Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
暂无分享,去创建一个
Hankui K. Zhang | Lu Liang | B. Xu | P. Gong | N. Clinton | Le Yu | H. Fu | Guangwen Yang | Zhiliang Zhu | Yanlei Chen | Jie Wang | Xiaoyi Wang | Yaomin Zheng | Yuanyuan Zhao | Lei Wang | Congcong Li | Xueyan Li | Luanyun Hu | Z. Niu | Yongchao Zhao | Xiaomeng Huang | Shuang Liu | Wei Fu | Caixia Liu | Yue Xu | Q. Cheng | W. Yao | P. Zhu | Ziying Zhao | Haiying Zhang | L. Ji | Yawen Zhang | Han Chen | A. Yan | Jianwei Guo | Liang Yu | Xiaojun Liu | Tingting Shi | Menghua Zhu | P. Tang | C. Giri | Jin Chen | Jun Chen | Bing Xu | L. Yu | Liang Yu | L. Yu | Liang Yu | Qu Cheng | L. Liang | Luyan Ji
[1] J. Townshend,et al. African Land-Cover Classification Using Satellite Data , 1985, Science.
[2] Peng Gong,et al. An assessment of some factors influencing multispectral land-cover classification , 1990 .
[3] Philip J. Howarth,et al. Land-use classification of SPOT HRV data using a cover-frequency method , 1992 .
[4] P. Gong,et al. Frequency-based contextual classification and gray-level vector reduction for land-use identification , 1992 .
[5] John R. Miller,et al. Forest canopy closure from classification and spectral unmixing of scene components-multisensor evaluation of an open canopy , 1994, IEEE Trans. Geosci. Remote. Sens..
[6] Christopher B. Field,et al. Mapping the land surface for global atmosphere‐biosphere models: Toward continuous distributions of vegetation's functional properties , 1995 .
[7] C. Elvidge,et al. A Technique for Using Composite DMSP/OLS "City Lights"Satellite Data to Map Urban Area , 1997 .
[8] N. Ramankutty,et al. Characterizing patterns of global land use: An analysis of global croplands data , 1998 .
[9] Margaret E. Gardner,et al. Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .
[10] Frédérique Seyler,et al. Land cover mapping and carbon pools estimates in Rondonia, Brazil , 1998 .
[11] J. Scepan,et al. Thematic validation of high-resolution Global Land-Cover Data sets , 1999 .
[12] J. Townshend,et al. Continuous fields of vegetation characteristics at the global scale at 1‐km resolution , 1999 .
[13] Gail P. Anderson,et al. Atmospheric correction for shortwave spectral imagery based on MODTRAN4 , 1999, Optics & Photonics.
[14] N. Ramankutty,et al. Estimating historical changes in global land cover: Croplands from 1700 to 1992 , 1999 .
[15] S. Adler-Golden,et al. Atmospheric Correction for Short-wave Spectral Imagery Based on MODTRAN 4 , 2000 .
[16] 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 .
[17] Limin Yang,et al. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .
[18] J. Cihlar. Land cover mapping of large areas from satellites: Status and research priorities , 2000 .
[19] G. Powell,et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth , 2001 .
[20] J. Townshend,et al. Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data , 2002 .
[21] L. S. Davis,et al. An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .
[22] Giles M. Foody,et al. Status of land cover classification accuracy assessment , 2002 .
[23] Peng Gong,et al. 3D Model-Based Tree Measurement from High-Resolution Aerial Imagery , 2002 .
[24] R. DeFries,et al. Effects of Land Cover Conversion on Surface Climate , 2002 .
[25] Alan H. Strahler,et al. Global land cover mapping from MODIS: algorithms and early results , 2002 .
[26] J. Townshend,et al. Carbon emissions from tropical deforestation and regrowth based on satellite observations for the 1980s and 1990s , 2002, Proceedings of the National Academy of Sciences of the United States of America.
[27] R. Dickinson,et al. The Common Land Model , 2003 .
[28] B. Xu,et al. Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic Ikonos image , 2003 .
[29] D. Roberts,et al. Sources of error in accuracy assessment of thematic land-cover maps in the Brazilian Amazon , 2004 .
[30] S. Liang,et al. Snail Density Prediction for Schistosomiasis Control Using Ikonos and ASTER Images , 2004 .
[31] Eric Bauer,et al. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.
[32] A. Belward,et al. GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .
[33] Peng Gong,et al. Land cover assessment with MODIS imagery in southern African Miombo ecosystems , 2005 .
[34] S. Carpenter,et al. Global Consequences of Land Use , 2005, Science.
[35] Robert H. Fraser,et al. Signature extension through space for northern landcover classification: A comparison of radiometric correction methods , 2005 .
[36] Alan H. Strahler,et al. Validation of the global land cover 2000 map , 2006, IEEE Transactions on Geoscience and Remote Sensing.
[37] Martin Jung,et al. Exploiting synergies of global land cover products for carbon cycle modeling , 2006 .
[38] Maggi Kelly,et al. A spatial–temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery , 2006 .
[39] Rich Caruana,et al. An empirical comparison of supervised learning algorithms , 2006, ICML.
[40] José M. C. Pereira,et al. Land-cover Mapping in the Brazilian Amazon Using SPOT-4 Vegetation Data and Machine Learning Classification Methods , 2006 .
[41] P. Gong,et al. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .
[42] S. Nilsson,et al. A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .
[43] 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 .
[44] Jianjun Ge,et al. Impacts of land use/cover classification accuracy on regional climate simulations , 2007 .
[45] Ruiliang Pu,et al. Development and analysis of a 12-year daily 1-km forest fire dataset across North America from NOAA/AVHRR data , 2007 .
[46] B. Xu,et al. Land-use/land-cover classification with multispectral and hyperspectral EO-1 data , 2007 .
