Use of Image Endmember Libraries for Multi-Sensor, Multi-Scale, and Multi-Site Mapping of Urban Areas
Frank Canters
Cartography and GIS Research Group, Department of Geography, Vrije Universiteit Brussel, Brussels, Belgium
Search for more papers by this authorSam Cooper
Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germany
Search for more papers by this authorJeroen Degerickx
Division of Forest, Nature and Landscape, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
Search for more papers by this authorUta Heiden
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany
Search for more papers by this authorMarianne Jilge
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany
Search for more papers by this authorAkpona Okujeni
Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germany
Search for more papers by this authorFrederik Priem
Cartography and GIS Research Group, Department of Geography, Vrije Universiteit Brussel, Brussels, Belgium
Search for more papers by this authorBen Somers
Division of Forest, Nature and Landscape, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
Search for more papers by this authorSebastian van der Linden
Institute of Geography and Geology, University of Greifswald, Greifswald, Germany
Search for more papers by this authorFrank Canters
Cartography and GIS Research Group, Department of Geography, Vrije Universiteit Brussel, Brussels, Belgium
Search for more papers by this authorSam Cooper
Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germany
Search for more papers by this authorJeroen Degerickx
Division of Forest, Nature and Landscape, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
Search for more papers by this authorUta Heiden
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany
Search for more papers by this authorMarianne Jilge
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Wessling, Germany
Search for more papers by this authorAkpona Okujeni
Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germany
Search for more papers by this authorFrederik Priem
Cartography and GIS Research Group, Department of Geography, Vrije Universiteit Brussel, Brussels, Belgium
Search for more papers by this authorBen Somers
Division of Forest, Nature and Landscape, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
Search for more papers by this authorSebastian van der Linden
Institute of Geography and Geology, University of Greifswald, Greifswald, Germany
Search for more papers by this authorXiaojun Yang
Search for more papers by this authorSummary
Building libraries of reference spectra for detailed mapping of urban areas at the level of building materials or plant species requires substantial effort. While in the last 15 years many approaches have been proposed to automatically extract pure material spectra from airborne hyperspectral imagery, the labeling of such spectra remains a tedious task. An interesting question, therefore, is to what extent the effort of building a library of reference spectra for a specific mapping task might be reduced by the re-use of image spectra collected from other imagery, covering multiple urban sites. In this chapter, we focus on methods for building multi-site libraries of reference spectra and for using these spectra in different urban mapping applications, and on the potential of generalized mapping models based on such libraries. We introduce the idea of a Generic Urban Library (GUL) offering users of remote sensing data the opportunity to share reference spectra, and to use spectra collected by others in their applications. Through specific case studies, we demonstrate the merits of sharing multi-site reference spectra using library-based mapping approaches. Building (more) generic urban libraries can be considered an important step in facilitating and fostering research on the transferability of urban mapping methods .
REFERENCES
- Advisory Committee for Environmental Research and Education (ACERE) ( 2018 ). Sustainable Urban Systems: Articulating a Long-Term Convergence Research Agenda . National Science Foundation .
- Ben-Dor , E. ( 2001 ). Imaging spectrometry for urban applications . In: Imaging Spectrometry - Basic Principles and Prospective Applications (ed. F.D. Meer and S.M. Jong ). Remote Sensing and Digital Image Processing 4 , 243 – 281 . Dordrecht : Springer .
- Bieniarz , J. , Müller , R. , Zhu , X.X. et al. ( 2012 ). On the use of overcomplete dictionaries for spectral unmixing . 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS 2012), Shanghai .
- Bieniarz , J. , Aguilera , E. , Zhu , X.X. et al. ( 2015 ). Joint sparsity model for multilook hyperspectral image unmixing . IEEE Geoscience and Remote Sensing Letters 12 ( 4 ): 696 – 700 . 10.1109/LGRS.2014.2358623.
- Boardman , J.W. , Kruse , F.A. , and Green , R.O. ( 1995 ). Mapping target signatures via partial unmixing of AVIRIS data . Proceedings of the Fifth JPL Airborne Earth Science Workshop , Pasadena, CA, USA (23–26 January 1995).
