. A consequence of this directive was that licences were issued to several American companies to commercialise hardware and software previously supplied to United States military space defence programmes (Petrie 1999).

Subsequent to Presidential Directive 23, President Clinton then signed a further Executive Order on 24 February 1995, directing the declassification of intelligence imagery acquired by the first generation of United States photo-reconnaissance satellites. As a result of this Executive Order more than 860,000 images of the Earth's surface, collected between 1960 and 1972, were declassified with the majority now being available for purchase through the United States Geological Survey or USGS (United States Geological Survey: 2008)

Steinberg (1998) reports that at the time President Clinton issued his Presidential Directive 23, American industry leaders were claiming that the Soviet military threat had been replaced by a commercial threat from French and Russian companies. These companies were preparing to enter the high-resolution space image market with sales that had already reached $700US million in 1994. Steinberg goes on to say that according to the French publication, Air and Cosmos Aviation International:

“By the early years of the next millennium, the number of countries owning Earth observation satellites will have doubled… Earth observation is going to enter a new phase this year, with more and more satellites going up, entry into service of the first high-resolution and hyper-spectral instruments, as well as deployment of the first private commercial systems and new systems co-financed by government and private industry. This should lead to broad changes of the landscape in this sector of activity over the next 10 years. The world market, which has seen no growth in the last decade, should finally see that long hoped for expansion.”

The change in the US restrictive policy also allowed US firms to compete in and capture a large portion of this market, enabling the US to maintain some control over distribution of high-resolution images, particularly in a crisis. In addition, these developments occurred at a time when the US defence budget was reduced. Hence the development of commercial high- resolution imaging systems was seen as a way of allowing the major firms that had mastered this technology to maintain capabilities in this strategic area, without the massive budgets

21 provided previously by defence agencies (Steinberg 1998).

Even though the relaxation of the strict US regulations meant the beginning of a new age in satellite imagery collection, it took until the end of the 1990s for the satellites to be realised, with some unexpected results. Petrie (2001) points out that several of the US licensed projects (for example GDE Systems, Boeing, Motorola) have progressed very slowly or been terminated. There have been numerous delays, cost overruns and several expensive failures either at launch or in actual operations.

It was not until the 24 September 1999 that the first high resolution imaging satellite, IKONOS II was successfully launched. This was followed by the EROS A satellite which was successfully launched on 5 December 2000. There was a 5 year delay between the issuing of President Clinton’s Presidential Directive 23 and the first successful launch which is a long time considering that the companies involved had previously developed high resolution imaging satellites for the US Government.

2.2.1 Business Ventures

It is of interest at this point to examine a number of commercial companies that have attempted or are attempting to develop the market for high resolution satellite imagery. The ones to be looked at here are DigitalGlobe, Space Imaging, ImageSat International, Centre National d'Etudes Spatiales (the French space agency, CNES), Orbimage and Geoeye.

2.2.1.1 ClearView and NextView Contracts

A significant factor that has assured the survival of the American companies that own and run high resolution commercial satellite images has been the National Geospatial-Intelligence Agency or NGA (previously known as the National Imagery and Mapping Agency or NIMA) ClearView and NextView contracts. In 2003 the White House U.S Commercial Remote Sensing Space Policy was released. This policy directed that all U.S. federal government agencies to utilise commercial satellite imagery to the maximum extent possible. (www.military-geospatial-technology.com website, accessed 5 February 2008).

22 Consequently the ClearView contract was signed in early 2003 and the NextView contract in late 2003, the difference between the two contracts being:

(1) ClearView: Government contracts for imagery and imagery services from three domestic satellites (two companies) at one meter spatial resolution.

(2) NextView: Government contracts for imagery and imagery services from current and next generation (half meter resolution) domestic satellites.

The significance of these contracts to the suppliers of the imagery and to the NGA can be seen from the following quote from the 2004 NGA Director, Lieutenant General James R. Clapper (Geoeye website:2008):

“The agreement is a key development for both NGA and the commercial remote sensing industry. We need the next generation of commercial satellites to help us do our job and we look forward to working with both NextView vendors to bring this new capability to fruition”

The degree to which these contracts are sustaining the companies can be seen from Geoeye’s (the current operators of the IKONOS and OrbView satellites) financial statement (Form 10- K) from March 2007:

“Revenues for the years ended December 31, 2006, 2005 and 2004 were $151.2 million, $40.7 million and $31 million, respectively. All of the $110.5 million increase in 2006 revenues over 2005 resulted from the operations acquired from Space Imaging. Revenues generated by the operations acquired from Space Imaging which are now reported by the Company’s SI Opco subsidiary, were $110.8 million for the period from January 10, 2006 to December 31, 2006. Excluding the acquired operations, revenues were comparable with the prior year. In 2006, our contracts under the ClearView program provide for NGA to pay us a minimum of $36 million for IKONOS-related imagery products and $13 million for Orbview-3 related imagery products.”

Further to this, Orbimage released a media statement in May 2005 that stated that revenues for the company from US government contracts was approximately 49%, 34% and 80% of

23 the total company’s revenue for the years ending December 31, 2004, 2003 and 2002 respectively (Geoeye website:2008).

2.2.1.2 Digital Globe

DigitalGlobes’ history dates back to 1993 when the United States Department of Commerce granted WorldView Imaging Corporation (Worldview), the first license to build and operate a satellite system capable of collecting high resolution digital imagery (DigitalGlobe Website: 2002).

In January 1995, Earthwatch Incorporated (EarthWatch) was formed with the merger of the commercial remote sensing ventures of Ball Aerospace and Worldview. Additional partners included Hitachi, Datron, Telespazio and MDA (Petrie 2001).

This merger resulted in their first attempted satellite launch on December 24 1997. The first satellite, Earlybird 1 was designed to collect 3m resolution panchromatic imagery and 15 metre multispectral imagery. It was successfully launched on a Start-1 rocket from Svobodny, Russia but the satellite failed in orbit four days later due to the failure of the onboard power system. In April 1998, EarthWatch declared the satellite a total loss, and used the insurance funds to complete the continuing construction of the Quickbird satellites which were designed to collect 1m panchromatic and 4m multispectral imagery. The company had decided not to launch the second Earlybird satellite as the market window had closed for 3m resolution data (DigitalGlobe Website: 2002).

In June 1998 EarthWatch’s CEO, Herb Satterlee secured substantial additional funding to enable Earthwatch to complete the Quickbird satellites and the company’s DigitalGlobe database. This was successfully achieved through a series of investments amounting to US$186 million made by ITT Industries, the Morgan Stanley Investment House and Capital Research in 1999. In addition, EarthWatch had a number of contracts from NASA for the supply of airborne radar imagery of Central America and Alaska and from NIMA (NGA) for the mapping of Panama. Even though a large part of these contracts were executed by Canadian Intermap Technologies as a subcontractor, they still helped to generate revenue for Earth Watch (Petrie 2001).

24 On the 20 November 2000, EarthWatch launched its second satellite, Quickbird 1 from the Plesetek cosmodrome in Russia. Unfortunately it failed to reach orbit and re-entered Earth’s atmosphere north east of Brazil and was destroyed (Petrie 2001). Then on August 31 2001 EarthWatch was granted permission by the U.S. Federal Communications Commission to modify the altitude of Quickbird-2 from 600km to 450 - 470km, the reason being given that it would improve the ground image resolution capability allowing EarthWatch to respond to changing technological, market and regulatory conditions. In September 2001 EarthWatch changed its name and became DigitalGlobe. This was followed by the successful launch of the second Quickbird satellite on October 18 2001 and the beginning of the successful collection of panchromatic and multispectral imagery (DigitalGlobe website: 2002).

It was from this successful launch that DigitalGlobe had to create its customer base and the first twelve months saw a change in the prices of imagery as displayed in Table 2.5.

Over the following years the most significant customer base for Quickbird imagery was Government and Universities. Of particular note is the NGA (formerly) NIMA ClearView and NextView contracts where DigitalGlobe was awarded significant amounts as displayed in Table 2.6.

It is quite clear that without the support of the NGA contracts DigitalGlobe would have struggled to maintain liquidity. In fact it was the award of the 30 September 2003 contract in excess of $500million USD that allowed DigitalGlobe to construct the first of the next generation high resolution commercial satellites, WorldView I.

When the Quickbird satellite commenced operations in 2002, DigitalGlobe was claiming that there were no plans to launch a comparable satellite until 2004. In the period between 2002 and 18 September 2007 when DigitalGlobe managed to successfully launch and deploy WorldView I (with 0.5m resolution) the company’s claims varied on their launch date for their next satellite as well as how the competition was faring, as is displayed (DigitalGlobe website: 2002 to 2008):

25 Date Prices/Licensing Reason

(a) Pan imagery from $30USD to $22.50USD per sq km

(b) Multispectral from $30USD to $25USD per sq km In response to customers and (c) Data-bundle containing panchromatic and resellers demands for more multispectral from $45USD to $30USD affordable products, (d) Pan-sharpened products from $37.50USD to Digitalglobe reduced the pricing $30USD 8 May 2002 for its Quickbird Imagery (e) Digitalglobe simplified their licensing scheme products. for multi-organisational purchases.

(f) Minimum purchase for standard imagery products delivered from archive was reduced from 64 sq km to 25 sq km. This meant that an archive order may be placed for well below $600USD.

(a) Pan/3-Band Colour: $13.50USD per sq km (40% discount) For the month of October (b) Multispectral: $15.00USD per sq km (40% pricing was reduced for discount) archived Quickbird orders in 7 Oct 2002 honour of the one year (c) 4-Band Colour: $20.00USD per sq km (33% anniversary of the satellite discount) launch.

(d) Pan/MS Bundle: $20.00USD per sq km (33% discount)

Table 2.5 - Quickbird Imagery Price Changes for 2002 (DigitalGlobe website: 2002 to 2008)

26 Date Details

DigitalGlobe awarded $72million USD contract to deliver high resolution satellite Jan 2003 imagery to NIMA (now NGA) over a three year period, as part of a ClearView contract not to exceed $500million USD.

DigitalGlobe awarded $9.8million USD ClearView contract with NIMA (now NGA) 15 Sept 2003 in competition with another ClearView contractor.

DigitalGlobe awarded a contract in excess of $500million USD under NIMA (now 30 Sept 2003 NGA) NextView contract.

DigitalGlobe awarded an additional $6.1million USD supplemental modification to July 2005 the ClearView contract.

DigitalGlobe awarded $24million USD satellite imagery capacity contract Jan 2006 modification by NGA. A ClearView contract.

DigitalGlobe awarded $12million USD satellite imagery capacity contract 16 Mar 2006 modification by the NGA. A ClearView contract.

Table 2.6 - DigitalGlobe ClearView and Next View Contract Awards (DigitalGlobe website: 2002 to 2008)

2002 - Currently there are no plans to launch a comparable commercial satellite until at least 2004.

2003 - The competition has no plans to launch a comparable satellite until at least 2006

2004 - March 23, 2004: The competition has no plans to launch a comparable satellite until at least 2007 - March 31, 2004: The superior technical capabilities of Digitalglobe’s Worldview system, scheduled for launch no later than 2006 - May 10, 2004: The competition has no plans to launch a comparable satellite until at least 2008

2005 - The company will launch its next generation Worldview 1 and Worldview 2 satellites no later than 2006 and 2008.

2006 - The company will launch its next generation Worldview 1 and Worldview 2 satellites in mid 2007 and anticipated launch in 2008 respectively.

2007 - 18 September – DigitalGlobe successfully launches WorldView I satellite

2008 - DigitalGlobe plans to complete construction of its next generation satellite WorldView II in late 2008.

27 The above shows the unpredictable nature of launching, sustaining and successfully marketing the high resolution commercial satellite imagery business. Indeed if it had not been for the support given by the NGA the success and survival of DigitalGlobe would have been extremely unlikely.

2.2.1.3 Space Imaging

In December 1994, Space Imaging was founded in order to build the world’s first high resolution commercial imaging satellite system. In November 1996 the company acquired the Earth Observation Satellite Company (EOSAT). Space Imaging was a limited partnership with Lockheed Martin Corp., Raytheon Company, Mitsubishi Corporation, Singapore’s Van Der Horst Ltd., Korea’s Hyundai Motor Co., Europe’s Remote Sensing Affiliates, Swedish Space Corporation, Thailand’s Loxley Public Company and other international investors (Space Imaging website:2002).

In addition to owning and operating the IKONOS satellite, Space Imaging had the exclusive rights to distribute outside India, the imagery from the Indian Remote Sensing Satellites (resolution of 5m to 180m). Also the company had the rights to distribute digital imagery to the US government’s National Imagery and Mapping Agency or NIMA (now NGA) from the Canadian RADARSAT satellites (resolution of 8m to 100m). Space Imaging also distributed European ERS imagery (resolution of 30m) and had access to an archive of imagery from the Japanese Earth Resource Satellite JERS (resolution of 18m) (Space Imaging website:2002).

Lockheed Martin had, as a result of a preliminary study, conceived the Space Imaging Satellite (SIS). When the appropriate licences were being issued in 1993 and 1994, Lockheed Martin had initiated the further development of its Commercial Remote Sensing Satellites (CRSS-1 and CRSS-2). In 1997 these were renamed IKONOS-1 and IKONOS-2 (IKONOS being derived from the Greek word for “image”) (Petrie 2001). The IKONOS satellite system was built by Lockheed Martin Commercial Space Systems, with the communications, image processing and customer service elements built by the Raytheon Company. The camera was built by Eastman Kodak. (Space Imaging website: 2002).

On the 27 April 1999 IKONOS-1 was launched by an Athena II (built by Lockheed Martin)

28 rocket from the Vandenberg Air Force Base in the United States. Unfortunately the satellite never reached orbit. Investigations determined that the Athena II’s Orbit Adjust Module (OAM) fourth stage with the payload fairing failed and the satellite did not separate properly. This resulted in the rocket not achieving sufficient velocity to place the satellite into earth orbit (Space Imaging website: 2002). Within five months Space Imaging had made ready the IKONOS-2 replacement satellite and on the 24 September 1999 successfully launched the satellite into orbit. By the 12 October 1999 the first high resolution image of Washington D.C. had been released and Space Imaging had begun selling imagery by January 2000. (Petrie 2001).

In December 1999, Space Imaging applied for a licence to build and launch a 0.5m resolution panchromatic and 2m resolution multispectral imaging satellite. This licence application was approved by the U.S. Government on 6 December 2000. The company expected to launch this satellite sometime between 2005 and 2006 (Space Imaging website: 2002).

Similar to DigitalGlobe, Space Imaging had relied heavily on US government funding. In fact in May 1998 Space Imaging had already signed a five-year contract with NIMA (now NGA) for ordering commercial satellite imagery. This contract had a minimum guarantee of $4.4million USD and the contract amount was expected to rise as NIMA further defined the need for additional commercial imagery products. This was then followed in January 2003 with an award for a multi-year ClearView contract that a had a minimum value of $120million USD for the first 3 years, with a 5 year ceiling of $500million USD (Geoeye website:2008).

Space Imaging suffered a setback later in 2003 when it missed out on a NextView contract that was awarded to Digitalglobe in September 2003 worth in excess of $500million USD. It was not until July 2005 that Space Imaging was awarded a $5.88million USD mid year supplemental contract by NGA. Then again on 6 January 2006 NGA awarded Space Imaging a $24million USD one year contract extension and within 6 days it was announced that Orbimage Holdings Inc. had finalised the acquisition of substantially all of Space Imaging’s assets, for the purchase price of approximately $58.5million USD. The resultant company was in future to be known under the brand name of Geoeye (Geoeye website:2008). Further details which are provided in Section 2.2.1.5.

29 2.2.1.4 OrbImage

There are currently three OrbView satellites in orbit, which were launched and are controlled by the Orbimage company. Orbimage could trace its origins back to an early proposal by Eyeglass International (Petrie 2001). The Eyeglass project could be considered as a commercial side product of remote sensing projects for the US intelligence community. The company was established in 1994 based on a consortium of three US firms: Orbital Sciences, Itek and GDE Systems. Each company was to undertake a specific role in the development of the satellite system. Orbital Sciences was to have built the satellite bus and provide the launch vehicle with their Taurus rocket. Itek was developing the sensors and on-board electronics and GDE Systems was to be responsible for the image processing and data handling tasks (Gupta 1994). When Itek and GDE Systems left the consortium, Orbital Sciences decided to continue, largely on its own. Orbital Sciences received its licences in the middle of 1994 and the Orbimage company was established later that same year. Effectively the company had operated as an affiliate or subsidiary of the Orbital Sciences Corporation, which had built launchers and satellites both for the US government agencies and non-government bodies and also had large interests in electronic and optical imagers (Petrie 2001).

In April 1995 Orbimage began operations with the launch of OrbView-1. This satellite was air launched from 40 000ft from a modified TriStar aircraft. OrbView-1 is a scientific satellite with its main objective being meteorological research. It is capable of producing low resolution images (spatial resolution 10km) of weather and is used for the mapping of lightning strikes. OrbView-2 followed with its successful launch in August 1997 and generates low to medium resolution imagery, mainly of oceanic areas. The imagery from OrbView-2 has eight spectral bands, six in the visible and two in the near-infrared spectrum with a spatial resolution of 1.1km. The imagery is used for oceanic and coastal research by NASA and for operational purposes by the US Navy and commercial fishing fleets (Petrie 2001).

The first two OrbView satellites are not classified as high resolution satellites but two other OrbView satellites, namely OrbView-3 and OrbView-4, were promoted as offering 1m panchromatic and 4m multispectral digital imagery, with OrbView-4 planned to be the world’s first commercial hyperspectral satellite (Orbimage website:2002). But as with other companies involved in such ventures, Orbimage had its share of problems.

30 Orbimage made a business decision to launch OrbView-4 first before OrbView-3, but on the 21 September 2001 OrbView-4 failed to achieve a stable orbit when launched. OrbView-3 though was successfully launched on 26 June 2003 from a Pegasus rocket provided by the Orbital Sciences Corporation from Vandenberg Air Force Base in California USA. The OrbView-3 satellite is capable of producing 1m panchromatic and 4m multispectral imagery (Orbimage website: 2003). Shortly after the failed launch of OrbView-4, Orbimage announced that it was financially restructuring with a new Chairman of the Board, Lt. General (Ret) James A. Abrahamson.

Similar to DigitalGlobe and Space Imaging, the Orbital Imaging Corporation (Orbimage) has signed a number of contracts with NIMA (now NGA). The first one of these was in January 2001 when it signed the NIMA Production Prototype (NPP) contract. The NPP was an Indefinite Delivery/Indefinite Quantity (IDIQ) type contract in which NIMA could order products and services for one base year and two optional years. The maximum value of the contract over the three year period was fixed at $100million USD. The objective of the NPP contract was to develop, prototype and demonstrate new or improved geospatial and intelligence products and data sets derived from commercial satellites and US government imagery sources (Geoeye website:2008).

In March 2004 Orbimage was awarded a Clearview contract for a guaranteed minimum over two years of $27.5million USD, with a minimum of $10.5million USD in the first year and $12million USD in the second year. This contract did not prevent Orbimage from announcing net losses for the three months ending March 31, 2003 and 2004 of $2.51million USD and $8.39million USD. It is worth noting that from 2001 to 2004 Orbimage was in a constant state of management and financial restructuring that included voluntary bankruptcy filing (Geoeye website:2008).

In September 2004 the company was then awarded a major contract valued at approximately $500million USD that would expire on the 30 September 2008. The awarding of this contract provided Orbimage with both long term revenue commitments as well as the capital for the development of what was their next satellite OrbView-5 (estimated project costs of $502million USD) (Geoeye website: 2008).

31 As mentioned previously, on September 16 2005, Orbimage purchased Space Imaging for approximately $58.5million USD and the resultant company was Geoeye Inc (Geoeye website:2008).

2.2.1.5 Geoeye Inc.

As a result of the formation of Geoeye Inc. in January 2006 when Orbimage Holdings Inc. acquired Space Imaging’s assets, Geoeye now operates a constellation of three remote- sensing satellites, which are IKONOS, OrbView-2 and OrbView-3. In addition, Geoeye successfully launched another satellite, Geoeye-1 on 6 September 2008. After a period of calibration and checks the company released the first colour half meter ground resolution image from Geoeye-1 on 8 Oct 2008. GeoEye-1 can simultaneously collect 0.41-meter ground resolution panchromatic imagery as well as 1.65-meter multispectral imagery. It is anticipated the company will begin selling imagery from Geoeye-1 towards the end of 2008 (Geoeye website: 2008).

