当前位置： 首页»科研成果» 2013 Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data 李丛丛、李雪艳、徐玥、赵子莹、徐冰

 

Peng Gong^a,b,c, Jie Wang^c, Le Yu^a, Yongchao Zhao^d,e, Yuanyuan Zhao^a, Lu Liang^b, Zhenguo Niu^c, Xiaomeng Huang^a, Haohuan Fu^a, Shuang Liu^c, Congcong Li^f, Xueyan Li^f, Wei Fu^c, Caixia Liu^c, Yue Xu^f, Xiaoyi Wang^c, Qu Cheng^a, Luanyun Hu^a, Wenbo Yao^a, Han Zhang^a, Peng Zhu^c , Ziying Zhao^f , Haiying Zhang^c , Yaomin Zheng^c, Luyan Ji^e, Yawen Zhang^g, Han Chen^g, An Yan^g, Jianhong Guo^h, Liang Yu^h, Lei Wang^c, Xiaojun Liu^g, Tingting Shi^g, Menghua Zhu^g, Yanlei Chen^b, Guangwen Yang^a, Ping Tang^c, Bing Xu^f, Chandra Giriⁱ, Nicholas Clinton^a, Zhiliang Zhuⁱ, Jin Chen^j, and Jun Chen^k

 

^aMinistry of Education Key Laboratory for Earth System Modelling, Centre for Earth System Science, Tsinghua University, Beijing 100084, China;

^bDepartment of Environmental Science, Policy and Management, University of California, Berkeley, CA 94720-3114, USA;

^cState Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences, and Beijing Normal University, Beijing 100101, China;

^dDepartment of Geography, University of South Florida, Tempa, FL 33620, USA;

^eInstitute of Electronics, Chinese Academy of Sciences, Beijing 100191, China;

^fCollege of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;

^gCollege of Remote Sensing Information Engineering, Wuhan University, Wuhan 430072, China;

^hBeijing Xiuying Environmental Information Technology Development Inc., Beijing 100101, China;

ⁱUnited States Geological Survey, Reston, VA 12201, USA;

^jState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China;

^kNational Geomatics Centre, Beijing 100830, China

 

Abstract

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

 

PUBLISHED BY: INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (7): 2607-2654.

 

DOWNLOAD PDF: http://www.tandfonline.com/doi/pdf/10.1080/01431161.2012.748992