[47] Philippe De Maeyer,et al. An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization , 2007, Expert Syst. Appl..
[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] Dengsheng Lu,et al. Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .
[50] Frédéric Achard,et al. GLOBCOVER : The most detailed portrait of Earth , 2008 .
[51] C. Justice,et al. Global characterization of fire activity: toward defining fire regimes from Earth observation data , 2008 .
[52] A. Ducharne,et al. Comprehensive data set of global land cover change for land surface model applications , 2008 .
[53] Laurence C. Smith,et al. Automated Image Registration for Hydrologic Change Detection in the Lake-Rich Arctic , 2008, IEEE Geoscience and Remote Sensing Letters.
[54] Peng Gong,et al. Using local transition probability models in Markov random fields for forest change detection , 2008 .
[55] Bicheron Patrice,et al. GlobCover - Products Description and Validation Report , 2008 .
[56] Steven W. Running,et al. Ecosystem Disturbance, Carbon, and Climate , 2008, Science.
[57] K. Koperski,et al. Land cover classification with multi-sensor fusion of partly missing data. , 2009 .
[58] L. Jiao,et al. Immune secondary response and clonal selection inspired optimizers , 2009 .
[59] Peng Gong,et al. Geographical characteristics of China’s wetlands derived from remotely sensed data , 2009 .
[60] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[61] 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.
[62] Xiao Cheng,et al. Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data , 2009, Sensors.
[63] Patrick Hostert,et al. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images , 2009 .
[64] Peng Gong,et al. Meta-prediction of Bromus tectorum invasion in Central Utah, United States. , 2009 .
[65] Obi Reddy P. Gangalakunta,et al. Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .
[66] J. Lelieveld,et al. Impact of future land use and land cover changes on atmospheric chemistry-climate interactions , 2010 .
[67] C. Potter,et al. Remote sensing-based time-series analysis of cheatgrass (Bromus tectorum L.) phenology. , 2010, Journal of environmental quality.
[68] Anthony C. Janetos,et al. Research priorities in land use and land‐cover change for the Earth system and integrated assessment modelling , 2010 .
[69] David J. Selkowitz,et al. A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska , 2010 .
[70] S. Fritz,et al. Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa , 2010 .
[71] M. Friedl,et al. Mapping global urban areas using MODIS 500-m data: new methods and datasets based on 'urban ecoregions'. , 2010 .
[72] Stéphane Couturier,et al. A fuzzy-based method for the regional validation of global maps: the case of MODIS-derived phenological classes in a mega-diverse zone , 2010 .
[73] Kathleen Neumann,et al. Challenges in using land use and land cover data for global change studies , 2011 .
[74] Yi Wang,et al. China’s wetland change (1990–2000) determined by remote sensing , 2010 .
[75] Misako Kachi,et al. Global Change Observation Mission (GCOM) for Monitoring Carbon, Water Cycles, and Climate Change , 2010, Proceedings of the IEEE.
[76] Qiong Ran,et al. Settlement extraction in the North China Plain using Landsat and Beijing-1 multispectral data with an improved watershed segmentation algorithm , 2010 .
[77] Laurent Ferro-Famil,et al. Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.
[78] Damien Sulla-Menashe,et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .
[79] Yang Liu,et al. Combining Spatial-Temporal and Phylogenetic Analysis Approaches for Improved Understanding on Global H5N1 Transmission , 2010, PloS one.
[80] M. Lefsky. A global forest canopy height map from the Moderate Resolution Imaging Spectroradiometer and the Geoscience Laser Altimeter System , 2010 .
[81] Yang Zhenzhong,et al. China's wetland change (1990-2000) determined by remote sensing , 2010 .
[82] C. Jeganathan,et al. Mapping the phenology of natural vegetation in India using a remote sensing-derived chlorophyll index , 2010 .
[83] T. Mitchell Aide,et al. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America , 2010 .
[84] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[85] Benjamin M. Sleeter,et al. Estimating California ecosystem carbon change using process model and land cover disturbance data: 1951–2000 , 2011 .
[86] Armel Thibaut Kaptué Tchuenté,et al. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale , 2011, Int. J. Appl. Earth Obs. Geoinformation.
[87] Niklaus E. Zimmermann,et al. Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO 2 airborne fraction , 2011 .
[88] Yuhong He,et al. Landsat-comparable land cover maps using ASTER and SPOT images: a case study for large-area mapping programmes , 2011 .
[89] A. Baccini,et al. Mapping forest canopy height globally with spaceborne lidar , 2011 .
[90] William J. Emery,et al. Using active learning to adapt remote sensing image classifiers , 2011 .
[91] Ashbindu Singh,et al. Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .
[92] Liang Lu. Development of an Integrated Software Platform for Global Mapping and Analysis , 2011 .
[93] M. E. Schaepman,et al. Using MERIS fused images for land-cover mapping and vegetation status assessment in heterogeneous landscapes , 2011 .
[94] P. Gong,et al. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China , 2011 .
[95] Ryutaro Tateishi,et al. Production of global land cover data – GLCNMO , 2011, Int. J. Digit. Earth.
[96] Guoqing Sun,et al. Hierarchical mapping of Northern Eurasian land cover using MODIS data , 2011 .
[97] P. Gong,et al. A phenology-based approach to map crop types in the San Joaquin Valley, California , 2011 .
[98] R. Houghton,et al. Characterizing 3D vegetation structure from space: Mission requirements , 2011 .
[99] Chengquan Huang,et al. Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges , 2012, Int. J. Digit. Earth.
[100] Le Yu,et al. Google Earth as a virtual globe tool for Earth science applications at the global scale: progress and perspectives , 2012 .
[101] C. Justice,et al. Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM + data , 2012 .
[102] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .