A Comparison of Landsat, Ikonos and Radarsat Satellite Imagery for Suburban Land Cover Mapping in the Township of Langley, British Columbia
A COMPARISON OF LANDSAT, IKONOS AND RADARSAT SATELLITE IMAGERY FOR SUBURBAN LAND COVER MAPPING IN THE TOWNSHIP OF LANGLEY, BRITISH COLUMBIA
Sarbjeet Kaur Mann B.Sc., University of Victoria 1999
RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTEROFRESOURCEMANAGEMENT
in the School of Resource and Environmental Management
Report No. 356
O Sarbjeet Kaur Mann 2004 SIMON FRASER UNIVERSITY April 2004
All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author Approval
Name: Sarbjeet Kaur Mann
Degree: Master of Resource Management
Title of Research Project: A comparison of Landsat, IKONOS and RADARSAT satellite imagery for suburban land cover mapping in the Township of Langley, British Columbia
Report No.
Examining Committee:
Chair: Marcela Olguin-Alvarez
Dr. Kristina D. Rothley, Assistant Professor School of Resource and Environmental Management Simon Fraser University Senior Supervisor
Dr. Suzana Dragicevic, Assistant Professor Department of Geography Simon Fraser University Committee Member
Pamela Zevit, Co-ordinator Greater Vancouver Region Biodiversity Strategy BC Ministry of Water, Land & Air Protection Committee Member
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It is understood that copying or publication of this work for financial gain shall not be allowed without the author's written permission.
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Bennett Library Simon Fraser University Burnaby, BC, Canada Increasing pressure from urban growth is placing heavy demands on local planners to ensure that biodiversity is maintained in the Greater Vancouver Regional
District. Tools and approaches for identifying and mapping the remaining natural areas are necessary. Traditionally, planners have identified land cover by aerial surveys, which are costly, time consuming and conducted on an as-needed basis. The current study tests and compares the feasibility of medium resolution Landsat (ETM+) and high- resolution IKONOS and RADARSAT satellite imagery for identification of land cover
(coniferous, deciduous, disturbed, water and wetland) at a study site in the Township of
Langley, British Columbia. Preliminary analysis showed that overall accuracy results for the classified RADARSAT image were marginal (64%). RADARSAT is therefore excluded from the main analysis. Maximum likelihood classification of principal components is used to classify the Landsat and IKONOS images. Air-photo interpreted polygons are used as reference data. Kappa analyses show that because of its additional mid-IR bands, the classified Landsat image has a significantly higher overall classification accuracy (79.8%) than IKONOS (70.7%). Overall accuracy increased with increasing minimum polygon size of the reference data. The highest classification accuracy (87.6%) was attained for the classified Landsat image when it was evaluated against test points from reference data polygons larger than 0.216ha.
iii Dedication
To my mother, for being everything a mother is supposed to be. Acknowledgements
Kristina Rothley has been a great mentor and I thank her for her generous guidance. I also thank Suzana Dragicevic and Pamela Zevit for their excellent advice and suggestions, and Dan Buffet, Arthur Roberts, Rob Knight, Marcela Olguin-Alvarez,
Ilona Naujokaitis-Lewis, Billie Gowans and the REM Departmental Staff for their assistance.
The RADARSAT images were obtained through the Canadian Space Agency and RADARSAT International administered RADARSAT-1 Data for Research Use program. Air-photo interpreted reference polygons were supplied by the Langley
Environmental Partners Society. Funding for this project was provided by Simon Fraser
University Graduate Fellowships and Applied Sciences Graduate Fellowships, and the
BC Ministry of Water, Land & Air Protection.
Finally, I thank my family and friends for their encouragement and support. Table of Contents
.. Approval ...... 11 ... Abstract ...... III Dedication ...... iv Acknowledgements ...... v Table of Contents ...... vi .. List of Tables ...... VII ... List of Figures ...... VIII List of Acronyms ...... ix Chapter One: Introduction ...... I 1. 1 Context of Research ...... 1 1.2 Research Objectives ...... 3 Chapter Two: Methods ...... 5 2.1 Study Site Selection ...... 5 2.2 Image Acquisition ...... 6 2.3 Image Pre-processing ...... 7 2.4 Classification Scheme Development ...... 8 2.5 Creation of Training Data ...... 8 2.6 Image Classification ...... 8 2.7 Accuracy Assessment ...... 9 Chapter Three: Results ...... 15 Chapter Four: Discussion...... 18 4.1 Radarsat ...... 18 4.2 Misclassification and Individual Class Performance ...... 19 4.3 Other Sources of Error ...... 21 4.3.1 Co-registration Errors ...... 22 4.3.2 Change in Land Cover ...... 22 4.3.3 Errors in Reference Data ...... 23 4.3.4 Boundary Error ...... 25 4.4 Landsat vs . IKONOS...... 26 4.5 Future Analyses ...... 27 4.6 Conclusions and Recommendations ...... 31 References ...... 36 Tables ...... 41 Figures ...... 75 List of Tables
Table 1. Characteristics of the satellite imagery...... 42 Table 2. Land cover classification scheme...... 43 Table 3. Area (ha) of the training regions for each land cover class...... 44 Table 4. Percentage (%) of the study site identified as each land cover class for each air-photo interpreted reference data set...... 45 Table 5. Error matrices for the classified Landsat image (7 original classes; all test points used regardless of the size of the reference data polygons; test point sampling interval = 100m)...... 46 Table 6. Error matrices for the classified IKONOS image (7 original classes; all test points used regardless of the size of the reference data polygons; test point sampling interval = 100m)...... 48 Table 7. Error matrices for the classified Landsat image as evaluated against interpretation 4 (5 classes; test point sampling interval = 100m)...... 50 Table 8. Error matrices for the classified Landsat image as evaluated against interpretation 3 (5 classes; test point sampling interval = 100m)...... 52 Table 9. Error matrices for the classified IKONOS image as evaluated against interpretation 4 (5 classes; test point sampling interval = 100m)...... 54 Table 10. Error matrices for the classified IKONOS image as evaluated against interpretation 3 (5 classes; test point sampling interval = 100m)...... 56 Table 11. Error matrices for the classified Landsat image as evaluated against the LEPS interpretation (5 classes; test point sampling interval = 1OOm)...... 58 Table 12. Error matrices for the classified IKONOS image as evaluated against the LEPS interpretation (5 classes; test point sampling interval = 1OOm)...... 60 Table 13. Error matrices for the classified Landsat image as evaluated against interpretation 4 (5 classes; test point sampling interval = 150m)...... 62 Table 14. Error matrices for the classified IKONOS image as evaluated against interpretation 4 (5 classes; test point sampling interval = 150m)...... 64 Table 15. Z-statistic values for kappa analysis comparisons between error matrices...... 66 Table 16. Principal components of the Landsat and IKONOS satellite images...... 70 Table 17. Habitat types identified by Lee & Rudd (2002) as important for the conservation of biodiversity in the GVRD...... 71
vii List of Figures
Figure 1. Maps of the GVRD and the Langley study site...... 76 Figure 2. The classified Landsat image of the study site showing the seven original land cover classes...... 77 Figure 3. The classified IKONOS image of the study site showing the seven original land cover classes...... 78 Figure 4. The classified Landsat image of the study site showing the disturbed land cover class...... 79 Figure 5. The classified IKONOS image of the study site showing the disturbed land cover class...... 80 Figure 6. Overall accuracy (%) as a function of the minimum polygon size (ha) of the reference data...... 81 Figure 7. Producer's accuracies for the land cover classes...... 82 Figure 8. Producer's accuracies for individual land cover classes as a function of the minimum polygon size of the reference data...... 83 Figure 9. Scattergram of the training regions used in the classification of the Landsat image...... 84 Figure 10.Scattergram of the training regions used in the classification of the IKONOS image...... 85 Figure 11.Close-up showing differences in the detail and resolution of reference data sets interpretation 4 and interpretation 3, and the corresponding areas on the classified Landsat and IKONOS images...... 86 List of Acronyms
GFOV: Ground Field of View
GVRD: Greater Vancouver Regional District
LEPS: Langley Environmental Partners Society
MWLAP: Ministry of Water, Land & Air Protection
NIR: Near Infrared
RMS: Root Mean Square Chapter One: Introduction
1.I Context of Research
The Greater Vancouver Regional District (GVRD), a 3292 km2 area in south-
western British Columbia, is situated within one of the most productive and diverse
natural settings in Canada. The Fraser River is the richest salmon producing freshwater
river in the world and on average over 100 000 salmon spawn in streams within the
GVRD. The estuary of the Fraser River is a stopover point for several million birds
annually as they migrate along the Pacific Flyway. North of the Fraser River estuary and
the Lower Fraser Valley, forested uplands, mountains, valleys and river systems provide
habitat for numerous plant and animal species. The GVRD, however, is also located
within one of the fastest growing regions of North America and has emerged to become
the premier commercial, industrial and transportation center in western Canada. The
population of the region now exceeds two million (2,016,000 people; BC Ministry of
Water, Air & Land Protection 2001). By 2021, an additional 800,000 people are
expected. As this region has become more populated, significant changes have taken
place on this landscape. Conversion of land for housing, industrial development, agriculture and other uses has resulted in fragmentation and alteration of much of the area that once provided habitat to a diverse array of species (BC Ministry of Water, Air &
Land Protection 2001 ).
Conservation planning and policy for the protection of biodiversity and its associated social and economic values has been identified as a priority at the federal, provincial and regional levels in Canada (Environment Canada 1998). As a result of this policy direction and the future expected growth in the region, the GVRD Biodiversity
Conservation Strategy was started in 1999 with an objective of assessing the status of
remaining green spaces and linkages in the GVRD and developing a strategy for
preserving and enhancing biodiversity throughout the region. Although green spaces in
urban areas may seem to be small and insignificant contributors to biodiversity, collectively these areas can have a major effect on the integrity of urban ecosystems and can represent a surprisingly high degree of biodiversity (Lee & Rudd 2002; Niemela
1999). Natural areas in urban settings provide habitat for plants and animals and conduits for their dispersal. Equally important, natural areas, greenways and open spaces provide human services such as storage and filtration for surface and groundwater and opportunities for recreation.
The need to identify and protect natural areas in the GVRD and other urban areas is urgent and requires accurate, up-to-date land cover maps. Reliable land cover information, especially in map form, is not readily available for the GVRD nor is it easy to acquire. An objective of the GVRD Biodiversity Conservation Strategy is to support development of a comprehensive land cover map of the entire GVRD. These baseline maps will be further used to produce maps describing currently undeveloped sites according to their value as habitats and corridors for plants and animals, as reservoirs for biodiversity, and as providers of human services (recreation and water quality).
Ultimately, these maps will serve as input to the planning process of the local 21 member municipalities comprising the GVRD, and form a central repository of easily accessible information.