-
Chang , C.I.
(
2013
).
Hyperspectral Data Processing: Algorithm Design and Analysis
.
Hoboken, NJ
:
John Wiley & Sons
.
10.1002/9781118269787 Google Scholar
- Chang , C.I. , Wu , C.C. , Liu , W. et al. ( 2006 ). A new growing method for simplex-based endmember extraction algorithm . IEEE Transactions on Geoscience and Remote Sensing 44 ( 10 ): 2804 – 2819 . 10.1109/TGRS.2006.881803.
- Chen , F. , Wang , K. , and Tang , T.F. ( 2016 ). Spectral unmixing using a sparse multiple-endmember spectral mixture model . IEEE Transactions on Geoscience and Remote Sensing 54 : 5846 – 5861 . 10.1109/TGRS.2016.2574331.
- Dams , J. , Dujardin , J. , Reggers , R. et al. ( 2013 ). Mapping impervious surface change from remote sensing for hydrological modelling . Journal of Hydrology 485 : 84 – 95 . 10.1016/j.jhydrol.2012.09.045.
- Degerickx , J. , Iordache , M.-D. , Okujeni , A. , et al. ( 2016 ). Spectral unmixing of urban land cover using a generic library approach . Proceedings SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments , SPIE Remote Sensing 2016, Edinburgh, UK (26–29 September 2016). Society of Photo-Optical Instrumentation Engineers. 10.1117/12.2241189.
-
Degerickx , J.
,
Hermy , M.
, and
Somers , B
. (
2017a
).
Mapping functional urban green types using hyperspectral remote sensing
.
Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE 2017)
, Dubai, United Arab Emirates (6–8 March 2017).
Red Hook, NY
:
Curran Associates, Inc.
10.1109/JURSE.2017.7924553 Google Scholar
- Degerickx , J. , Okujeni , A. , Iordache , M-D . et al. ( 2017b ). A novel spectral library pruning technique for spectral unmixing of urban land cover . Remote Sensing 9 . 10.3390/rs9060565.
- Demarchi , L. , Canters , F. , Chan , J.C.-W. et al. ( 2012 ). Multiple endmember unmixing of CHRIS/Proba imagery for mapping of impervious surfaces in urban and suburban environments . IEEE Transactions on Geoscience and Remote Sensing 50 ( 9 ): 3409 – 3424 . 10.1109/TGRS.2011.2181853.
- Demarchi , L. , Canters , F. , Cariou , C. et al. ( 2014 ). Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land cover mapping . ISPRS Journal of Photogrammetry and Remote Sensing 87 : 166 – 179 . 10.1016/j.isprsjprs.2013.10.012.
- Deng , C. ( 2016 ). Automated construction of multiple regional libraries for neighborhoodwise local multiple endmember unmixing . IEEE Journal of Selected Topics in Applied Earth Obervations and Remote Sensing 9 : 4232 – 4246 . 10.1109/JSTARS.2016.2541660.
- Deng , Y. and Wu , C. , ( 2016 ). Development of a Class-based Multiple Endmember Spectral Mixture Analysis (C-MESMA) approach for analyzing urban environments . Remote Sensing 8 : 349 . 10.3390/rs8040349.
- Dennison , P.E. and Roberts , D.A. ( 2003 ). Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE . Remote Sensing of Environment 87 : 123 – 135 . 10.1016/S0034-4257(03)00135-4.
- Dennison , P.E. , Halligan , K.Q. , and Roberts , D.A. ( 2004 ). A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper . Remote Sensing of Environment 93 : 359 – 367 . 10.1016/j.rse.2004.07.013.
- Dudley , K.L. , Dennison , P.E. , Roth , K.L. et al. ( 2015 ). A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients . Remote Sensing of Environment 167 : 121 – 134 . 10.1016/j.rse.2015.05.004.
- Fan , F. and Deng , Y. ( 2014 ). Enhancing endmember selection in multiple endmember spectral mixture analysis (MESMA) for urban impervious surface area mapping using spectral angle and spectral distance parameters . International Journal of Applied Earth Observation and Geoinformation 33 : 290 – 301 . 10.1016/j.jag.2014.06.011
- Foody , G.M. and Mathur , A. ( 2006 ). The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM . Remote Sensing of Environment 103 : 179 – 189 . 10.1016/j.rse.2006.04.001.
