Satellites Could Soon Map Every Tree on Earth

Total Page:16

File Type:pdf, Size:1020Kb

Satellites Could Soon Map Every Tree on Earth News & views fully convolutional neural networks. This Ecology deep­learning method is designed to recog­ nize objects (in this case, tree canopies) on the basis of their characteristic shapes and colours within a larger image. Convolutional networks Satellites could soon rely on the availability of training data, which in this case consisted of satellite images in map every tree on Earth which the visible outlines of tree and shrub canopies were manually traced. Through train­ Niall P. Hanan & Julius Y. Anchang ing using these samples, the computer learnt how to identify individual tree canopies with An analysis of satellite images has pinpointed individual tree high precision in other images. The result is a canopies over a large area of West Africa. The data suggest that wall­to­wall mapping of all trees larger than it will soon be possible, with certain limitations, to map the 2 m in diameter across the whole of southern Mauritania, Senegal and southwestern Mali. location and size of every tree worldwide. See p.78 A previous estimate2 of the total number of trees on a global scale was obtained using field data from approximately 430,000 forest Terrestrial ecosystems are defined in large Brandt et al. provide a striking demon­ plots around the world. The authors of that part by their woody plants. Grasslands, shrub­ stration of this transformation in terrestrial study used statistical regression models to lands, savannahs, woodlands and forests rep­ remote sensing. The authors analysed more estimate tree density between the field sites, resent a series of gradations in tree and shrub than 11,000 images, at a spatial resolution of on the basis of vegetation type and climate. density, from ecosystems with low­density, 0.5 m, to identify individual trees and shrubs Their analysis suggested that there are approx­ low­stature woody plants to those with taller with canopy diameters of 2 m or more. The imately three trillion trees globally. However, trees and overlapping canopies. Accurate authors completed this giant task using arti­ this approach to tree­density estimation has information on the woody­vegetation struc­ ficial intelligence — exploiting a computa­ inherent errors and uncertainties, particularly ture of ecosystems is, therefore, fundamental tional approach that involves what are called for drylands, for which relatively few field to our understanding of global­scale ecology, biogeography and the bio geochemical cycles a of carbon, water and other nutrients. On 150 Trees per page 78, Brandt et al.1 report their analysis of hectare a massive database of high­resolution satellite MAURITANIA 0 1–25 images covering more than 1.3 million square 26–50 kilometres of the western Sahara and Sahel 51–100 300 regions of West Africa. The authors mapped > 100 the location and size of more than 1.8 billion individual tree canopies; never before have trees been mapped at this level of detail across such a large area. SENEGAL 600 The spatial resolution of most satellite data MALI is relatively coarse, with individual image pixels generally corresponding to areas on the GAMBIA ground that are larger than 100 square metres, and often larger than one square kilometre. b c d This limitation has forced researchers in the field of Earth observation to focus on meas­ uring bulk properties, such as the proportion of a landscape covered by tree canopies when viewed from above (a measurement known as canopy cover). However, during the past two decades, a variety of commercial satellites have begun to collect data at a higher spatial resolution, 5 km 5 km 50 m capable of capturing ground objects measur­ JULIUS ANCHANG ing one square metre or less. This resolution Figure 1 | Large-scale tree mapping. Accurate information about tree distribution provides useful ecological improvement places the field of terrestrial insights, but such data are difficult to obtain for large areas of land. a, A previous study2 estimating global remote sensing on the threshold of a funda­ tree density per hectare relied on data from field plots — samples of these data are shown for western Africa. mental leap forward: from focusing on aggre­ The inset box in a is in a dry region (with an average annual rainfall of less than 600 millimetres per year), and corresponds to b. Dotted lines indicate the boundaries of average rainfall in millimetres per year. c, d, Brandt gate landscape­scale measurements to having et al.1 report the detection of individual tree canopies across western Africa, obtained using an artificial­ the potential to map the location and canopy intelligence approach to analyse high­resolution satellite images. The authors found a higher tree density size of every tree over large regional or global in dry regions of Africa than did the earlier study. For example, Brandt and colleagues’ analysis of the area scales. This revolution in observational capa­ corresponding to the inset box in a produced the tree density per hectare shown in c. They