To create these maps tools are required to analyze and update spatial information quickly and efficiently and to assess their accuracy. Remote sensing and geographic information systems (GIs) are attractive options for the cost-effective production of land cover maps. Because there is a high correlation between variation in remotely sensed data and variation across the earth's surface remotely sensed data
provides an excellent basis for making maps of land cover (Lillesand & Kieffer 2000).
We use remotely sensed data to make maps because:
land cover maps derived from ground-based surveys are time-consuming and expensive to produce and become quickly outdated as the landscape is altered. it offers a perspective from above (the 'bird's eye view'), allowing for a better understanding of spatial relationships at the landscape scale it permits capturing types of data undectectable by the human eye such as the infrared portions of the electromagnetic spectrum, which allow for superior discrimination of certain land cover types (Congalton & Green 1999).
Remote sensing is available at a range of spatial and temporal scales and offers
a means for repetitive mapping of natural resources in a cost-effective manner. Its
application for sustainable resource management has been widely demonstrated and
the production of thematic maps, such as those depicting land cover, using an
appropriate image classification is one of the most common applications of remote
sensing (Foody 2002). Before remote sensing technology can be applied, however,
analysis is required to identify and refine appropriate procedures in order to produce
satisfactory mapping results for the region of interest (Green et al. 1994; Yang & Lo
2002). Further, if decisions based upon map information are to have reliable results,
then the accuracy of the maps must be known. Otherwise decisions based on these
maps may yield unexpected and unacceptable results (Congalton & Green 1999; Foody
2002).
1.2 Research Objectives
A critical component to ensuring effective management and conservation of
natural areas in the GVRD is an up-to-date, high-resolution spatial data set describing
current land cover (forest, water bodies, impervious surfaces, etc.). The automated
classification of satellite images can efficiently generate up-to-date land cover maps. However, given the accuracy required for the land cover maps, the costs associated with obtaining the satellite images, and the challenges presented by spectrally heterogeneous urban landscapes, it is first necessary to demonstrate the accuracy of this technique. In this study images from three satellites, I)the Landsat Enhanced
Thematic Mapper Plus (ETM+) carried by the Landsat 7 satellite, 2) the IKONOS carried by IKONOS 2, and 3) the Synthetic Aperture Radar (SAR) carried by RADARSATI, are compared for identification of land cover types at a study site in the Township of
Langley, British Columbia, to determine the most effective imagery for discerning land cover in the region.
The primary motivation and goal for this project is to provide key technical advice to support management directions for biodiversity conservation in the GVRD. The specific research objectives guiding this study are:
1. To determine the mapping accuracy of each classified satellite image relative to reference data, using a commonly applied classification method. 2. To compare how the classified satellite images perform relative to each other 3. To determine the accuracy with which each land cover class is mapped. 4. To analyze how decreasing the resolution of the reference data affects the accuracy of the classified satellite images. 5. To analyze how accuracy changes with different sources of reference data.
This report describes the fundamental procedures used to extract land cover data from remotely sensed images and assess accuracy of the land cover maps that are produced. Chapter 2 begins with the classification and accuracy assessment methods.
Chapter 3 provides the analysis results. Chapter 4 is devoted to the discussion and provides recommendations for future research and for management. Chapter Two: Methods
2.1 Study Site Selection
The GVRD lies in the Fraser Lowlands, a physiographic area that extends from the Georgia Strait to Chilliwack (Figure 1). This area consists of extensive upland separated by wide, flat-bottomed valleys. These low elevation lands are mostly in the
Coastal Western Hemlock biogeoclimatic zone. The GVRD is also situated in the Coast
Forest Region where the dominant natural tree species are coastal Douglas fir, western
hemlock and western red cedar (Meidinger & Pojar 1991).
The Langley study site was chosen as the focus for this study because the
Langley Environmental Partners Society (LEPS) provided a ground-truthed land cover
map for this area in the form of GIs-based polygons (minimum polygon size = 0.01 ha)
based on aerial photograph interpretation of 1:20000 air photos from 2002. This map was to be used as the reference data in this study. Five percent of the polygons in this map were ground-truthed and the interpretation was approximately 80% correct
(Caroline Astley 2003, personal communication). Furthermore, the variety and relative abundance of land cover classes in the bounds of the Langley study site are considered characteristic of many other locations across GVRD. Langley is also one of the fastest growing municipalities in the GVRD and therefore a high priority for the GVRD
Biodiversity Conservation Strategy. The study site (2.7km x 4.4 km) borders the
Canadian - US border and encompasses Little Campbell River Regional Park (Figure
1>. Topography in Langley varies from level areas to gently rolling hillsides to ravines along major watercourses. Most of Langley has been logged or cleared, and treed
areas are now a mixture of second growth coniferous and deciduous trees. Langley is a
major agricultural community in the province and approximately three-quarters of the
municipality is in the Agricultural Land Reserve. The complex geological history of the
area has resulted in a variety of deposits, landforms and soil types, and this diversity in
soil types combined with the long growing season and proximity to the Vancouver
market results in production of a large variety of agricultural products (Township of
Langley 1979).
2.2 Image Acquisition
Summer and winter RADARSAT and Landsat images and a summer IKONOS image were purchased for the study site (a winter IKONOS image was unavailable;
Table 1). The first IKONOS satellite was launched in 1999 and only more recent publications describe its applicability for land cover mapping (Zanoni & Goward 2003).
In particular, mapping of impervious vs. non-impervious areas and forested vs. non- forested areas has been successful, with reported overall classification accuracies greater than 90% and 84% (Cablk & Minor 2003; Goetz et. al2003). Sugumaran et. al
(2002) reported an overall IKONOS classification accuracy of greater than 85% for the mapping of seven different land cover types in Columbia, Missouri. A weakness of the high resolution (4m pixels) IKONOS imagery is that clouds, a frequent occurrence in the
GVRD skies, can obscure the images. Further, spectral information is recorded in only four bands.
The Landsat series of satellites is much older (the first Landsat satellite was launched in 1972) and numerous published studies demonstrate the usefulness of
Landsat images for a wide range of thematic mapping (Lillesand & Kieffer 2000), including land cover mapping in urban areas. Yang and Lo (2002) and Seto et. al (2002) reported overall Landsat classification accuracies greater than 85% for the mapping of land-uselland cover change in urban areas. Landsat has moderate resolution 30 meter pixels, detects radiation in a larger range of the electromagnetic spectrum and may also be distorted by clouds. The most cloud free IKONOS and Landsat images were purchased for this study.
RADARSAT was designed for ice reconnaissance, coastal surveillance, land cover mapping, and agricultural and forestry monitoring (Lillesand & Kieffer 2000).
Applications of RADARSAT for mapping of wetlands have been widely studied and overall classification accuracies greater than 80% have been reported (Parmuchi et. a1
2002). The RADARSAT-1 sensors generate and record radiation in a single band of the
microwave range, providing high resolution 8m pixels regardless of weather conditions but do not allow for the statistical, multi-band land cover discrimination possible with multi-spectral images. However, previous research has found that classification accuracies based on Landsat TM data may increase when RADARSAT image tone and texture data is included (Presutti et al. 2001).
2.3 Image Pre-processing
The Landsat, IKONOS and RADARSAT satellite images were clipped to match the boundaries of the Langley study site and then georeferenced to the reference data
(polygons derived from air-photo interpretations) by applying a polynomial transformation
(ER Mapper 2002). Root mean square (RMS) errors were kept below 1 pixel. Pixel sizes for the satellite images are provided in Table 1. 2.4 Classification Scheme Development
Classification schemes are fundamental to any mapping project because they reduce the total number of land cover types that must be dealt with to some reasonably small number. The classification scheme (Table 2) used to describe land cover in the test sites was based on a ground truthed land coverlland use map recently derived by
LEPS for the Langley study site, but modified to accommodate the anticipated uses of the land cover maps to be produced. The detail of the adopted scheme was also dictated by the land cover types that can be discerned with satellite data. The scheme was relatively simple so that it could be applied across the GVRD, but exhaustive and exclusive with hierarchical elements.
2.5 Creation of Training Data
To run the statistical classifications of the satellite images, areas of known land cover in the images, called training regions, must first be identified. For this project, the training data set was derived by comparing polygons from air-photo interpretations to
Red-Green-Blue (RGB) colour composites of the images and to the results of unsupervised classifications (maximum number of classes: 25) (ER Mapper 2002). A small subset of the interpreted polygons was used as training data (Table 3). These areas were distributed across the study site, and apart from a few exceptions, were excluded from the accuracy assessment.
2.6 Image Classification
Land cover type was predicted for the study site using the standard maximum likelihood classifier. Two RADARSAT (summer and winter) images, one IKONOS image
(summer), and two Landsat images (summer and winter) were analyzed using ER
Mapper 6.3 (ER Mapper 2002). Before the RADARSAT images could be classified it was necessary to remove speckle from the images. Mean spectral values and standard deviation statistics of the different land cover training regions were used to determine the appropriate filter and/or texture analyses to do this. The objective was to have distinct non-overlapping means and confidence intervals for the spectral signatures of each class. Results (not reported) showed that applying the Average 5x5 filter and extracting Maximum Probability texture data allows for the best distinction between spectral means of the land cover training
regions in the RADARSAT images. Therefore, speckle was removed from the
RADARSAT images with the Average 5x5 filter, and Maximum Probability texture data was extracted. A supervised classification (maximum likelihood enhanced) was then
performed on each RADARSAT image.
Before the supervised classification (maximum likelihood enhanced) was
performed on the Landsat and IKONOS images, principle components analysis was
used to derive new axes that would improve the explanatory power of the raw image data (Singh & Harrison 1985). Two principal components were derived from the multi-
spectral bands of each image and were classified using the maximum likelihood classifier. Two principal components explained 90% of the spectral variation in the
Landsat image and 99% of the spectral variation in the IKONOS image.
The summer Landsat image was also combined with the filtered summer
RADARSAT image and texture data, and classified using principal components to determine if there was an improvement in classification accuracy. The winter and summer Landsat images were also combined and classified in a similar manner.
2.7 Accuracy Assessment
Accuracy assessment is not an easy task as it is necessary to balance the
requirements for rigor and defensibility with practical limitations of cost and time. In this study, reference data was collected for the study site through air photo interpretation.
Air photos are a good reference data source because they allow for more consistent measurements over large areas as the interpretation is done in the laboratory with one or a few well-trained interpreters rather than in the field by many, frequently volunteer, observers (Congalton & Green 1999).
As I began the accuracy assessment of the classified images using the LEPS reference data (described above), it soon became apparent that this reference data was of limited use because the LEPS classification scheme was quite different to the one adopted for this study. As a result three different individuals completed additional air- photo interpretations of a 1:24000 August 2002 colour air photo for the study site based on the classification scheme that was used to generate the classified satellite images.