Due to its heritage, Geoeye is a company that has enjoyed substantial revenue contracts from NGA and up until March 2006 the value of Geoeyes’ ClearView contracts was $49million USD (Geoeye website:2008). In fact as a result of contract awards from one of its founding companies, NGA is supporting the project costs of the new Geoeye-1 satellite under a cost sharing arrangement to a total of $237million USD out of the estimated project costs of $502million USD. The NextView contract also provides for NGA to order approximately $197million USD of imagery products beginning 1 February 2007 and continuing until six quarters after the launch of Geoeye-1. It could be considered that as a result of the announced delays in entering Geoeye-1 into service in February 2007, Geoeye and NGA agreed to purchase $54million USD of imagery products from the company’s existing satellites from 1 February 2007 until 31 December 2007. The reliance for financial support on the contract with NGA, seen in Geoeye’s statement in their Form 10-K, shows that they anticipate that NGA will account for approximately half of the satellites’ imagery capacity during the period previously stated (Form 10-K for Geoeye, Inc: 2007)

32 2.2.1.6 ImageSat

ImageSat is an international company established in 1997, originally named West Indian Space. The two originating Israeli companies were Israel Aircraft Industries (IAI) a manufacturer of aircraft, satellites and electronic systems, Electro-Optics Industries (El-Op) a supplier of advanced military and commercial electro-optical systems. A third American company, Core Software Technology (CST) was the supplier of software and services to handle large databases of geospatial information (Petrie 1999).

Despite ImageSat International’s (including predecessor) existence only since 1997, Israel has been involved in developing high resolution satellites since the late 1980s though little information is available. In 1988, Israel launched the Ofeq-1 (Horizon-1) test satellite on a three stage Shavit launcher. In order to avoid flying over Israel’s Arab neighbours an unusual northeast flight path was used over the Mediterranean placing the satellite into a retrograde orbit at an inclination of 143°. This orbit limited the satellite’s view to areas 37° north and south of the equator. This 156kg satellite was explained to be a test vehicle designed to lead the development of an orbital reconnaissance capability. Ofeq-1 re-entered the Earth’s atmosphere in January 1989. Ofeq-2 was launched in April 1990 with similar weight and technical characteristics to Ofeq-1 and an orbital life of three months (Steinberg 1998).

Then on 5 April 1995, Ofeq-3 which was Israel’s first fully operational reconnaissance satellite, was successfully launched into orbit. This satellite was also placed in a retrograde orbit with an inclination of 143.4° (Petrie 1999). Its higher perigee (369 km) and an orbital maneuvering capability allowed for a longer life of one to three years. Its orbit took it over sites in the Middle East, including Iraq. The head of the Israeli Space Agency (ISA) described Ofeq-3 as “a very sophisticated platform on which many things can be placed.” (Steinberg 1998). Whilst not much detail is available on the payload, it is reported to be capable of obtaining both visible and ultraviolet imagery with a resolution of approximately 1m (Encyclopedia Astronautica Website:2002). Ofeq-3 eventually re-entered and burned up in the Earth’s atmosphere in October 2000. Ofeq-4 was launched as a replacement satellite but failed (Petrie 1999).

On 28 May 2002, Ofeq-5 was launched on an IAI/MLM Shavit satellite launcher from the Palmachim missile test center on the Israeli Mediterranean coast. The satellite carries a

33 remote sensing payload that will enable it to acquire high resolution data for Israels’ national needs. The satellite is believed to be capable of delivering both panchromatic and colour images at approximately 0.8m (Defence Update International Website: 2002). Ofeq-6 was subsequently launched on 6 September 2004 but unfortunately crashed into the Mediterranean Sea during a launch which was attempting to place the satellite in a low Earth orbit (LEO). The imagery from the Ofeq-6 satellite was intended to supplement coverage of the Ofeq-5 satellite, which Israel was using to monitor troop movements, missile launches and nuclear development efforts in neighbouring countries (Space News website: 2008). Despite the significant setback caused by the loss of Ofeq-6 on launch, Israel managed to successfully launch Ofeq-7 from the Palmahim Air Force Base on 11 June 2007. Details of the satellite’s characteristics are difficult to obtain though its velocity is known to be approximately 8m/second in an elliptical orbit at an altitude between 311 and 600km (Space News website: 2008).

In December 1996, Israeli press published unconfirmed reports of an agreement between IAI, Lockheed Martin and Mitsubishi in which IAI agreed to supply Ofeq images to the civilian market through a satellite to be launched by the end of 1997 (Steinberg 1998). Consequent events have shown this has not been the case and that imagery obtained from the Ofeq series of satellites have appeared to have remained for the use of the Israeli government only. The Israeli Ministry of Defence also has exclusive access to Middle Eastern coverage provided by the commercial EROS remote sensing satellites, also built by IAI (Space News website: 2008).

The development of the Ofeq satellites is important, as their design is the one on which the EROS satellites of ImageSAT International are based (Petrie 2001). ImageSat International had intended to launch a constellation of up to six satellites by 2007 (ImageSat Website: 2002). In common with the American satellite projects, the launch of the first EROS satellite, EROS-A had its share of both technical and financial problems. At first EROS-A was to be launched in December 1999, then it was postponed until February 2000. In July 2000 the financial problems were solved by a group of U.S. and French investors providing a US$90 million package. The acceptance of the financial assistance also resulted in the reorganisation (including a change in top management) of the company and it was here the company’s name was changed from West Indian Space to ImageSat International NV, incorporated in the Netherlands Antilles. The EROS-A satellite, following the company’s reorganisation, was

34 then to be launched in October 2000, but it was not until 5 December 2000 that the satellite finally launched on a Russian Start-1 launcher from Svobodny. The first images were received successfully four days later, while the first images shown publicly were released on the 18 January 2001. One of the limitations of the EROS satellite is that it has no on-board recording and storage device so the satellite relies entirely on the images being transmitted in real-time to ground receiving stations (Petrie 2001). ImageSat International though, has signed strategic partnership agreements with a network of 12 Acquisition, Archiving and Distribution (AAD) ground receiving, processing and distribution centres worldwide, but by November 2001 there were only six countries with functioning AAD centres: Sweden, Argentina, Japan, Italy, South Africa and Taiwan. In developing these partnerships ImageSat International was dealing with receiving stations that already processed Landsat and SPOT data. They supplied the stations with a free upgrade of hardware and software for them to receive, process and archive imagery from the EROS satellites. Through this arrangement ImageSat International obtains a share of the revenue (Wagner 2001).

EROS B which was successfully launched on 25 April 2006 from a Russian Start-1 rocket, was designed to capture black and white (panchromatic) images at 0.7m resolution, compared to its predecessor, EROS A which collects images at 1.9m resolution. In addition, EROS B has larger onboard storage, improved pointing accuracy and a faster data communication link than EROS A.

As with their U.S. based counterparts, ImageSat has had to consider financial constraints and market influences in deciding company direction. In June 2004 they announced that they would not launch the satellite formerly known as EROS B1 (now known as EROS C) but instead an upgraded version of EROS A, the EROS B satellite. The ImageSat Chief Executive Officer (CEO) at the time, Menashe Broder claimed the revised plan was a direct response to a market demand in which national security customers were in need of multiple collections of panchromatic, high resolution imagery as opposed to the civilian market that the multispectral imagery was intended for (Space News website: 2008). In fact ImageSat operates a purchasing arrangement whereby customers can exclusively buy collection areas around the world and no one else will be able to obtain EROS imagery over these locations.

Whilst it is difficult to find details of ImageSat’s financial position, in 2003 Menashe Broder claimed that government customers for intelligence quality imagery accounted for 99% of

35 ImageSat business.

The EROS C satellite is anticipated to be launched in 2009 into a sun synchronous orbit at an altitude of about 500km. It will be equipped with sensors capable of producing both panchromatic imagery at a resolution of 0.7m and multispectral imagery at a resolution of 2.8m with a swath of 11km at nadir. Its expected life is considered to be ten years (Apogee website: 2008)

2.2.1.7 Centre National d'Etudes Spatiales (CNES)

Whilst not included in the category of high resolution satellites, the SPOT system continues to be a source of satellite imagery. The SPOT Earth observation satellite system was first approved in 1978 being designed by the French Space Agency (CNES) with cooperation from Belgium and Sweden. The entire system consists of a series of orbiting satellites and associated ground facilities for satellite control, acquisition programming, data reception and imagery production. CNES has responsibility for the satellite in-orbit control and execution and the satellite acquisition plan. Spot Image, a CNES subsidiary has responsibility for the satellite’s daily activity plan definition, the reception of images transmitted to the Toulouse station, the processing of the image telemetry to update image catalogues, the production and development of products derived from satellite data and their commercialisation (CNES Internal Newspaper: 2002).

The SPOT system has been operational since 1986 with the launch of SPOT 1 in February 1986, SPOT 2 in January 1990, SPOT 3 in September 1993 (failed November 1996), SPOT 4 in March 1998 and finally SPOT 5 in May 2002 (ACRES:2002). As the ground system was only ever configured for the management of three satellites, at present only three satellites are in use, being SPOT 2, 4 and 5. SPOT 1 has been temporarily deactivated in favour of SPOT 5 (CNES Internal Newspaper:2002), though in late 2003 SPOT 1 was de-orbited to place it on a destructive re-entry into the Earth’s atmosphere in 15 years (Space News website: 2008). SPOT 5 is considered as the last of the medium-resolution wide-area collection systems with future imaging satellites systems being of the 1m resolution category (Geoinformatics:2002).

Similar to the other commercial ventures of high resolution commercial satellite imagery, the

36 SPOT series of satellites are linked both financially and technically with France’s military reconnaissance satellite program. France’s military reconnaissance program is known as Helios and is divided into two phases Helios I and Helios II, both comprising of two satellites. In the Helios I phase, Helios IA and Helios IB were launched in July 1995 and December 1999. These satellites both had a resolution of 1m and no infrared capability. Italy and Spain reportedly have invested in Helios I and receive data at an amount proportional to their funding (Hitchens: 2006).

Helios IIA was launched in December 2004 and produced its first images in January 2005. Helios IIB, the second satellite in the series is due for launch in 2008. The Helios IIA which weighs 4 200kg, was built by EADS Astrium as the prime contractor, with Alcatel Space providing the imaging system and has a contractual service life of five years. Its resolution according to the French Defense Ministry is “several 10s of centimetres”. The Helios II platform is based on a nearly identical platform built by EADS Astrium to SPOT 5 (Space News website: 2008).

In 2005 Spot Image reported revenues of approximately $85million USD which was a 19% increase from 2004, though this was actually 12% after taking into account a partnership cancellation with competitor DigitalGlobe. It was though, the company’s third straight year of double digit revenue growth. Despite this, previously it was reported in 2003 that 60% of images taken by SPOT satellites are used for defence purposes. With the advent of the Pleiades satellite system in the near future, similar to their counterparts in the U.S. and Israel, it can be anticipated that the French high resolution commercial satellite imaging industry will be heavily supported by not only the French military but also the military of its European partners (Space News website: 2008).

The Pleiades satellite system is the high resolution optical French component of the Franco- Italian ORFEO (Optical and Radar Federated Earth Observation) program. These satellites are claimed to be dual use satellites for both military and civilian users. Other partners in the Pleiades project include Austria, Belgium, Spain and Sweden who will receive data in proportion to their investments. The actual system will consist of two satellites capable of a resolution of 0.7m. The first satellite is due to be launched in late 2008 and the second in 2009 or early 2010 (Hitchens: 2006).

37 IKONOS EROS A EROS B Quickbird Worldview - 1 Orb View -3

Launch Date 24 Sept 1999 5 Dec 2000 25 April 2006 18 Oct 2001 18 September 2007 26 Jun 2003 Resolution 1 m Panchromatic 1.8 m Panchromatic 0.7m Panchromatic 0.61-0.72m Panchromatic only 1.0m Panchromatic 4 m Multispectral Panchromatic 0.50m at Nadir 4.0m Multispectral 2.44-2.88m 0.59m at 25Deg off- Multispectral nadir Swath Width 11 km at Nadir 12 km -- 17.6km at Nadir 8 km Orbit Information

Altitude 680km 480 km 500km 450 km 496km 470 km Inclination 98.1° 97.3° 98° 97.29

Orbit Type Sun Synchronous Sun Synchronous Sun Synchronous Sun Synchronous Sun Synchronous

Revisit Frequency 2.9 days at 1 meter 3 days 1 to 3.5 days 1.7 days at 1meter or Less than 3 days 1.5 days at 1.5 meter less. Note: Frequency will vary 4.6 days at 25Deg off- according to latitude of nadir or less with collection 0.59m resolution Viewing Angle Agile spacecraft – Agile spacecraft – Agile spacecraft – Nominally +/-45Deg off-nadir. in track and cross track in track and cross in track and cross Higher angles pointing track pointing track pointing selectively available Spectral Bands

Panchromatic 0.45 - 0.90 microns 0.5 - 0.9 microns 0.45 - 0.90 microns Panchromatic only 0.45 - 0.90 microns Multispectral #1: Blue 0.45 - 0.52 None Blue: 0.45 - 0.52 0.45 - 0.52 #2: Green 0.52 - 0.60 Green: 0.52 - 0.60 0.52 - 0.60 #3: Red 0.63 - 0.69 Red: 0.63 - 0.69 0.625 - 0.695 #4: Near IR 0.76 - 0.90 Near IR: 0.76 - 0.90 0.76 - 0.90 Table 2.7 Satellite Operational Parameters

38 2.3 Satellite Systems

This section will describe the technical characteristics of commercial satellites that are capable of acquiring high resolution imagery.

2.3.1 IKONOS

Figure 2.6 - IKONOS Satellite (Source: Geoeye Website, 2008)

The IKONOS satellite is currently in a 680km sun synchronous orbit at an inclination of 98.1°. In addition to collecting panchromatic and multispectral imagery, the IKONOS satellite has the capability to collect stereo pairs. It has a revisit frequency of 2.9 days at 1m resolution and 1.5 days at 1.5m resolution. Revisit times are more frequent for latitudes greater than 40° and less frequent for latitudes closer to the equator. The nominal swath width is 11km at nadir and 13km at 26° off-nadir (Space Imaging Website: 2002). Operational parameters are listed in Table 2.7.

Figure 2.7 IKONOS Imagery - Dalrymple Bay, Queensland, Australia. (May 23, 2005) (Source: Geoeye Website, 2008)

39 Zhou and Li (2000) also describe the special characteristics of IKONOS, and compare them with other systems. Their main points are:

(a) The system is based on a new optical system: a push broom camera with a 10m focal length, folded to 2m through the use of a mirror system. It is designed to capture both panchromatic images with a 1m resolution and multispectral images with a 4m resolution.

(b) In addition to along-track stereo capability, the satellite imaging system is able to roll in orbit to collect cross-track images at distances of 725 km on either side of the ground track. Due to the satellite’s 680 km altitude, imagery maintains at least a 1m ground sample distance (GSD) out to 350 km either side of nadir (Corbley: 1996).

(c) The system is also equipped with GPS antennas and three digital star trackers to establish precise camera positions and attitudes. A rigid satellite platform has been built to reduce the motion vibration of the platform and this contributes to the integrity of the line-of-sight determination. The satellite orbits the Earth in a sun- synchronous polar orbit, allowing it to traverse the planet every 98 minutes, crossing the equator at the same local time (around 10:30 am) on each pass (Folchi, 1996).

2.3.2 EROS A

Figure 2.8 – EROS A Satellite (Source: ImageSat Website: 2008)

EROS A is a light weight low earth orbit (LEO) satellite with a single electro optical camera system. The satellite is capable of capturing only high resolution panchromatic image data. It

40 orbits the Earth almost 15 times a day in a circular sun-synchronous near polar orbit at an altitude of 480km, with the capability of delivering data in real time to 16 ground receiving stations worldwide. As the satellite is highly maneuverable it can be quickly pointed and stabilised to image customer specified sites on nadir or at oblique angles up to 45°, this enables the satellite to view almost any site on Earth as often as two or three times a week. The standard image resolution is 1.8m with an over sampled resolution of 1.0m at a swath of 12.7km at nadir for the standard resolution. (ImageSat Website: 2002). A list of the main operational parameters can be seen in Table 2.7.

Figure 2.9 - EROS Imagery - Adelaide Cricket ground, 5th March 2002 (Source: Apogee Website: 2008)

2.3.3 EROS B

Figure 2.10 – EROS B Satellite (Source: ImageSat Website: 2008)

ImageSat claims to have launched the EROS B satellite to address market demand for higher resolution and faster revisit of EROS satellites (ImageSat Website: 2008). Like its predecessor it is a light, low earth orbiting satellite that is designed for fast maneuvering

41 between imaged targets (Apogee Website: 2008). It is slightly larger in appearance to the EROS A satellite but has greater capabilities which include a larger improved camera which provides a standard panchromatic resolution of 0.70m from an altitude of approximately 500km (ImageSat Website: 2008). The EROS B satellite has an increased onboard storage capacity, which when combined with its agility permits collection of 190km long strip scenes at any angle to the ground track (Apogee Website: 2008).

Figure 2.11- EROS Imagery - Circular Quay, Sydney, 17 May 2006 (Source: Apogee Website: 2008)

2.3.4 Quickbird

Figure 2.12 – Quickbird Satellite (Source: DigitalGlobe Website: 2008)

The Quickbird satellite which was manufactured by Ball Aerospace & Technologies Corp, orbits the Earth at a 450km in a 98° sun-synchronous orbit (DigitalGlobe Website: 2002). The satellite has a 61-72cm resolution for panchromatic imaging and 2.44-2.88m resolution for multispectral imaging, depending upon the off-nadir viewing angle (0°-25°). It also has an

42 along-track and across track stereo capability (which has been used in the past, but is not currently being offered for commercial customers), which provides a high revisit frequency of 1 to 3.5 days, depending on the latitude (Toutin & Cheng 2002). Operational parameters are listed in Table 2.7.

Figure 2.13- Quickbird Imagery - Singapore, 21 March 2004 (Source: DigitalGlobe Website: 2008)

2.3.5 Worldview 1

Figure 2.14 – WorldView – 1 Satellite (Source: DigitalGlobe Website: 2008)

The Worldview satellite is advertised by DigitalGlobe as the most agile satellite ever flown commercially. Even though it is only capable of obtaining panchromatic imagery, the resolution is from 0.5m to 0.59m, which currently exceeds any of its competitors. It not only has improved maneuverability when compared to Quickbird but also positional accuracy of 6.5m 90%CE compared to 23m 90%CE, resolution of 0.5m at nadir compared to 0.6m at nadir and a larger swath width of 17.6km at nadir compared to 16.5km at nadir. It is now the

43 only DigitalGlobe satellite that is used to collect stereo imagery. Operational parameters are listed in Table 2.7.

Figure 2.15 - WorldView – 1 Imagery - Sydney, 31 December 2007 (Source: DigitalGlobe Website: 2008)

2.3.6 OrbView - 3

Figure 2.16 – OrbView–3 Satellite (Source: Geoeye Website, 2008)

OrbView-3 collects 1m panchromatic and 4m multispectral imagery at a swath width of 8km. It has a revisit rate of less than three days as a result of its ability to collect data up to 50° off nadir and is capable of collecting 21 000 square kilometres per ten minute pass. (Geoeye Website: 2008)

44 Figure 2.17 – Orbview Imagery (Source: Global Land Cover Facility Website, 2008)

2.4 Terrestrial Based Methods

High resolution commercial satellite imagery provides a new source of imagery and spatial data products. The following section provides a comparison of the current terrestrial technology available that either complements or competes with high resolution commercial satellite imagery. The methods detailed here are considered to be the more “traditional” methods or “earth based systems” normally familiar and accessible to surveyors and related spatial industries.

Existing methods of survey include:

(1) Ground Survey

(2) Aerial Photogrammetry

(3) Airborne InSAR

(4) Light Detection and Ranging (LIDAR)

45 A discussion on cost is only given for ground surveys. A detailed discussion on cost comparison between Aerial Photogrammetry and Satellite Imagery is contained in Chapter 3.

2.4.1 Ground Survey

The usual method of information collection by ground survey is by the use of an Electronic Total Station which incorporates an electronic theodolite, electronic distance measuring (EDM) equipment and a data recorder. Whilst there are Electronic Total Station systems that can be operated by one person, it is more commonly a two person operation, where one person operates the Electronic Total Station and the other controls the prism or “target” to which physical measurement to the object is required (see Figure 2.16).

Figure 2.18 - Typical Ground Survey Party (Source: United States Army Publication, EM 1110-1-1005)

Ground survey is the most accurate and detailed method for obtaining spatial data. It has the advantage that objects can be measured direct at close range and hence produce easily consistently accurate results. Typically accuracy of +/- 1cm in the X and Y direction (or Easting and Northing coordinates) and a vertical accuracy of +/- 5cm on soft surfaces (such as grass) to +/- 1cm on hard surfaces (such as concrete) is obtainable (Uren & Price: 1992: 198)

When determining the required resolution of the Digital Elevation Model (or DEM) consideration must be taken of how the spacing or grid size will affect the accuracy of the final DEM. In terms of contour interval, Table 2.8 gives an indication of spacings between spot heights or measurements in terms of a required contour interval (Uren & Price: 1992:

46 200).

Scale 1:50 1:100 1:200 1:500 1:1000 Contour vertical interval 0.05m 0.1m 0.25m 0.5m 1m Spot level grid size 2m 5m 10m 20m 40m Table 2.8 – Spacings between Spot Heights or Measurements

The establishment costs of a field survey party can vary but include a total station between $10 000 to $20 000AUD, vehicle and ancillary equipment (such as spades, axes, chainsaws etc) $20 000 to $30 000AUD. Typically a field survey party in Australia will cost approximately $150 to $200 per hour depending on the circumstances. In typical field operations a field survey party can observe between 250 to 1 000 points per day for a detail survey. The numbers of observations are dependent on the complexity and ruggedness of the site as well as the purpose for the survey.

During a ground survey the ability of “walking the ground” means that it is quite easy to obtain accurate spatial data correctly representing the details of the ground. The disadvantage of field surveys is that the cost can rapidly escalate for large areas that require a detailed digital elevation model (DEM) and topographical and cultural details. As a result, ground survey techniques and equipment are commonly used for such work as detail surveys, engineering layout, “as constructed” or house block surveys covering small or discrete areas.

Figure 2.19 Typical Detail Survey

47 When used in conjunction with high resolution commercial satellite imagery and its derivative spatial data, ground survey can be a complementary source. As mentioned above ground survey can be uneconomical both in time and money for large areas, but for a large area project which contains some areas that cannot be observed on satellite images (due to cloud or vegetation) or requiring higher positional accuracy or detail, ground survey can be complementary.

2.4.2 Aerial Photogrammetry

Photogrammetry can be described as:

“the art, science, and technology of obtaining reliable information about physical objects and the environment through processes of recording, measuring and interpreting photographic images and patterns of recorded radiant electromagnetic energy and other phenomena” (Wolf & Dewitt:2000:1)

Only images or photographs taken from airborne vehicles will be considered in this section.

As with satellite imagery, stereo imagery must be obtained to determine heights or relief from aerial photography. In the case of aerial photography this means a 60% overlap between successive photographs along each strip and 30% sidelap between strips if more than one photograph width is required to cover the area. Suitable ground control points are also required to ensure referencing to the ground coordinate system.

Aerial photography can be classified as either vertical or oblique. Vertical photography refers to photos taken with the camera axis directed within 2° of vertical. By using detailed photogrammetric instruments, computer software and hardware it is possible to rigorously correct for tilt in the photos with no loss of accuracy (Wolf & Dewitt: 2000:5).

48 Figure 2.20 – Aerial Stereo Photography collect

Oblique aerial photography refers to photography exposed with the camera axis tilted about the flight direction. High oblique photography includes the horizon while low oblique photography does not (Wolf & Dewitt: 2000:6).

The process of obtaining spatial data from aerial photogrammetry involves four separate phases:

(a) Planning

(b) Establishment of ground control

(d) Processing or extracting the data.

Due to the amount of work and coordination required in using this method of spatial data production, detailed planning is required. The initial point of determination which will govern the nature of all subsequent steps, is the actual purpose for which the photography is being

49 flown. Normally aerial photography has either good metric qualities or high pictorial qualities. Good metric qualities means that the photographs or images are to be used for topographic mapping or purposes that require quantitative photogrammetric measurements. Images of high pictorial qualities are used for qualitative analysis, for example photographic interpretation or for the construction of orthophotos, photomaps and aerial mosaics (Wolf & Dewitt:2000:409).

In recent years the advent of digital aerial cameras such as the Leica ADS40 and the Vexcel UltraCam have caused a re-evaluation of aerial photography. These systems have many enhanced features over their “wet film” predecessors. Their advantages include the almost complete elimination of the need for ground control due to the integration of Global Positioning Systems (GPS) technology and an Inertial Measuring Unit (IMU). Also they are capable of obtaining multispectral as well as panchromatic images.

From the computing aspect the use of digital cameras or sensors allow for greater contrast in images by using the digital images radiometric resolution. As computers store information in binary format every number has a value of 0 or 1, to obtain more larger numbers, more binary numbers need to be stringed together. Hence 8 bit data has 28 or 256 possible values which when applied to imagery means that 8 bit data has 256 potential values for each pixel (when viewing an image in gray tones 0 = black and 255 = white and there are 254 shades of grey in between). Similarly 11 bit data (211 equates to 2048) increases the range to 2048 shades of grey.

There are distinct similarities between digital aerial cameras and high resolution commercial satellite imagery, such as the way the images are electronically captured, stored and then transferred. This allows for more rapid turnaround for processing or recapture in the event of images being found to be unsuitable. Whilst digital aerial cameras are primarily designed for large scale mapping with pixel sizes as small as 5cm, they can be suitable for medium or smaller scale imagery collects, particularly for aircraft which are capable of a ceiling of approximately 8000m. In this instance the maximum ground sampling distance (GSD) would be less than 1m which would then be comparable with high resolution commercial satellite imagery. This with the ability to collect 12 bit imagery (IKONOS imagery being 11bit) makes the use of digital aerial cameras a serious alternative to high resolution commercial satellite imagery (Trinder: 2008).

50 A more detailed comparison between aerial photography and high resolution commercial satellite imagery is contained in Chapter 3. Aerial photography has the advantages that it provides higher resolution, greater positional accuracy and also, depending on the cloud cover, can be flown on demand at any time to achieve a desire outcome. Despite this, the financial costs of aerial photography are generally greater, as well as the greater complexity of collection when compared to high resolution commercial satellite imagery, such as the establishment of ground control, flight planning, airspace clearance and staff skills required to process.

2.4.3 Airborne Interferometric Synthetic Aperture Radar (InSAR)

Airborne Interferometric Synthetic Aperture Radar (InSAR) uses a variation of a conventional Synthetic Aperture Radar (SAR). It combines a SAR system with another spatially separated receiving antenna, to determine elevations. When a radar pulse is emitted from the SAR antenna, the returning echoes are received by both SAR antennas, and the phase difference between the two signals is measured. This phase difference is related to the difference in geometric path length from the ground point. From the geometry of the antennas, the phase difference can be converted into heights of ground points. Using the interferometric phase, in addition to the standard along and cross track location of an image point obtained with conventional SAR, the three dimensional coordinates of a point can be determined within an accuracy of 0.3m to 3.0m (ASPRS: 2001:153).

InSAR has the advantage of having near weather-independent operation, cloud penetrating capability and quick turnaround time. In areas which are notorious for cloud clover, such as Papua New Guinea, InSAR provides an effective solution for mapping as it is capable of penetrating weather that optical sensors cannot. Its disadvantage is that there are very few InSAR systems in the world. Its application can be expensive for a small project or third world application without outside financial and technological assistance.

In comparison to high resolution commercial satellite imagery, InSAR can be used to produce a high resolution digital elevation model that can be used to orthorectify satellite imagery or aerial photography. Whilst InSAR data has been used for feature extraction of spatial data unless it is automated it can be a difficult and operator exhausting process. For this reason

51 InSAR provides a good source of digital elevation model data (which does not necessarily need to be recent), for use in the production of orthorectified images using high resolution satellite imagery or aerial photography.

Figure 2.21 – Concept of IFSAR Mapping (Source: Xiaopeng Li, Intermap website, 2008)

Figure 2.22 - Intermap’s LearJet 36 STAR-3i System (Source: Xiaopeng, Intermap website, 2008)

2.4.4 Light Detection and Ranging (LIDAR)

Laser scanning or LIDAR (light detection and ranging) is a recent development in the area of topographical data collection. The equipment consists essentially of a laser scanner, inertial navigation system, GPS receiver, and controller and data recording computers fitted to an airborne platform. It is capable of obtaining measurements of a very large number of points every second, to 10 – 20cm accuracy with swathes several kilometers wide on the Earth’s surface (Wolf & Dewitt: 2000:303).

52 The principle of operation is that as the aircraft flies over the subject area a laser pulse is transmitted to the terrain below and its return signal is detected by a sensor. The distance from the scanner to the terrain can then be determined from the time delay between the transmission and the return signal. This distance data is then combined with information from the inertial navigation system, which determines the orientation of the aircraft, and the GPS receiver which records the XYZ positions of the antenna. The results from the laser pulses then effectively define vector displacements from specific points in the air to points on the ground. As the laser is capable of generating pulses at the rate of thousands of pulses per second the output is a dense pattern of measured X, Y, Z points on the terrain. At present the accuracy obtained using this technology is of the order of 10 to 20cm (Wolf & Dewitt: 2000:304).

Figure 2.23 - LIDAR system. (Source: Burtch, 2002)

The use of LIDAR has a number of advantages which include:

(a) Measuring ground and above ground features. The density of points collected mean that it is possible to determine manmade objects and infrastructure such as building and powerlines.

(b) Defining the terrain under vegetation. Due to the large number of points collected

53 a sufficient number of laser pulses will penetrate the tree canopy and return, enabling the determination of the terrain under the tree canopy.

(Jones:2008)

Figure 2.24 – LIDAR feature collection methodology (Source: AAM Hatch:2008)

Similar to InSAR the main use for LIDAR is the production of digital elevation models which then can be used for a variety of applications. LIDAR though does have the distinct advantages in that it can be used in the determination of heights of infrastructure such as power lines or tree canopy heights due to the large number of laser pulses emitted. Which means features can be extracted as with InSAR data.

Table 2.9 provides a comparison between the sources of spatial data and their data output accuracy and where applicable image resolution. Whilst it can be seen that imagery and digital elevation models from high resolution commercial satellite imagery are of a lower accuracy and resolution of terrestrial based sources, what is not represented here is the

54 advantages of spatial data sourced from spaced based means which in some situations can counter the loss of accuracy and resolution. These advantages as well as the disadvantages are examined in the following chapter.

High Ground Aerial InSAR LIDAR Resolution Survey Photogrammetry Commercial (Aerial Digital Satellites Cameras) Image Less than - 5 to 50cm - 15 to 40cm Resolution 1m from integrated cameras Digital +/- 2m +/- 5cm (soft 5 to 50cm @ 1 0.3m to 3.0m 10 to 50cm Elevation vertical surfaces) sigma on clear @ 1 sigma Model accuracy @ +/- 1cm (hard ground on clear Accuracy 1 sigma on surfaces) ground clear ground (with control) Source AAMHatch Uren & AAMHatch 2008 ASPRS:2001: AAMHatch 2008 price:1992:198 153 2008 Table 2.9 – Comparison of Spatial Data Sources

2.5 Summary

The availability of high resolution commercial satellite imagery has opened a new spatial data collection source and methodology not previously available. Its ability to collect high resolution imagery in both mono and stereo remotely from space means that not only can imagery for a variety of uses be collected, but also other spatial data such as features and digital elevation models can be extracted. Whilst the accuracy is not that of ground survey, aerial photogrammetry or scanning (such as InSAR or LIDAR), it is sufficient to satisfy a variety of requirements.

As a business venture the cost of establishing such a data source or industry has not been cheap both in time and finance for any organisation or country involved. Few business ventures have not had at least one major set back and none would have survived if it was not for significant funding through work programs from their respective governments. Despite these problems there is still progress being made to ensure that the next generation of high

55 resolution imaging commercial satellites will be constructed and successfully launched, such as Digital Globe’s WorldView 1 and 2 satellites.

When compared to terrestrial based survey technologies such as aerial photogrammetry, InSAR or LIDAR it has become clear that in some areas depending on the requirement for small to medium scale mapping or revision, it is a direct competitor. It would be better to consider high resolution commercial satellite imagery as a source or methodology in its own right providing an alternative and or complement to other technologies.

56 CHAPTER 3

IMAGERY APPLICATIONS IN A GEOGRAPHICAL INFORMATION SYSTEM (GIS)

3.1 Introduction – Composition of a Geographical Information System (GIS)

The significance of the advent of commercial high resolution satellite imagery is that it has added another versatile source of data that can be used by the spatial community. Consideration still needs to be given to accuracy assessments, suitability, timeliness and cost compared with other sources of data to ensure that it is utilised to its full capacity. This chapter will briefly cover a description of Geographical Information Systems (GIS) and provide some points for consideration in regards to the use of high resolution satellite imagery in a GIS.

Wolf (2000) describes a Geographical Information System or a GIS as any information management system which can:

(a) Collect, store, and retrieve information based on its spatial location;

(b) Identify locations within a targeted environment which meet specific criteria;

(d) Analyse the related data spatially as an aid to making decisions about that environment;

(e) Facilitate selecting and passing data to application-specific analytical models capable of assessing the impact of alternatives on the chosen environment; and

57 (f) Display the selected environment both graphically and numerically either before or after analysis.

As a note of caution Chernin and LeRoux (1999) state:

“As a result, GIS challenges us to understand the complexities of scale, accuracy, and the photogrammetric process. To ignore or misunderstand these complexities can cause unnecessary expenditures of money for the development of highly accurate land bases.”

This view highlights the point that whilst spatial data has been available for thousands of years, its only form until the advent of computer technologies was that of a map. These maps came in a variety of themes, but were all in hardcopy form. Giger (2001) highlights the point from Goodchild (1988) that:

“The ability of a Geographic Information System to analyse spatial data is frequently seen as a key element in its definition and has often been used as a characteristic to distinguish a GIS from systems whose primary objective is map production.”

This can best be displayed by comparing a Computer Aided Drafting (CAD) package such as AutoCAD or Microstation with a GIS. A CAD package is capable of creating spatial data and cartographically presenting it in the form of a plan or map, which will generally consist of a number of thematic layers with a common grid or coordinate system, capable of being overlaid to provide a compiled product. A GIS will present this data and its associated topological information, which describes its spatial relationship with respect to neighbouring objects, and be capable of analysis. For example, by using a CAD package an engineer or surveyor can design and draft a housing estate subdivision including all the roads and allotment details. By importing this data into a GIS and building the topology, it is possible then to perform analysis of relationships to space facilities such as bus stops, community centres etc to ensure they satisfy minimum or maximum requirements for residential dwellings.

A GIS exploits the full potential of analysis of spatial data for providing information

58 of benefit to most areas of our community and businesses. These benefits are derived from the way spatial data is stored in a GIS with textual attributes, position and relationships in a digital format thus enabling manipulation with application software.

Building Footprints

Road Network

Cadastral Information

Digital Terrain Model (DTM)

IKONOS Satellite Imagery 2001

Aerial Photography 1995

Analytical Output

Analysis and Manipulation Software

Relational Database

Figure 3.1 – Relationship of Data in a GIS

Before looking at the possible uses of satellite imagery in a GIS it is important to understand the composition of a geographic information system. This concept can be partly explained by the previous example comparing a CAD package application versus a GIS, but the concept with a modern GIS goes further than this. With a GIS, textual data is stored with the spatial data so relationships can be created and manipulated to provide analysis of a geographical area or thematic topic. For example, a digital street map compiled from cadastral data will enable a street address to be located in an automobile’s Global Positioning System (GPS) navigation system, in an automated map. If the same combination of data were augmented by tabulated text data such as rates, unimproved land values, house hold income, utilities

59 consumption, persons per dwelling, it would then become possible to monitor and model future trends in geographical areas from a suburb to a town council to a regional area. Such modelling would allow the analysis of essential future services, or considerations of planning as demographics of an area change or remain static over a period of time.

A GIS, through linking data from various sources and giving it a spatial context, provides a dynamic environment in which to exploit all the data. The following description (Star and Estes: 1990) of the five basic components of a GIS is provided to illustrate this point:

(a) Data acquisition;

(b) Preprocessing;

(d) Manipulation and analysis;

(e) Product generation.

3.1.1 GIS Data Acquisition

This is the process of identifying and collecting the varying data sets for the application of the GIS and could involve a number of techniques and sources. In the first instance it can be sourced from existing data such as computer scanned hardcopy map products, ground surveys including feature vector information, aerial and satellite imagery and terrain data. Spatially linked to this would be tabulated textual data such as land rates, population demographics, crime incidents and home ownership, depending on the output application or purpose of the GIS. This phase can be considered the true foundation of the GIS since the correct data for the intended use must be identified and located. The costs and time taken to acquire the data should not be underestimated. The suitability and quality of any decisions and analysis derived

60 by using spatial data is limited and linked to the accuracy and precision of the datasets from which they were derived (Star and Estes: 1990).

Field Surveys Data Acquisition Spatially Linked Textual Data

Preprocessing CAD Drawings - Ingest Vector Data - Data Translation - Transformation Sets - Storage - Quality Control - Metadata Scanned Hardcopy Maps (Raster Datasets) Data Management - Maintenance - Quality Control - Procedures Digital Terrain Models (Various Resolution) Manipulation and Analysis - Training for users - Training for Imagery specialist staff - Various Resolution - Software tools - Various Source - Aerial - Satellite Product Generation - Textual report - Hardcopy (Paper) products - Derived vector and raster data (softcopy)

Figure 3.2 – Data flow of a Geographic Information System (GIS)

The issue of cost versus accuracy and quality is a critical problem not only for this phase of the creation of a GIS but also the maintenance of the system. It should not be assumed that expenditure of all available funding on highly accurate and detailed data will ensure a system capable of high quality output. Data must be acquired that is appropriate and suitable for the intended user base. For example, a local authority may use a GIS to assist town planners and engineers to design and maintain services in an urban environment. The acquisition of high resolution aerial photography may be justified for this purpose as it will allow town planners to monitor development and identify potential unauthorised construction. The digital elevation model (DEM)

61 derived from the photography would be suitable for town planning purposes only, such as identifying land parcel zoning and road corridors. Similarly, engineers would be able to use the aerial photography and DEM to perform preliminary design work, both in identifying features and approximating the topography. But for detailed design a ground survey would need to be used to meet engineering construction specifications of the order of centimetres, and ensure the most current information is used, since the aerial photography could be dated (in excess of twelve months) when design work is commenced or ground detail may be obscured by obstacles such as vegetation

3.1.2 GIS Preprocessing

Preprocessing can be divided into two tasks, the first being collation, conversion and extraction of existing data, such as maps, imagery and textual records and then depositing this information into the computer database. This task can be extremely time consuming especially when initially establishing a GIS. The importance of this phase also cannot be underestimated since, unless the appropriate data is collected, the GIS may not suit the applications it was intended for.

After importing the data the second task involves establishing a system for recording and specifying the locations of the objects in the datasets. This means that all the data must be related to a spatial reference, street address or Real Property description, and/or underlying coordinate system. When this is complete it is possible to derive the characteristics of any specified location in terms of the contents of any data layer in the system (Star and Estes: 1990).

3.1.3 GIS Data Management

Whilst the quality of data is critical to the success of a GIS, once it is loaded appropriate data management procedures are required allowing the creation of, access to, and maintenance of the database. Effective data management ensures consistent methods for data entry, update, deletion and retrieval. If this is done well, the system

62 will run efficiently and responsively ensuring the functionality and suitability of the system to the user (Star and Estes: 1990).

3.1.4 GIS Manipulation and Analysis

From the user’s perspective this component is often the focus of a GIS. It is in this area of the system where the analytic operations are undertaken on the database contents to derive information about the location. This can be displayed for example, by determining of the gradients of the slopes of an area to establish potential routes for a new road construction. Whilst the terrain information resides in the database in the form of a DEM, software tools are required to derive the gradients from the database (Star and Estes: 1990).

The manipulation and analysis of data in a GIS is not limited to engineering or town planning requirements in an urban environment. Its application can be applied to other areas to assist in either a decision making process or the determination of the delivery of services. For example, a hardware retailer may use a GIS to assist in determining the location of a new large retailing outlet by using the following data sets:

(a) Percentage of home ownership versus rental properties in a designated area (data source: Bureau of Statistics);

(b) Traffic congestion patterns (data source: Government Transport Authority);

(d) Availability and location of suitable land parcels for development (data source: Local and State Governments);

(e) Average financial income range and disposable income of house holds (data source: Bureau of Statistics);

63 (f) Age range of population (data source: Bureau of Statistics).

The suitability of a site or service requirements for the retail outlet could be determined by applying the following principles:

(a) Home owners are more likely to do renovations than tenants;

(b) Low traffic congestion on weekends assists in traveling to and from retail sites, particularly with bulky goods with short notice requirements;

(d) An area with a population suffering from financial stress (due for example to high mortgage repayments) will have less income available for renovations or improvements.

(e) An older or retired population may indicate the need or potential for a larger service catering to tradespersons who maybe called to work in the area.

