Advances in environmental remote sensing sensors, algorithms, and applications

Tai Lieu Chat Luong Advances in Environmental Remote Sensing Sensors, Algorithms, and Applications Taylor & Francis Series in Remote Sensing Applications 6HULHV (GLWRU 4LKDR :HQJ ,QGLDQD 6WDWH 8QLYHUVLW\ 7HUUH +DXWH ,QGLDQD 86$ Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, edited by Qihao Weng Remote Sensing of Coastal Environments, edited by Yeqiao Wang Remote Sensing of Global Croplands for Food Security, edited by Prasad S Thenkabail, John G Lyon, Hugh Turral, and Chandashekhar M Biradar Global Mapping of Human Settlement: Experiences, Data Sets, and Prospects, edited by Paolo Gamba and Martin Herold Hyperspectral Remote Sensing: Principles and Applications, Marcus Borengasser, William S Hungate, and Russell Watkins Remote Sensing of Impervious Surfaces, Qihao Weng Multispectral Image Analysis Using the Object-Oriented Paradigm, Kumar Navulur Advances in Environmental Remote Sensing Sensors, Algorithms, and Applications Edited by Qihao Weng Boca 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Clearance Center, Inc (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400 CCC is a not-for-profit organization that provides licenses and registration for a variety of users For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Contents Acknowledgments����������������������������������������������������������������������������������������������������������������������� vii Editor�����������������������������������������������������������������������������������������������������������������������������������������������ix Contributors������������������������������������������������������������������������������������������������������������������������������������xi Introduction��������������������������������������������������������������������������������������������������������������������������������� xv Section I Sensors, Systems, and Platforms Remote Sensing of Vegetation with Landsat Imagery Conghe Song, Joshua M Gray, and Feng Gao Review of Selected Moderate-Resolution Imaging Spectroradiometer Algorithms, Data Products, and Applications 31 Yang Shao, Gregory N Taff, and Ross S Lunetta Lidar Remote Sensing 57 Sorin C Popescu Impulse Synthetic Aperture Radar 85 Giorgio Franceschetti and James Z Tatoian Hyperspectral Remote Sensing of Vegetation Bioparameters 101 Ruiliang Pu and Peng Gong Thermal Remote Sensing of Urban Areas: Theoretical Backgrounds and Case Studies 143 Qihao Weng Section II Algorithms and Techniques Atmospheric Correction Methods for Optical Remote Sensing Imagery of Land 161 Rudolf Richter Three-Dimensional Geometric Correction of Earth Observation Satellite Data 173 Thierry Toutin Remote Sensing Image Classification 219 Dengsheng Lu, Qihao Weng, Emilio Moran, Guiying Li, and Scott Hetrick v vi Contents 10 Object-Based Image Analysis for Vegetation Mapping and Monitoring 241 Thomas Blaschke, Kasper Johansen, and Dirk Tiede 11 Land-Use and Land-Cover Change Detection 273 Dengsheng Lu, Emilio Moran, Scott Hetrick, and Guiying Li Section III Environmental Applications-Vegetation 12 Remote Sensing of Ecosystem Structure and Function 291 Alfredo R Huete and Edward P Glenn 13 Remote Sensing of Live Fuel Moisture 321 Stephen R Yool 14 Forest Change Analysis Using Time-Series Landsat Observations 339 Chengquan Huang 15 Satellite-Based Modeling of Gross Primary Production of Terrestrial Ecosystems 367 Xiangming Xiao, Huimin Yan, Joshua Kalfas, and Qingyuan Zhang 16 Global Croplands and Their Water Use from Remote Sensing and Nonremote Sensing Perspectives 383 Prasad S Thenkabail, Munir A Hanjra, Venkateswarlu Dheeravath, and Muralikrishna Gumma Section IV Environmental Applications: Air, Water, and Land 17 Remote Sensing of Aerosols from Space: A Review of Aerosol Retrieval Using the Moderate-Resolution Imaging Spectroradiometer 423 Man Sing Wong and Janet Nichol 18 Remote Estimation of Chlorophyll-a Concentration in Inland, Estuarine, and Coastal Waters 439 Anatoly A Gitelson, Daniela Gurlin, Wesley J Moses, and Yosef Z Yacobi 19 Retrievals of Turbulent Heat Fluxes and Surface Soil Water Content by Remote Sensing 469 George P