Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

Global land cover mapping using earth observation satellite data : recent progresses and challenges

[1]  Yifang Ban,et al.  Improving Urban Change Detection From Multitemporal SAR Images Using PCA-NLM , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[3]  B. Liu,et al.  A 2010 update of National Land Use/Cover Database of China at 1:100000 scale using medium spatial resolution satellite images , 2014 .

[4]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[5]  Yifang Ban,et al.  Fusion of Quickbird MS and RADARSAT SAR data for urban land-cover mapping: object-based and knowledge-based approach , 2010 .

[6]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

[7]  Peng Gong,et al.  Remote sensing of environmental change over China: A review , 2012 .

[8]  Arthur P. Cracknell,et al.  An overview of small satellites in remote sensing , 2008 .

[9]  P. Gong,et al.  Reduction of atmospheric and topographic effect on Landsat TM data for forest classification , 2008 .

[10]  Hankui K. Zhang,et al.  Meta-discoveries from a synthesis of satellite-based land-cover mapping research , 2014 .

[11]  J. Townshend,et al.  Use of Landsat and Corona data for mapping forest cover change from the mid-1960s to 2000s: Case studies from the Eastern United States and Central Brazil , 2015 .

[12]  Eric F. Lambin,et al.  The surface temperature-vegetation index space for land cover and land-cover change analysis , 1996 .

[13]  Le Yu,et al.  Towards a common validation sample set for global land-cover mapping , 2014 .

[14]  S. de Bruin,et al.  Assessing global land cover reference datasets for different user communities , 2015 .

[15]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[16]  M. Friedl,et al.  A new map of global urban extent from MODIS satellite data , 2009 .

[17]  John R. Townshend,et al.  A new global raster water mask at 250 m resolution , 2009, Int. J. Digit. Earth.

[18]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[19]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[20]  Zhiqiang Yang,et al.  Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms , 2010 .

[21]  P. Gong,et al.  Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery , 2014 .

[22]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[23]  Francesca Bovolo,et al.  Classification of Time Series of Multispectral Images With Limited Training Data , 2013, IEEE Transactions on Image Processing.

[24]  Paolo Gamba,et al.  Spaceborne SAR data for global urban mapping at 30 m resolution using a robust urban extractor , 2015 .

[25]  N. Ramankutty,et al.  Characterizing patterns of global land use: An analysis of global croplands data , 1998 .

[26]  Alexander Jacob,et al.  Object-Based Fusion of Multitemporal Multiangle ENVISAT ASAR and HJ-1B Multispectral Data for Urban Land-Cover Mapping , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Michael K. Ng,et al.  Super-Resolution Reconstruction Algorithm To MODIS Remote Sensing Images , 2009, Comput. J..

[28]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[29]  N. Pettorelli,et al.  Essential Biodiversity Variables , 2013, Science.

[30]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[31]  Yifang Ban,et al.  Unsupervised Change Detection in Multitemporal SAR Images Over Large Urban Areas , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  F. Achard,et al.  Object‐oriented and textural image classification of the Siberia GBFM radar mosaic combined with MERIS imagery for continental scale land cover mapping , 2007 .

[33]  Chandra P. Giri,et al.  Next generation of global land cover characterization, mapping, and monitoring , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[34]  S. Fritz,et al.  Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa , 2010 .

[35]  Le Yu,et al.  Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution , 2015 .

[36]  Le Yu,et al.  A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST , 2014, Science China Earth Sciences.

[37]  Lu Liang,et al.  China’s urban expansion from 1990 to 2010 determined with satellite remote sensing , 2012 .

[38]  A. Dobson Monitoring global rates of biodiversity change: challenges that arise in meeting the Convention on Biological Diversity (CBD) 2010 goals , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[39]  Frédéric Achard,et al.  GLOBCOVER : The most detailed portrait of Earth , 2008 .

[40]  Chengquan Huang,et al.  Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover , 2014, Remote. Sens..

[41]  Peng Gong,et al.  Geographic stacking: Decision fusion to increase global land cover map accuracy , 2015 .

[42]  Le Yu,et al.  Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach , 2013 .

[43]  Nazmul Hossain,et al.  Change of impervious surface area between 2001 and 2006 in the conterminous United States , 2011 .

[44]  Jie Wang,et al.  Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery , 2014, Remote. Sens..

[45]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[46]  Shu Peng,et al.  A web-based system for supporting global land cover data production , 2015 .

[47]  Ryutaro Tateishi,et al.  Production of global land cover data – GLCNMO , 2011, Int. J. Digit. Earth.

[48]  Martin Herold,et al.  Some challenges in global land cover mapping : An assessment of agreement and accuracy in existing 1 km datasets , 2008 .

[49]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Steffen Fritz,et al.  Building a hybrid land cover map with crowdsourcing and geographically weighted regression , 2015 .

[51]  Timothy A. Warner,et al.  Does single broadband or multispectral thermal data add information for classification of visible, near‐ and shortwave infrared imagery of urban areas? , 2009 .

[52]  A. S. Belward,et al.  Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites , 2015 .

[53]  Thomas Esch,et al.  Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.

[54]  J. Cihlar Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[55]  Le Yu,et al.  FROM-GC: 30 m global cropland extent derived through multisource data integration , 2013, Int. J. Digit. Earth.

[56]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[57]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[58]  Anna Barbati,et al.  Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[59]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[60]  Laurence C. Smith,et al.  How well do we know northern land cover? Comparison of four global vegetation and wetland products with a new ground‐truth database for West Siberia , 2007 .

[61]  A. Simmons,et al.  The Concept of Essential Climate Variables in Support of Climate Research, Applications, and Policy , 2014 .

[62]  R. Lucas,et al.  New global forest/non-forest maps from ALOS PALSAR data (2007–2010) , 2014 .

[63]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[64]  R. Finkel,et al.  Terrestrial Cosmogenic Nuclide Geochronology Data Reporting Standards Needed , 2010 .

[65]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[66]  José A. Sobrino,et al.  Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[67]  M. Herold,et al.  Revisiting land cover observation to address the needs of the climate modeling community , 2011 .

[68]  J. Townshend,et al.  Bidirectional effects in Landsat reflectance estimates: Is there a problem to solve? , 2015 .

[69]  Kathleen Neumann,et al.  Challenges in using land use and land cover data for global change studies , 2011 .

[70]  Steffen Fritz,et al.  The Need for Improved Maps of Global Cropland , 2013 .

[71]  Andrew Nelson,et al.  Delivering a Global, Terrestrial, Biodiversity Observation System through Remote Sensing , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[72]  Huadong Guo,et al.  A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[73]  Paolo Gamba,et al.  Fast and Efficient Urban Extent Extraction Using ASAR Wide Swath Mode Data , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[74]  J. Townshend,et al.  NDVI-derived land cover classifications at a global scale , 1994 .

[75]  Anton J. J. van Rompaey,et al.  he effect of atmospheric and topographic correction methods on land cover lassification accuracy , 2013 .