These air-photo interpretations were completed using table stereoscopes and grease pencils, and the resulting low-resolution interpretations were later scanned and digitized.
These three reference data sets are in the form of GIs-based polygons and are referred to as: interpretation 1, interpretation 2, interpretation 3. The exact spatial resolution of these three reference data sets is not known. Later on, the 1:24000 August 2002 air photo of the study site was scanned to produce a digital air-photo with high resolution
4.1 m pixels. An on-screen interpretation of this digital air photo was completed to produce an additional polygon-based reference data set, which is referred to as interpretation 4. lnterpretation 4 has a minimum polygon size 0.012ha (1 1m2)'.
Each of the 1:24000 air-photo interpreted reference data sets differ in the amount of coverage allocated to each land cover class in the study site (Table 4). lnterpretation
' Ground resolution is often incorrectly equated with Ground Field of View (GFOV). Spatial resolution is defined as the minimum separation of two objects that can be actually separated in an image. Separation requires at least one pixel to be between two separate objects. Thus the objects need to be more than twice the square root of two, times the GFOV, to be resolved. Thus a 4.lm pixel digital image offers 11m spatial resolution (Hastings 2001). I and interpretation 2 identified very few coniferous polygons compared to interpretation
3 and interpretation 4. Interpretation I also identified very few deciduous polygons. This highlights the fact that different interpreters can introduce different degrees of error for particular land cover types. Reference data sets interpretation I and interpretation 2 were not used in the accuracy assessment because based on my familiarity with the study site, they lacked sufficient coverage of the coniferous and deciduous land cover classes.
ArcView 3.2 (ESRI 2000) was used to complete the accuracy assessment. The classification accuracy of the classified Landsat and IKONOS images was evaluated using test points from interpretation 3 and interpretation 4 reference data sets. The test points were distributed across the study site in a grid style where the points were spaced a) 100m apart, and b) 150m apart. The required number of test points to be extracted from the test point grid was calculated according to the following formula (Congalton &
Green 1999):
where:
land cover type with the greatest coverage in the study site
B - a constant derived from the Chi-squared distribution - rri - the proportion of land covered by i
bi - the desired level of precision
Based on the formula above, I calculated that 717 was the minimum number of test points required to adequately assess the accuracy of the classified images. For the rarer classes where the test point grid did not yield at least 50 test points, I manually added test points until 50 test points were attained for each class. The test point grid in combination with the test points that were added manually yielded a total of approximately 950 test points for the 100m grid. Of these 950 test points, up to 95 points were added manually to the water, wetland and coniferous categories. Similarly,
166 points were added manually to the approximately 550 points of the 150m grid.
Reference data test points in larger homogenous areas are more likely to be correctly labelled by air-photo interpreters than test points in smaller heterogenous areas. Furthermore, in satellite imagery, larger objects have proportionally fewer mixed pixels and georeferencing inaccuracies than smaller objects and are more likely to be correctly classified. For these reasons it is expected that as the minimum polygon size of the reference data increases, so will overall accuracy results. To test this, I successively assessed accuracy of the classified images using only those test points that fell within minimum sized interpreted polygons of 0.024 ha, 0.096 ha, and 0.216 ha.
To complement the analysis, the IKONOS and Landsat classified satellite images were also evaluated against the LEPS air-photo interpretation (from now on referred to as LEPS interpretation) for the area where all three data sets overlapped (area approximately 2.7km x 6.2km). Again, test points were distributed in a grid style where the points were spaced 100m apart. Where a minimum of 50 test points was not selected for rare classes, test points were added manually to reach this amount. LEPS used different criteria for their classification scheme and only those LEPS polygons that had labels analogous to labels in my classification scheme were included in the analysis.
These labels included: deciduous, coniferous, water, wetland, herbs, and soil. The
LEPS interpretation did not label any polygons analogous to impervious.
The classified RADARSAT images, the combined LandsatlRadarsat image and the combined summerlwinter Landsat image were only evaluated against the LEPS interpretation using an accuracy assessment methodology similar to the one described above. Preliminary accuracy assessments against the LEPS interpretation data indicated that the classified RADARSAT images performed poorly relative to the
IKONOS and Landsat images, with overall accuracy ranging from 54% to 64%.
Because of its poor preliminary performance, RADARSAT was excluded from further analysis. Preliminary analyses also indicated that the classification of the Landsat image with the addition of the RADARSAT data did not result in an improvement of overall accuracy results. Further, the classification of the combined summer and winter Landsat images did not result in an improvement in overall accuracy either. These classifications were also excluded from further analysis.
In summary, a classified Landsat image and a classified IKONOS image were evaluated in detail against test points from three reference data sets: 1) interpretation 4
(minimum polygon 0.012 ha), 2) interpretation 3 (minimum polygon size not known), and
3) the LEPS interpretation (minimum polygon size = 0.01 ha). For all of the reference data sets, accuracy was also assessed using only the subset of test points that fell within minimum sized interpreted polygons of 0.024 ha, 0.096 ha, and 0.216 ha. For all of the analyses, the classified image and reference data labels for the test points were compared to one another in an error matrix, from which the overall accuracies, user and producer's accuracies, and kappa values were computed.
A kappa analysis is used in accuracy assessment for statistically determining if two kappa values, and therefore if two error matrices, are significantly different. This allows one to statistically compare two images, classification algorithms, etc., to determine which produces statistically higher overall accuracy results. A kappa value is computed for each error matrix and is a measure of how well the remotely sensed classification agrees with the reference data (Bishop et al. 1975; Congalton & Green
1999). The measure of agreement is based on the difference between the actual agreement between the classification and the reference data (as indicated by the major diagonal) and the chance agreement which is indicated by the row and column totals. A kappa value greater than 0.80 represents strong agreement, a value between 0.40 and
0.80 represents moderate agreement, and a value below 0.40 represents poor agreement.
The kappa value for an error matrix is calculated as follows (Congalton & Green
1999):
Let k = the number of classes
i= row number
j = column number
n = total number of test points
nV= number of test points falling in the cell corresponding to row i and
column j
pii = nVln
Then let
(the actual agreement)
k
PC = 1 P~+P+, (the chance agreement) i= I
Finally, Chapter Three: Results
Despite applying the Average 5x5 filter and extracting the Maximum Probability texture data, it became apparent early on in the study that classified RADARSAT images performed poorly relative to IKONOS and Landsat images. Preliminary accuracy assessments against the LEPS interpretation indicated that overall accuracy for the classified RADARSAT images ranged from 54% to 64%. Because of its poor preliminary performance, RADARSAT was excluded from further analysis. Preliminary analyses also showed that the classification of the Landsat image with the addition of the
RADARSAT data did not result in an improvement of overall accuracy results. The classification of the combined summer and winter Landsat images did not result in an improvement in overall accuracy either.
Individual class results for the classified Landsat and IKONOS images (Figures 2
& 3) are provided in Tables 5-1 2, as evaluated against interpretation 4, interpretation 3 and LEPS interpretation reference data sets. Several of the land cover classes tend to have high reflectance (so called "bright" pixels). These include impervious surfaces, soils and herbs (which often have lots of bare soil mixed in with the vegetation). These
"bright" feature classes have similar spectral properties resulting in their being confused with one another and ultimately being misclassified in both the Landsat and IKONOS images (Tables 5 and 6). For example, in the classified Landsat image 111 of the 132 impervious test points are misclassified as herbs, and 49 of the 56 soil test points are misclassified as herbs (Table 5a). In the classified IKONOS image, out of the 132 impervious test points, 58 are misclassified as soil and 35 are misclassified as herbs.
Twenty-two of the 57 soil test points are misclassified as herbs (Table 6a). Because of this confusion, the herbs, soil and impervious classes were merged. The resultant new class was called disturbed and subsequent accuracy assessments and kappa analyses were performed using this new class (Figures 4 & 5; Tables 7 to 12; Table 15). Bright feature confusion has been reported in other studies (Sawaya et. al 2003) between concrete, bare fields and recreational fields.
A kappa analysis showed that Landsat has a significantly higher overall classification accuracy (79.8%) than IKONOS (70.7% overall accuracy) when evaluated against all the test points from interpretation 3, interpretation 4 and the LEPS interpretation (Figure 6; Tables 8a, IOa, 15a). Overall accuracy for both Landsat and
IKONOS was a function of the resolution of the reference data against which the classified image was evaluated: overall accuracy increased with increasing minimum size of the polygons in which the test points were located (Figure 6; Table 15b). The highest overall accuracy (90.7%) was attained for the classified Landsat image when it was evaluated against test points from LEPS interpretation reference polygons larger than 0.21 6ha (Table IId). This result exceeds the 85% level that was set as a target for overall classification accuracy by Anderson et. al (1976), although not all classes exceed
70% accuracy. The highest accuracy level for IKONOS (80.2%) was also attained when it was evaluated against test points from LEPS interpretation reference polygons larger than 0.216ha (Table 12d).
When compared to accuracy assessments results assessed against interpretation 4, overall accuracy results increased marginally when the classified
Landsat and IKONOS images were assessed against the LEPS interpretation and decreased marginally when they were assessed against interpretation 3 (Figure 6; Table
15c). However, the differences were generally not significant. Interpretation 4 is considered to be the most accurate reference data set since it has more precise higher resolution polygons than interpretation 3, and since it was based on the same classification scheme that was used to create the classified satellite images. The LEPS interpretation was based on a classification scheme different to the one used in this study and did not map impervious areas.
When all of the test points are considered, the error matrices (and derived producer's and user's accuracy's) indicate that the match between the test points and the classified IKONOS and Landsat images was poor for the water and wetland classes
(accuracy <56%) (Figure 7; Tables 7a & 8a). Both Landsat and IKONOS mapped coniferous and deciduous areas reasonably well (60%-80%). Disturbed areas were mapped very well by Landsat (92.2% accuracy) and reasonably well by IKONOS (76.9% accuracy). Before the soil, impervious and herbs classes were merged, Landsat mapped herbs very well (87.6% accuracy) and IKONOS mapped them poorly (56.5% accuracy).
As already described, producer's and user's accuracies for each class generally increased with increasing minimum size of reference polygons, especially for the coniferous and deciduous classes (Figure 8; Tables 7 & 8). However, this was not the case for the classification for wetlands, the accuracy of which remained around 50% for
Landsat and 30% for IKONOS, regardless of the size of the reference polygons. The accuracy of the disturbed class for the classified Landsat image remained around 92%.
It was not possible to assess how the accuracy of water changed, as the number of test points dropped dramatically with increasing minimum polygon size.
Overall accuracy results were not significantly different when evaluated against test points spaced 150m apart (as opposed to 100m apart) for both the classified
Landsat and IKONOS image, regardless of the minimum size of the reference polygon data (Tables 13, 14 & 15d). Chapter Four: Discussion
In this chapter, after a brief explanation of the RADARSAT results, individual class results and sources of error will be discussed. This discussion will provide background information to explain the differences in performance between Landsat and
IKONOS. Recommendations to improve any future work on this project will follow. This chapter ends with conclusions and recommendations for management.