- Foody , G.M. , Boyd , D.S. , and Cutler , M.E.J. ( 2003 ). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions . Remote Sensing of Environment 85 : 463 – 474 . 10.1016/S0034-4257(03)00039-7.
- Franke , J. , Roberts , D.A. , Halligan , K. et al. ( 2009 ). Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments . Remote Sensing of Environment 113 : 1712 – 1723 . 10.1016/j.rse.2009.03.018.
- García-Haro , F.J. , Sommer , S. , and Kemper , T. ( 2005 ). A new tool for variable multiple endmember spectral mixture analysis (VMESMA) . International Journal of Remote Sensing 26 : 2135 – 2162 . 10.1080/01431160512331337817.
- Green , R.O . ( 2018 ). Global VSWIR imaging spectroscopy and the 2017 decadal survey . IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , Valencia, Spain (22–27 July 2018). IEEE. 10.1109/IGARSS.2018.8518744.
- Grimm , N.B , Faeth S.H , Golubiewski , N.E. et al. ( 2008 ) Global change and the ecology of cities . Science 319 : 756 – 760 . 10.1126/science.1150195.
- Gruninger , J.H. , Ratkowski , A.J. , and Hoke , M.L . ( 2004 ). The sequential maximum angle convex cone (SMACC) endmember model . Proceedings SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Defense and Security 2004 , Orlando, FL, USA (12 August 2004). International Society for Optics and Photonics. https://doi.org/10.1117/12.543794.
- Guanter , L. , Kaufmann , H. , Segl , L. et al ( 2015 ). The EnMAP spaceborne imaging spectroscopy mission for Earth observation . Remote Sensing 7 : 8830 . 10.3390/rs70708830.
- Heiden , U. , Segl , K. , Roessner , S. et al. ( 2007 ). Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data . Remote Sensing of Environment 111 : 537 – 552 . 10.1016/j.rse.2007.04.008.
- Heremans , S. , Bossyns , B. , Eerens , H. et al. ( 2011 ). Efficient collection of training data for sub-pixel land cover classification using neural networks . International Journal of Applied Earth Observation and Geoinformation 13 : 657 – 667 . 10.1016/j.jag.2011.03.008.
- Herold , M. and Roberts , D. ( 2005 ). Spectral characteristics of asphalt road aging and deterioration: Implications for remote-sensing applications . Applied Optics 44 : 4327 – 4334 . 10.1364/AO.44.004327.
- Herold , M. , Roberts , D.A. , Gardner , M.E. et al. ( 2004 ). Spectrometry for urban area remote sensing -development and analysis of a spectral library from 350 to 2400 nm . Remote Sensing of Environment 91 : 304 – 319 .
- Heylen , R. , Parente , M. , and Gader , P. ( 2014 ). A review of nonlinear hyperspectral unmixing methods . IEEE Journal of Selected Topics in Applied Earth Obervations and Remote Sensing 7 : 1844 – 1868 . 10.1109/JSTARS.2014.2320576.
- Iordache , M.-D. , Bioucas-Dias , J.M. , and Plaza , A. ( 2011 ). Sparse unmixing of hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing 49 ( 6 ): 2014 – 2039 . 10.1109/TGRS.2010.2098413.
- Iordache , M.-D. , Bioucas-Dias , J.M. , Plaza , A. et al. ( 2014 ). MUSIC-CSR: Hyperspectral unmixing via multiple signal classification and collaborative sparse regression . IEEE Transactions on Geoscience and Remote Sensing 52 : 4364 – 4382 . 10.1109/TGRS.2013.2281589.
- Jacobson , C.R. ( 2011 ). Identification and quantification of the hydrological impacts of imperviousness in urban catchments: A review . Journal of Environmental Management 92 : 1438 – 1448 . 10.1016/j.jenvman.2011.01.018.