Similarly the determination of health services in an residential area can be established or improved by spatially linking population data sets in a GIS and then performing a range of analysis to determine the requirement; be they aged care, new born clinics or public transport restrictions which may highlight the requirement for a major medical facility such as a hospital.

3.1.5 Product Generation

Product generation is the final component and could also be termed the output phase, which could be in the form of a textual report based on an analysis derived by linking statistical data such as housing prices of cadastral lots in a suburb, or a hardcopy map plotting the location of bush fires in relation to houses.

64 3.2 Imagery in Data Maintenance

The data used in a GIS must satisfy standards in quantity, quality and timeliness and therefore it must be maintained or updated whenever changes occur. The updating process can be a difficult problem both in terms of time, money and availability of data. Updating or revision of maps has traditionally been achieved by techniques such as obtaining information as new developments occur, when the number of changes that have occurred has rendered the existing maps useless, or by invoking a programme of map revision, such as in the case of Switzerland which updates its national maps every six years. A significant issue with such methods is that changes between revisions maybe lost or economic or political circumstances may prevent the scheduled revision, resulting in the loss of historical data. High resolution satellite imagery would be beneficial for updating purposes particularly in terms of time and cost, when compared to aerial photography. High resolution satellite imagery provides large area coverage cost effectively, which whilst at a lower resolution as will be shown in this thesis, provides enough detail and accuracy when used in conjunction with aerial photography and survey control.

3.3 Satellite Imagery versus Aerial Photography (Imagery)

In the collection of imagery data for compilation or revision of a GIS, metric imagery or photography can play a significant role in spatial analysis and terrain representation for a GIS. Of issue is whether to use aerial imagery or satellite sourced imagery or both. Holland and Marshall (2003) provided a comparison of high resolution Quickbird imagery and aerial imagery as follows:

Advantages of satellite derived imagery over aerial imagery include:

(a) A satellite is operational 365 days a year;

(b) Potentially no extra expense is incurred in attempting more than one image capture as vendors of satellite imagery will continue to attempt to collect imagery until parameters are met (for example until the agreed minimum

65 percentage of cloud cover is achieved);

(d) Imagery can be post processed reasonably quickly;

(e) Air Traffic Control restrictions do not apply;

(f) Satellite derived imagery typically has a large ground coverage which in the case of Quickbird is 16.5km by 16.5km. This reduces the need for block adjustments and the creation of image mosaics;

(g) A satellite can easily access remote or restricted areas, weather permitting;

(h) No aircraft cameras are required to be maintained and financed and depending on level of imagery collected there are minimum specialised software and hardware requirements (such as Photogrammetric Workstations).

Drawbacks of satellite imagery:

(a) Totally cloud free imagery can be difficult to collect and in tropical areas often impossible;

(b) The typical off nadir viewing angle of up to 25 degrees may not be acceptable in a dense urban area, or where the DEM is not ideal;

(c) The production processes required for high resolution satellite imagery can be different to those of traditional photogrammetric data capture. In particular, extra equipment, different production work practices and more training may be required;

(d) the reliability of capture and delivery of imagery is an unknown quantity;

66 (e) satellite image resolution still cannot meet the resolution of large scale aerial imagery.

3.3.1 Cost of High Resolution Commercial Satellite Imagery

The cost of high resolution satellite imagery is often subject to change due to varying offers from the vendors. The table below (Table 3.1) contains prices for imagery from each of the subject satellites. Where possible a comparison has been obtained for previous years’ pricing in order to provide an indication of the financial evolution of the satellite imagery product.

The gaps between years and non continuance of pricing from year to year is an indication of the changing policies within the vendor companies depending on whether their pricing is readily available or only available “upon application”. The varying pricing policy is particularly evident for different locations around the world. For example, in 2002 the Space Imaging price for Geo 1m imagery varied from $18 USD/sq km in North America, $35 USD/sq km in Asia, $35 USD/sq km in Middle East, to $44 USD/sq km in Japan.

However the cost of imagery has not varied greatly from 2002 to 2008. What has changed is the way in which the image vendors have altered the licensing arrangements in order to make the purchase of high resolution commercial satellite imagery more attractive to organisations. An example is the introduction of multi- organisation licensing, whereby a government department which purchases an image is permitted to share the imagery with a certain number of government departments within the same jurisdiction. This is different to when this type of imagery was first available when each government department was required to purchase their own copy of the imagery at full or a negotiated price. The continued competitiveness of the pricing, particularly of the archive imagery is further indication of satellite imagery’s lack of attraction to certain markets, such as a replacement for aerial photography.

67 Satellite Imagery Scene Area 2002 2004 2007 2008 Cost Cost Cost Cost Quickbird Archive 25 sq km $750 AUD ($30 AUD /sq km) Standard 25 sq km $18 USD (Archive) /sq km Standard and 64 sq km $18 USD Priority /sq km Stereo 544 sq km $9792 USD a pair

Worldview Prebooked due to U.S. Defence Commitments

IKONOS Geo 1m Archive 49 sq km $35 USD New 100 sq km /sq km 0.8m Colour (4 50 sq km $7.70 USD band) /sq km New Capture 100 sq km $19.80 0.8m Colour (4 50 sq km (Multi- USD Band) Site) /sq km New Capture 100 sq km $36 USD 0.8m Stereo Pan /sq km Multi-Site 50 sq km (Three $1500 product sites collected in AUD per 12 months) site/date

Orbview Basic Enhanced 384 sq km $21 USD (Did not start Pan /sq km commercial Multispectral 384 sq km $21 USD sales until /sq km 2004) Stereo (Pan) 384 sq km $52.50 USD /sq km

Eros Standard Scene 12.5km x 12.5km $1500 USD (Nov 2001) 25 sq km $5 AUD (offer only) /sq km

SPOT 5 2.5m Colour Full Scene 60km $10 125 Merge (Archive) x 60km USD 2.5m Pan Full Scene 60km $6 750 (Archive) x 60km USD 2.5m Colour Full Scene 60km $11 475 Merge x 60km USD 2.5m Pan Full Scene 60km $7 425 x 60km USD 2.5m Pan Full Scene 60km $8 600 x 60km AUD Table 3.1 Cost Comparison of Satellite Imagery (Source: Vendor websites 2002 to 2008)

3.3.2 Application of Imagery

Despite earlier unsuccessful attempts by the vendors of high resolution commercial satellite imagery to prove how applicable or comparable their product was to aerial

68 sourced imagery, the greatest determining factor remains the application or outcome required (Fraser N: 2008).

If there is a requirement to provide an image in order to non quantitatively report or to assess the extent of an incident such as a natural disaster, satellite imagery provides a versatile source through its ability to collect a large area of any point of the Earth weather permitting. This is opposed to obtaining more quantitative results which is possible with aerial metric photography or imagery, but this source is limited by its area coverage and physical access to sites for acquiring the imagery and support data, such as ground control points if required (Fraser N: 2008). An example of non quantitative reporting from high resolution commercial satellite imagery can be seen in Figure 3.3 below.

Khao Lak, Thailand, 13 Jan 2003 Khao Lak, Thailand, 29 Dec 2004 Figure 3.3a Figure 3.3a Source: Images acquired and processed by CRISP, National University of Singapore, IKONOS Imagery © CRISP 2004

The IKONOS images shown in Figure 3.3 were taken twelve months prior and four days after the 2004 Boxing Day Tsumani Disaster in South East Asia. From these images it is immediately possible to obtain an initial assessment of the damage inflicted for preliminary planning purposes in an area prior to dispatching rescue parties.

69 A more detailed quantitative example is acquisition of imagery over an education facility, of the size of a typical university campus. It would take an estimated $3 000 AUD to have the aircraft and imaging equipment on site and a further $5 000 AUD to fly over the site to obtain the imagery. This would provide an up-to-date image, but if there have been no significant changes over a site, an option could be to obtain archived mono Quickbird imagery, which can be obtained for $750 AUD covering a minimum area of 25 square kilometers. Even if the requirement called for more recent satellite imagery, it could be tasked for approximately $3 500 AUD, which is still below the amount required for aerial photography. If the requirement were to provide a DEM and three dimensional extraction and location of features over the area, this would not be possible with mono satellite imagery and uneconomical with stereo satellite imagery at approximately $18 000 AUD a stereo pair (Fraser N: 2008). From this example it can seen that whilst high resolution commercial satellite imagery can be competitive in price in certain circumstances, the competitiveness can lead to a lack in versatility of the imagery.

The competitiveness of the market is demonstrated by how some suppliers of aerial imagery have marketed their products, as in the case of the AUSIMAGE product. The pricing in Table 3.2 for smaller imagery tiles at greater resolution (urban at 75 to 150mm and rural at 200 to 300mm) and spatial accuracy (urban at +/- 150mm to 200m and rural at +/- 500m) provides a significant viable alternative to high resolution satellite imagery, particularly archived imagery.

3.3.3 Processing Tools Required

A variety of software capable of handling high resolution commercial satellite imagery exists. The cost, both in financial terms and for training in the use of the software is dependent on the desired outcome of the manipulation of the imagery. The greatest cost is at the more detailed photogrammetric application and processing level of the imagery, with the least being at the aesthetic enhancement level. The range of software can be categorised into three levels:

70 Table 3.2 – AUSIMAGE 2007 pricing (Source: SKM website 2008)

(a) Aesthetic Enhancement, where the purpose is simply to manipulate the imagery in order to make it aesthetically pleasing (create a picture only), but not cartographically manipulate or correct the imagery; any software accepting the format of the imagery could be used, such as Corel Draw.

(b) Spatial or Spectral Analysis: this includes the derivation of dimensions of features represented in an image and the spectral manipulation of the image in order to derive analysis and extract relevant information. For example it can include the spectral classification of an image to determine the nature of vegetation cover in a subject area, and requires a more detailed software package capable of spectral classification and specific image manipulation. Software such as ERDAS Imagine, ENVI or IDIRISI all have this functionality.

(c) Photogrammetric Processing: this software is capable of providing the full range of photogrammetrically derived products such as orthoimages, DEMs

71 and feature extraction to required industry and government standards. A package such as BAE Socet Set is capable of performing these functions

The functionality of the software has a direct relationship to its cost. Software capable of the functionality described in (a) and (b) above can cost between $1000 AUD to $10 000 AUD but software capable of (c) such as Socet Set can cost in excess of $55 000 AUD together with similar hardware costs, depending on the functionality and processing speed required.

Once the data (imagery) and tools (software and hardware) have been acquired the question of training and skills sets need to be addressed. Most vendors of software will provide, at a cost, training programs of various lengths to develop initial proficiency in a software package. Depending on the desired outcome of the training, its length may vary from a few days for a basic image manipulation package, through to a number of weeks for a more detailed package such as Socet Set. A further consideration is the one of skills sets of the operators. They can be partly developed from theory lessons but they vary according to types of imagery. Aerial photography is more difficult to use than satellite imagery due to the extensive use of ground control, orientation and triangulation procedures required (Fraser N: 2008). This means that in situations where skills sets maybe limited, such as those of local staff in aid work conducted in a developing country, the simplicity of use of high resolution commercial satellite imagery makes it more suitable than aerial photography in such a situation. In such circumstances the final product may not adhere to strict cartographic standards but it maybe “fit for purpose”.

3.4 Imagery Applications and Considerations

Imagery from any source be it aerial or satellite has a direct application within a GIS as described previously, but each application or service specific GIS has unique requirements of the imagery to meet the aims and outputs of the GIS. The following section presents the requirements of imagery from a range of application specific GIS, and then provides a comparison between the use of aerial photography and high resolution commercial satellite imagery in each application.

72 The areas to be discussed in this section further are:

(a) Local Council Requirements;

(b) Emergency Services;

(d) Land Use Identification.

3.4.1 Local Council Requirements

Potentially local governments in Australia are one of the significant users of satellite imagery. Most local governments have a GIS in which to store and manipulate their spatial data, where the level of detail is dependent on each local government’s priorities, revenue, requirements and access to skilled staff. Typical local government spatial and spatially linked data includes land rates, land zoning, drainage, cadastre, as constructed/as built, water reticulation, sewerage with an orthoimage background derived from aerial photography or satellite imagery.

Imagery has been found to be of distinct benefit to local governments to assist their staff in a variety of tasks, such as:

(a) Town planning;

(b) Engineering works;

(d) Emergency services.

In the areas of town planning and engineering, image data is used for initial

73 overviews, including the proximity of residential dwellings to proposed developments eg. entertainment venues such as hotels, or for displaying existing services and infrastructure for upgrading engineering works, such as road widenings. For health and emergency services, imagery together with other information such as statistical and residential data, provide information to assist the decision maker in determining needs of potential services. For example, an environmental officer may record refuse overflow in certain areas and then use the imagery as a back drop to the statistical and land use data to establish “choke points”. This can then be used to allocate more refuse collection services and possibly different equipment to ensure efficient and economic collection for the benefit of community health.

A major task for local governments is asset management for which satellite imagery can provide assistance. However in the case of satellite imagery, despite the reassurance of the vendors, the resolution of 0.6m to 1.0m is not adequate to record features such as access holes and power lines as displayed in Figure 3.4. Experience has shown that most local governments require image resolutions of better than 0.15m or in mapping scale terms, 1:5 000.

Whilst aerial photography has the potential of providing greater detail this is dependent on flying height, with the greater level of detail, being obtainable from a lower flying height. This can be displayed in the case of a 152mm focal length aerial camera, theoretically a flying height of 152m would produce a photo scale of 1:1 000 covering 5.3ha (53 000sq m) whereas a flying height of 760m will produce a photo scale of 1:5 000 covering 130ha (1 300 000sq m) (Wolf: 2000). The result being that whilst it is possible to obtain greater detail from aerial photography, image acquisition costs increase as more images are required to cover the same area with greater detail.

Whilst the preference of some users is to source the highest resolution imagery possible (typically by aerial photography), the cost of such collection can be prohibited on a annual basis. For this reason some local governments opt for high resolution aerial photography data collection for one year followed by a lower resolution collection every three to five years, and then higher resolution data collection (more expensive) five to seven years after the original collect. (Author, 2005: Appendix A). This has the advantage of limiting costs but also ensuring

74 suitable time intervals between acquisitions to be used for analysis for change detection, as opposed to asset management. For example, in Figure 3.6 Mackay City Council, chose aerial photography over satellite imagery. This was due to the greater resolution afforded by aerial photography and the ability to update the authority’s DEM. In addition, the effects of cloud in a tropical environment played a part as it was possible for an aircraft to fly below the clouds to obtain the required imagery, whereas a satellite which requires a cloud free area (MiMAPS, Mackay City Council: 2007).

Accesshole IKONOS 11 bit Imagery

Power lines IKONOS 11 bit Imagery Figure 3.4 – Inability to identify assets in high resolution satellite imagery

75 1:7000 scale Aerial Photography

Figure 3.5 – Level of detail from aerial photography

Figure 3.6a – City of Mackay May 2004 Urban Aerial Photography

76 A-1

B-1

C-1

Figure 3.6b – City of Mackay May 2006 Urban Aerial Photography

The advantage of regular imagery collects, whether aerial or satellite, for monitoring change within the urban environment can also be seen in Figure 3.6. Both images were taken in May of each year and late in the week or on the weekend. Three areas of change have been indicated in the two year period which assists in urban planning and monitoring. The three areas of change can be interpreted as follows:

(a) A and A-1: increase in the patronage of the facility by the development of car parking and extension of buildings.

(b) B and B-1: construction of a structure in a back yard of a dwelling or the structure is revealed due to vegetation clearing.

Both set of images were taken with similar parameters, such as a pixel size of 0.11m and a positional accuracy of 0.3m and 0.4m.

Local governments undertake engineering works in the form of sewerage

77 construction, drainage and water reticulation. Sourcing DEMs that satisfy the design and construction of this infrastructure is of significant importance to local governments. It is not unusual for local governments to forgo the expense of creating their own DEMs and instead, use topographic data from mapping sources at various scales such as 1:25 000 topographic maps for initial planning only and then use existing higher accuracy DEMs derived from traditional detail field survey techniques for engineering design work. This approach has the additional advantage of reducing costs for data acquisition as the field survey performed to meet the engineering design requirements can have a secondary use as a DEM data source in a GIS for other applications (Boler, 2002 to 2003).

Some state governments have dealt with the issue of cost effectiveness of satellite imagery by taking the initiative to recommend consolidated acquisition. An Example of consolidated acquisition were revealed by the Study of Spatial Imagery Use & Management in Queensland Government in August 2004 (Queensland Government, Geoimage Pty Ltd, Spatial 3i Pty Ltd: 2004). The benefits of joint projects can be displayed by the following stereo pair over the Sydney metropolitan area.

Figure 3.7 – Quickbird Stereo Pair over Sydney

Such a stereo pair covers a large area and with licensing arrangements catering for multi users, this type of joint project becomes a realistic option to a consortium of

78 organisations such as local governments. A stereo pair collect can be used to derive the following information:

(a) Road layout;

(b) DEM;

(d) Feature extraction.

3.4.2 Emergency Services

Emergency Services such as fire, ambulance, natural disaster response and defence are significant consumers of high resolution commercial satellite imagery. The use of any remote sensing data for emergency services (in regards to imagery collects, dissemination and exploitation) can pose significant problems, resulting in restricting the application of high resolution commercial satellite imagery. Previous experience has shown that in order to bridge the gap between the emergency management community and the remote sensing community, there is a need for:

(a) The specification of the type of remotely sensed information required by emergency managers;

(b) Data brokers to provide emergency managers with information from remotely sensed data;

Of particular importance is the need to establish procedures for the acquisition of imagery prior to the emergency to enable timely exploitation.

At an international level as a result of United Nations General Assembly resolution

79 61/110 of 14 December 2006 the United Nations under its Office for Outer Space Affairs (UNOOSA) established the programme, United Nations Platform for Space- based Information for Disaster Management and Emergency Response (UN- SPIDER). The purpose is to provide access to all countries and relevant international and regional organisations to all types of space-based information and services relevant to disaster management, including support and training to developing countries. Further it also allows entities within the United Nations and international bodies that work on space-related issues and disaster management to benefit from increased coherence and synergy in using space science and technology to assist in humanitarian developments (UNOOSA website, 2008)

There have been a number of examples in professional literature and general media to highlight the usage of high resolution commercial satellite imagery for emergency services. The most significant use prior to and during any emergency is the visualisation aspect which allows situation awareness and planning to occur. Prior inspection can reveal to Police offence locations, while during and after an event for Fire Services the imagery can be used for example in Computer Aided Dispatch (CAD), fire investigation, planning and incident management roles.

A particular problem for emergency services is the skill sets of the available staff, particularly in the case of volunteer organisations such as Rural Fire Brigades and State Emergency Services. Even more established emergency services such as the Police often have only one qualified spatial professional, which affects the level of exploitation available and hence restricts the use of high resolution commercial satellite imagery as backdrop imagery. These volunteer organisations also face the issue of acquiring suitable and current imagery and are often reliant on state governments to provide funding or access.

Though emergency services clearly have difficulties accessing high resolution commercial satellite imaging, an example of where a lower resolution imaging satellite has significantly catered for emergency services is the Sentinel web service provided by the Australian Federal Government. Imagery from the MODIS satellite (ground resolution ranging from 250m to 1km) is downloaded and processed to determine the location of probable bush fires within Australia. Output from the

80 service provided by the Sentinel website is shown in Figure 3.8 below.

Figure 3.8 – Example of Sentinel Product

It is more likely that the satellite imagery will be used by emergency services in conjunction with other sources of data such as aerial imagery. This was displayed in the September 11 2001 terrorist attacks on the World Trade Centre in New York as reported by Williamson and Baker (2002). Overhead imagery was used for the purposes of:

(a) Orientating emergency workers since the environment was constantly changing and devoid of landmarks;

(b) Locating smoldering fires that could flare up, posing a safety hazard to emergency workers;

(c) Providing emergency planners an accurate method for estimating the changing volume of the rubble piles as they were screened and removed;

(d) Assisting planners to create transportation routes for moving around the site.

81 Whilst aerial photography was used to provide the more detailed assessment, use of high resolution satellite imagery was significant in the first days after the terrorist attacks as civil and commercial aircraft were restricted from flying over the site.

3.4.3 Public Information (Street Directories, Mapping, General Spatial Data, Analytical Applications)

Satellite imagery can be useful when acquiring data for digital mapping. An othophotograph or orthorectified satellite image is commonly used in a GIS as a base map over which vectors are laid. If the vector information is accurately positioned, all the street segments, parcel boundaries and other vectors will coincide with their locations in the image.

As described earlier in Chapter 2 it is not only the geometric quality that is important in an image but also the information content. Jacobsen (2002) provided a comparison between Swiss topographic maps of 1:25 000, Swiss orthoimages resampled to 1m resolution from orthophotos of 0.3m pixel size, and IKONOS panchromatic images. The results of this comparison indicated that on first view the information content is similar, but further analysis revealed some misidentifications and unrecognised buildings in a map based on the IKONOS image.