4.1 Radarsat
Early analyses indicated that RADARSAT consistently performed poorly (overall accuracy ~65%).The poor performance could be attributed to the fact that RADARSAT captures information in a single band, as opposed to the multi-spectral Landsat and
IKONOS sensors. Artificially bright pixels caused by corner reflection may also be a factor in the poor performance of RADARSAT (Lillesand and Kiefer 2000). A considerable level of corner reflection was obvious throughout the RADARSAT images.
Buildings with distinctly vertical surfaces adjacent to distinctly horizontal surfaces produced corner reflection as would be expected. These bright corners were simply merged with the bright pixels normally associated with impervious surfaces. However, corner reflection also occurred along the edges of forested patches that were adjacent to disturbedlwetland patches. These bright corners were mistakenly classified as being impervious surfaces. In a less disturbed landscape where the transition between land cover classes would be smoother, these errors would be less likely to occur.
The poor RADARSAT results were supported by Presutti et al. (2001) who reported that the use of Landsat data alone provided superior classification accuracy compared to the use of RADARSAT data alone, and that the use of texture improved
RADARSAT classification marginally. Still, the filtered summer and winter RADARSAT images offered revealing visual information on the study site. Building tops and water bodies were easily discernable, and vegetated versus non-vegetated areas could be readily distinguished.
4.2 Misclassification and Individual Class Performance
Suburban and urban environments represent one of the most challenging areas for remote sensing analysis due to high spatial and spectral diversity of land cover types.
Major types of spectral confusion and misclassification can be identified in the current study, especially in regards to the herbs category. In the classified Landsat image: (1) impervious areas are misclassified as herbs, (2) soil is misclassified as herbs, (3) wetlands are misclassified as herbs, (4) water is misclassified as herbs, and (5) water is misclassified as coniferous (Table 5). In the classified IKONOS image: (1) impervious areas are misclassified as herbs and soil, (2)soil is misclassified as herbs, (3) wetlands are misclassified as herbs, (4) water is misclassified as coniferous, and (5) herbs are misclassified as soil (Table 6).
The spectral variation in the herbs training region is very large (Figures 9 & 10) for both IKONOS and Landsat, accounting for the confusion associated with this category and the need to create the new disturbed class. An ideal scattergram should have no spectral gaps and no spectral overlap between the class ellipses. The variation for herbs is so large, that 'when in doubt' it makes statistical sense to label a pixel as herbs as opposed to another class. One land cover class is not enough to describe the variation within the herbs category. It is recommended that the existing class be split further into more specific classes (i.e. sparse herbs, dense herbs, etc.) where practical, to help alleviate spectral confusion. These new spectral classes representing a similar class can be later regrouped (Ma et al. 2001). Training data for these more specific classes should be collected through field surveys. Reference and satellite data should also be collected at the same time since the herbs class changes dramatically between the different seasons.
The analyst creating the reference data had familiarity with the site that let her distinguish wetland from herbs. However, during the summer these wetlands are very herbaceous and another analyst unfamiliar with the site would have likely labeled the wetland areas as herbs. As indicated by Figures 9 & 10, it is indeed difficult to distinguish the two classes spectrally, accounting for the poor accuracy results for the wetland class.
For IKONOS, the spectral variation in the soil training region is also large (Figure
1O), accounting for the confusion between impervious and soil, and herbs and soil. This variation is not present for Landsat indicating that either Landsat is better than IKONOS at distinguishing this land cover type or perhaps that improper areas were included in the soil training regions for IKONOS. This highlights the need for ground-truthing of areas chosen as training regions.
It is difficult and/or impossible for a statistically based classification algorithm like the maximum likelihood classifier to label areas in the study site that have reflectance values characterized by the unlabelled spectral zones in the scattergrams. These reflectance values are not represented well enough by the training regions making it difficult to statistically assign a land cover label to areas characterized by these reflectance values. Such is the case for areas on the ground that are spectrally characterized by the zone between the coniferous and water ellipses in the Landsat scattergram (Figure 9). This accounts for the misclassification between these two classes in the classified Landsat image. It is normally expected that these two classes should be easy to distinguish spectrally (Lillesand and Kieffer 2000). The confusion is less for IKONOS, as the gap between the coniferous and water ellipses is small in the
IKONOS scattergram (Figure 10). Part of the confusion between impen/ious and herbs land cover classes can also be attributed to a gap between the two ellipses in the
Landsat scattergram.
The coniferous, deciduous, and disturbed classes were identified with reasonable accuracy by Landsat, as was expected from the literature. IKONOS identified coniferous and disturbed areas well. These results can be applied with a high level of confidence to immediately derive a land cover map of suburban GVRD for these classes.
4.3 Other Sources of Error
Map inaccuracies or error can occur at many steps throughout any remote sensing project. Accuracy assessment is conducted to understand the quality of map information by identifying and assessing map errors (Congalton & Green 1999).
Accuracy assessment is never an easy task. It requires obtaining reference data of higher quality with adequate coverage of space and classes to test a map. However, the ability to obtain an ideal reference data set is constrained by practical limits of technology, logistics, and cost (Crist & Deittner 2000). Disagreements between the classified image and the reference data are typically interpreted as errors in the land cover map derived from the remotely sensed data (Congalton 1991). This interpretation has driven research that aims to decrease the error in image classification. This research has typically focused on the derivation and assessment of different classification algorithms. However, there are many other possible sources of error, in addition to misclassification. These include co-registration errors, error in the reference data, change in land cover between the collection date of the reference data and the collection date of the satellite images, and difficulty in assessing boundary areas (Foody
2002). 4.3.1 Co-registration Errors
Even if the classified satellite image and the reference data are perfect, error can
result from misregistration of the two data sets (Czaplewski 1992; Stehman 1997a;
Foody 2002). This problem is most apparent in heterogeneous landscapes with a
complex land cover mosaic, such as the Langley study site (Scepan 1999). Locational
accuracy is important if we are trying to match up small polygons. Unfortunately, such
landscapes are frequently the ones for which it is most important to map and monitor
land cover. Without perfect co-registration, however, the confusion matrix may contain errors due to misregistration as well as thematic mislabeling which will complicate the
interpretation of derived accuracy metrics. Co-registration error is assumed to be
minimal in the Langley study site, which was relatively flat (as noted above, all RMS
pixel errors were kept below 1 during co-registration).
However, as an example, several months were spent trying to analyze another
study site on the North Shore, BC. Satellite images were classified and air-photos were
interpreted as reference data. However, due to the higher elevations and complex terrain of the North Shore study site, it proved impossible at that time to line up the two data sets accurately enough to complete a proper accuracy assessment.
4.3.2 Change in Land Cover
It is unreasonable to report as errors those areas where land cover changes have occurred after the collection date of the satellite imagery. In practice, the reference data may not be collected until long after (or long before) the satellite imagery is acquired. Therefore, it may not be obvious whether the discrepancy between the map and the reference data is due to temporal land cover change or simply to misclassification (Crist & Deittner 2000). Temporal error is most definitely present in the reference data, as up to two years passed between collection date of the satellite imagery and the collection date of the reference data. Also, part of the study site is situated in an agricultural area where land cover tends to change rapidly, not only over the long term, but also over seasonal cycles.
4.3.3 Errors in Reference Data
A meaningful accuracy assessment requires that the ground data are accurate.
However, ground data sets are themselves a classification which may contain error and sometimes more error than the remotely sensed product they are being used to evaluate
(Congalton & Green 1999; Foody 2002). The two most obvious sources include misidentification of the class by the interpreter and data entry errors.
In this study, except for the LEPS interpretation, no accuracy assessment was performed on reference data sets interpretation 4 and interpretation 3. Without an accuracy assessment, it is impossible to estimate the degree to which error exists in the reference data. During the air-photo interpretation, it was difficult to distinguish between coniferous and deciduous land cover types in mixed forest areas and it was difficult to distinguish between soil and herbs land cover types in agricultural areas. To avoid mislabelling areas, polygons were labelled as unknown when in doubt as to the correct land cover type. Comparatively, because it was derived from an on-screen interpretation of air-photos, reference data set interpretation 4 is assumed to be the more accurate and precise than reference data set interpretation 3, particularly for the water, wetland, and impervious land cover types. On-screen interpretation allowed for greater magnification and precise delineation of boundaries. Interpretation 3 was derived by tracing areas on the air-photos under limited magnification and was particularly prone to error in boundary areas (to be discussed below) because the grease pencils used to do the tracing were not fine enough to create thin polygon borders along boundaries and along narrow features such as roads. Furthermore, Interpretation 4 coverage values for coniferous, deciduous, and soil land cover types differ from interpretation 3 coverage values (Table
4). This indicates that error, or at least differences, exists in the different reference data sets and requires consideration.
Overall accuracy results were marginally higher when the classified images were evaluated against interpretation 4 (minimum polygon size = 0.012ha) as opposed to interpretation 3 (minimum polygon size not known). This is expected because a more generalized interpretation masks fine-scale heterogeneity in the landscape and a given point within a polygon may actually be incorrectly labelled even though the polygon as a whole is correct. One of the challenges of accuracy assessment of high-resolution images is to match the resolution of the reference data and the classified image. Ideally, the method of collecting the reference data must identify land cover at the same level of detail as the map (Crist & Deittner 2000). Reference data that might be appropriate for evaluating moderate spatial resolution imagery (10 - 30m pixels) may be inadequate for high-resolution imagery (1- 10m pixels). A reference data set mapped at a resolution of at least 0.012 ha (II m) is necessary to adequately evaluate IKONOS. As discussed previously, ground resolution is often incorrectly equated with Ground Field of View
(GFOV). Spatial resolution is defined as the minimum separation of two objects that can be actually separated in an image. Separation requires at least one pixel to be between two separate objects. Thus the objects need to be more than twice the square root of two, times the GFOV, to be resolved. Thus a 4m IKONOS image offers II m spatial resolution, just as a 10m SPOT image offers 29m spatial resolution (Hastings 2001).
Reference data sets interpretation 4 and the LEPS interpretation were of adequate resolution (minimum polygon sizes of 0.01 ha and 0.012 ha) to assess the accuracy of
IKONOS. However, the minimum polygon size of interpretation 3 is not known. 4.3.4 Boundary Error
Boundary errors occur at class boundaries due to the occurrence of spectral mixing within a pixel. Sampling is often consciously constrained to large homogeneous regions of the classes with regions in and around the vicinity of complexities such as boundaries excluded (Dicks & Lo 1990; Richards 1996; Wickham et al. 1997) as a deliberate action to minimize misregistration problems and ensure a high degree of confidence in the reference data labels. However, as a result of this type of strategy, the accuracy statement derived may be optimistically biased (Hammond & Verbyla 1996;
Zhu et al. 2000) and only relevant to a small part of the image. Further, as polygons
become smaller in highly heterogeneous landscapes, edge avoidance becomes very difficult and removes even more of the map from the sampling pool. To be applicable to the entire map, the test points used in forming the confusion matrix have to be
representative of the conditions found in the region (Foody 2002)
If boundary areas are masked or excluded from the analysis, we might not be
able to adequately compare the capabilities of IKONOS and Landsat in classifying
boundary areas. It is expected that because of its coarser spatial resolution, the Landsat
image contains more mixed pixels than the IKONOS image. A problem with the
reference data set used in this study is that areas that were difficult to identify were
labeled as unknown and removed from the analysis. The study site is spatially complex
and approximately 30% of the study site was removed from the analysis (Table 4).
These areas that were removed from the current study often included mixed forests,
residential areas, and boundary areas near edges and transition zones that did not accurately represent the polygon. 4.4 Landsat vs. IKONOS
For this study site, Landsat consistently outperforms IKONOS, regardless of the resolution or source of the reference data. The spatial and spectral resolution of the satellite images will determine the types of patches that may be extracted. An examination of the principal components used in the classifications is very revealing
(Table 16). Landsat principal component 1, which explains 63% of the variation, heavily weights bands 5 and 6, which are mid-IR bands that IKONOS lacks. Landsat principal component 2 heavily weights band 4, the near-lR (NIR) band. IKONOS principal component 1 also very heavily weights the NIR band. It appears that the infrared bands are very important in explaining the spectral variation in the study site, and that the
higher overall accuracy results for Landsat can be attributed in part to its additional
infrared bands. The spectral limitations of IKONOS for urban land cover mapping has
been confirmed by other studies (Herold et. al 2003; Goetz et. al 2003). In general, vegetation discrimination, which is important for identification of wildlife habitat, is
enhanced through the incorporation of data from one of the mid-IR bands (band 5 or 7)
(Lillesand & Kieffer 2000). The present results support this fact as Landsat especially outperforms IKONOS in the deciduous and herbs categories.
Given that suburban environments are generally characterized by highly
heterogeneous surface covers with substantial inter-pixel and intra-pixel changes, it is
generally believed that higher spatial resolution is better for suburban land cover
mapping. Therefore, the lower overall accuracy of the classified IKONOS image was a
surprise given its fine resolution and multi-spectral quality. However, the usefulness of a
given type of imagery for suburban and urban applications should not be based solely on
its spatial characteristics (Jensens and Cowen 1999; Yang & Lo 2002). Higher spatial
resolution imagery has not improved classification accuracy in rural-urban fringe settings because each feature in a rural-urban fringe scene can have its own spectrally unique signature. Research in the past has shown that improved spatial resolution can lead to an increase not only in the inter-class variability but also in the intra-class variability, which can produce poor image classification accuracy if a classic pixel based classification method (such as the maximum likelihood classifier) is used (Irons et a1.1985, Haack et al. 1987; Malcolm et al. 2001). Yang & Lo (2002) demonstrated that it was the spectral and radiometric resolution and not the spatial resolution that was most relevant for land cover assessment.
Accuracy may be assessed using a range of spatial units and the unit selected can have a major impact on the estimated magnitude of classification accuracy (Zhu et al. 2000; Foody 2002; Knight & Lunetta 2003). In this study, overall accuracy for both
Landsat and IKONOS is a function of the minimum polygon size of the reference data.
The larger the homogeneous area around a test point, the greater the probability it will be correctly classified. There are many reasons why this may be, and most have already been touched upon. First, it is easier for the interpreter to correctly identify larger polygons. Second, larger polygons exhibit fewer mixed pixels and boundary areas, which tend to be prone to misinterpretation and misclassification. Lastly, although it is not thought to be significant in this study site, larger polygons are less affected by poor misregistration.
4.5 Future Analyses
In this study the widely used maximum likelihood statistical classifier was applied in the image classification. However, in this complex suburban landscape, some cover types likely vary in distribution of digital numbers, where one simple mean value may not provide the best description, such that two or more spectral classes are associated with a single cover type. For instance, herb coverage can vary from very sparse to very dense, or impervious surfaces may be very dull or extremely bright. It is recommended that the existing classes be split further where practical, to help alleviate spectral confusion. These new spectral classes representing a similar class can be later regrouped (Ma et al. 2001). Further, if the probability of a land cover type occurring in the landscape is known beforehand, Bayesian classifiers can be used to further refine the classification. This is expected to be most effective in classifications at the local level (Hepinstall and Sader 1997).
The automated image classification method, which is preferred for large amounts of data over large study areas, relies mainly upon brightness and spectral elements with limited use of image spatial contents. These types of classification methods generally work well in spectrally homogeneous areas, such as forests, but not in highly heterogeneous regions, such as urban landscapes (Yang & Lo 2002). Many other strategies have been developed for improving automated classification, including decision tree classifiers (de Colstoun et al. 2003; Oetter et al. 2000; Rogan et al. 2002), and artificial neural networks (Civco 1993). However, few have found their way into routine use because these techniques can vary greatly in terms of their performance, depending on image characteristics and mapping objectives (Campbell 1996). Several other classification techniques or procedures are also quite promising because they have been shown experimentally to be not only accurate but also comparatively simple and easy to implement in a conventional image processing platform. For example, the present analysis could benefit from the incorporation of spatially referenced ancillary data (i.e. a transportation layer) in the classification procedure (Oetter et al. 2000).
Alternative classifiers were not included in the present study because the objective of this study was to test a non-specific spectrally based methodology that could be easily transferred and applied at a regional level. The study could benefit from the application of post-classification spatial processing. This could be (I) localized contextual reclassification (Barnsley & Barr
1996), for example by overlaying a drainage network and identifying a buffer zone as
'riparian', or (2) modal filtering reclassification where areas smaller than a user identified threshold are identified, declassified, and re-labelled on the basis of their surrounding pixels/polygons (Presutti et al 2001 ; Yang & Lo 2002). For example, a modal filter could be applied to the IKONOS image to remove the 'speckle' pixels and replace them with class values of their surroundings. The high resolution IKONOS sensors pick up more variation in land cover than do the interpreters creating the reference data polygons
(Figure 11). The result is a classified IKONOS image that is highly 'speckled' compared to the original reference polygons. Landsat on the other hand, because it is characterized by lower resolution 30m pixels, produces a smoother image that is less
'speckled' and agrees more frequently with the reference data.
Newer accuracy assessment techniques also attempt to address the mixed pixel problem. For example, a source of error is the implicit assumption that the image is composed of pure pixels. Unfortunately, remotely sensed data are often dominated by pixels that represent areas containing more than one class and these are a major problem in accuracy assessment (Foody 1996, 1999). As already discussed, mixed pixels are common especially in coarse spatial resolution data sets and/or where the land cover mosaic is complex, such as the Langley study site (Campbell 1996; Foody
2002). In a standard classification of data containing mixed pixels, the interpretation of the class labels is difficult as many of the errors observed may be only partial errors because the pixel may represent an area that is partially comprised of the allocated class. Similarly however, some of the apparently correct class labels may be partly erroneous. In attempting to solve the mixed pixel problem, fuzzy classifications have been used increasingly (Gopal & Woodcock 1994). These typically are fuzzy in the sense that they allow each pixel to have multiple and partial class membership. Since mixed pixels often dominate remotely sensed imagery and will not disappear with the use of fine spatial resolution data, techniques that allow their inclusion into the assessment of classification accuracy are required (Foody 2002). Fuzzy logic may also provide more useful information where, for example, a given point within a polygon may actually be incorrectly classified even though the polygon as a whole is correct, or where different magnitudes of error exist. For instance, misclassifying a polygon as coniferous instead of deciduous is much less dramatic than misclassifying it as water (Crist &
Deittner 2002).
Some users might benefit from having a measure of accuracy by polygon or geographic area indicating the level of reliability (Corves & Place 1994; Crist & Deittner
2000). Often there is a distinct pattern to the spatial distribution of thematic errors with, for example, errors spatially correlated at the boundaries of classes (Congalton 1988;
Steele et al. 1998). Much of the error occurring at the boundaries is associated with misregistration of the data sets and mixed pixels. Classified Landsat and IKONOS images may differ in the spatial distribution of error. Unfortunately, the confusion matrix and the accuracy metrics do not provide this kind of information (Steele et al. 1998).
The utility of this study to decide the most efficient strategy for the development of a GVRD land cover map is dependent upon the degree to which land cover conditions in the Langley site are characteristic of the rest of the GVRD. As already mentioned, it would be beneficial to repeat the analysis for another study sites in the GVRD, for example the North Shore or Delta, where different land cover types may be present.
A classified satellite image would either replace or provide additional data for air- photo interpretations. In order to provide a complete analysis of all similar alternatives, it would be ideal to compare results of this type of study with the classification of multi- spectral aerial photography (Arthur Roberts 2003, personal communication). Recent studies have also examined the applications of IKONOS and other high resolution imagery for mapping of only one or two land cover types at a time, such as impervious surface or water quality mapping (Cablk & Minor 2003; Sawaya et. al2003;
Masuoka et. al 2003). For instance, mapping of transportation surfaces has shown significant improvement as the spectral resolution of the sensor improves (Herold et. al.
2003).
This study was started one year prior to another GVRD Biodiversity Conservation
Initiative project which identified specific habitat types (Table 17) that were of particular importance to maintaining biodiversity in the region (Lee & Rudd 2002). The results of this study can be used to determine the suitability of remotely sensed images for the mapping of these specific habitat types, because the land cover types mapped in this study overlapped with some of these identified habitat types. Table 17 explains which of the GVRD identified habitat types were mapped in this study, which ones were not mapped but have the potential to be mapped through satellite imagery, and which ones could be mapped with the addition of ancillary data layers to the satellite imagery.
These are my opinions as pertaining to the GVRD, and the references provided in Table
17 explore the mapping of these habitat types, but may not necessarily provide the best approach.
4.6 Conclusions and Recommendations
Monitoring and decision support tools are important in the management and planning of natural resources, especially in urban areas like the GVRD where growth and change is occurring rapidly. Determining the applicability of satellite remote sensing for land cover mapping is thus a valuable undertaking, as it has the potential to offer information on land cover in a timely fashion. For the GVRD Biodiversity Conservation
Strategy, remotely sensed data has the potential to provide information that will lead to (1) better understanding of the state of existing biodiversity values and conservation within the region, (2) better refinement of policy and planning priorities through development of realistic management objectives for conservation and protection, and (3) more effective allocation of financial, technological and human resources needed to achieve desired outcomes (BC Ministry of Water, Land & Air Protection 2001)
There are over a dozen major research journals devoted to the field of remote sensing. With all of the research in this growing field, numerous image classification methods have been developed. In this study, I applied the widely used maximum likelihood statistical classifier on the principal components derived from the Landsat and
IKONOS images. In order to maintain transferability of the methodology to other parts of the GVRD, ancillary data layers were not used in the classification.
This study has demonstrated the usefulness of satellite remote sensing, digital image processing and GIs techniques in producing land cover maps for the GVRD. The results show that the spectral resolution of the satellite images and the spatial resolution of the reference data affect the accuracy of computer based image classifications.
Because of its fine spatial resolution, the classified IKONOS image was initially expected to be superior over the classified Landsat image. However, the reference data used for this study suggest that the lower resolution classified Landsat image giver higher classification accuracy results than IKONOS. It is thought that the spectral resolution of
Landsat, particularly the presence of the mid-IR bands, gives Landsat the edge over
IKONOS.
The utility of this study to decide the most efficient strategy for the development of a GVRD land cover map is dependent upon the degree to which land cover conditions in the Langley site are characteristic of the rest of the GVRD. The approach used in the study is expected to be transferable to other suburban areas of the GVRD, however, this has not yet been assessed. A digital elevation model (DEM) would be necessary for similar studies of the mountainous North Shore area.
From a biodiversity conservation management and planning perspective, the present study indicates that Landsat offers greater potential than IKONOS in accurate land cover classification of suburban areas in the GVRD. In particular, the study shows that the disturbed, coniferous and deciduous classes were mapped accurately enough, such that the results could be applied immediately across suburban areas of the GVRD for these classes. A better study site, with more coverage of open water, is necessary to assess the ability to classify water, which is normally expected to be easy to classify.
The wetland class was mapped poorly, indicating that air-photo interpretation is necessary to identify this class correctly. Further, if other habitat types (Table 17) are to be mapped, air-photo interpretation or alternative digital image processing methods may be required. If it is not to be used for classification purposes, satellite imagery is still an excellent aid and complementary interpretive tool during manual air-photo interpretation.
Lillesand and Kieffer (2000) recommend that Landsat images should not be a replacement for low altitude aerial photographs.
The increasing classification accuracy with increasing minimum reference data polygon size for the classified Landsat and IKONOS images suggests that it may be possible to obtain an acceptable overall accuracy rating (85%; see Anderson et. al 1976) if larger minimum test polygon sizes are acceptable, or if the landscape is characterized by land cover types with larger patch sizes. As long as the scale of resolution at which the classified image meets accuracy requirements is consistent with planning needs, the classified satellite image will be a useful tool for planning.
Of course, local biodiversity conservation planners would prefer to map smaller features because small parcels are more consistent with cadastral maps and tend to be more susceptible to impacts than larger parcels. However, it is important to ensure that land use decisions are based on correct information. Therefore, it is more important to have correctly identified parcels, even if they are larger than desired. As discussed previously, the classification of small parcels in urban settings is expected to be problematic using pixel based statistical classifiers because improved spatial resolution can lead to (I)an increase in the inter-class spectral variability and the intra-class spectral variability, (2) an increase in the mixed pixel problem, and (3) greater misregistration problems in accuracy assessments. Until the efficacy of IKONOS is proven in the classification of small parcels, it is recommended that the lower resolution
Landsat imagery be used to produce classified land cover maps of the disturbed, coniferous and deciduous classes. The relative cost of Landsat images is considerably cheaper than IKONOS images (Table I),and in the meantime, managers can wait for the development of more effective high resolution technology, wait for the results of similar studies in the literature, or provide support for studies to improve upon the present methodology. For instance, it would be valuable to compare present results to the classification of multi-spectral aerial photography. For now, because of its crisp image and detail, it is recommended that IKONOS images, while not the best for creating land cover maps, be used as an aid to air-photo interpretation.
There is considerable interest in the use of remote sensing to study thematic change. However, a variety of factors influence the accuracy of land cover change products. Basic issues are the accuracies of the component classifications as well as more subtle issues associated with the sensors and data preprocessing methods used, together with the atmospheric conditions at the times of the different image acquisitions.
When mapping land cover change, the problems discussed previously in relation to the registration of data sets and boundaries are generally magnified. Error in the individual classifications may also be confused with change (Khorram 1999). As a consequence of these and other issues, the estimation of the accuracy of a change product is a substantially more difficult and challenging task than the assessment of the accuracy of a single image classification (Congalton & Green 1999). This is a major limitation in environmental studies where the magnitude of change is often important. (Foody, 2002).
In conclusion, the classified Landsat map approached the 85% accuracy level stipulated by the Anderson classification (Anderson et al. 1976). This is good evidence that the image processing approach adopted in this study has been effective, and that satellite imagery does provide a viable source of data from which updated land cover information can be extracted to improve the effectiveness and efficiency of conservation efforts in suburban areas of the GVRD. References
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Landsat image coniferous deciduous herbs impervious soil water wetland Row total accuracy (%)
coniferous 68 8 3 5 0 28 1 113 60.2
deciduous 4 124 13 2 0 1 2 146 84.9 herbs 27 24 339 111 49 20 21 59 1 57.4
impervious 0 0 0 4 0 0 0 4 100.0
soil 2 0 13 8 6 0 0 29 20.7
water 0 0 0 0 0 5 0 5 100.0 wetland 1 6 19 2 1 0 25 54 46.3 Column total 102 162 387 132 56 54 49 942 Producer's Overall accuracy (%) 66.7 76.5 87.6 3.0 10.7 9.3 51.0 accuracy (%): 60.6 5b. As evaluated against interpretation 3. Kappa = 0.29 Classified Interpretation 3 User's Landsat image coniferous deciduous herbs impervious soil water wetland Row total accuracy (%) coniferous 43 6 7 1 2 26 4 89 48.3 deciduous 11 88 20 1 8 2 8 138 63.8 herbs 38 15 251 134 137 17 17 609 41.2 impervious 0 0 0 9 0 0 0 9 100.0 soil 1 0 9 7 11 0 0 28 39.3 water 0 0 0 0 0 5 0 5 100.0 wetland 2 1 13 0 7 0 25 48 52.1 Column total 95 110 300 152 165 50 54 926 Producer's Overall
P accuracy (%) 45.3 80.0 83.7 5.9 6.7 10.0 46.3 accuracy (%): 46.7 4 Table 6. Error matrices for the classified IKONOS image (7 original classes; all test points used regardless of the size of the reference data polygons; test point sampling interval = 100m). 6a. As evaluated against interpretation 4. Kappa = 0.36 Classified Interpretation 4 User's IKONOS image coniferous deciduous herbs impervious soil water wetland Row total accuracy (%) coniferous 79 28 37 12 4 12 4 176 44.9 deciduous 9 109 11 14 1 2 9 155 70.3 herbs 9 2 1 221 35 22 2 21 331 66.8 impervious 0 0 6 1 3 0 0 10 10.0 soil 3 9 79 58 21 8 2 180 11.7 water 0 0 0 0 1 30 0 3 1 96.8 wetland 2 6 37 12 5 0 14 76 18.4
P 03 Column total 102 173 39 1 132 57 54 50 959 Producer's Overall accuracy (%) 77.5 63.0 56.5 0.8 36.8 55.6 28.0 accuracy (%): 49.5 6b. As evaluated against interpretation 3. Kappa = 0.27 Classified Interpretation 3 User's IKONOS image coniferous deciduous herbs impervious soil water wetland Row total accuracy (%) Coniferous 62 26 23 18 15 17 11 172 36.0 Deciduous 8 76 16 11 3 1 10 125 60.8 Herbs 14 7 162 45 77 2 21 328 49.4 Impervious 0 0 6 2 4 0 0 12 16.7 soil 6 3 63 65 49 6 2 1 94 25.3 water 0 0 4 0 2 24 0 30 80.0 wetland 6 3 3 1 12 17 0 12 81 14.8 Column total 96 1 15 305 153 167 50 56 942 Producer's Overall
P accuracy (%) 64.6 66.1 53.1 1.3 29.3 48.0 21.4 accuracy(%): 41 .I a Table 7. Error matrices for the classified Landsat image as evaluated against interpretation 4 (5 classes; test point sampling interval = 100m). 7a. All test points used regardless of the size of the reference data polygons. Kappa = 0.64 Classified Interpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 68 8 8 28 1 1 13 60.2 deciduous 4 124 15 1 2 146 84.9 disturbed 29 24 530 20 21 624 84.9 water 0 0 0 5 0 5 100.0 wetland 1 6 22 0 2 5 54 46.3 ------Column total 102 162 575 54 49 942 Producer's accuracy (%) 66.7 76.5 92.2 9.3 51.0 Overall accuracy (%): 79.8 Cn 0 7b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.68. Classified Interpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) Coniferous 56 7 2 24 1 90 62.2% Deciduous 3 106 4 1 2 116 91.4% Disturbed 9 9 270 17 20 325 83.1% water 0 0 0 5 0 5 100.0% wetland 1 3 19 0 23 46 50.0% Column total 69 125 295 47 46 582 Producer's accuracy (%) 81.2 84.8 91.5 10.6 50.0 Overall accuracy (%): 79.0 7c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.78 Classified Intermetation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) Coniferous 37 5 1 6 1 50 74.0% Deciduous 2 78 1 0 1 82 95.1% Disturbed 3 1 162 3 19 188 86.2% water 0 0 0 3 0 3 100.0% wetland 0 0 9 0 21 30 70.0% Column total 42 84 173 12 42 353 Producer's accuracy (%) 88.1 92.9 93.6 25.0 50.0 Overall accuracy (%): 85.3
7d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.81
5 Classified Interpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) Coniferous 26 1 1 0 1 29 89.7% Deciduous 1 56 1 0 1 59 94.9% Disturbed 1 1 98 1 13 114 86.0% water wetland Column total 28 58 106 3 31 226 Producer's accuracy (%) 92.9 96.6 92.5 66.7 51.6 Overall accuracy (%): 87.6 Table 8. Error matrices for the classified Landsat image as evaluated against interpretation 3 (5 classes; test point sampling interval = 100m). 8a. All test points used regardless of the size of the reference data polygons. Kappa = 0.56 Classified Interpretation 3 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 43 6 10 26 4 89 48.3 deciduous 11 88 29 2 8 138 63.8 disturbed 39 15 558 17 17 646 86.4 water wetland Column total 95 110 61 7 50 54 926 Producer's accuracy (%) 45.3 80.0 90.4 10.0 46.3 Overall accuracy (%): 77.6
8b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.61 Classified Interpretation 3 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 40 5 7 22 2 76 52.6 deciduous 7 75 14 1 6 103 72.8 disturbed 26 10 380 11 14 44 1 86.2 water 0 0 0 4 0 4 100.0 wetland 1 0 15 0 22 38 57.9 Column total 74 90 416 38 44 662 Producer's accuracy (%) 54.1 83.3 91.3 10.5 50.0 Overall accuracy (%): 78.7 8c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.69