identified the size bilities will undoubtedly drive fundamental and location of individual tree canopies (green), as shown in d for an area corresponding to the inset box in changes in how we think about, monitor, model c. Tree information at this level of detail was not available in the earlier study. (Images made using data from and manage global terrestrial ecosystems. refs 1 and 2.) (Springer Nature remains neutral with regard to jurisdictional claims in published maps.) 42 | Nature | Vol 587 | 5 November 2020 ©2020 Spri nger Nature Li mited. All rights reserved. ©2020 Spri nger Nature Li mited. All rights reserved. measurements are available to calibrate the be particularly tricky at regional and global wood biomass. Continuing research will models. scales and across biodiverse ecosystems. be needed to develop more­efficient cano­ A comparison (Fig. 1) of that earlier result The mapping of individual tree canopies by py­classification algorithms. In the meantime, with Brandt and colleagues’ findings in the species will probably remain at the top of the Brandt and colleagues have clearly demon­ western Sahel, for example, shows that the Earth­observation research community’s wish strated the potential for future global mapping previous study tended to underestimate the list for some time6. of tree canopies at submetre scales. number of trees in the drier regions (areas In the years ahead, remote sensing will with annual rainfall of less than 600 milli­ undoubtedly provide unprecedented detail Niall P. Hanan is with the Jornada Basin metres). Moreover, the previous estimates about vegetation structure as data from a Long-Term Ecological Research Program, provided no information on the location and range of sources — including light detection Department of Plant and Environmental size of individual trees within each square kilo­ and ranging (lidar), radar and high­resolution Sciences, and Julius Y. Anchang is in the metre, whereas Brandt and colleagues provide visible and near­infrared sensors — become Department of Plant and Environmental detailed information on the location and size more readily available7. Satellite­derived Sciences, New Mexico State University, of every individual canopy. The improvement Las Cruces, New Mexico 88003, USA. provided in the latest study can also be seen “Never before have trees e-mails: [email protected]; in the much higher level of detail it gives for [email protected] the wetter regions (those with annual rainfall been mapped at this level greater than 600 mm), and shows local spatial of detail across such variability in trees that is presumably associ­ a large area.” ated with contrasting soil types, water availa­ bility, land use and land­use history. There are, of course, caveats and limita­ high­resolution data on tree canopy size and 1. Brandt, M. et al. Nature 587, 78–82 (2020). tions to Brandt and colleagues’ work and the density could contribute to the inventory 2. Crowther, T. W. et al. Nature 525, 201–205 (2015). potential for scaling up their approach to a and management of forests and woodland, 3. Axelsson, C. R. & Hanan, N. P. Biogeosciences 14, 3239–3252 (2017). global analysis. Successful canopy detection deforestation monitoring, and assessment 4. Reichstein, M. et al. Nature 566, 195–204 (2019). declined drastically below a canopy diameter of the carbon sequestered in biomass, tim­ 5. Engler, R. et al. Forest Ecol. Mgmt 310, 64–73 (2013). of 2 m, owing to constraints imposed by the ber, fuel wood and tree crops. The ability to 6. Draper, F. C. et al. Trends Ecol. Evol. https://doi. org/10.1016/j.tree.2020.08.003 (2020). spatial resolution of the imagery, and consist­ map the size and location of individual tree 7. National Academies of Sciences, Engineering, and 3 ent with earlier work . Although we can expect canopies using such satellite data will com­ Medicine. Thriving on Our Changing Planet: A Decadal further improvements in the spatial resolu­ plement information available from other Strategy for Earth Observation from Space (Natl Academies Press, 2018). tion of satellite images, it becomes pertinent instruments that provide data for tree height, to ask what minimum canopy size is needed vertical canopy profiles and above­ground This article was published online on 14 October 2020. to characterize woody­plant communities in various regions. For global tree­canopy map­ Astronomy ping, if we assume that the computational and storage challenges associated with large data volumes can be overcome, the biggest roadblock would lie in developing efficient A fast radio burst approaches for automated classification and delineation of canopies. Brandt and col­ in our own Galaxy leagues’ deep­learning method required an input of approximately 90,000 manually digi­ Amanda Weltman & Anthony Walters tized training points. This approach becomes untenable for work on a global scale, and The origins of millisecond­long bursts of radio emissions, more­automated (unsuper vised) methods for known as fast radio bursts, from beyond our Galaxy have been extracting information from satellite imagery 4 enigmatic.
Recommended publications
  • In-Depth Review of Satellite Imagery / Earth Observation Technology in Official Statistics Prepared by Canada and Mexico
    In-depth review of satellite imagery / earth observation technology in official statistics Prepared by Canada and Mexico Julio A. Santaella Conference of European Statisticians 67th plenary session Paris, France June 28, 2019 Earth observation (EO) EO is the gathering of information about planet Earth’s physical, chemical and biological systems. It involves monitoring and assessing the status of, and changes in, the natural and man-made environment Measurements taken by a thermometer, wind gauge, ocean buoy, altimeter or seismograph Photographs and satellite imagery Radar and sonar images Analyses of water or soil samples EO examples EO Processed information such as maps or forecasts Source: Group on Earth Observations (GEO) In-depth review of satellite imagery / earth observation technology in official statistics 2 Introduction Satellite imagery uses have expanded over time Satellite imagery provide generalized data for large areas at relatively low cost: Aligned with NSOs needs to produce more information at lower costs NSOs are starting to consider EO technology as a data collection instrument for purposes beyond agricultural statistics In-depth review of satellite imagery / earth observation technology in official statistics 3 Scope and definition of the review To survey how various types of satellite data and the techniques used to process or analyze them support the GSBPM To improve coordination of statistical activities in the UNECE region, identify gaps or duplication of work, and address emerging issues In-depth review of satellite imagery / earth observation technology in official statistics 4 Overview of recent activities • EO technology has developed progressively, encouraging the identification of new applications of this infrastructure data.
    [Show full text]
  • The Contribution of Earth Observation Technologies to Monitoring Strategies of Cultural Landscapes and Sites
    The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W5, 2017 26th International CIPA Symposium 2017, 28 August–01 September 2017, Ottawa, Canada THE CONTRIBUTION OF EARTH OBSERVATION TECHNOLOGIES TO MONITORING STRATEGIES OF CULTURAL LANDSCAPES AND SITES B. Cuca a* a Dept. of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio 31, 20133 Milan, Italy – [email protected] KEY WORDS: Cultural landscapes, Earth Observation, satellite remote sensing, GIS, Cultural Heritage policy, geo-hazards ABSTRACT: Coupling of Climate change effects with management and protection of cultural and natural heritage has been brought to the attention of policy makers since several years. On the worldwide level, UNESCO has identified several phenomena as the major geo-hazards possibly induced by climate change and their possible hazardous impact to natural and cultural heritage: Hurricane, storms; Sea-level rise; Erosion; Flooding; Rainfall increase; Drought; Desertification and Rise in temperature. The same document further referrers to satellite Remote Sensing (EO) as one of the valuable tools, useful for development of “professional monitoring strategies”. More recently, other studies have highlighted on the impact of climate change effects on tourism, an economic sector related to build environment and traditionally linked to heritage. The results suggest that, in case of emergency the concrete threat could be given by the hazardous event itself; in case of ordinary administration, however, the threat seems to be a “hazardous attitude” towards cultural assets that could lead to inadequate maintenance and thus to a risk of an improper management of cultural heritage sites. This paper aims to illustrate potential benefits that advancements of Earth Observation technologies can bring to the domain of monitoring landscape heritage and to the management strategies, including practices of preventive maintenance.
    [Show full text]
  • Improving the Usability of Nighttime Imagery from Low Light Sensors
    Improving the Usability of Nighttime Imagery from Low Light Sensors Thomas F. Lee F. Joseph Turk Jeffrey D. Hawkins Cristian Mitrescu Naval Research Laboratory, Monterey California (USA) Steven D. Miller Cooperative Institute for Research in the Atmosphere Fort Collins Colorado (USA) Mike Haas Aerospace Corporation Silver Spring Maryland (USA) 1. Abstract This article presents the nighttime visible sensor on the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument to be flown aboard the upcoming NPOESS and NPP polar-orbiting satellites. First, we will introduce the sensor and explain how it improves on a similar sensor, the Operational Linescan System (OLS) flown on the Defense Meteorological Satellite Program (DMSP) series. A brief summary of the new instrument will be given, followed by a few example products from the OLS. The paper will close with a discussion of how the crossing time of the satellite affects the number of scenes that will have lunar illumination. 2. Introduction Low light imagery from the Operational Linescan System (OLS) aboard the Defense Meteorological Satellite Program (DMSP) satellites (Johnson et al. 1994) has prompted a number of important applications never foreseen by the United States Air Force which originally designed and launched the sensor in the early 1970’s. The unforeseen applications include composites of worldwide city lights at night; detection of fires; imaging of the aurora; monitoring of fishing boats; imaging of snow fields; detection of bioluminescence (Miller et al. 2005); and the detection of power outages. However, the sensor is still underutilized for the application for which it was originally designed, the imaging of clouds using moonlight.
    [Show full text]
  • Using the Spatial and Spectral Precision of Satellite Imagery to Predict Wildlife Occurrence Patterns
    Remote Sensing of Environment 97 (2005) 249 – 262 www.elsevier.com/locate/rse Using the spatial and spectral precision of satellite imagery to predict wildlife occurrence patterns Edward J. Laurenta,T, Haijin Shia, Demetrios Gatziolisb, Joseph P. LeBoutonc, Michael B. Waltersc, Jianguo Liua aCenter for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources Building, East Lansing, MI 48824-1222, USA bPacific Northwest Research Station, United States Forest Service, 620 Main Street Suite 400, Portland, OR 97205, USA cDepartment of Forestry, Michigan State University, 126 Natural Resources Building, East Lansing, MI 48824-1222 USA Received 27 July 2004; received in revised form 26 April 2005; accepted 29 April 2005 Abstract We investigated the potential of using unclassified spectral data for predicting the distribution of three bird species over a ¨400,000 ha region of Michigan’s Upper Peninsula using Landsat ETM+ imagery and 433 locations sampled for birds through point count surveys. These species, Black-throated Green Warbler, Nashville Warbler, and Ovenbird, were known to be associated with forest understory features during breeding. We examined the influences of varying two spatially explicit classification parameters on prediction accuracy: 1) the window size used to average spectral values in signature creation and 2) the threshold distance required for bird detections to be counted as present. Two accuracy measurements, proportion correctly classified (PCC) and Kappa, of maps predicting species’ occurrences were calculated with ground data not used during classification. Maps were validated for all three species with Kappa values >0.3 and PCC >0.6. However, PCC provided little information other than a summary of sample plot frequencies used to classify species’ presence and absence.
    [Show full text]
  • Satellite Imagery and Change Over Time
    R E S O U R C E L I B R A R Y A C T I V I T Y : 4 5 M I N S Satellite Imagery and Change Over Time Students view satellite images of places past and present and analyze the changes over time. G R A D E S 5, 6 S U B J E C T S Geography C O N T E N T S 5 Images, 1 Link, 1 PDF OVERVIEW Students view satellite images of places past and present and analyze the changes over time. For the complete activity with media resources, visit: http://www.nationalgeographic.org/activity/satellite-imagery-and-change-over-time/ DIRECTIONS 1. Discuss different ways to capture images of Earth from above. Have a whole-class discussion about how it’s possible to capture images of Earth from above. Project the satellite image of New York City and the aerial image of LaCrosse, Wisconsin. Ask: What technologies are used to capture images of Earth from above? Write students’ ideas on the board; these may include planes, helicopters, kites or balloons with cameras, and satellites. 2. Examine the changes in Las Vegas, Nevada, and its surroundings. Tell students that they will be looking at changes over time in different places on Earth using satellite imagery. Project the Growth in the Desert image of Las Vegas, Nevada, in 2007 and minimize the caption. Invite volunteers to point to different areas on the image as you use the prompts below. Ask: Where is the city? What patterns do you see in the city? (Straight lines are streets; the layout is a grid, with some diagonal roads.) What does the land look like outside of the city? (rugged, mountainous, like a desert) What landforms do you see? (mountains, lakes) Point out that the black area to the east of the city is Lake Meade, a reservoir created by the damming of the Colorado River.
    [Show full text]
  • GPS/GIS Mapping Satellite Imagery
    GPS/GIS Mapping Zooms in on OOM IN ON THE FINE DETAIL. Zoom out on the broad sp an. Satellite With the convergence of three well-established techn olo­ gies, satellite imagery, Global Position ing Systems (G PS), and geographic in fonnation systems (GIS), remote sensing is quickly moving frorn an abstract art to high realism. Imagery ZGPSIGIS mapping now takes satellite imagery directly into the field, greatly extending its value and uses for applications as di verse as envi­ ronmemal monitoring. precision farming. and measuring urban sprawl. Sciemists who rely on raw satellite imagery to provide broad brush By Suzanne Richardson strokes of planetary phenomena can now bring th at same imagery down to earLh to create detailed and high ly accurate base map products. Complete GPSIGIS fi eld mapping systems. such as the patented GeoLink Mapping System from GeoResearch Inc .. feature full sc reen rasler or vector background map capability that can accurately register background maps through known control points. Satellite imagery, such as Lh ose from SPOT Image Corp.. can be registered and brought into the field as a background coverage. Other vector layers of infomlation, such as a visual trace of the user's path or mul tiple GIS fil es may be overlaid on the satellite image for further efficiency in the fi eld. GPS/GIS mapping technology uses GPS data to continuously pin­ point position and create an accurate digital map of the user 's pmh. Using small, li ghtweight, pen-based computers. field mapping personnel can quickly elller multiple layers of attribute data describing elIcit point, while watching their GIS maps evolve on the screen.
    [Show full text]
  • Image-Based Sociocultural Prediction Models
    Working Paper No. 178 Viewing society from space: Image-based sociocultural prediction models by John M. Irvine, Richard J. Wood, and Payden McBee | December 2017 1 Afrobarometer Working Papers Working Paper No. 178 Viewing society from space: Image-based sociocultural prediction models by John M. Irvine, Richard J. Wood, and Payden McBee | December 2017 John M. Irvine (corresponding author) is chief scientist for data analytics at Charles Stark Draper Laboratory. Email: [email protected]. Richard J. Wood is a principal member of the technical staff at Charles Stark Draper Laboratory. Payden McBee is a fellow at Charles Stark Draper Laboratory. Abstract Applying new analytic methods to imagery data offers the potential to dramatically expand the information available for human geography. Satellite imagery can yield detailed local information about physical infrastructure, which we exploit for analysis of local socioeconomic conditions. Combining automated processing of satellite imagery with advanced modeling techniques provides a method for inferring measures of well-being, governance, and related sociocultural attributes from satellite imagery. This research represents a new approach to human geography by explicitly analyzing the relationship between observable physical attributes and societal characteristics and institutions of the region. Through analysis of commercial satellite imagery and Afrobarometer survey data, we have developed and demonstrated models for selected countries in sub-Saharan Africa (Botswana, Kenya, Zimbabwe). The findings show the potential for predicting people’s attitudes about the economy, security, leadership, social involvement, and related questions, based only on imagery-derived information. The approach pursued here builds on earlier work in Afghanistan. Models for predicting economic attributes (presence of key infrastructure, attitudes about the economy, perceptions of crime, and outlook toward the future) all exhibit statistically significant performance.