- Jilge , M. , Heiden , U. , Habermeyer , M et al. ( 2016 ). Identifying pure urban image spectra using a learning urban image spectral archive (LUISA) . Proceedings SPIE 10008, Remote Sensing Technologies and Applications in Urban Environments , SPIE Remote Sensing 2016, Edinburgh, UK (26–29 September 2016). Society of Photo-Optical Instrumentation Engineers. 10.1117/12.2241370.
- Jilge , M. , Heiden , U. , Habermeyer , M. et al. ( 2017 ). Detecting unknown artificial urban surface materials based on spectral dissimilarity analysis . Sensors 17 : 1826 . 10.3390/s17081826.
- Jilge , M. , Heiden , U. , Neumann , C. et al. ( 2019 ). Gradients in urban material composition: A new concept to map cities with spaceborne imaging spectroscopy data . Remote Sensing of Environment 223 : 179 – 193 . 10.1016/j.rse.2019.01.007.
- Jiménez , M. , González , M. , Amaro , A. et al. ( 2014 ). Field spectroscopy metadata system based on ISO and OGC standards . ISPRS International Journal of Geo-Information 3 : 1003 – 1022 . 10.3390/ijgi3031003.
- JPI Urban Europe ( 2019 ). Strategic Research and Innovation Agenda 2.0 . JPI Urban Europe .
- Kale , K.V. , Solankar , M.M. , Nalawade , D.B. ( 2019 ). Hyperspectral endmember extraction techniques . In: Processing and Analysis of Hyperspectral Data (ed. J. Chen , Y. Song , and H. Li ). London, UK : IntechOpen Ltd. 10.5772/intechopen.88910.
- Kaspersen , P. , Fensholt , R. , and Drews , M. ( 2015 ). Using Landsat vegetation indices to estimate impervious surface fractions for European cities . Remote Sensing 7 : 8224 . 10.3390/rs70608224.
- Kennedy , C. , Steinberger , J. , Gasson , B. et al. ( 2009 ). Greenhouse gas emissions from global cities . Environmental Science & Technology 43 : 7297 – 7302 . 10.1021/es200849z.
- Kotthaus , S. , Smith , T.E.L. , Wooster , M.J. et al. ( 2014 ). Derivation of an urban materials spectral library through emittance and reflectance spectroscopy . ISPRS Journal of Photogrammetry and Remote Sensing 94 : 194 – 212 . 10.1016/j.isprsjprs.2014.05.005.
- Lemajic , S. , Vajsova , B. , and Astrand , P.J. ( 2018 ). New sensors benchmark report on PlanetScope: Geometric benchmarking test for Common Agricultural Policy (CAP) purposes . JRC Technical Reports . Luxembourg : Publications Office of the European Union . 10.2760/178918.
- Li , J. and Roy , D.P. ( 2017 ). A global analysis of Sentinel-2A, Sentinel-2B and Landsat 8 data revisit intervals and implications for terrestrial monitoring . Remote Sensing 9 : 902 . 10.3390/rs9090902.
- van der Linden , S. and Hostert , P. ( 2009 ). The influence of urban structures on impervious surface maps from airborne hyperspectral data . Remote Sensing of Environment 113 : 2298 – 2305 . 10.1016/j.rse.2009.06.004.
- van der Linden , S. , Okujeni , A. , Canters , F. et al. ( 2019 ). Imaging spectroscopy in urban environments . Surveys in Geophysics 40 ( 3 ): 471 – 488 . 10.1007/s10712-018-9486-y.
- Liu , T. and Yang , X. ( 2013 ). Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis . Remote Sensing of Environment 133 : 251 – 264 . 10.1016/j.rse.2013.02.020.
- Meerdink , S.K. , Roberts , D.A. , Roth , K.L. et al. ( 2019 ). Classifying California plant species temporally using airborne hyperspectral imagery . Remote Sensing of Environment 232 : 111308 . 10.1016/j.rse.2019.111308.
- Meganem , I. , Déliot , P. , Briottet , X. et al. ( 2014 ). Linear – quadratic mixing model for reflectances in urban environments . IEEE Transactions on Geoscience and Remote Sensing 52 : 544 – 558 . 10.1109/TGRS.2013.2242475.