Petropoulos and Toby N Carlson 20 Remote Sensing of Urban Biophysical Environments 503 Qihao Weng 21 Development of the USGS National Land-Cover Database over Two Decades 525 George Xian, Collin Homer, and Limin Yang Index 545 Acknowledgments I extend my heartfelt thanks to all the contributors of this book for making this endeavor possible Moreover, I offer my deepest appreciation to all the reviewers, who have taken precious time from their busy schedules to review the chapters submitted for this book Finally, I am indebted to my family for their enduring love and support It is my hope that the publication of this book will facilitate students to understand the state-of-the art knowledge of environmental remote sensing and to provide researchers with an update on the newest development in many sub-fields of this dynamic field The reviewers of the chapters of this book are listed here in alphabetical order: Thomas Blaschke Hubo Cai Toby Carlson Paolo Gamba Anatoly Gitelson Chengquan Huang Stefaan Lhermitte Lin Li Desheng Liu Hua Liu Dengsheng Lu Janet Nichol Ruiliang Pu Dale Quattrochi Yang Shao Conghe Song Junmei Tang Xiaohua Tong Guangxin Wang George Xian Xiangming Xiao Jian-sheng Yang Ping Yang Zhengwei Yang Fei Yuan Yuyu Zhou vii Editor Dr Qihao Weng is a professor of geography and the director of the Center for Urban and Environmental Change at Indiana State University From 2008 to 2009, he visited the National Aeronautics and Space Administration (NASA) as a senior research fellow He is also a guest/adjunct professor at Wuhan University and Beijing Normal University, and a guest research scientist at the Beijing Meteorological Bureau in China His research focuses on remote sensing and GIS analysis of urban environmental systems, land-use and land-cover change, urbanization impacts, and human– environment interactions Dr Weng is the author of more than 120 peer-reviewed journal articles and other publications and three books (Urban Remote Sensing, 2006, CRC Press; Remote Sensing of Impervious Surfaces, 2007, CRC Press; and Remote Sensing and GIS Integration: Theories, Methods, and Applications, 2009, McGraw-Hill Professional) He has been the recipient of some significant awards, including the Robert E Altenhofen Memorial Scholarship Award (1998) from the American Society for Photogrammetry and Remote Sensing (ASPRS), the Best Student-Authored Paper Award from the International Geographic Information Foundation (1999), the Theodore Dreiser Distinguished Research Award from Indiana State University (2006), a NASA senior fellowship (2008), and the 2010 Erdas Award for Best Scientific Paper in Remote Sensing from ASPRS (first place) Dr Weng has worked extensively with optical and thermal remote sensing data, with research support from the National Science Foundation (NSF), NASA, USGS, the U.S Agency for International Development (USAID), the National Geographic Society, and the Indiana Department of Natural Resources Professionally, Dr.  Weng was a national director of ASPRS (2007–2010) He also serves as an associate ­editor of ISPRS Journal of Photogrammetry and Remote Sensing, and is the series editor for both the Taylor & Francis series in remote sensing applications, and the McGraw-Hill series in GIS&T ix 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Figure 13.7 Fire season summaries in chronological sequence Notes: – Because satellite imagery was not available for all periods spanning March through July, the following fire season summaries are approximations: – Catalina: 1990, 1999, and 2003 – Chiricahua: 1990, 1991, and 1999 – Huachuca: 1990 – White pixels indicate nondata Hua Chi Cat 2003 The figure below shows 15–18 years of fire season fuel moisture stress in three sky island regions of southeastern Arizona Each column represents a year and each row represents a site Fire season summaries in chronological sequence for the three southeastern Arizona study sites 2004 2005 2006 Catalina FMS/climate grid updated 1/3/07 Fuel moisture stress (FMS): 1989 to 2006 Low FMS High FMS Temperature climatology: 1959–2002 Precipitation climatology: 1985–2002 Far below average precipitation Below average precipitation Average precipitation Above average precipitation Far above average precipitation 1.7 1.5 Far above average precipitation 1.3 0.9 1986 0.7 Above average precipitation 0.5 0.3 1985 0.1 −0.1 −0.3 Average precipitation 1987 1988 Below average precipitation Temperature of current season (z-score) 1.1 −0.5 −0.7 −0.9 Far below average precipitation −1.1 −1.3 −1.5 −1.5 −1.3 −1.1 −0.9 −0.7 −0.5 −0.3 −0.1 0.1 0.3 0.5 0.7 Precipitation of preceding winter (z-score) 0.9 1.1 1.3 1.5 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Note: – White pixels indicate nondata (large regions of 1990 and 2003 are nondata) – 1990, 1999, and 2003 show approximated fire seasons truncated out to gaps in satellite imagery – 2000 and 2002 share the same grid cell Figure 13.8 The FMSI/climate grid for the Catalina-Rincon study site Fire season summaries for 18 years are plotted using precipitation and temperature z-scores for each year Predisturbance image Postdisturbance image 1994 1996 1988 1990 2003 2005 Disturbance year map Legend for the disturbance year map Persisting nonforest Persisting forest Persisting water Preobservation 1986 1996 1988 1998 1990 2000 1991 2002 1993 2003 1994 2005 Figure 14.9 Visual validation of three mapped disturbances using pre- and post-disturbance Landsat images The disturbance year map was selected from a 17.1 × 11.4 km area in the Uwharrie national forest located in North Carolina (WRS path 16/row 36) The size of each Landsat image chip shown to the left is 2.85 × 2.85 km (From Huang, C et al Remote Sens Environ, 113, 7, 2009 With permission.) N 60 90 120 Kilometers Persisting nonforest Persisting forest Water Pre-1986 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Land cover and forest disturbance year Figure 14.10 Disturbance year map derived using the LTSS-VCT approach for Mississippi (left) and Alabama (right) Landsat-derived forest disturbances Mississippi, 1984–2007 15 30 Water Pre-1985 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 County boundary Persisting nonforest Persisting forest Land cover and forest disturbance year 60 90 120 Kilometers Landsat-derived forest disturbances Alabama, 1984–2007 15 30 N Cropland fraction < 0.01 0.01–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0 Figure 16.1 Global cropland map at nominal 5-minutes (0.083333 decimal degrees) resolution using national statistics and geospatial techniques for the nominal year 2000 Total area of croplands is 1.47 billion hectares (Adopted from Ramankutty, N et al Global Biogeochem Cycles, 22, 2008.) −180 −120 −60 60 120 60 180 60 0 Legend 01 Irrigated, major (major and minor reservoirs) 02 Irrigated, minor (ground water, small reservoirs, and tanks) 03 Rainfed croplands 04 Rainfed croplands and grasslands/shrublands 05 Natural vegetation with rainfed fragments 00 Other area and ocean 1000 1000 2000 Kilometers −60 −180 −120 −60 60 −60 120 180 Figure 16.2 Global cropland map at nominal 1-km resolution using remote sensing for the nominal year 2000 Total ­cropland area was determined to be 1.53 billion hectares, of which 399 Mha was irrigated area Because irrigated areas often had more than one crop per year, the total annualized irrigated area was 467 Mha (Adapted from Thenkabail, P.S et al Rem Sens, 1, 2009b http://www.mdpi.com/2072-4292/1/2/50; Thenkabail, P.S et al Remote Sensing of Global Croplands for Food Security, CRC Press/Taylor & Francs, Boca Raton, FL, 2009c.) 23.80 23.40 23.00 22.60 22.20 22.20 22.60 23.00 23.40 23.80 112.20 112.60 113.00 113.40 113.80 114.20 114.60 112.20 112.60 113.00 113.40 113.80 114.20 114.60 AOT_550 nm_MOD04_C005 0.0 0.4 0.8 (a) 1.2 1.6 2.0 23.80 23.40 23.00 22.60 22.20 22.20 22.60 23.00 23.40 23.80 112.20 112.60 113.00 113.40 113.80 114.20 114.60 112.20 112.60 113.00 113.40 113.80 114.20 114.60 AOT_550 nm_MODIS_500 m 0.0 012 km 0.4 0.8 (b) 1.2 1.6 2.0 AOT_550 nm_MODIS_500 m 0.0 0.25 0.50 0.75 1.00 1.25 (c) Figure 17.4 Aerosol optical thickness image at 550 nm, (a) derived from MODIS collection-5 algorithm, (b) derived from MODIS 500-m data, and (c) derived from MODIS 500-m data overlaid with road layer 23.80 23.40 23.00 22.60 22.20 22.20 22.60 23.00 23.40 23.80 112.20 112.60 113.00 113.40 113.80 114.20 114.60 112.20 112.60 