    [Show full text]
  • Applications of Satellite Remote Sensing to Forested Ecosystems
    Landscape Ecology vol. 3 no. 2 pp 131-143 (1989) SPB Academic Publishing bv, The Hague Applications of satellite remote sensing to forested ecosystems Louis R. Iverson', Robin Lambert Graham2 and Elizabeth A. Cook' 'Illinois Natural History Survey, 607 E. Peabody, Champaign, IL 61820 and Department of Forestry, University of Illinois, Champaign, IL 61820, USA; 2Environmental Sciences Division, Oak Ridge National Laboratory, P.O. Box 2006, Oak Ridge, TN 37831, USA Keywords: satellite, remote sensing, forest ecosystems, GIS, monitoring Abstract Since the launch of the first civilian earth-observing satellite in 1972, satellite remote sensing has provided increasingly sophisticated information on the structure and function of forested ecosystems. Forest classifica- tion and mapping, common uses of satellite data, have improved over the years as a result of more dis- criminating sensors, better classification algorithms, and the use of geographic information systems to incor- porate additional spatially referenced data such as topography. Land-use change, including conversion of forests for urban or agricultural development, can now be detected and rates of change calculated by superim- posing satellite images taken at different dates. Landscape ecological questions regarding landscape pattern and the variables controlling observed patterns can be addressed using satellite imagery as can forestry and ecological questions regarding spatial variations in physiological characteristics, productivity, successional patterns, forest structure, and forest decline. Introduction over varying scales of spatial resolution. The sophistication of applications evident in re- Since the launching of the first earth-observing cent years has been made possible by (1) the use of civilian Landsat satellite in 1972, satellite remote more spectrally and/or spatially discriminating sensing has been used for gathering synoptic infor- sensors; (2) the improvement of hardware and mation on forests.
    [Show full text]
  • Digital Mapping Using High Resolution Satellite Imagery Based on 2D Affine Projection Model
    Ono Tetsu DIGITAL MAPPING USING HIGH RESOLUTION SATELLITE IMAGERY BASED ON 2D AFFINE PROJECTION MODEL Tetsu ONO*, Susumu HATTORI**, Hiroyuki HASEGAWA***, Shin-ichi AKAMATSU* *Graduate School of Engineering, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, JAPAN [email protected], [email protected] **Faculty of Engineering, Fukuyama University, 1 Sanzo, Gakuen-cho, Fukuyama, 729-0292, JAPAN [email protected] ***Geonet Inc, 1265-1-203, Shin-yoshida, Minato-ku, Yokohama, 223-0056, JAPAN [email protected] KEY WORDS: High-resolution satellite imagery, Topographic mapping, Digital ortho-imagery, 2D affine projection model ABSTRACT High-resolution satellite imagery is expected to reduce cost for medium and small scale topographic mapping. Because 1m high-resolution satellite imagery has a much narrower field angle, the projection of images is nearly approximated by parallel rather than central one. In this situation, the orientation model based on affine projection is effective for satellite imagery triangulation. Furthermore under the assumption that the satellite attitude is stable and the movement of the sensor position is almost linear, 2D affine projection model is applicable to basic equations for mapping. This paper discusses the application of the 2D affine projection model to high-resolution satellite imagery and SPOT scenes at various terrain area in JAPAN. The first topic is the approach to generate ortho-imagery using the model. The second topic is the real-time image positioning on a softcopy photogrammetric workstations for satellite imagery. 1 INTRODUCTION Precise digital maps generated from satellite imagery are assuming growing importance in the spatial information industry.
    [Show full text]
  • The Significance of Using Satellite Imagery Data Only in Ecological Niche Modelling of Iberian Herps
    Acta Herpetologica 7(2): 221-237, 2012 The significance of using satellite imagery data only in Ecological Niche Modelling of Iberian herps Neftalí Sillero1, José C. Brito2, Santiago Martín-Alfageme3, Eduardo García- Meléndez4, A.G. Toxopeus5, Andrew Skidmore5 1 Centro de Investigação em Ciências Geo-Espaciais (CICGE), Universidade do Porto, Faculdade de Ciências, Rua do Campo Alegre, 687, 4169-007 Porto, Portugal. Corresponding author. E-mail: neftali. [email protected] 2 CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos da Universidade do Porto, Campus Agrário de Vairão, Rua Padre Armando Quintas, 4485-661 Vairão, Portugal 3 Servicio Transfronterizo de Información Geografica, Universidad de Salamanca, C.M. San Bartolome, Pz. Fray Luis de León 1-8, 37008 Salamanca, Spain 4 Área de Geodinámica Externa, Facultad de Ciencias Biológicas y Ambientales, Universidad de León, Campus de Vegazana s/n, 24071, León, Spain 5 ITC, Faculty of Geo-Information Science and Earth Observation, University of Twente, Hengeloses- traat 99, 7514 AE Enschede, The Netherlands Submitted on: 2011, 24th October; revised on: 2012, 20th March; accepted on: 2012, 11th April. Abstract. The environmental data used to calculate ecological niche models (ENM) are obtained mainly from ground-based maps (e.g., climatic interpolated surfaces). These data are often not available for less developed areas, or may be at an inappro- priate scale, and thus to obtain this information requires fieldwork. An alternative source of eco-geographical data comes from satellite imagery. Three sets of ENM were calculated exclusively with variables obtained (1) from optical and radar images only and (2) from climatic and altitude maps obtained by ground-based methods.
    [Show full text]
  • Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping
    Remote Sens. 2013, 5, 6026-6042; doi:10.3390/rs5116026 OPEN ACCESS Remote Sensing ISSN 2072-4292 www.mdpi.com/journal/remotesensing Article Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping Qiong Hu 1,2, Wenbin Wu 1,2,*, Tian Xia 1,2, Qiangyi Yu 1,2, Peng Yang 1,2, Zhengguo Li 1,2 and Qian Song 1,2 1 Key Laboratory of Agri-informatics, Ministry of Agriculture, Beijing 100081, China; E-Mails: [email protected] (Q.H.); [email protected] (T.X.); [email protected] (Q.Y.); [email protected] (P.Y.); [email protected] (Z.L.); [email protected] (Q.S.) 2 Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +86-10-8210-5070; Fax: +86-10-8210-5070. Received: 9 September 2013; in revised form: 10 November 2013 / Accepted: 11 November 2013 / Published: 15 November 2013 Abstract: Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB.
    [Show full text]
  • Imaging Technology Using Google Earth and NASA World Wind
    NCSR Education for a Sustainable Future www.ncsr.org Imaging Technology Using Google Earth and NASA World Wind Northwest Center for Sustainable Resources (NCSR) Chemeketa Community College, Salem, Oregon DUE #0455446 Funding provided by the National Science Foundation opinions expressed are those of the authors and not necessarily those of the foundation 1 Table of Contents Imaging Technology Using Google Earth and NASA World Wind.................................... 4 Introduction..................................................................................................................... 4 Google Earth................................................................................................................... 5 Site Description........................................................................................................... 5 Minimum System Requirements................................................................................. 5 NASA World Wind ......................................................................................................... 5 Site Description........................................................................................................... 5 Minimum System Requirements................................................................................. 6 Examples of Imagery Use............................................................................................... 7 Application of World Wind............................................................................................
    [Show full text]