It can be seen from Table 3.3 that many of the feature types required for 1:10 000 to 1:50 000 scale mapping can be satisfactorily identified and captured with high resolution commercial satellite imagery. Some features required for larger scale mapping, such as roads and woodland boundaries at 1:2 500 can also be captured. Exceptions to this are narrow linear features, such as electricity transmission lines, walls, fences and hedges, which can be considered impossible to determine with imagery of this resolution. Further to this, a combination of panchromatic and multispectral imagery can help to differentiate between hedges and walls but in general the imagery is unsuitable for the capture of these features. Holland and Marshall (2003) concluded that results of the feature capture and geometric accuracy indicated that Quickbird imagery showed the potential as a data source for 1:10 000

82 scale mapping but could be used to derive topographic data up to scales as large as 1:6 000. The significant drawback is this type of imagery’s inability to resolve small linear features, which if required for a product or data set would have to be derived from another data source.

Mapping agencies are also required to identify changes to the landscape and add these changes to the topographic database as soon as possible after they occur. These can be done in several ways such as:

(a) Observations by field surveyors;

(b) Provision of planning information by local planning authorities or commercial change detection agencies; and

Use of satellite imagery could complement these sources of data by allowing surveyors to find areas of change which could not be detected using other methods. Holland and Marshall (2003) use the example of central London, where satellite imagery potentially could provide regular images of an area, enabling field surveyors to constantly monitor and capture topographic and cultural detail. By comparison in rural areas, change intelligence requirements are different due to remoteness. In this environment buildings may be constructed without planning permission, fields subdivided or merged and vegetation removed or planted. In these areas imagery can be a valuable tool for change intelligence, especially if this data is also used as a source for the subsequent capture of the topographic change.

83 Scale

Features Notes 1:1 250 250 1:1 500 1:2 000 1:10 000 1:25 000 1:50 Housing and All housing easy to identify. Capture of housing satisfactory if of associated uniform shape. Complex shapes with juts and recesses not N N M Y Y features possible to accurately depict. Other buildings All uniform large buildings easy to detect, particularly industrial units. Complex, multilevel and roof structures difficult to clearly N N Y Y Y identify. Communication Kerbs and traffic calming features not clearly defined. Most networks - roads white lines possible to identify. General road alignments very N Y Y Y Y clear. Dual Barriers not clear. General alignment very clear. Slopes (and any Y Y Y Y Y carriageways other height data) impossible to identify in a monoscopic image. Airports Edge of metalling clear. Major buildings okay. Small walkways N N Y Y Y and fine detail around buildings difficult to define. Railways Railway furniture not visible (signal posts, points etc). Single lines not visible. General alignment okay (centre of track). Major N N Y Y Y station detail clearly visible. Electricity Impossible to define actual lines. Transmission pylons possible to transmission see in some instances. Single poles very difficult to see. N N N N N Lines Major sea defences to All features clear to see. Groynes and promenades very clear. Y Y Y Y Y reduce flooding Non-coastal sea Weirs and dams stand out clearly. Finer detail not clear to see. Y Y Y Y Y defences Major property Large fences easy to see. Small fences very difficult to identify. N N N N Y boundaries Major landscape Very clear. Associated fences not so clearly defined. Y Y Y Y Y changes Quarries and other Clear to see, but quarry permanent detail (e.g. conveyor belts), surface difficult to fully identify identify. M M Y Y Y workings Field boundaries Clear to see, but difficult to classify. M M M M Y Water features Clear to see. Small streams sometimes difficult. M M Y Y Y All vegetation Vegetation is clearly defined if using pan-sharpened imagery. Y Y Y Y Y

Tracks and path Tracks clear to see. Unmade paths difficult to make out. Made M M M M M paths in urban areas can be difficult to define. Telephone boxes Difficult to define N N N N N

Extensions to Major shape possible to define. Small juts and recesses not so M M Y Y Y commercial buildings easy. Minor property Possible to see, but difficult to classify. M M M M Y boundaries Tide lines Edge of water-line easy to see, but doubtful if imagery can be N N N N N captured to coincide with high tide times. Garages built after Clearly defined Y Y Y Y Y initial development Key: Y = Yes – feature can be captured N = No – feature cannot be successfully captured M = Maybe – in some circumstances Note: For 1:1 250 and 1:2 500 scales, even if features can be clearly identified, the geometric accuracy is not sufficient to meet the mapping specification

Table 3.3 – Features which can be captured from Quickbird imagery at national (UK) scales (Holland and Marshall, 2003)

84 3.4.4 Land Use Identification

The multispectral applications of satellite imagery must also be assessed when looking at the practical use of satellite imagery. Whilst much has been written on the potential of using the multispectral component of the high resolution commercial satellite imagery, little has indicated the practicalities. The availability of colour information in this instance would assist feature definition in that it would be easier to locate objects featuring a contrasting colour such as roofs, trees, green areas and water surfaces. An option in such an instance would be to use pan-sharpened images, which are created by merging the colour information in a multispectral image with the higher spatial resolution of a panchromatic image (Gianinetto et al, 2005).

A particular point to note is that the complexity of a scene directly affects the selection and success of an automatic procedure for detecting building features (Chen et al, 2001). This means that whilst the information content may exist in a high resolution commercial satellite multispectral image, the complexity of the scene may hinder its use for analysis or data extraction. This can be due to the variety of spectral responses available in close proximity in an area, such as from roof tops, roads and garden vegetation in the Central Business District of a city. This is opposite to the situation with an urban scene where the transition from different ground cover is less abrupt, as can be seen in Figure 3.9 below.

Trinder (2008) provides a warning in a review of research into the viability of spectral classification using high resolution commercial satellite imagery. Of particular note is the determination that the spectral resolution of high resolution commercial satellite imagery is inadequate for extracting ground cover information based on typical pixel- based classification techniques. In Table 3.4 Hirose et al (2004) provides a summary of the potential of extracting thematic information from IKONOS images.

85 Urban

Central Business District Figure 3.9 – Complexity of Scenes due to Ground Cover (IKONOS imagery)

86 Class Results of Maximum Likelihood Classification of an IKONOS Image Water plants + Field check necessary Grass & Deciduous + Have similar appearance trees Coniferous trees & * Difficult to distinguish them bamboo Bare soil + Relatively easy to identify Orchards & * Visual interpretation must be used in conjunction with vegetable fields maximum likelihood classification Rice Fields - Difficult to distinguish from agricultural fields if imagery is not when fields are submerged Residential areas * Distinction from residential area and manmade structures is and manmade problematic structures Open Water + Relatively easy to distinguish Key: + : Acceptable, *: Moderate, -: Unacceptable Table 3.4 – Summary of the classification results for an IKONOS image using maximum likelihood classification (Hirose et al:2004, Trinder: 2008)

3.5 Summary

A GIS provides an efficient and effective means in which to link spatial and textual data to increase the efficiencies and usability of the spatial datasets. The utility of storing and manipulating data in such a system means that more informed decisions can be made to help organisations or individuals by the variety of ways by which the data can be presented and sourced.

Imagery is a significant dataset available for the analysis or service provided by a GIS. Both aerial and satellite imagery have roles, which in some cases compete, but more often they complement each other as a result of their individual advantages and disadvantages. As discussed, when only a representation of an area is required, high resolution satellite imagery provides a good and economical alternative to aerial photography both in terms of financial constraints and skills sets. But if more quantitative information is required such as DEMs, three dimensional extraction and location of features at larger scales, aerial photography provides a better solution. In this case high resolution commercial satellite imagery cannot compete in terms of resolution and accuracy but in some cases this depends on the area coverage and cost.

87 Further each of the applications of GIS discussed in this chapter has a different requirement in respect of the types of datasets. It is this area where the utility of imagery, in particular from satellites capable of high resolution imagery is examined in this thesis. It is not enough to simply supply imagery once. It must also be capable of being supplied repeatedly at various time intervals, be they days, months or years depending on the application, with the same or better quality, as well as being suitable for the purposes of the GIS.

CHAPTER 4

THE SUITABILITY OF HIGH RESOLUTION COMMERCIAL SATELLITE IMAGERY AS A SPATIAL DATA SOURCE

4.1 Introduction

As seen from previous chapters high resolution commercial satellite imagery is a unique source of spatial data. It is not only imagery providing a graphic representation of an area but also can be used for feature extraction for mapping, digital elevation model creation and change monitoring over a period of time. A major aim of this study is an evaluation of the potentials and limitations of high resolution commercial satellite imagery. This chapter is an investigation of the accuracy and the suitability of the imagery to fulfil the requirements of representing an urban environment in a spatial data context. The topics investigated are part of a typical spatial data creation process in a mapping organisation and are listed below with the aims of the investigation for each component:

(a) Ground control points determination: Fundamental to the creation of any spatial data is the establishment and use of control points which define the horizontal and vertical ground positions of monuments or discernable features on an image. A comparison is conducted in this chapter to assess the quality of coordinates of control points derived photogrammetrically from a high resolution commercial satellite image stereo pair when compared to values determined from a ground survey conducted by Real Time Kinematic (RTK) Global Positioning System (GPS).

(b) Feature interpretation and extraction: The aim of this component is to determine the potential of high resolution commercial satellite imagery to portray to a user both cultural and topographical features in an area of interest. This work not only assesses the ability of observers to distinguish certain features of interest but also the level of detail that can be extracted.

(c) Digital Elevation Model (DEM) creation: The final area is the creation of Digital Elevation Models or DEMs. High resolution commercial satellite stereo imagery enables the creation of a DEM by the process of image matching using photogrammetric software such as Socet Set as described in Chapter 3. This investigation gives a comparison between existing data sets and those derived from the IKONOS stereo model over the two study areas.

Whilst there is a multispectral component of high resolution commercial satellite imagery as described in Chapter 3, given the complexity of the use of multispectral imagery as detailed by Hirose et al (2004) and summarised by Trinder (2008) an evaluation of this component of the imagery is outside the scope of this study.

4.1.1 Imagery and Study Areas

For the conduct of this work IKONOS images consisting of a stereo triplet, dated 22 February 2003, of the Greater Hobart area was obtained from Space Imaging (now Geoeye Inc.) through the University of Melbourne. Reference data for comparison was obtained from the Tasmanian government and two Hobart local councils.

The geometric collection parameters of the IKONOS imagery are shown below.

Source Image ID Start Time Sensor Sensor Scan (Date 22 February 2003) (GMT) Azimuth Elevation Azimuth 2003022200270380000011614288 00:27:03.8 329.4° 69.1° 180° 2003022200272480000011614290 00:27:24.8 293.7° 75.1° 0° 2003022200275430000011614289 00:27:54.3 235.7° 69.2° 180° Table 4.1 – IKONOS Imagery Geometric Collection Parameters (Source: IKONOS Test Imagery Cover Letter: 2004)

90 The IKONOS imagery details are:

Bits Dynamic Bands Format Product Remarks Range Identificatio Adjustment n (POIDs) (DRA) 8 Yes 1m RGB GeoTIFF 150442 Pan-sharpened, natural 150443 colour 151127 11 No 1m Pan and GeoTIFF 149875 Absolute MSI radiometry 4-bands of 4m 149879 with DRA off Multispectral (MSI) 149883 11 No 1m Pan and NITF 2.0 149877 ClearView product format 4-bands of 4m 149881 Multispectral (MSI) 149885 11 No 1m Pan Stereo GeoTIFF 153452 UTM map-projected stereo. Relative orientation performed Table 4.2 – IKONOS Imagery Set Details (Source: IKONOS Test Imagery Cover Letter: 2004)

Two different study areas were selected to provide a range of ground cover types, both built and natural, and terrain, as follows:

(a) Urban area at Binya St, Glenorchy, Hobart (Figure 4.2);

(b) Central Business District (CBD) of Hobart (bounded by Bathurst St, Argle St, Davey St and Harrington St) (Figure 4.3).

All photogrammetric work such as control point measurement, feature extraction and digital elevation model creation were performed on the University of New South Wales Socet Set Photogrammetric Workstation. The selection of the software was based on commonly used or readily available software in regards to cost and training.

The stereo model was created without using ground control points or performing an absolute orientation. The purpose of this methodology was to assess the inherit positional and geometric accuracy of the imagery using the direct orientation from the Rational Polynomial Coefficients or RPC of the images provided by Space Imaging. RPC, also known as Rapid Positioning Capability or Rational Polynomial Model, are an approximation to the rigorous sensor model and used for mapping the imaging system’s pixel coordinates to ground points. The RPC model uses a pair of polynomial ratios to 91 approximate the geometric relationship between the camera and the surface of the Earth (Hinson: 2007). In the case of IKONOS it is necessary to use this method since the imaging systems sensor model is not supplied.

To further assess how well IKONOS imagery represents an urban environment, both natural and built, in a spatial data context by comparison with reference data, a ground truthing validation exercise was undertaken in Hobart on the 14 and 15 May 2007. The field exercise was done in two phases:

(a) 14 May 2007 - survey of the urban study area at Binya St Glenorchy;

(b) 15 May 2007 - survey of the central business district study area of Hobart (bounded by Bathurst St, Argle St, Davey St and Harrington St).

The scope of the ground truthing covered:

(a) comparison of built environment features extracted;

(b) extent of utilities that can be determined or monitored using the imagery, such as water, sewerage and power lines;

92 Urban Area

CBD Area

Mt Wellington N

Figure 4.1 – Extent of IKONOS Study Imagery over Hobart (Source: IKONOS POID: 153452)

93 Binya St

Figure 4.2a - Urban area at Binya St, Glenorchy, Hobart (Source: IKONOS POID: 150442)

Figure 4.2b – Anaglyph of Urban area at Binya St, Glenorchy, Hobart (Source: Derived from IKONOS Stereo Pair POID: 153452)

94 Bathurst St Argle St

Harrington St Davey St

Figure 4.3a - CBD Central business district (CBD) of Hobart (Source: IKONOS POID: 150442)

Figure 4.3b - Anaglyph of CBD Central business district (CBD) of Hobart (Source: IKONOS POID: 150442)

95 4.2 Ground Control Points Comparison

4.2.1 Methodology

As discussed in 4.1 the purpose of this section is to assess between and as a result the quality of coordinates extracted from IKONOS stereo imagery. As mentioned previously in Chapter 2 the IKONOS satellite is equipped with GPS antennas and three digital star trackers to establish precise camera positions and attitudes.

To support the IKONOS imagery data set, a control point data file consisting of 114 points created by the University of Melbourne was supplied. The control points ground coordinates were determined by RTK GPS survey with a ground accuracy of approximately 10 to 20cm in the X (Easting), Y (Northing) & Z (Height) direction. The ground control points were spread over an area of approximately 12km by 13km with a height variation of -2.49m to 1260.6m with respect to the WGS84 ellipsoid. The RTK GPS control points were used as the reference data in the comparison of the X, Y & Z coordinates of the points extracted from the IKONOS stereo model created in Socet Set.

4.2.2 Comparison of GPS RTK Coordinate Values to IKONOS Stereo Model Values

Once coordinate values were extracted from the IKONOS stereo model, four differences between the data sets for each control point coordinate value were determined:

(a) X values;

(b) Y values;

96 (d) The vector distance derived for the X and Y differences for each point X, Y coordinates from each data set.

The detailed results are contained in Appendix B with a summary of the results displayed Table 4.3 below

X, Y Vector Distance X (m) Y (m) Z (m) (X² + Y²) (m) Mean 1.9 -0.9 2.3 2.3 Minimum -0.4 -2.1 -1.9 0.8 Maximum 3.1 0.6 4.9 3.3 Table 4.3 – Mean, Minimum and Maximum Component Differences and Vector Distance

To provide a statistical evaluation, the Root Mean Square Error or RMSE of each coordinate difference and the vector distance between the data sets were determined, as summarised in Table 4.4.

X, Y Vector Statistical Distance Level of X (m) Y (m) Z (m) (X² + Y²) Confidence (m)

68% 0.6 0.6 0.9 0.6 90% 0.9 1.0 1.6 0.9 95% 1.2 1.2 1.9 1.1 99% 1.6 1.6 2.5 1.4 Table 4.4 – Root Mean Square Error of Component and Vector Distance

According to Kay et al (2003) the RMSE approximately correspond to the residuals of the observations within ± 1 standard deviation or 1-sigma of the measurements ie approximately 68% of the residuals observed in an image would be expected equal to or 97 less than the RMSE, with remainder being larger. Further the 90%, 95%, or 99% levels can be determined by multiplying the RMSE value by 1.65, 1.96 or 2.58 respectively. The values are described as the statistical levels of confidence in Table 4.4.

The RMSE of the difference between reference and observed control points coordinate values indicate that point coordinates derived from IKONOS stereo imagery would be suitable for planimetric mapping between 1:1 000 to 1:5 000 scale. This scale range requires a positional accuracy between ±0.3m to ±1.5m. It is doubtful that heights derived from IKONOS stereo imagery would be suitable for this scale range as it is considered that no point should be more in error that half the contour interval, the contour interval for 1:1000 is 1m and 1:5000 is 5m.

Toutin, Chenier and Carbonneau (2002) make the point that IKONOS point accuracy deteriorates in mountainous areas if the images are acquired with off-nadir viewing, and so the product for these conditions will only meet requirements for mapping scales at 1:100 000. To determine if there was a relationship between point accuracy and location, ie in terms of the Easting (X), Northing (Y) or Height (Z) values across the stereo model, the X, Y vector distance ((X² + Y²)) and the Height difference (Z) between the data sets were plotted against increasing Easting and Northing and ellipsoidal Heights (Figure 4.5). The correlation coefficient in order to determine interdependence was then calculated for each data set represented in the graphs in Figure 4.5 by using the following formula (Koffman et al: 1990):

BB yyxx Correlation Coefficient of (x, y) = (4.1) 2 BB yyxx 2

Where x and y are the data sets with x and y being the mean values. A correlation coefficient of close to or equal to -1.0 or 1.0 would indicate interdependence.

98 It can be concluded from these figures that in this case there is no relationship between differences between the control point coordinate values of the data sets and the Easting, Northing and Height value of the coordinates.