- - Classified lnterpretation 3 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 37 3 4 14 1 59 62.7 deciduous 3 65 2 1 4 75 86.7 disturbed 15 2 218 3 11 249 water 0 0 0 3 0 3 wetland 1 0 12 0 14 27 Column total 56 70 236 21 30 41 3 Producer's accuracy (%) 66.1 92.9 92.4 14.3 46.7 Overall accuracy (%): 81.6
8d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.80
Cn Classified Interpretation 3 User's W Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) Coniferous 27 2 0 1 1 31 87.1 Deciduous 0 46 0 0 2 48 95.8 Disturbed 9 0 135 0 11 155 87.1 water 0 0 0 3 0 3 100.0 wetland 0 0 4 0 11 15 73.3 Column total 36 48 139 4 25 252 Producer's accuracy (%) 75.0 95.8 97.1 75.0 44.0 Overall accuracy (%): 88.1 Table 9. Error matrices for the classified IKONOS image as evaluated against interpretation 4 (5 classes; test point sampling interval = 1OOm). 9a. All test points used regardless of the size of the reference data polygons. Kappa = 0.52 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 79 28 53 12 4 176 44.9 deciduous 9 109 26 2 9 155 70.3 disturbed 12 30 446 10 23 521 85.6 water 0 0 1 30 0 3 1 96.8 wetland 2 6 54 0 14 76 18.4 Column total 102 173 580 54 50 959 Producer's accuracy (%) 77.5 63.0 76.9 55.6 28.0 Overall accuracy (%): 70.7
U1 P 9b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.59 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) Coniferous 58 23 15 7 4 107 54.2 Deciduous 8 89 10 2 7 116 76.7 disturbed 2 16 239 9 23 289 82.7 water 0 0 0 29 0 29 100.0 wetland 1 5 31 0 13 50 26.0 Column total 69 133 295 47 47 591 Producer's accuracy (%) 84.1 66.9 81 .O 61.7 27.7 Overall accuracy (%): 72.4 9c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.66 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 36 12 4 2 3 57 63.2 deciduous 4 7 1 1 0 6 82 86.6 disturbed 2 4 148 0 2 1 175 84.6 water 0 0 0 10 0 10 100.0 wetland 0 3 20 0 13 36 36.1 Column total 42 90 173 12 43 360 Producer's accuracy (%) 85.7 78.9 85.5 83.3 30.2 Overall accuracy (%): 77.2
9d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.62 ul Classified Interpretation 4 User's ul IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) Coniferous 23 8 3 0 1 35 65.7 Deciduous 3 50 0 0 3 56 disturbed 2 2 85 0 18 107 water 0 0 0 3 0 3 wetland 0 2 18 0 9 29 Column total 28 62 106 3 3 1 230 Producer's accuracy (%) 82.1 80.6 80.2 100.0 29.0 Overall accuracy (%): 73.9 Table 10. Error matrices for the classified IKONOS image as evaluated against interpretation 3 (5 classes; test point sampling interval = 100m). 10a. All test point used regardless of the size of the reference data polygons. Kappa = 0.46 Classified Interpretation 3 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 62 26 56 17 11 172 36.0 deciduous 8 76 30 1 10 125 60.8 disturbed 20 10 473 8 23 534 88.6 water 0 0 6 24 0 30 80.0 wetland 6 3 60 0 12 8 1 14.8 Column total 96 1 15 625 50 56 942 Producer's accuracy (%) 64.6 66.1 75.7 48.0 21.4 Overall accuracy (%): 68.7
UI Q, lob. Test points from reference data polygons larger than 0.024ha. Kappa = 0.53 Classified Interpretation 3 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 53 24 28 10 9 1 24 42.7 deciduous 5 62 15 0 9 9 1 68.1 disturbed 13 5 335 6 18 377 88.9 water 0 0 6 22 0 28 78.6 wetland 4 2 37 0 10 53 18.9 Column total 75 93 42 1 38 46 673 Producer's accuracy (%) 70.7 66.7 79.6 57.9 21.7 Overall accuracy (%): 71.6 10c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.58 Classified Interpretation 3 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 41 17 11 5 6 80 51.3 deciduous 3 55 7 0 7 72 76.4 disturbed 11 0 195 4 13 223 87.4 water 0 0 3 12 0 15 80.0 wetland 2 1 23 0 6 32 18.8 Column total 57 73 239 21 32 422 Producer's accuracy (%) 71.9 75.3 81.6 57.1 18.8 Overall accuracy (%): 73.2
10d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.63
U1 Classified Interpretation 3 User's 4 IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 26 6 3 0 4 39 66.7 deciduous 2 44 3 0 7 56 78.6 disturbed 7 0 118 1 11 137 86.1 water 0 0 0 3 0 3 100.0 wetland 1 0 16 0 5 22 22.7 Column total 36 50 140 4 27 257 Producer's accuracy (%) 72.2 88.0 84.3 75.0 18.5 Overall accuracy (%): 76.3 Table 1 1. Error matrices for the classified Landsat image as evaluated against the LEPS interpretation (5 classes; test point sampling interval = 100m). I I a. All test points used regardless of the size of the reference data polygons. Kappa = 0.61 Classified LEPS Interpretation User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 90 12 5 11 6 1 24 72.6 deciduous 9 100 22 1 4 136 73.5 disturbed 52 34 485 7 18 596 81.4 water 0 0 1 31 0 32 96.9 wetland 1 6 30 0 21 58 36.2 Column total 152 152 543 50 49 946 Producer's accuracy (%) 59.2 65.8 89.3 62.0 42.9 Overall accuracy (%): 76.8
I I b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.72 Classified LEPS Interpretation User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 78 6 0 10 5 99 78.8% deciduous 6 80 13 0 3 102 78.4% disturbed 13 11 391 6 16 437 89.5% water 0 0 0 30 0 30 100.0% wetland 0 0 28 0 20 48 41.7% Column total 97 97 432 46 44 716 Producer's accuracy (%) 80.4 82.5 90.5 65.2 45.5 Overall accuracy (%): 83.7 II c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.79 Classified LEPS Interpretation User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 53 4 0 9 3 69 76.8% deciduous 2 60 1 0 2 65 92.3% disturbed 3 4 29 1 4 11 313 93.0% water 0 0 0 29 0 29 100.0% wetland 0 0 20 0 16 36 44.4% Column total 58 68 312 42 32 512 Producer's accuracy (%) 91.4 88.2 93.3 69.0 50.0 Overall accuracy (%): 87.7
IId. Test points from reference data polygons larger than 0.216ha. Kappa = 0.84 Classified LEPS Interpretation User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 45 2 0 5 3 55 81.8% deciduous 0 44 0 0 0 44 100.0% disturbed 2 3 220 2 8 235 93.6% water 0 0 0 27 0 27 100.0% wetland 0 0 11 0 15 26 57.7% Column total 47 49 23 1 34 26 387 Producer's accuracv (%) 95.7 89.8 95.2 79.4 57.7 Overall accuracy (%): 90.7 Table 12. Error matrices for the classified IKONOS image as evaluated against the LEPS interpretation (5 classes; test point sampling interval = 100m). 12a. All test points used regardless of the size of the reference data polygons. Kappa = 0.48 Classified LEPS Inter~retation User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 99 37 49 2 11 198 50.0 deciduous 14 89 32 1 10 146 61 .O disturbed 38 27 399 5 17 486 82.1 water 0 0 2 42 2 46 91.3 wetland 0 7 6 5 0 10 82 12.2 Column total 151 160 547 50 50 958 Producer's accuracy (%) 65.6 55.6 72.9 84.0 20.0 Overall accuracy (%): 66.7 cn 0 12b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.58 Classified L EPS Interpretation User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 75 21 29 2 9 136 55.1% deciduous 8 69 2 1 0 9 107 64.5% disturbed 13 10 335 3 15 376 89.1% water 0 0 0 41 2 43 95.3% wetland 0 3 48 0 9 60 15.0% Column total 96 103 433 46 44 722 Producer's accuracv (%) 78.1 67.0 77.4 89.1 20.5 Overall accuracv (%): 73.3 12c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.64 Classified LEPS Interpretation User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 49 14 14 2 5 84 58.3% deciduous 5 51 10 0 7 73 69.9% disturbed 4 7 257 3 12 283 90.8% water 0 0 0 37 1 38 97.4% wetland 0 1 3 1 0 7 39 17.9% Column total 58 73 312 42 32 517 Producer's accuracy (%) 84.5 69.9 82.4 88.1 21.9 Overall accuracy (%): 77.6
12d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.69
2 Classified L EPS Interpretation User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 42 7 10 1 5 6 5 64.6% deciduous 4 40 4 0 5 53 75.5% disturbed 1 5 196 0 10 21 2 92.5% water 0 0 0 33 1 34 97.1% wetland 0 1 22 0 5 28 17.9% Column total 47 53 232 34 26 392 Producer's accuracy (%) 89.4 75.5 84.5 97.1 19.2 Overall accuracy (%): 80.6 Table 13. Error matrices for the classified Landsat image as evaluated against interpretation 4 (5 classes; test point sampling interval = 150m). 13a. All test points used regardless of the size of the reference data polygons. Kappa = 0.60 Classified Interpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 34 2 5 29 2 72 47.2 deciduous 3 60 5 1 2 7 1 84.5 disturbed 13 14 284 23 19 353 80.5 water 0 0 0 5 0 5 100.0 wetland 0 1 9 0 3 1 41 75.6 Column total 50 77 303 58 54 542 Producer's accuracy (%) 68.0 77.9 93.7 8.6 57.4 Overall accuracy (%): 76.4 ln 10 13b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.65 Classified Interpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 29 0 1 26 1 57 50.9 deciduous 2 49 1 1 1 54 90.7 disturbed 4 7 167 17 18 21 3 78.4 water 0 0 0 5 0 5 100.0 wetland 0 1 5 0 3 1 37 83.8 Column total 35 57 174 49 51 366 Producer's accuracy (%) 82.9 86.0 96.0 10.2 60.8 Overall accuracy (%): 76.8 13c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.77 Classified lnterpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 24 0 1 6 1 32 75.0 deciduous 1 37 0 0 1 39 94.9 disturbed 2 1 96 1 16 116 82.8 water 0 0 0 3 0 3 100.0 wetland 0 1 4 0 30 35 85.7 Column total 27 39 101 10 48 225 Producer's accuracy (%) 88.9 94.9 95.0 30.0 62.5 Overall accuracy (%): 84.4
13d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.80 Classified Interpretation 4 User's Landsat image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 17 0 1 2 I 21 81 .O deciduous 1 26 0 0 1 28 92.9 disturbed 1 0 52 0 9 62 83.9 water wetland Column total 19 27 55 2 34 137 Producer's accuracy (%) 89.5 96.3 94.5 0.0 67.6 Overall accuracy (%): 86.1 Table 14. Error matrices for the classified IKONOS image as evaluated against interpretation 4 (5 classes; test point sampling interval = 150m). 14a. All test points used regardless of the size of the reference data polygons. Kappa = 0.51 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 35 19 30 12 6 102 34.3 deciduous 8 48 10 0 16 82 58.5 disturbed 6 9 248 12 25 300 82.7 water 0 0 0 34 0 34 100.0 wetland 1 5 16 0 10 32 31.3 Column total 50 8 1 304 58 57 550 Producer's accuracy (%) 70.0 59.3 81.6 58.6 17.5 Overall accuracy (%): 68.2 cn P 14b. Test points from reference data polygons larger than 0.024ha. Kappa = 0.59 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 30 12 5 8 5 60 50.0 deciduous 5 39 4 0 16 64 60.9 disturbed 0 6 159 10 25 200 79.5 water 0 0 0 3 1 0 3 1 100.0 wetland 0 3 6 0 8 17 47.1 Column total 35 60 174 49 54 372 Producer's accuracy (%) 85.7 65.0 91.4 63.3 14.8 Overall accuracy (%): 71.8 14c. Test points from reference data polygons larger than 0.096ha. Kappa = 0.63 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 24 5 1 1 5 36 66.7 deciduous 3 33 1 0 15 52 63.5 disturbed 0 2 96 0 23 121 79.3 water 0 0 0 9 0 9 100.0 wetland 0 1 3 0 8 12 66.7 Column total 27 41 101 10 51 230 Producer's accuracy (%) 88.9 80.5 95.0 90.0 15.7 Overall accuracy (%): 73.9
14d. Test points from reference data polygons larger than 0.216ha. Kappa = 0.64 Classified Interpretation 4 User's IKONOS image coniferous deciduous disturbed water wetland Row total accuracy (%) coniferous 18 1 1 0 2 22 deciduous 1 27 0 0 9 37 disturbed 0 0 52 0 19 7 1 water 0 0 0 2 0 2 wetland 0 1 2 0 5 8 Column total 19 29 55 2 35 140 Producer's accuracy (%) 94.7 93.1 94.5 100.0 14.3 Overall accuracy (%): 74.3 Table 15. Z-statistic values for kappa analysis comparisons between error matrices. The error matrices being compared are indicated by the corresponding table numbers. The critical z-value is 1.96. Error matrices are significantly different from each other where the z-statistic is greater than 1.96 (highlighted in bold).
a = all test points used b = test points from reference polygons > 0.024 ha c = test points from reference polygons > 0.096 ha d = test points from reference polygons > 0.216 ha
15a. Overall accuracy results are consistently significantly higher for Landsat than IKONOS when evaluated against all of the reference data sets. Landsat evaluated against Landsat evaluated against Landsat evaluated against interpretation 4. interpretation 3 LEPS interpretation 1 Table 7a 7b 7c 7d 8a 8b 8c 8d Ila Ilb Ilc Ild IKONOS evaluated against 9a interpretation 4. 9b 9c 9d IKONOS evaluated against 10a interpretation 3. lob 1oc 10d IKONOS evaluated against 12a LEPS interpretation. 12b 12c 12d 15b. Overall accuracy results for Landsat and IKONOS are consistently significantly higher when evaluated against test points from reference polygons larger than 0.096ha and 0.216ha
Landsat Landsat Landsat IKONOS IKONOS IKONOS evaluated evaluated evaluated evaluated evaluated evaluated against against against LEPS against against interpretation 4. interpretation 3. interpretation. interpretation 4. Table 9a Landsat evaluated against interpretation 4.
Landsat evaluated against interpretation 3.
Landsat evaluated against LEPS interpretation.
IKONOS evaluated against interpretation 4.
IKONOS evaluated against interpretation 3.
IKONOS evaluated against Ilb LEPS interpretation. Ilc IId 15c. The reference data set against which the classified Landsat and IKONOS images are evaluated against do not significantly affect overall accuracy results. The only exceptions are when the classified Landsat image is evaluated against all the test points from interpretation 3 and test points from interpretation 3 polygons larger than 0.096ha.
Landsat evaluated against interpretation 4. IKONOS evaluated against interpretation 4. Table 7a 7b 7c 7d Landsat evaluated against 8a 2.36 interpretation 3. 8b 1.88 8c 2.02 8d 0.26 Landsat evaluated against Ila 0.88 L EPS interpretation. Ilb 1.32 Ilc 0.38 IId 0.00 IKONOS evaluated against 10a interpretation 3. lob 1Oc 10d IKONOS evaluated against 12a LEPS interpretation. 12b 12c -12d 15d. Overall accuracy results are not significantly affected by changing the test point sampling interval from 100m to l5Om.
Landsat evaluated against IKONOS evaluated against interpretation 4. interpretation 4. Test point sampling interval = 100m. Test point sampling interval = 100m. Table 7a 7b 7c 7d 9a 9b 9c 9d Landsat evaluated against interpretation 4. Test point sampling interval = 150m.
I IKONOS evaluated against interpretation 4. Test point sampling interval = 150m. Table 16. Principal components of the Landsat and IKONOS satellite images. 16a. Landsat principal components 1 and 2 and the corresponding eigenvectors2for each band. Landsat bands Landsat principal Landsat principal component 1 component 2 eigenvectors eigenvectors 1 (Blue) 0.266 -0.1 10 2 (Green) 0.300 -0.024 3 (Red) 0.490 -0.110 4 (NIR) 5 (Mid IR) 7 (Mid IR) 8 (Panchromatic) % of the total scene variance represented 63 27 in each principal component
16b. IKONOS principal components 1 and 2 and the corresponding eigenvectors of each band. IKONOS Bands IKONOS principal IKONOS principal component 1 component 2 eigenvectors eigenvectors 1 (Red) 0.015 0.644 2 (Green) 0.038 0.61 1 3 (Blue) -0.010 0.459 4 (NIR) 0.999 -0.029 % of the total scene variance represented 67 32 in each principal component
Eigenvectors are the variance contribution from each original input band to each transformed principal component band.
70 Table 17. Habitat types identified by Lee & Rudd (2002) as important for the conservation of biodiversity in the GVRD. This table indicates whether the habitat type was mapped in the present study, and if not, whether I believe it is possible to map distinctly with the use of satellite imagery alone or with that addition of ancillary data layers Habitat Types of Interest Mapped in Possible to map using only satellite Possible to map with satellite imagery and current study imagery addition of ancillary data layers WETLAND ECOSYSTEMS yes - Marsh and Swamp no yes (Parmuchi et. a1 2002) Bog no yes (Takeuchi et. al2003) - Vernal Pool no no, need ground sampling to identify - OPEN WATER Yes - - ECOSYSTEMS Lake no no, unable to distinguish from other yes, with addition of hydrologic data layer open water ecosvstems Pond no no, unable to distinguish from other yes, with addition of hydrologic data layer open water ecosystems River no no, unable to distinguish from other yes, with addition of hydrologic data layer open water ecosystems no no, unable to distinguish from other yes, with addition of hydrologic data layer open water ecosystems Reservoir no no, unable to distinguish from other yes, with addition of hydrologic data layer open water ecosystems Ditch and Stormwater no no, unable to distinguish from other yes, with addition of hydrologic data layer Detention Pond open water ecosystems Estuary no yes (Donoghue & Mironnet 2002) -
Habitat Types of Interest Mapped in Possible to map using only satellite Possible to map with satellite imagery and current study imagery addition of ancillary data layers URBAN AND RURAL yes (Sugumaran et. al2002) ECOSYSTEMS Boulevard and Street Trees no no, determined by spatial proximity to yes, with addition of transportation data layer roads ------Hedgerows, Rights-of-way no no, difficult to distinguish from other yes, with addition of transportation data layer vegetation Shrub Communities and no yes (Goslee et. al 2003) - Thickets Lawns and Gardens no no, determined by spatial proximity to yes, with addition of cadastral data layer residential development BLUFF AND BEDROCK no no, confused with impervious surfaces OUTCROPPINGS
'1 Marine no no, confused with impervious surfaces - Scree and Talus Slopes no no, confused with impervious surfaces yes, with the addition of a digital elevation layer Inland and Upland Bluffs no no, confused with impervious surfaces Habitat Types of Interest Mapped in Possible to map using only satellite Possible to map with satellite imagery and current study imagery addition of ancillary data layers HERB AND GRASS Yes - - ECOSYSTEMS Old Field no no, need ground sampling to identify - Pasture no no, confused with cropland and athletic yes, with addition of agricultural land use fields data layer Cropland no no, confused with pasture and athletic yes, with addition of agricultural land use fields data laver Athletic Fields and Golf no no, confused with pasture and cropland yes, with addition of land use data layer Courses ARTIFICIAL STRUCTURES Yes - - Buildings no no, confused with other impervious yes, with addition of land use data layer surfaces 4 P Transmission Towers no no, confused with other impervious yes, with addition of land use data layer surfaces Nest Boxes no no, too small of a feature to identify - Other Built Environments Yes - - EXPOSED OR DISTURBED Yes - - SITES Quarries and Gravel Pits no no, confused with other impervious yes, with the addition of a land use data layer surfaces Barren Land Yes - - Figures
deciduous
impervious
soil
water
wetland
0 1000 2000 Meters I I
Figure 2. The classified Landsat image of the study site showing the seven original land cover classes. Pixel size = 30m. Iconiferous
Isoil 7 water
1wetland
0 1000 2000 Meters
Figure 3. The classified IKONOS image of the study site showing the seven original land cover classes. Pixel size = 4m. deciduous
1 disturbed
1 wetland
0 1000 2000 Meters I
Figure 4. The classified Landsat image of the study site showing the disturbed land cover class. Pixel size = 30m. - coniferous r l deciduous 1disturbed water
wetland
0 1000 2000 Meters I D l
Figure 5. The classified IKONOS image of the study site showing the disturbed land cover class. Pixel size = 4m. +Landsat evaluated against interpretation 4
+Landsat evaluated against interpretation 3
+Landsat evaluated against LEPS interpretation
IKONOS evaluated againsi interpretation 4
l KONOS evaluated againsi interpretation 3
IKONOS evaluated agains. LEPS interpretation
0,05 0.1 0 0.1 5 0.26 0.25 Minimum polygon size of the reference data (ha)
Figure 6. Overall accuracy (%) as a function of the minimum polygon size (ha) of the reference data. ILandsat IKONOS
coniferous deciduous disturbed water wetland Land cover classes
Figure 7. Producer's accuracies for the land cover classes. (evaluated against interpretation 4; all test points included; test point sampling interval = 100m) U K -([I C
Ti 03