- Miao , L. and Qi , H. ( 2007 ). Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization . IEEE Transactions on Geoscience and Remote Sensing 45 : 765 – 777 . 10.1109/TGRS.2006.888466.
- Michishita , R. , Jiang , Z. , and Xu , B. ( 2012 ). Monitoring two decades of urbanization in the Poyang Lake area, China through spectral unmixing . Remote Sensing of Environment 117 : 3 – 18 . 10.1016/j.rse.2011.06.021.
- Mitraka , Z. , Del Frate , F. , and Carbone , F. ( 2016 ). Nonlinear spectral unmixing of Landsat imagery for urban surface cover mapping . IEEE Journal of Selected Topics in Applied Earth Obervations and Remote Sensing 9 : 3340 – 3350 . 10.1109/JSTARS.2016.2522181.
-
Nascimento , J.M.
,
Dias , J.M.
(
2003
).
Vertex component analysis: A fast algorithm to extract endmembers spectra from hyperspectral data
. In:
Pattern Recognition and Image Analysis
(ed.
F.J. Perales
,
A.J.C. Campilho
,
N.P. Blanca
et al.). Lecture Notes in Computer Science 2652.
Berlin, Heidelberg
:
Springer-Verlag
. 10.1007/978-3-540-44871-6_73.
10.1007/978-3-540-44871-6_73 Google Scholar
- National Academies of Sciences, Engineering, and Medicine ( 2018 ). Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space . Washington, DC : The National Academies Press . 10.17226/24938.
-
Neville , R.A.
,
Staenz , K.
,
Szeredi , T.
et al. (
1999
).
Automatic endmember extraction from hyperspectral data for mineral exploration
.
Proceedings of the 21st Canadian Symposium on Remote Sensing
,
21
–
24
.
Ottawa, Canada
:
Canadian Aeronautics and Space Institute
.
10.4095/219526 Google Scholar
-
Oke , T.R.
,
Mills , G.
,
Christen , A.
et al. (
2017
).
Urban Climates
,
Cambridge University Press
. 10.1017/9781139016476.
10.1017/9781139016476 Google Scholar
- Okujeni , A. , van der Linden , S. , Tits , L. et al. ( 2013 ). Support vector regression and synthetically mixed training data for quantifying urban land cover . Remote Sensing of Environment 137 : 184 – 197 . 10.1016/j.rse.2013.06.007.
- Okujeni , A. , van der Linden , S. , and Hostert , P . ( 2015 ). Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning . Remote Sensing of Environment , 158 : 69 – 80 . 10.1016/j.rse.2014.11.009.
- Okujeni , A. , van der Linden , S. , Suess , S. et al. ( 2016 ). Ensemble learning for quantifying urban land cover with support vector regression and synthetically mixed training data . IEEE Journal of Selected Topics in Applied Earth Obervations and Remote Sensing 10 ( 4 ): 1640 – 1650 . 10.1109/JSTARS.2016.2634859.
- Okujeni , A. , Canters , F. , Cooper , S.D. et al. ( 2018 ). Generalizing machine learning regression models using multi-site spectral libraries for mapping vegetation-impervious-soil fractions across multiple cities . Remote Sensing of Environment 216 : 482 – 496 . 10.1016/j.rse.2018.07.011.
- Panagopoulos , T. , Gonzalez Duque , J.A. , and Bostenaru Dan , M. ( 2015 ). Urban planning with respect to environmental quality and human well-being . Environmental Pollution 208A : 137 – 144 . 10.1016/j.envpol.2015.07.038.
- Pesaresi , M. , Corbane , C. , Julea , A. et al. ( 2016 ). Assessment of the added-value of Sentinel-2 for detecting built-up areas . Remote Sensing 8 ( 4 ): 299 . 10.3390/rs8040299.
- Plaza , J. , Hendrix , E.M. , García , I. et al. ( 2012 ). On endmember identification in hyperspectral images without pure pixels: A comparison of algorithms . Journal of Mathematical Imaging and Vision 42 : 163 – 175 . 10.1007/s10851-011-0276-0.
- Powell , R. , Roberts , D. , Dennison , P. et al. ( 2007 ). Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil . Remote Sensing of Environment 106 : 253 – 267 . 10.1016/j.rse.2006.09.005.
- Priem , F. and Canters , F. ( 2016 ). Synergistic use of LiDAR and APEX hyperspectral data for high-resolution urban land cover mapping . Remote Sensing 8 ( 10 ): 787 . 10.3390/rs8100787.
-
Priem , F.
,
Canters , F.
,
Okujeni , A.
et al. (
2017
).
Optimizing mixed spectra generation for regression-based unmixing of land cover in urban areas
.
Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE 2017)
, Dubai, United Arab Emirates (6–8 March 2017).
Red Hook, NY
:
Curran Associates, Inc.
10.1109/JURSE.2017.7924554.
10.1109/JURSE.2017.7924554 Google Scholar
- Priem , F. , Okujeni , A. , van der Linden , S. et al. ( 2019 ). Comparing map-based and library-based training approaches for urban land cover fraction mapping from Sentinel-2 imagery . International Journal of Applied Earth Observation and Geoinformation 78 : 295 – 305 . 10.1016/j.jag.2019.02.003.
- Rasaiah , B.A. , Jones , S.D. , Bellman , C. et al. ( 2014 ). Critical metadata for spectroscopy field campaigns . Remote Sensing 6 ( 5 ): 3662 – 3680 . 10.3390/rs6053662.
- Rasmussen , C.E. and Williams , C. ( 2006 ). Gaussian Processes for Machine Learning . Cambridge, MA : MIT Press .
- Ridd , M.K. ( 1995 ). Exploring a V-I-S (Vegetation-Impervious surface-Soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities . International Journal of Remote Sensing 16 : 2165 – 2185 . 10.1080/01431169508954549.
- Roberts , D.A. , Gardner , M. , Church , R. et al. ( 1998 ). Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models . Remote Sensing of Environment 65 : 267 – 279 . 10.1016/S0034-4257(98)00037-6.
- Roberts , D.A. , Dennison , P.E. , Gardner , M.E. et al. ( 2003 ). Evaluation of the potential of Hyperion for fire danger assessment by comparison to the airborne visible/infrared imaging spectrometer . IEEE Transactions on Geoscience and Remote Sensing 41 : 1297 – 1310 . 10.1109/TGRS.2003.812904.
-
Roessner , S.
,
Segl , K.
,
Bochow , M.
et al. (
2011
).
Potential of hyperspectral remote sensing for analyzing the urban environment
. In:
Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment
,
First Edition
(ed.
X Yang
.),
49
–
62
.
Wiley-Blackwell
.
10.1002/9780470979563.ch4 Google Scholar
- Roth , K.L. , Dennison , P.E. , and Roberts , D.A. ( 2012 ). Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data . Remote Sensing of Environment 127 : 139 – 152 . 10.1016/j.rse.2012.08.030.
- Schaaf , A.N. , Dennison , P.E. , Fryer , G.K. et al. ( 2011 ). Mapping plant functional types at multiple spatial resolutions using imaging spectrometer data . GIScience and Remote Sensing 48 : 324 – 344 . 10.2747/1548-1603.48.3.324.
- Schneider , A. , Friedl , M.A. , and Potere , D. ( 2010 ). Mapping global urban areas using MODIS 500-m data: New methods and datasets based on “urban ecoregions” . Remote Sensing of Environment 114 : 1733 – 1746 . 10.1016/j.rse.2010.03.003.
- Seto , K.C. and Shepherd , J.M. ( 2009 ). Global urban land-use trends and climate impacts . Current Opinion in Environmental Sustainability 1 ( 1 ): 89 – 95 . 10.1016/j.cosust.2009.07.012.
- Sexton , J.O. , Song , X.-P. , Huang , C. et al. ( 2013 ). Urban growth of the Washington, D.C.–Baltimore, MD metropolitan region from 1984 to 2010 by annual, Landsat-based estimates of impervious cover . Remote Sensing of Environment 129 : 42 – 53 . 10.1016/j.rse.2012.10.025.