Distance between Measured to Control against increasing Height

3.5

3.0

2.5

2.0

1.5

1.0 Distance betweenControl toMeasured

0.5

0.0

3 1 1 3 1 7 8 7 9 9 4 3 1 9 70 42 40 42 62 12 29 05 74 21 074 .523 .278 .204 .934 .794 .624 .006 .530 .480 .830 .464 86 40 03 26 88 41 34 26 75 02 0.16243.19833.21374.94066.22679. 2. 3. 5. 0. 1. 3. 6. 3. 6. 9. -2.492 -0.023 13 19 23 28 37 41 50 56 82 84 94 10 12 15 19 25 27 35 42 125 125 Height

Figure 4.5a – Vector Distance against Ellipsoidal Height Correlation Coefficient: -0.07

99 Distance between Measured to Control against increasing Easting

3.5

3.0

2.5

2.0

1.5

1.0 Distance between Control to Measured Control between Distance

0.5

0.0

7 8 2 3 4 5 1 2 0 5 8 7 2 1 0 9 9 3 9 2 2 7 3 3 81 591 850 43 541 500 264 652 39 0 462 14 120 10 784 16 5 .50 .5788 3 .73 .9450 9 .8 .12 7 1 .25 .4528 5 .24 .0803 7 .7 .7 1 .5 .34 .2441 5 .6 .61 6 .9 .87 4 1 8 0 2 3 8 1 7 8 5 4 7 6 31. 85. 97. 50 03. 00. 35. 78. 54 82. 50 19. 89 33. 11 5 94 8 40 6 1 2 8 94 4 67 0 3 59 6 7 82 1 2 2 3 8 0 2 3 4 5 5 5 6 8 1 2 2123 2 2271 2 2408 2 2492 2 25 2 2 2597 2 2633 2 2854 516 5 51932 519 5 5 521 5 5 523 523 5 5 524 5 5 525 5 5 525 5 5 5 526 5 5 527 5 5 Easting

Figure 4.5b – Vector Distance against Easting Correlation Coefficient: -0.119

Distance between Measured to Control against increasing Northing

3.5

3.0

2.5

2.0

1.5

1.0 Distance between Control to Measured Control between Distance

0.5

0.0

0 0 0 0 0 0 0 0 0 0 0 0 60 40 40 00 10 00 00 70 30 00 40 00 20 8 0 2 0 14 9 5 2 31 7 29 9 9 44 0 78 8 82 5 .5 .1 .5 .0 0 .0 .2 .2 .4 .6 2 .8 .9 0 .9 2 .3 8 .4 6 5 5 5 1 4 7 5 0 4 0 7 5 7 4 4 74. 7 5 6 80 2 44. 1 8 92. 45. 5 39. 7 7 6 2 7 8 6 9 8 3 4 5 4 0 5 2 1 7 8332.7398 9055.31500 0498.38600 2 3 3833.0173 4578.8354 5335.40705 54 5 5 5977.1926 6839.1847 7 9 0 0570.409 4 4 5 5 5 5 5 5 5 5 5 5 5 5 6 24 24 25 25 25 25 25 25 25 25 257380 2581 26 5 52 5 52 5 52 5 5250839.312052 5 52 5 52 5 52 52 5 52 5 52 5 52 52 5 52 5 52 5 52 Northing

Figure 4.5c – Vector Distance against Northing Correlation Coefficient: 0.025

100 Delta Z (Measured to Control) against increasing Height

Z 02 04 15 Delta Z between Controlto Measured 207 837 512 097 935 9 8 857 594 164 189 533 3629 .1 3 3 5 5 8 7 7 1978 6078 .6752 2201 0.05 1. 3.21174.93834.98488.9504 4.7 3.8 8. - -0.0035 13 17. 21. 27. 36. 41.502547.110752.9 78.3 8 9 96. 32. 56. -1 114.2219137. 183. 2 2 351 423.0093689.8207 125

-2

-3 Height

Figure 4.5d – Z (m) against Ellipsoidal Height Correlation Coefficient: -0.184

Delta Z (Measured to Control) against increasing Easting

2 3 6 5 9 8 X 91 68 88 84 83 23 68 46 95 16 22 Delta Z between ControlMeasured to 1 8 615 584 1 7 2 88 526 939 6 4 3 72 41 904 3 3 9 64 45 885 573 4 5 5 4 .8 .1 6 8 6 9 .4 .5 .1 9 0 0 .4 9 7 4 5 5 6. 0. 3. 0. 3 8 7. 8. 2. 6. 6 1 5 0. 72.09 6. 6. 1 4. 0. 5. 4. 8. 7 4 3 4 8 -1 65 37 66 91 34 55 6 99 24 33 731 932 204 262 303 472 498 1 1 19 20 21204.709821561.92812 2 2 23 23 240 24415.2052 2 2 25 52 25 257 25 26 26 266 281 5 5 5 5 5 5 5 5 5 5 5 5 5 52457 5 5 5 5 5 525786.052590 5 5 5 5 5 528461.5954

-2

-3 Easting

Figure 4.5e – Z (m) against Easting Correlation Coefficient: 0.158

101 Delta Z (Measured to Control) against increasing Northing

7 4 9 2 Y 7 Delta Z between Control to Measured 744 .322 899 .977 513 275 066 145 361 081 781 677 213 363 569 4 656 7.322 5. 0 0. 6 6. 8. 6. 1. 9.765 8. 9.29 4. 3.54 1. 2. 3. 7.67 3. 6. 9. 2. 5.513 6 -1 31 50 253464.45 5537 5545 5734 24855 5249563.05 5 25923 5248 5 524900 525040525069525082525269 5253785253955254 5254835255299.29552 5255395255440.83152 5255805256336.99952566252570252 5257455258095 52600152605

-2

-3 Northing

Figure 4.5f – Z (m) against Northing Correlation Coefficient: -0.120

It is significant that when each of the vector distances between the IKONOS stereo model and the RTK GPS reference data coordinate values are plotted as vectors the majority are in the west-north-west direction (see Figure 4.6) indicating a directional bias within the model.

Willneff and Poon (2006) indicated a similar bias with IKONOS imagery over Hobart and attributed it to a RPC bias. Willneff and Poon (2006) also indicated that the RPC bias could be corrected if ground control points of the image area are available. In fact Baltsavias et al (2001) makes the point that by using 4 to 7 accurate and well distributed ground control points and a simple translation or bias removal, absolute accuracies in X, Y and Z of 0.4m to 0.5m and 0.6m to 0.8m can be achieved. It is clear that the ability to resolve this directional bias without ground control points would be of significant advantage to the application of IKONOS imagery. This would allow positional accuracies approaching that of an earth based mapping system as detailed in Section 2.4 whilst still maintaining the advantage of avoiding visiting the site by ground or accessing airspace.

102 This ability though is currently not available for stereo models based on orientations by RPCs.

Figure 4.6 - Vector distances between IKONOS stereo model control points and the RTK GPS reference data (Note: In order to visualize the direction vector each error distance has been multiplied by a factor of 100.)

The directional bias may also be due to a coordinate datum error. As both the images and the RTK GPS control points are derived using the WGS84 datum, there maybe discrepancies between the application of this datum within the satellites’ buses due to the differing orbits or orientations. As they are both using the same datum and the IKONOS satellite derives its position from the GPS satellites and star trackers, the directional bias could be considered a systematic Observer Distortion as described in Chapter 2.

In summary the RPC of the IKONOS stereo imagery is capable of providing a horizontal positional accuracy between 0.3m to 1.5m, though in heighting accuracy it cannot match

103 its horizontal component. A directional bias only was confirmed in this work; currently it is only possible to resolve this bias by the introduction of ground control points to the stereo model. The introduction of such points would unfortunately remove one advantage of high resolution commercial satellite imagery that being remote access.

4.3 Feature Interpretation and Extraction

As detailed in Chapter 3 for high resolution commercial satellite imagery to be useful it must have an application in the collection of spatial information in the form of ground features. This section examines the factors affecting the suitability for high resolution commercial satellite imagery to fulfill this role, when compared to other data sources, such as aerial photography and spatial data sourced from ground survey methods.

4.3.1 Development of a Civil NIIRS Rating for Imagery Comparison

In order to provide an initial comparison across all images, it was considered appropriate to assess the ability to extract features using the Civil NIIRS (National Imagery Interpretability Rating Scale). A full description of Civil NIIRS can be found in Beloken et al (1997). NIIRS was originally developed within the United States of America (USA) defence community in the 1970s to include categories such as natural, agricultural and urban/industrial and not simply military equipment as described in the original NIIRS. It therefore can be utilised as a measure of image interpretability for:

(a) communicating the potential usefulness of imagery.

(b) specifying requirements for imagery.

(d) assisting the design and assessment of future imaging systems.

104 (e) qualitatively determining the performance of sensor systems and imagery exploitation devices.

In order to assign a NIIRS rating to an image, analysts need to determine what tasks they could achieve and/or what features they can see in the imagery, by taking into account local scene content and image acquisition conditions. In effect the imagery analyst judges the information potential of the image as opposed to making judgments about what was or was not actually imaged.

The procedure for establishing an image’s Civil NIIRS rating is as follows:

(a) Decide which NIIRS rating level best describes the interpretability of the image being viewed, by judging what interpretation tasks can or could be done, and what items of interest can or could be seen on imagery of that interpretability. This analysis will also not only reveal what items cannot be seen but also what tasks cannot be done on imagery of that quality.

(b) Determine an appropriate rating level ranging from 0 to 9 according to the criteria described in Table 4.5.

In determining the Civil NIIRS rating for imagery over the study areas presented in Table 4.6, first a rating level was determined from the values in Table 4.5 then a decimal rating graded further on how well the image defined the Rating Level was applied. For example in the case of Image ID 150442RGB for the CBD it was determined that it satisfied the information requirements for Rating Level 4 to the satisfaction of a score of 8 out of 10: hence a final Civil NIIRS score of 4.8 resulted.

105 Rating Level 0 Rating Level 5 ( 0.75m to 1.2m GSD) Interpretability of the imagery is precluded by Identify Christmas tree plantations. obscuration, degradation, or very poor resolution. Identify individual rail cars by type (eg gondola, flat, box) and locomotive by type (eg steam, diesel). Detect open bay doors of vehicle storage buildings. Identify tents (larger than two persons) at established recreational camping areas. Distinguish between stands of coniferous and deciduous trees during leaf off condition. Detect large animals (eg elephants, rhinoceros, giraffes) in grasslands. Rating Level 1 ( 9.0m GSD) Rating Level 6 ( 0.40m to 0.75m GSD) Distinguish between major land use classes (eg Detect narcotics intercropping based on texture. urban, agricultural, forest, water, barren). Distinguish between row (eg corn, soybean) crops Detect a medium sized port facility. and small grain (eg wheat, oats) crops. Distinguish between runways and taxiways at a Identify automobiles as sedans or station wagons. large airfield. Identify individual telephone/electric poles in Identify large area drainage patterns by type (eg residential neighbourhoods. dendritic, trellis, radial) Detect foot trails through barren areas. Rating Level 2 ( 4.5m to 9.0m GSD) Rating Level 7 ( 0.20m to 0.40m GSD) Identify large (ie greater than 160 acre) center-pivot Identify individual mature cotton plants in a known irrigated fields during the growing season. cotton field. Detect large buildings (eg hospitals, factories). Identify individual railroad ties. Identify road patterns, such as clover leaves, on Detect individual steps on a stairway. major highway systems. Detect stumps and rocks in forest clearings and Detect ice-breaker tracks. meadows. Detect the wake from a large (eg greater than 300’) ship. Rating Level 3 ( 2.5m to 4.5m GSD) Rating Level 8 ( 0.10m to 0.20 GSD) Detect large area (ie larger than 160 acres) contour Count individual baby pigs. plowing. Identify a USGS benchmark set in a paved surface. Detect individual houses in residential Identify grill detailing and/or the license plate on a neighbourhoods. passenger/truck type vehicle. Detect trains or strings of standard rolling stock on Identify individual pine seedlings. railroad tracks (not individual cars). Identify individual water lilies on a pond. Identify inland waterways navigable by barges. Identify windshield wipers on a vehicle. Distinguish between natural forest stands and orchards. Rating Level 4 ( 1.2m to 2.5m GSD) Rating Level 9 ( less than 0.10 GSD) Identify farm buildings as barns, silos or residences. Identify individual grain heads on small grain (eg Count unoccupied railroad tracks along right of wheat, oats, barley). ways or in a railroad yard. Identify individual barbs on a barbed wire fence. Detect basketball court, tennis court, volleyball Detect individual spikes in railroad ties. court in urban areas. Identify bunches of pine needles. Identify individual tracks, rail pairs, control towers, Identify an ear on large game animals (eg deer, elk, switching points in rail yards. moose). Detect jeep trails through grassland. Table 4.5 – Definition of Civil NIIRS ratings (Source: Beloken & Emmons et al:1997)

106 Civil NIIRS Image ID/ Comment Description Software Rating Name CBD Urban 150442RGB 8 Bit, 1m Red-Green-Blue RemoteView 4.8 4.8 - Good true (GeoTIFF) DRA – ON, Pan- Reader colour sharpened, natural color 150443RGB 8 Bit, 1m Red-Green-Blue RemoteView 4.9 4.9 - CBD - “crisp” (GeoTIFF) DRA – ON, Pan- Reader image sharpened, natural color - Urban - Brown tone reveals more 151127RGB 8 Bit, 1m Red-Green-Blue RemoteView 4.8 4.8 - Washed out (GeoTIFF) DRA – ON, Pan- Reader sharpened, natural color 149875 11 Bit, 1m Pan, Remote 4.8 4.8 (GeoTIFF) DRA - OFF View Reader 149879 11 Bit, 1m Pan Remote 4.8 4.8 (GeoTIFF) DRA - OFF View Reader 149883 11 Bit, 1m Pan Remote 4.8 4.8 (GeoTIFF) DRA - OFF View Reader 149877 11 Bit, 1m Pan Remote 5.1 5.1 (NITF) DRA - OFF View Reader 149881 11 Bit, 1m Pan Remote 5.1 5.1 (NITF) DRA - OFF View Reader 149885 11 Bit, 1m Pan Remote 5.1 5.1 (NITF) DRA - OFF View Reader 153452 11 Bit, 1m Pan Stereo, Remote 5.0 5.0 (GeoTIFF)) UTM map-projected View Reader stereo, Relative orientation performed DRA - OFF 153452_001 11 Bit, 1m Pan Stereo, Remote 5.0 5.0 (GeoTIFF)) UTM map-projected View Reader stereo, Relative orientation performed DRA - OFF 1:7000 “The List” DPIE, Remote 6.0 Does not Sullivans Cove 1:7000 Sullivans Cove, View Reader cover (GeoTIFF) Colour Orthophoto Urban area 1:24 000 “The List” DPIE Remote 5.00 4.5 - CBD – Clear Orthophoto View Reader - Urban – (GeoTIFF) Washed out CBD_15m_Ortho Created from 11 Bit, 1m Remote 4.8 Does not (GeoTIFF) Pan Stereo GeoTIFF, 15m View Reader cover post spacing DEM Urban area Urban_ortho Created from 11 Bit, 1m Remote Does not 4.8 (GeoTIFF) Pan Stereo GeoTIFF, 30m View Reader cover post spacing DEM CBD area Table 4.6 – Civil NIIRS ratings for imagery over Hobart

107

4.3.2 Factors Affecting the Interpretation of Features in High Resolution Commercial Satellite Imagery.

Experience and evidence has shown that there are a variety of factors affecting the quality and hence visual interpretability. This section provides a brief description of the factors which affect image quality:

(a) Radiometric Resolution;

(b) Whether Dynamic Range Adjustment applied (DRA on or off);

(d) National Imagery Transfer Format (NITF) versus Geographic Tag Image Format (GeoTIFF);

(e) Spatial Resolution and Ground Sampling Distance;

(f) Angle of collect of imagery (or obliquity).

(a) Radiometric Resolution

The principles of radiometric resolution or “bit depth” are described in Chapter 2. The use of 11 bit imagery in situations such as low-level lighting conditions can be advantageous as it improves the ability to enhance images or increase the “tonal” range of the image. The difference can clearly be seen by comparing the 8 bit IKONOS imagery to the 11 bit IKONOS imagery. As determined in this study despite the use of colour in the 8 bit data the panchromatic 11 bit data has the same NIIRS rating.

108 (b) Effect of Dynamic Range Adjust applied (DRA on or off)

IKONOS imagery can be supplied with and without Dynamic Range Adjust (DRA) applied, though only GeoTIFF imagery allows this option, as NITF imagery is only available with DRA off. When DRA is turned on a tonal transfer curve converts the absolute radiometry, which separately measures the intensity at each wavelength, achieving a more natural appearance for visual interpretation. The effect is when the Civil NIIRS was applied during this study that the 8 bit imagery with DRA-on achieved the same or very nearly the same Civil NIIRS rating as the 11 bit data with DRA-off.

Panchromatic imagery whilst black and white, covers the whole of the visible spectrum. The advantage of colour is that the 8 bit colour imagery can achieve a similar Civil NIIRS rating to the 11 bit Panchromatic image. A significant advantage of panchromatic imagery is that the file size is substantially smaller than the 3 bands of the colour imagery. Despite this, colour imagery has the distinct advantage of its visual appeal, which can be more important than a higher dynamic range black and white image for the casual observer.

(d) National Imagery Transfer Format (NITF) versus Geographic Tag Image Format (GeoTIFF)

Two formats for IKONOS imagery were provided, GeoTIFF (Geographic Tagged Image Format File) and NITF (National Imagery Transfer Format). The documentation accompanying the trial imagery indicates that the GeoTIFF is supplied to civilian users, while NITF is reserved for government users, presumably from the United States of America. It can be noted in the Table 4.6 that the NITF file format has a higher NIIRS rating than the GeoTIFF images, indicating that the NITF image format presents better imagery quality.

109 (e) Spatial Resolution and Ground Sampling Distance (GSD)

Spatial resolution is a function of the GSD, the smaller the GSD the greater the spatial resolution, as well as the contrast of objects. This is apparent in the IKONOS trial imagery where it is possible to detect a vehicle and depending on the size of the vehicle determine the type, such as a bus versus a family car, but it is not possible to discriminate between a sedan and station wagon. The spatial resolution of the 1:7000 Sullivans Cove Aerial Colour Orthophoto provided by the Tasmanian Department of Primary Industries and Water (DPIW) allows the discrimination between a sedan and station wagon.

(f) Angle of collect of imagery (or obliquity)

The angle of obliquity has the effect of increasing the GSD of the imagery and as a result its geometric accuracy and resolution. The effect of obliquity is such that at nadir the GSD for a IKONOS panchromatic image is 0.82m whereas at 26° off nadir it is 1.0m (www.geoeye.com, products, 11/12/06). The off nadir angle is defined as the angle between Nadir, the point directly below the sensor, and the point on the ground the sensor is pointed at.

Poon et al (2006) also developed a rating scale for orthoimages based on all aspects of digital imagery such as high and low contrast as well as area and linear features. For this study the Civil NIIRS system was considered more suitable as it rates an image based simply on what information can be determined and as such the usefulness of the image, it is also a standard recognised rating system. This differs to Poon et al (2006) where the characteristics of the image, such as how well features are defined or contrasted, are rated.

This section demonstrates that there is more to considering the use of high resolution commercial satellite imagery than simply the ground resolution for determining the maximum scale and employment of final products. It has been shown that through the application of the rating scale that there is no significant difference between the worst assigned rating and the best even though there is a significant difference in the scale of

110 the imagery. For instance as described earlier, IKONOS by empirical means is suited to 1:20 000 scale mapping based on its best rate on the Civil NIIRS of 5.0, whereas the 1:7 000 Sullivans Cove Orthophoto could only be considered one rating scale better at a Civil NIIRS of 6.0. Further, the IKONOS 11 bit panchromatic imagery was rated the same as the 8 bit colour imagery, this indicates that despite the appeal of colour, the 11 bit panchromatic or “black and white” imagery can be considered more useful. Finally near vertical aerial photography must be acquired with minimum tilt of the aircraft to ensure integrity of the metric imagery, whereas for IKONOS imagery the obliquity can vary from 21 degrees to 15 degrees with no loss in Civil NIIRS, further indicating the versatility of the imagery and platform used in the image acquisition.

4.3.3 Evaluation of Feature Interpolation and Extraction from IKONOS Imagery

This section provides a comparison of what ground features (both built and natural) can be detected or identified using IKONOS imagery. Compiled drawings extracted from IKONOS stereo imagery showing features, contours and photo locations are contained in Appendix C.

Due to the varying terrain and ground cover of the natural and built environment, the two Study Areas are treated individually for comparison with other data sources and ground truthing.

111 4.3.3.1 Urban Trial area at Binya St, Glenorchy, Hobart

Change in roof line

Figure 4.7 – Change in roof line, not apparent on IKONOS imagery or feature extraction (Photo P1)

Figure 4.7a – IKONOS 8 bit Imagery Figure 4.7b – IKONOS 11 bit Imagery

Figure 4.7c – IKONOS derived Orthophoto Figure 4.7d – 1:24 000 Orthophoto

112 Figure 4.7 shows the roof line of a typical suburban house, the resolution displayed by all comparison images makes it difficult to determine the actual outline. Data from Glenorchy City Council indicated the level of detail they depict on the circled building is a regular rectangular as displayed in Figure 4.8.

Figure 4.8 – Building Data from Glenorchy City Council

This is compared to the level of detail extracted from IKONOS stereo imagery using Socet Set software in Figure 4.9. This segment of data better approximates the actual shape of the building. Though the extra area at the lower end of the building could be due to an extension of the original structure given the different reflectance of the roof (see Figure 4.7a) and the indication from the Glenorchy City Council data dictionary that the data was sourced from design plans.

Extra Building Area

Figure 4.9 – Features Extracted from IKONOS Imagery

113 Figure 4.10 shows two stormwater drains and a survey mark both of which clearly cannot be distinguished on any of the imagery due to their small size as shown below by the orange circles.

Survey Mark 10432

Stormwater Drain

Figure 4.10 – Stormwater drains and Survey Mark (No.10432) (Photo P2)

Figure 4.10a – IKONOS 8 bit Imagery Figure 4.10b – IKONOS 11 bit Imagery

Figure 4.10c – IKONOS derived Orthophoto Figure 4.10d – 1:24 000 Orthophoto

114 Figure 4.11 displays an example of an unformed track and a steep slope. The Glenorchy City Council data indicates the tracks are derived from engineering drawings, with a similar level of detail as displayed in Figure 4.12. The IKONOS derived features are shown in Figure 4.13 which shows the level of detail available was only a single line, as opposed to both sides of the track being available from the City Council data.

Figure 4.11 – Example of track and steepness (Photo P3)

Figure 4.11a – IKONOS 8 bit Imagery Figure 4.11b – IKONOS 11 bit Imagery

115

Figure 4.11c – IKONOS derived Orthophoto Figure 4.11d – 1:24 000 Orthophoto

Figure 4.12 – Track Detail from Glenorchy City Council

Figure 4.13 – Track Detail extracted from IKONOS stereo imagery

116 Figure 4.14 shows a significant power line which is only detectable through ground clearing or scarring with the poles not even detected by the shadow they cast on any of the imagery

Figure 4.14 – Example of power line visible only by clearing not lines and poles (Photo P4)

Figure 4.14a – IKONOS 8 bit Imagery Figure 4.14b – IKONOS 11 bit Imagery

117

Figure 4.14c – IKONOS derived Orthophoto Figure 4.14d – 1:24 000 Orthophoto

Figure 4.15 – Example of water main, corresponds with Glenorchy City Council plans (Photo P5)

The water main marker shown in Figure 4.15 above relates to the Glenorchy City Council plans shown below by the dashed blue line and orange arrow in Figure 4.16.