113.00 113.40 113.80 114.20 114.60 20070280315_AOT_500 m 0.4 0.8 (a) 1.2 1.6 2.0 112.20 112.60 113.00 113.40 113.80 114.20 114.60 112.20 112.60 113.00 113.40 113.80 114.20 114.60 20070300300_AOT_500 m 0.0 0.4 0.8 (b) 1.2 1.6 23.80 23.40 23.00 22.60 22.20 22.20 22.60 23.00 23.40 23.80 0.0 2.0 Figure 17.5 Aerosol optical thickness image at 550-nm and 500-m resolution over Hong Kong and the Pearl River Delta region on (a) January 28, 2007 and (b) January 30, 2007 (a) 22.20 22.20 22.60 22.60 23.00 23.00 23.40 23.40 23.80 23.80 112.20 112.60 113.00 113.40 113.80 114.20 114.60 112.20 112.60 113.00 113.40 113.80 114.20 114.60 20073340300_AOT_550 m 0.0 0.4 0.8 (b) 1.2 1.6 2.0 22.20 22.20 22.60 22.60 23.00 23.00 23.40 23.40 23.80 23.80 112.20 112.60 113.00 113.40 113.80 114.20 114.60 112.20 112.60 113.00 113.40 113.80 114.20 114.60 MODIS_10 km_collection5_2007334.0300 0.0 0.4 0.8 (c) 1.2 1.6 2.0 Figure 17.6 (a) Rayleigh-corrected RGB image on November 30, 2007, (b) aerosol ­optical thickness (AOT) image at 500-m resolution, and (c) AOT collection-5 image at 10-km resolution Satellite image Max transpiration VImax Min transpiration (Tair) Vegetation index Full cover Pixel window Dry edge (M0) = Wet edge (M0) = Partial cover Bare soil VImin Max evaporation Ts Surface temperature (Ts) Min evaporation Ts max Figure 19.2 Summary of the main physical properties and interpretations of the satellite-derived (or airborne) Ts/VI feature space Dots represent the measurements at pixels observed by a VIS/IR radiometer at various fractional vegetation covers (Fr) and surface temperatures (Ts) In this illustration, pixels classified as water or clouds are assumed to have been masked out (Adapted from Petropoulos, G et al Adv Phys Geogr, 33, 2, 2009a.) Seattle, Washington Sioux Falls, South Dakota N 250 500 1,000 km Land-cover class Open water Low-int, resident High-int, resident Comm/indust/trans Bare Quarry/strip mine Transitional Deciduous forest Evergreen forest Figure 21.1 NLCD1992 land cover for the Seattle, Washington and Sioux Falls, South Dakota areas Mixed forest Shrub Grass Pasture Row crop Small grains Woody wetland Herb wetland km N (a) km (95) Emergent herbaceous wetlands (41) Deciduous forest km N 11%–20% 61%–70% 11%–20% 61%–70% 1%–10% 51%–60% Impervious surface 1%–10% 51%–60% Tree canopy 71%–80% 21%–30% 21%–30% 71%–80% (c) 81%–90% 31%–40% 31%–40% 81%–90% km N 91%–100% 41%–50% 41%–50% 91%–100% (d) Figure 21.2 NLCD 2001 (a) land cover and (b) percent tree canopy cover for the Seattle, Washington; (c) land cover and (d) percent impervious surface for the Sioux Falls, South Dakota area (90) Woody wetlands (31) Barren land (81) Hay/pasture (23) Developed, medium intensity (82) Cultivated crops (71) Grassland/herbaceous (22) Developed, low intensity (b) (24) Developed, high intensity (52) Shrub/scrub (21) Developed, open space N (43) Mixed forest (42) Evergreen forest (12) Perennial ice/snow (11) Open water Land-cover class 2 Wetland N Agriculture km Urban Open water Class Ice/snow Barren Change class Forest km Grassland/shrub N Figure 21.3 The NLCD 1992–2001 retrofit land cover changes for the Seattle and Sioux Falls areas The change class represents changes in land-cover type from 1992 to 2001 N N (e) km (a) km 2 4 N (f ) km N (b) km 2 4 (g) km (c) km N N 2 4 N N (h) km (d) km Figure 21.5 Landsat imagery in (a) 2001 (b) 2006, (c) 2006 land cover, (d) 2006 percent tree canopy in the Seattle area Landsat imagery in (e) 2001 (f) 2006, (g) 2006 land cover, and (h) 2006 impervious surface in the Sioux Falls area The color legends for land cover, tree canopy, and impervious surface are the same as in Figure 21.2 2 km (a) N No change Tree canopy change Decrease km N (b) Increase km N (c) 61%–70% 51%–60% 11%–20% 1%–10% Tree canopy 71%–80% 21%–30% km 81%–90% 31%–40% (d) N 91%–100% 41%–50% Figure 21.6 (a) Tree canopy in 2006 and (b) the tree canopy change from 2001 to 2006 in the Seattle area estimated by using Landsat image (c) Tree canopy estimated using 2006 AWiFS image (d) The tree canopy change from 2001 to 2006 in 56-m resolution for the same area In both (b) and (d), red and green represent tree canopy decrease and increase from 2001 to 2006, respectively