- Small , C. ( 2003 ). High spatial resolution spectral mixture analysis of urban reflectance . Remote Sensing of Environment 88 : 170 – 186 . 10.1016/j.rse.2003.04.008.
- United Nations, Department of Economic and Social Affairs ( 2018 ). 68% of the world population projected to live in urban areas by 2050, says UN . https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html (accessed 23 February 2020).
- Van De Voorde , T. , Vlaeminck , J. , and Canters , F. ( 2008 ). Comparing different approaches for mapping urban vegetation cover from Landsat ETM+ data: A case study on Brussels . Sensors 8 ( 6 ): 3880 – 3902 . 10.3390/s8063880.
- Van de Voorde , T. , De Roeck , T. , and Canters , F. ( 2009 ). A comparison of two spectral mixture modelling approaches for impervious surface mapping in urban areas . International Journal of Remote Sensing , 30 ( 18 ): 4785 – 4806 . 10.1080/01431160802665918.
- Verbeiren , B. , Van de Voorde , T. , Canters , F. et al. ( 2013 ). Assessing urbanisation effects on rainfall-runoff using a remote sensing supported modelling strategy , International Journal of Applied Earth Observation and Geoinformation 21 : 92 – 102 . 10.1016/j.jag.2012.08.011.
- Walton , J.T. ( 2008 ). Subpixel urban land cover estimation: Comparing Cubist, Random Forests and Support Vector Regression . Photogrammetric Engineering and Remote Sensing 74 : 1213 – 1222 . 10.14358/PERS.74.10.1213.
- Wang , W. , Yao , X. , Zhai , J. et al. ( 2014 ). A tetrahedron-based endmember selection approach for urban impervious surface mapping . PLoS One 9 : 1 – 17 . 10.1371/journal.pone.0093479.
- Ward , D. , Phinn , S.R. , and Murray , A.T. ( 2000 ). Monitoring growth in rapidly urbanizing areas using remotely sensed data . Professional Geographer 52 : 371 – 386 . 10.1111/0033-0124.00232.
- Weng , Q. , Hu , X. , and Lu , D. ( 2008 ). Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: A comparison . International Journal of Remote Sensing 29 : 3209 – 3232 . 10.1080/01431160701469024.
- Wetherley , E.B. , McFadden , J.P. , and Roberts , D.A. ( 2018 ). Megacity-scale analysis of urban vegetation temperatures . Remote Sensing of Environment 213 : 18 – 33 . 10.1016/j.rse.2018.04.051.
- Winter , M.E . ( 1999 ). N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data . Proceedings Volume 3753, Imaging Spectrometry V, SPIE's International Symposium on Optical Science, Engineering, and Instrumentation 1999 , Denver, CO, United States . 10.1117/12.366289.
- Woodcock , C.E. , Macomber , S.A. , Pax-Lenney , M. et al. ( 2001 ). Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors . Remote Sensing of Environment 78 : 194 – 203 . 10.1016/S0034-4257(01)00259-0.
- Wu , C. ( 2004 ). Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery . Remote Sensing of Environment 93 : 480 – 492 . 10.1016/j.rse.2004.08.003.
- Wu , C. and Murray , A.T. ( 2003 ). Estimating impervious surface distribution by spectral mixture analysis . Remote Sensing of Environment 84 : 493 – 505 . 10.1016/S0034-4257(02)00136-0.
- Zhang , C. , Cooper , H. , Selch , D. et al. ( 2014 ). Mapping urban land cover types using object-based multiple endmember spectral mixture analysis . Remote Sensing Letters 5 : 521 – 529 . 10.1080/2150704X.2014.930197.
- Zhu , Z. , Wulder , M.A. , Roy , D.P. et al. ( 2019a ). Benefits of the free and open Landsat policy . Remote Sensing of Environment 224 : 382 – 385 . 10.1016/j.rse.2019.02.016.
- Zhu , Z. , Zhou , Y. , Seto , K.C. et al. ( 2019b ). Understanding an urbanizing planet: Strategic directions for remote sensing . Remote Sensing of Environment , 228 : 164 – 182 . 10.1016/j.rse.2019.04.020.