118

Figure 4.16 – Glenorchy City Council plan detail of water main

The orange circle in Figures 4.17a and 4.17b indicate the approximate area of the water main which cannot be detected by visible ground scarring (depression or clearing) or by change in tone in the ground surface or vegetation.

Figure 4.17a – IKONOS 8 bit Imagery Figure 4.17b – IKONOS 11 bit Imagery

119

Figure 4.18 – Example of tops of reservoirs, older one (above) is Figure 4.18a – Example of tops of reservoirs, newer one (above) circular (Photo P6) is polygon shaped (Photo P7) Old Old Old Old

New New New New

Figure 4.18e– 1:24 000 Figure 4.18b– IKONOS 8 bit Figure 4.18c – IKONOS 11 bit Figure 4.18d – IKONOS Orthophoto Imagery Imagery derived Orthophoto

120 The reservoirs shown in Figures 4.18 and 4.18a give an example of the difficulty of detecting the true shape of similarly shaped features, as shown for the old and new reservoirs. During feature extraction using IKONOS 11 bit stereo imagery, it was not possible to detect the difference between the new roof which is a polygon shape and the old roof which is a circle, as displayed in Figure 4.19.

Figure 4.19 – Features extracted from 11bit IKONOS Stereo Imagery

Both reservoirs are represented as circles and not as they should be.

B A

Figure 4.20 – Example of roof tops and their roof line (Photo P12)

121 A A

B B

Figure 4.20a – IKONOS 8 bit Imagery Figure 4.20b – IKONOS 11 bit Imagery

A A

B B

Figure 4.20c – IKONOS derived Orthophoto Figure 4.20d – 1:24 000 Orthophoto

As in Figure 4.7, Figure 4.20 shows the details of the roof line in the Glenorchy City Council feature data, compared with features extracted from 11 bit IKONOS imagery below.

122 A

Figure 4.20e – Glenorchy City Council Data

Figure 4.20f – Features extracted from 11bit IKONOS Stereo Imagery

Whilst the purpose of this study is to assess the suitability of IKONOS imagery, in this instance with both examples the information derived was incorrect in at least one part of the structure, highlighting the importance of ground truthing and the danger of relying on one source of data.

123

Figure 4.21 – Example of number of power poles and lines that cannot be seen on IKONOS imagery (Photo P13)

Figure 4.21a – IKONOS 8 bit Imagery Figure 4.21b – IKONOS 11 bit Imagery

Figure 4.21c – IKONOS derived Orthophoto Figure 4.21d – 1:24 000 Orthophoto

124 Figure 4.21 gives not only an idea of the level of complexity of data that can be derived from overhead imagery but also the level of undetectable data such as urban power lines, similar to Figure 4.14. Despite the amount of overhead wires as seen in Figure 4.21 it is not possible to detect them in Figures 4.21a to d.

Figure 4.22 – New construction (mobile telephone relay station) Photo P8

Figure 4.23 – Example of new construction (children’s’ playground) (Photo P17)

125 Both Figures 4.22 and 4.23 give an example of the change detection capability of high resolution commercial satellite imagery. The IKONOS imagery was taken in 2003 and since then two constructions have occurred, which it can be easily seen were not in existence in 2003. Regular collection plans at set time intervals have the potential for monitoring developments at a local level. Also the coverage of one satellite image (16km x 16km) allows trend analysis of an entire region to be potentially achieved with one image collect.

4.3.3.2 Central Business District (CBD) area of Hobart

Detail of Building Line

Figure 4.24 – Example of detail (Photo P24)

126

Figure 4.24a – IKONOS 8 bit Imagery Figure 4.24b – IKONOS 11 bit Imagery

Figure 4.24c – IKONOS derived Orthophoto Figure 4.24d – 1:24 000 Orthophoto

Figure 4.25 - Hobart City Council data

127

Figure 4.26 – Features extracted from 11bit IKONOS Stereo Imagery

Comparing the IKONOS imagery in Figure 4.24 with other imagery, such as higher resolution aerial orthophotos, shows the level of building detail is comparable in this case in all images regardless of source. This is in contrast to features extracted in both the Hobart City Council data (Figure 4.25) and those derived from IKONOS stereo imagery (Figure 4.26). Neither example of vector data gives a complete representation of ground features. In the case of features derived from IKONOS imagery this maybe due to interpreter/observer error due to unfamiliarity of the ground features. In the case of the Hobart City Council data, which is based on a combination of digitised and ground survey data, it may simply be due to incomplete data collection.

128

Figure 4.27 – Hobart Midcity Hotel – example of roofline and level of detail extracted possible (Photo P26)

Figure 4.27b – IKONOS 11 bit Imagery

Similar to Figure 4.24, the difference in the interpretation of features can be seen in Figures 4.27, 4.28 and 4.29. In this case the IKONOS extracted features more closely resemble the situation on the ground. This shows the usefulness of high resolution commercial satellite imagery in defining the urban landscape.

129

Figure 4.28 - Hobart City Council data

Figure 4.29– Features extracted from 11bit IKONOS Stereo Imagery

Figures 4.30, 4.31 and 4.32 provide an example of consistency across all forms of imagery (Figure 4.30) as well as the feature representation in a vector or line form (Figures 4.31 and 4.32). It can be clearly seen that the arrowed building roof line is accurately represented in all the images (Figure 4.30) and the building footprint is consistently represented in the vector diagrams (Figures 4.31 and 4.32).

130

Figure 4.30 – Example of roof line (Photo P29)

Figure 4.30a – IKONOS 8 bit Imagery Figure 4.30b – IKONOS 11 bit Imagery

Figure 4.30c – IKONOS derived Orthophoto Figure 4.30d – 1:24 000 Orthophoto

131

Figure 4.31 - Hobart City Council data

Figure 4.32 – Features extracted from 11bit IKONOS Stereo Imagery

Figures 4.33, 4.34 and 4.35 are a good demonstration that the level of detail that is obtainable from satellite imagery compares extremely favourably with the aerial orthophoto. The deficiencies of the Council data are more probably due to the lack of traditional survey detail in the Council data. This is a clear example where satellite or aerial imagery can be of benefit.

132

Figure 4.33 – Example of building detail. This multistory car Figure 4.33a – Top of multistory carpark (Photo P32) park does not appear as one from a vertical perspective. (Photo Looking south P30)

Figure 4.33b – IKONOS 8 Figure 4.33c – IKONOS 11 bit Figure 4.33d – IKONOS Figure 4.33e – 1:24 000 bit Imagery Imagery derived Orthophoto Orthophoto

133

Figure 4.34 - Hobart City Council data

Figure 4.35 – Features extracted from 11bit IKONOS Stereo Imagery

Figures 4.36, 4.37 and 4.38 provide a good view of the complexity of city roof scapes at close range. The extracted features from IKONOS imagery (Figure 4.38) compare favorably with more traditional data and imagery sources in this example.

134

Figure 4.36 – Example of roof line and detail from top of multistory car park (Photo P33) Looking North

Figure 4.36a – IKONOS 8 bit Imagery Figure 4.36b – IKONOS 11 bit Imagery

Figure 4.36c – IKONOS derived Orthophoto Figure 4.36d – 1:24 000 Orthophoto

135

Figure 4.37 - Hobart City Council data (orange arrow indicates direction of photograph P33 in Figure 4.36)

Figure 4.38 - Features extracted from 11bit IKONOS Stereo Imagery (orange arrow indicates direction of photograph P33 in Figure 4.36)

Figure 4.39 is an example of a mis-identified feature. Whilst the detail of the building footprint is clearly apparent, the shape of the building has been interpreted incorrectly. During feature extraction using the IKONOS stereo imagery the single building has been interpreted as two buildings. This example demonstrates that whilst high resolution satellite imagery can remove the requirement to visit a location for feature extraction, it cannot remove the requirement to visit a location for ground truthing purposes.

136

Figure 4.39 – Example of roof top detail and the possibility of to misinterpretation. (Photo P36)

Figure 4.39a – IKONOS 11 bit Imagery

Figure 4.39b - Features extracted from 11bit IKONOS Stereo Imagery

137

Figure 4.40 – Example of detail and interpretability Figure 4.40e – 1:7000 Sullivans Cove Orthophoto (Photo P37)

Figure 4.40b – IKONOS 11 bit Figure 4.40a – IKONOS 8 bit Figure 4.40c – IKONOS Figure 4.40d – 1:24 000 Imagery Imagery derived Orthophoto Orthophoto

138

Figure 4.41 – Example of roof line and detail (Photo P38) Figure 4.41e – 1:7000 Sullivans Cove Orthophoto

Figure 4.41a – IKONOS 8 bit Figure 4.41b – IKONOS 11 bit Figure 4.41c – IKONOS Figure 4.41d – 1:24 000 Imagery Imagery derived Orthophoto Orthophoto

139 Fig 4.41

Fig 4.40

Figure 4.42 – Hobart City Council data

Fig 4.41

Fig 4.40

Figure 4.43 - Features extracted from 11bit IKONOS Stereo Imagery

Both Figures 4.40 and 4.41 show the comparison of IKONOS imagery with large scale aerial orthophoto (1:7000). This larger scale imagery is clearly far superior to either the IKONOS imagery or the 1:24 000 orthophoto, and displays best that currently it is difficult to replace high resolution aerial photography with satellite imagery for certain purposes or that satellite imagery has its limitations.

140 4.3.3.3 Summary of Observations from Feature Interpolation and Extraction of Study Areas

From the examples shown in Sections 4.3.3.1 and 4.3.3.2 the following conclusions on the use of high resolution commercial satellite imagery can be made:

(a) Due to the size of utility features, such as water or sewerage, as displayed below it is not possible to detect or analyse these typical urban features. Even though sewer manholes are approximately one metre in diameter, for the GSD of the IKONOS imagery, they are undetectable. The level of detail that can be extracted varies and is a function of the contrast of the object with respect to the background. Some one metre size objects can be detected if they have high contrast compared to their background. Generally objects of low contrast must be approximately 3 to 4 metres, or size of a small car, to be identifiable.

Figure 4.44a – Sewerage Manhole (P25) Figure 4.44b- Water Service – Size approx 0.15m (P14)

Figure 4.44c – IKONOS 8 bit Imagery Figure 4.44d – IKONOS 8 bit Imagery

141 (b) Whilst it is difficult to detect utility features, it is possible to detect some through “ground scarring” in the form of vegetation clearing as displayed in Figure 4.14, but it is not possible to determine the nature of the feature, only the evidence of human activity.

(c) The level of detail that can be discerned within an image varies for a given size of feature, depending on its orientation and structure, for example:

- tops of the reservoirs. The two newer ones actually have polygons tops. Figure 4.18 shows the difference between the older reservoir with a circular top and a newer one with a polygon shaped top.

- roof shape (roof line) of dwellings can be difficult to establish as resolution, sun angle and tonal range appears to affect the level of detail that can be determined (Figure 4.7).

(d) Mis-interpretation can easily occur on high resolution satellite imagery as depicted in Figure 4.33 which shows a multi-storey carpark. With no cars parked on the roof (IKONOS imagery was captured on Saturday 22 February 2003) and with the colour of concrete similar to the ground, it is not possible to determine the building purpose unless ground knowledge is obtained. This compares with another multistory carpark from another image on another day, as shown below which is easily identified because cars are parked on the roof. This clearly indicates that also timing of capture of the imagery is an important factor in its usability.

Figure 4.45 – 1:7000 Sullivans Cove Orthophoto

142 (e) There is a rivulet running under the Hobart central business district which has recently been built over. There is no evidence of this in the IKONOS imagery. Whilst in this case it is not possible to detect an “old” and apparently lost natural feature there are many cases where aerial and satellite imagery have been utilised to help locate the extent of an archeological site that is “invisible” at ground level.

As described earlier in this section the purpose of using the Civil NIIRS rating is to determine the information potential of an image not quantify the number of features that could be correctly identified. This is not the normal mapping approach and significant studies, such as Holland and Marshall (2003 and 2004) have been performed to relate the suitability of high resolution commercial satellite imagery to map scale requirements. The utility and versatility of the imagery through such properties as 11bit radiometric resolution and single image large area coverage, add new properties for assessing suitability of imagery for mapping purposes. This can be seen in that the IKONOS 11bit imagery rates better than any of the sample imagery except the 1:7 000 scale aerial orthophotos.

Further, in the sample imagery used the one metre resolution or GSD should put the Civil NIIRS ratings for all the IKONOS imagery at five, but this is not the case as it ranges from 4.8 to 5.1. This indicates that whilst it is possible to establish the scale from the resolution, the information potential of the image may vary as a result of other factors such as radiometric resolution or even the acquisition obliquity of the imagery. An example of how other factors, which were not able to be explained during this study, can affect interpretability is the difference between the quality of the aerial 1:24 000 Orthophoto from the CBD area with a Civil NIIRS rating of 5.0 to the Urban area, Civil NIIRS rating 4.5. Whilst both images are produced at the same scale and so should be of the same quality they are not.

As indicated in Chapter 2, using empirical formula a suitable map scale using IKONOS imagery would be 1:20 000. But many studies have indicated, as previously described in this thesis, that it could be used for scales up to 1:5 000 if the requirement to collect

143 linear features such as roads or watercourses were eliminated. The work in this section confirms that it is difficult without additional information, such as ground scarring, to detect some linear features such as power lines on IKONOS imagery. It must also be noted that this shortfall is not restricted to high resolution commercial satellite imagery as aerial photography of the two scales used in this study also presented difficulties for adequately interpreting linear features such as power lines and tracks.

To provide a comparison with other work an evaluation was conducted using the IKONOS 11 bit imagery against the classes of features evaluated for extraction at a scale of 1:10 000 in Table 3.3 from Holland et al (2003), the results are contained in Table 4.7.

From Table 3.3 Image Civil Identification on IKONOS 11 bit Imagery Feature 1:10 000 NIIRS Housing and associated Yes - though discrete changes in roof line as Maybe 5.0 features in Fig 4.7 not possible to detect Other buildings Yes Yes – all large or commercial buildings. 5.0 Communications networks Yes – though road markings on secondary 5.0 Yes - roads roads not clear Yes – Barriers and road furniture maybe 5.0 Dual carriageways Yes detectable but not clear Airports Yes No example on image 5.0 Railways Yes Yes – Cannot determine detail 5.0 Electricity transmission No – detectable only through ground scarring 5.0 No lines and truthing see Fig 4.14 Major sea defences to 5.0 Yes Yes – Port facilities clearly identifiable reduce flooding Yes – Dams an be identified, but detail 5.0 Non-coastal sea defences Yes unclear Major property boundaries No Yes – Only through ground cover change 5.0 Major landscape changes Yes Yes - Distinct 5.0 Quarries and other surface 5.0 Yes Yes - Distinct workings Field boundaries Maybe Yes - Only through ground cover change 5.0 Water features Yes Yes – Small creeks difficult 5.0 All vegetation Yes Maybe – Depends on nature of vegetation. 5.0 Maybe – Depends on size and width of track, 5.0 Tracks and path Maybe See Figs 4.11 and 4.17 Telephone boxes No No 5.0 Extensions to commercial 5.0 Yes Yes building Minor property boundaries Maybe Yes – Only through ground cover change 5.0 Tide lines No No 5.0 Garages built after initial 5.0 Yes Yes development Table 4.7 - Feature Classes identified in IKONOS Imagery

144 Using Table 4.7 it can be seen that there is agreement in the ability to detect the majority of urban ground features or 70% of the feature classes tabulated using IKONOS 11 bit panchromatic imagery with a Civil NIIRS of at least 5.0.

In order to ensure the quality of mapping data, mapping organisations attempt to quantify the numbers of features that can be determined. Such as in the case of the UK Ordnance Survey, which as part of its performance monitoring, requires that a minimum of 99.6% of significant real world features are represented in their database within six months of their completion (Holland and Marshall: 2004). In this study it was found to be difficult to determine a definitive number of features that could be identified or extracted in an IKONOS image when compared to other spatial datasets. This was because, regardless of the dataset, there were always omissions or misinterpretations either due to the age or incompletion of the dataset as displayed in Figures 4.7 to 4.9 and Figures 4.24 to 4.26. This highlights the importance of ground truthing as part of the process of creating spatial data. Whilst a significant advantage of high resolution commercial satellite imagery is the ability to image areas where access is restricted, unquantifiable errors that occur due to feature misinterpretation must be factored into any quality measured and consequently risk assessment in the employment of the resultant spatial data, and therefore decisions made as a result of analysing this derived data.

In summary when using IKONOS imagery for feature identification or extraction the following need to be considered:

(a) Only one metre objects with high contrast can be detected. In reality objects need to be at least 3 to 4 metres in size to be reliably detected on IKONOS imagery;

(b) It is possible to detect finely detailed linear features through ground scarring or associated indicators such as change in vegetation or relief, but it is usually not possible to identify the feature itself;

145 (c) The level of detail that can be discerned will vary for a given size of feature depending on its orientation, structure and contrast;

(d) Misinterpretation can easily occur and with all feature extraction or identification ground truthing or other confirming checks should be conducted to provide validation;

(e) Errors or omission exist in all spatial data sets and this needs to be considered in any decision or product creation involving the data.

4.4 Digital Elevation Model (DEM) Creation

4.4.1 Derivation of Digital Elevation Models

As described in Chapter 2 one of the advantages of high resolution commercial satellite imagery is the stereo imaging capacity, in particular its ability to obtain “in track” stereo, enabling a large base-height ratio to be achieved, which is a favourable geometric condition for elevation determination. Whilst stereo imagery allows the creation of DEMs, the actual process used in softcopy photogrammetry is image matching where corresponding points on the images of a stereo pair are matched and their object space coordinates calculated by an intersection computation for DEM creation (Wolf: 2000).

For this comparison a DEM was created from the IKONOS stereo pair over each Study Areas using the Socet Set Photogrammetric Workstation at the University of New South Wales. The DEM over the CBD area had a grid post spacing of 15m and the Urban DEM had 30m grid post spacing. Both DEMs were edited to “First Surface” as opposed to “Bald Earth”. “First Surface” digital elevation models represent the ground but include objects such as buildings and trees whereas “Bald Earth” digital elevation models are reduced to ground level eliminating buildings and vegetation.

146

As described in Section 4.1.1 the stereo models were created without using ground control points, being only based on RPCs for exterior orientation. Consequently the heights for the IKONOS derived DEM are based on WGS84 spheroidal heights as this is the acquisition datum of the IKONOS imagery used. Once the IKONOS derived DEMs were created they were then compared to two reference DEMs with post spacings of 100m and 12.5m supplied by the Tasmanian Department of Primary Industries and Water (DPIW). The 100m post spacing data is based on 1:25 000 series mapping and the 12.5m post space data DEM is derived from contours from 1:25 000 series mapping or better. Both use the Australian Height Datum (AHD). Whilst it is stated in Chapter 2 that IKONOS imagery would be suitable for 1:20 000 mapping, which is larger than the 1:25 000 mapping derived data that was used as the references in this study, these were the best datasets commercially available without a specific collect occurring (such as LIDAR). They represent DEMs that would be readily available for use in an urban GIS.

As noted the two reference DEMs are based on the AHD which approximates the Geoid and the IKONOS derived DEM uses WGS84 spheroidal heights, at this point this difference in height datum can only be resolved by applying ground control points to the stereo model. Poon et al (2006) reports that readily available softcopy photogrammetric workstations and software can extract elevation information from high resolution commercial satellite imagery based on ground control points to accuracies of 4 to 9m overall. Since many researchers report that a minimum number of ground control points such as 4 to 8 are all that is required to achieve maximum possible accuracy for this type of imagery, this would indicate that regardless of the use of ground control points in such a stereo model, there is finite accuracy level that can be achieved. This will prevent the imagery’s use for applications requiring more detail, such as civil engineering design and restrict it to preliminary or proposal level design work at best.

147 4.4.2 Comparison of Digital Elevation Models

From each of the data sets contours of 5m intervals were derived as shown in Figures 4.46 and 4.47 which display the 5m contours derived from each reference DEM overlaid on the IKONOS derived contours. Although the potential accuracy of the IKONOS imagery and the reference data sets would suit a larger contour interval such as 10m, a 5m contour interval was only used for comparison purposes between the data sets. Also for comparison between the data sets, two longitudinal sections for each DEM were extracted for each Study Area, as shown in Figures 4.48 and 4.49.

The area size and height differences along the longitudinal sections meant that there were only a small number of points able to be used in the analysis, such as in the urban area four and five points and the CBD ten and eleven points, along the longitudinal sections. This limits the results as there is not enough data to perform a detail statistical analysis. The use of this reference data set though is realistic as mentioned previously as it is typical of the height data that is available in such an urban environment and to local governments as opposed to LIDAR or InSAR data.

In order to identify the variations of heights along the longitudinal sections of the reference data sets and the IKONOS derived DEM, an RMSE value was derived from the differences in the heights of the data sets at 50m chainage intervals along the longitudinal sections, the results being:

(a) CBD Study Area – Longitudinal Section 1 - Liverpool St

- RMSE IKONOS /100m GDA DTM = 0.3m - RMSE IKONOS / 12.5m Hobart DTM = 1.7m

(b) CBD Study Area – Longitudinal Section 2 - Macquarie St

- RMSE IKONOS / 100m GDA DTM = 1.0m - RMSE IKONOS / 12.5m Hobart DTM = 1.2m

148 (c) Urban Study Area – Longitudinal Section 1 - South

- RMSE IKONOS / 100m GDA DTM = 7.4m - RMSE IKONOS / 12.5m Hobart DTM = 3.0m

(d) Urban Study Area Longitudinal Section 2 - North

- RMSE IKONOS / 100m GDA DTM = 2.7m - RMSE IKONOS / 12.5m Hobart DTM = 1.3m

The overall accuracy result of the test is better than that expected from the reference data based on 1:25 000 mapping, which has a horizontal accuracy of ±17.5m and a vertical accuracy of ±5m, this is due to the small sample size.

It can be seen from the above data and that there is consistency in all study areas data sets in the form or shape of the terrain. The lack of gradient displayed in Figure 4.48 for the CBD area as well as the difficulty in creating a DEM in a significantly built up area makes it difficult to confirm the elevation in the CBD area. The urban area provides a better comparison, primarily because it is less built up. Toutin et al (2002) reports a similar difference in magnitude by comparing elevations from IKONOS stereo models using ground control points. In his work Toutin et al (2002) reports a 6.5m linear error at the 68% level of confidence (LE68) and a 10m linear error at the 90% level of confidence (LE90) for a DEM, which includes ground cover and an accuracy over bare soils of 1.5m LE68 and 3.5m LE90. The results from this study indicate that IKONOS derived DEMs are comparable to the reference data sets in quality and accuracy. Apart from the large RMSE error in the Urban Study Area Longitudinal Section 1 – South for the IKONOS / 100m GDA DTM of 7.4m all the results would indicate that without ground control IKONOS stereo imagery would be suitable for the production of 10m interval contours making the imagery suitable for 1:10 000 scale mapping. The difference in the Urban Study Area Longitudinal Section 1 – South can be attributed to the large difference in post spacing in the data sets and the undulating nature of the terrain, particularly between chainages 250m and 300m along this longitudinal section shown in Figure 4.49a. Better results overall could only be achieved by utilising ground control points when establishing the IKONOS stereo model as discussed in Section 4.2 of this chapter.

149 Figure 4.46a – CBD Study Area: 5m IKONOS derived contours and 5m Hobart 12.5m DTM contours Figure 4.46b – CBD Study Area: 5m IKONOS derived contours and 5m 100m GDA DTM contours Figure 4.47a – Urban Study Area: 5m IKONOS derived contours and 5m Hobart 12.5 DTM contours Figure 4.47b – Urban Study Area: 5m IKONOS derived contours and 5m 100m GDA DTM contours Figure 4.48a - CBD Longitudinal Section – Liverpool St 150 Figure 4.48b - CBD Longitudinal Section – Macquarie St 151 Figure 4.49a - Urban Longitudinal Section – South 152 Figure 4.49b - Urban Longitudinal Section – North 153 4.5 Summary

The studies conducted in this Chapter have shown that the positional accuracy and the ability to extract features from high resolution commercial satellite imagery would indicate that it is suitable for mapping for at least at the 1:10 000 scale level without the use of ground control points. This represents the most versatile characteristic of high resolution commercial satellite imagery.

The RPC provided by Space Imaging with the IKONOS stereo imagery is capable of providing a horizontal positional accuracy between 0.3m to 1.5m, and even though its heighting accuracies capability is lower, elevations derived from the imagery will meet the requirements in both vertical and horizontal positional accuracy for 1:10 000 mapping.

In regards to feature identification or extraction, the IKONOS imagery can be seen as no worse than aerial photography derived with the same ground resolution. The imagery is capable of identifying ground features, both built and natural, regardless of scale subject to its shortfalls in identification of linear features, which are also usually not visible on higher resolution aerial photography. The IKONOS imagery also brings the advantage of 11 bit depth radiometric resolution which can increase the identification capability of the imagery by allowing the detection of high contrast one metre size objects that would not normally be visible at such a resolution.

The examples provided in this chapter have shown that the level of detail that can be interpreted and positioned is at the individual building level without accessing the area, with a minimum of technical expertise. This advantage of high resolution commercial satellite imagery is an important one. What the imagery lacks in detail or positional accuracy, it counters with its versatility provided by remote processing with minimal ground survey required.

154 The latest high resolution imaging commercial satellite, Geoeye-1 has similar characteristics as the IKONOS satellite in that it is capable of acquiring 11bit imagery at a maximum off nadir imaging of 60 degrees. Geoeye-1 differs in that it has a spatial resolution for panchromatic imagery of 0.41m and multispectral imagery of 1.65m with a claimed stereo positional accuracy of three metres within actual locations on the Earth surface. This is opposed to IKONOS which has spatial resolution for panchromatic imagery of 0.82m and multispectral imagery 3.2m and a precision stereo horizontal positional accuracy of four meters and vertical accuracy of five metres (Geoeye Website: 2008). Given that this study has shown that the IKONOS stereo positional accuracy is greater than the vendors claims it could be anticipated similar results for Geoeye-1, potentially enabling satellite imagery to achieve the positional accuracy of aerial photography. Further as can be seen from Figure 4.50 the increase spatial resolution increases the ability to detect linear features, such as rowing shells of an approximate width of 0.6m, an issue highlighted in this study and others (Geoeye Website: 2008).

Figure 4.50 – Geoeye-1 Imagery – Cambridge, Massachusetts, 18 October 2008 (Source: Geoeye Website: 2008)

In summary, high resolution commercial satellite imagery provides a source of information from which to derive a range of spatial data such as vectors, points, features (built and natural), land use identification through spectral analysis, and DEMs creation. Whilst this shows the versatility of this imagery source, the practicalities and success of

155 application of the data can vary. Whilst the positional accuracy and feature identification can permit use for map scales between 1:1 000 to 1:6 000, if elevations are required as well this will restrict the suitability of the imagery to a minimum of 1:10 000 scale.

156 CHAPTER 5

CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions

5.1.1 Objectives and Strategies

The successful implementation of the commercial accessibility of high resolution satellite imagery is a significant technological and business step in the spatial science industry around the world. Access to and the availability of the service and imagery from these satellites has provided a new data source in the terms of imagery, DEM creation and feature extraction, as well as potential spatial science services such as the long term monitoring of areas that are physically difficult to access due to physical, political, disaster or time constraints.

The objectives of this thesis were:

(a) To provide an understanding of the imagery collection techniques and methodology of high resolution commercial imaging satellites.

(b) To give an understanding of the way the commercial and government interests have influenced the development and sustainability of high resolution commercial imaging satellites;

(c) To allay practical concerns in the application of using the imagery in regards to cost, equipment (hardware and software) and training;

157 (d) To give an assessment of the imagery’s ability to represent features and phenomena on the ground to a practical level in a variety of applications.

(e) to provide a guide to possible uses and disadvantages of such imagery away from the traditional qualifiers;

Overall the work in this thesis has determined that the RPC of the IKONOS stereo imagery can provide horizontal positional accuracy of between 0.3m to 1.5m, and though the heighting accuracy is not at the same level it is suitable for the production of 10m interval contours. This makes the imagery suitable for 1:10 000 scale mapping if heighting is required to be sourced from the stereo model without the use of ground control or for planimetric mapping between 1:1 000 to 1:5 000 scale if heighting is not required.

Feature identification or extraction using IKONOS imagery can satisfy a variety of requirements, but there are some points that need to be considered in the application. Whilst the imagery is supplied at one metre GSD, only objects with high contrast can be detected of this size, resulting in practical terms that an object needs to be at least 3 or 4 metres minimum in size to be detected on IKONOS imagery. Also even if the object or feature is greater than 3 to 4 metres it can be difficult to detect finely detailed linear features such as tracks or power lines on identification of the feature itself without some associated indicators such as change in vegetation or relief. Similarly the level of detail that can be discerned will vary for a given size of feature depending on its orientation and structure.

Each of the objectives has been achieved. This study has shown that whilst high resolution commercial satellite imagery is capable of producing reasonable spatial data detail both in quality and cost for use in a urban GIS, its greatest advantage is its ability to remotely access a site to obtain imagery for the creation of spatial data. In order to take advantage of this benefit it must be accepted that there will be lower positional accuracy as well as the potential of errors due to misidentification of features if ground truthing is

158 not conducted. Consequently the only way that high resolution commercial satellite imagery can compete with sources of higher resolution spatial data such as aerial photography, is by the application of ground control and ground truthing. This will reduce the advantage of the imagery. For this reason high resolution commercial satellite imagery should be assessed as a spatial data source on its own merits and not as a substitute or replacement for another technique.

To reach the conclusions, this study has reviewed and examined all aspects of the development and application potential of high resolution commercial satellite imagery, such as:

(a) Historical Development;

(b) Business Evolution;

(d) Evaluation and comparison as a spatial data source.

5.1.2 Historical Development

The historical development of high resolution commercial satellite imagery is one of successes and failures. The initiator of this development can be found in decisions by the former Soviet Union then subsequently the United States of America, to declassify high resolution satellite technologies that were developed for military advantage during the Cold War to be used for commercial application. These events led to a “challenge” from other countries such as France and Israel which ensured that the U.S. was not going to

159 easily dominate the market for the supply of high resolution commercial satellite imagery, that in 1994 was already estimated to have reached $700US million.

Even though a number of National Governments around the world such as the U.S., France and Israel have heavily sponsored their countries’ commercial ventures into the high resolution commercial satellite imagery market, the successful outcomes took a number of years to achieve, as well as incurring financial losses. There is not one company regardless of country of origin that did not suffer setbacks of either a technical nature, such as failure to achieve orbit or malfunction when in orbit, or a financial loss as a result of not being able to achieve a suitable market. If it were not for the support of each of these companies’ governments by either supplying lucrative contracts or injecting financial support, none of these companies or consortiums commercialising high resolution satellite imagery would have been able to achieve results.

5.1.3 Business Evolution

The business of high resolution commercial satellite imagery started with extreme confidence in its applicability and value to its targeted audiences. However the fortunes of the companies resulted in several having to restructure to ensure their commercial survival and return on investor’s capital.

A significant development in this evolution has been the licensing arrangements for distribution of the high resolution commercial imagery. Initially the purchase of the imagery was strictly on a one user per purchase or licence. Whilst the cost of the imagery has not varied since the early years of its availability, the form of licensing has, since it is now possible to purchase multi-organisation licenses. This increases the competitiveness and attractiveness of using such imagery by government departments or authorities, since sharing cost makes the imagery financially viable to source.

160 A factor that has ensured the survival of many if not all of these commercial ventures is access of government contracts. In particular defence contracts such as the U.S. ClearView and NextView have ensured millions of dollars worth of business being made available to such companies as DigitalGlobe and Geoeye Inc. This methodology of funding is not limited to the U.S.; both Israel and France have used similar financial techniques to continue to develop their countries’ imaging satellite capabilities.

Finally since there is no benefit to put in place such a infrastructure or service there are also practical concerns in the implementation to ensure the imagery’s use and marketing success. These practical concerns include cost, in time and money of such items as hardware and software acquisition, training and data or imagery acquisition. It has been shown that depending on the circumstances or application, the cost of the use of high resolution commercial satellite imagery is no greater or less than other survey or spatial data creation techniques.

5.1.4 Geographical Information System (GIS) Requirements and Applications

This study highlights the philosophy and aims behind the establishment, use and consequently the maintenance of spatial data in a GIS. As a result the differing requirements between the purposes for a particular GIS being established are revealed, be it for Emergency Services or Local Government. The demands and requirements of the users of a GIS determine whether high resolution commercial satellite imagery and derived spatial data meets the subject GIS requirements.

It can be seen from this study that if a representation or overview of an area is required, high resolution commercial satellite imagery provides an economical alternative at the cost of resolution and positional accuracy. This loss of resolution and positional accuracy for the sake of economies whilst initially could be seen as “penny pinching” is also revolutionary in the utilisation of imagery. The ease of availability and competitive pricing of high resolution commercial satellite imagery, in particular archived imagery,

161 allows for monitoring and recording of the earth surface at a resolution not previously available to the general public. This imagery can be of use in most public authorities and local governments when it is supplemented in suitable intervals with higher resolution and more expensive imagery or terrain data from aerial platforms.

5.1.5 Evaluation and Comparison of High Resolution Commercial Satellite Imagery as a Spatial Data Source

As discussed in this thesis high resolution commercial satellite imagery provides more than simply imagery. It can also be a source of feature data (both the natural and built environment), terrain information (DEM) and land use identification using the multispectral component of the imagery.

The suitability of the spatial data derived from the imagery is, as previous mentioned, dependent on the purpose for which the data is intended to be used. From this study it is clear that high resolution commercial satellite imagery cannot compete with the geometric or resolution accuracy of a ground survey or aerial photography. But its accuracy and resolution is such that it is suitable for planning or reconnaissance tasks as well as an interval supplement to more accurate spatial data source collections.

In addition despite its lower accuracy and resolution, high resolution commercial satellite imagery’s ability to provide reasonably accurate position and resolution without ground control ensures its vendors a niche market.

5.2 The Future and Recommendations

December 2007 and September 2008 saw the launch of the Worldview-1 and Geoeye-1 satellites respectively, both of which boast capability of providing imagery of a resolution of 0.5m or better. Other commercial imaging satellites from other countries due for

162 launch in the near future include the ImageSat EROS C satellite from Israel with a planned panchromatic resolution of 0.7m and the French CNES Pleiades satellite system also with a resolution of 0.7m. The successful launch of these two satellites and the pending launch of the others herald in the next chapter of the use of high resolution commercial satellite imagery. Geoeye-1 in particular as mentioned in Chapter 4 promises greater positional accuracy and resolution; this with the marketing lessons from the sales of the first high resolution commercial satellite images will make the gap between imagery sources smaller. Whilst high resolution commercial satellite imagery is finding a place in the remote sensing and spatial industry market, it has not achieved its original promise of revolutionising spatial data creation and imagery exploitation, as its vendors attempted to make potential markets believe.

The next generation of high resolution commercial satellite imagery promises greater accuracy, accessibility and more competitive costing. Only time and world events (such as natural disasters and conflicts) will determine the success and nature of the business that surrounds high resolution commercial satellite imagery.

Areas in which further investigation or development should be considered are:

(a) A change of attitude by spatial science professionals in not relating data to scale, but to usability or “fit for purpose”, to make full use of modern imaging techniques that allow for the sourcing and exploitation of 11 bit or greater imagery and resolution;

(b) Investigating techniques to take full advantage of the 11 bit panchromatic imagery capability;

(c) Methodology to improve the absolute accuracy of the imagery without the use of ground control so as to remove the current requirement for ground or aerial access to enhance the imagery’s inherent positional capability.

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169 APPENDIX A

MASTERS IMAGERY USERS SURVEY – 2005 Masters Imagery Users Survey - 2005

As part of the thesis a survey was conducted, this involved approaching current providers and users of spatial data. The aim was to determine the nature of their existing uses of spatial data (in particular imagery) and whether they have considered using high resolution satellite imagery, and if not, why not? The survey was conducted in the middle of 2005.

Each survey participant was asked to complete a questionnaire. Survey participants varied from private companies, government, mining and emergency services.

The composition of the participants and the returns are contained in Table A-1 below

Percentage Government/Authority returned 50 Percentage Private Companies returned 43.75 Percentage Emergency returned 27.59 Percentage Mining returned 0

Total Percentage Returned 35.44

Total Sent: 79 Government/Authority: 26 Private Companies: 16 Emergency: 29 Mining:8

Table A-1 – Survey Participants

The results are detailed in the Tables below

A-1 Ground Survey Aerial Photography Satellite Imagery Purchased Data Sets

Government 10 8 4 10

Private 2 5 4 4

Emergency 2 2 1 8

Mining

Total 14 15 9 22

Note: (1) Aerial includes airborne sensors such as LIDAR (2) Purchased data sets includes topographic and vector data or supplied direct from Government Departments (3) Some organisations use a variety of sources

Table A-2 – Source of Data

Standard Remote Survey Survey GIS Photo- Mobile External Sensing Equipment Software Software grammetric Units Provider Software

Government 4 4 12 2 1 2

Private 3 3 5 4 1 1

Emergency 6 1 1

Mining

Total 7 723 6313 Note: Some organisations use a variety of software and equipment

Table A-3 – Facilities for Processing

A-2 Terrestrial Aerial Satellite

Government 1 11 5

Private 3 6 4

Emergency 2 6 5

Mining

Total 62314 Note: Some organisations use a variety of imagery

Table A-4 – Types of Imagery

Landsat IKONOS Quickbird SPOT Aster Hyperion

Government 3 4 3

Private 5 4 4 5 1 1

Emergency 3 1 4

Mining

Total 11 4 9 12 1 1 Note: Some organisations use a variety of imagery

Table A-5 – If used, Satellite Data from what system

A-3 Too Expensive No Suitable Software Not suitable No suitable Other suitable data No suitable experienced accuracy or reason or task or trained staff resolution

Government 2 1 5 1

Private 2 1 3

Emergency 2 12

Mining

Total 61 1 8 13

Table A-6 – Reason for Not Using Satellite Data

Town Emergency Land Environmental Insurance Mineral Mapping Local Asset Web Planning Management Development Planning Assessment Exploration Assessment

Government 7 3 4 7 1 2 1

Private 2 2 4 3 1 1 1 1

Emergency 6 1

Mining

Total 9118 112 11 31

Table A-7 – Using Imagery Data for what? (Includes Terrestrial, Aerial and Satellite)

A-4 Town Emergency Land Environmental Insurance Mineral Mapping Local Asset Web Planning Management Development Planning Assessment Exploration Assessment

Government 3 4 2 4 1

Private 2 2 3 3 1 1 1 1

Emergency 5 1

Mining

Total 5115 8 1 1111

Table A-8 – Using Satellite Imagery Data for what?

Panchromatic Multispectral Ortho-Corrected (includes Pan Shapened) Stereo

Government 5 5 2

Private 4 4 3 2

Emergency 4 5

Mining

Total 13 14 5 2

Table A-9 – Level of Processing (Satellite Imagery)

A-5 Vector Event Data Locations Ortho Address Building (eg image Drainage Cadastral As Built Water Transport Point Features (Various) Roads Footprints Sewer Asset offences) Zoning

Government 8 11 13 8 8 2 1 1 1 2 2 2 2 2

Private 5 4 4 4 4 1 1 1 1 1 1 1 1

Emergency 5 4 5 2 3 1 1 1 2 1 1

Mining

Total 18 19 22 14 15 4 3 3 4 4 3 3 3 1 2

Table A-10 – Data in GIS

A-6 APPENDIX B

HOBART GROUND CONTROL POINTS COMPARISON Resultant Bearing Delta X Point Delta Y (Measured Delta Z (Measured to (dd.mmss) Resultant Distance Bearing Bearing Bearing X Y Z Measured X Measured Y Measured Z (Measured to Bearing (0

RMSE RMSE RMSE RMSE Confidence X, Y Resultant Interval Delta X (m) Delta Y (m) Delta Z (m) Distance (m) 68% 0.606 0.621 0.982 0.55 90% 0.9996 1.024 1.62 0.907 95% 1.187 1.217 1.924 1.078 99% 1.563 1.602 2.533 1.419

Table B-1 - Hobart Ground Control Points Comparison B-1 APPENDIX C

COMPILED DRAWINGS EXTRACTED FROM IKONOS STEREO IMAGERY SHOWING FEATURES, CONTOURS AND PHOTO LOCATIONS Figure C-1 - Compiled Drawings Extracted from IKONOS Stereo Imagery showing Features, Contours and Photo Locations over Urban Study Area C-1 Figure C-2 - Compiled Drawings Extracted from IKONOS Stereo Imagery showing Features, Contours and Photo Locations over CBD Study Area C-2