Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

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 g...

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Published in:	International journal of remote sensing Vol. 34; no. 7; pp. 2607 - 2654
Main Authors:	Gong, Peng, Wang, Jie, Yu, Le, Zhao, Yongchao, Zhao, Yuanyuan, Liang, Lu, Niu, Zhenguo, Huang, Xiaomeng, Fu, Haohuan, Liu, Shuang, Li, Congcong, Li, Xueyan, Fu, Wei, Liu, Caixia, Xu, Yue, Wang, Xiaoyi, Cheng, Qu, Hu, Luanyun, Yao, Wenbo, Zhang, Han, Zhu, Peng, Zhao, Ziying, Zhang, Haiying, Zheng, Yaomin, Ji, Luyan, Zhang, Yawen, Chen, Han, Yan, An, Guo, Jianhong, Yu, Liang, Wang, Lei, Liu, Xiaojun, Shi, Tingting, Zhu, Menghua, Chen, Yanlei, Yang, Guangwen, Tang, Ping, Xu, Bing, Giri, Chandra, Clinton, Nicholas, Zhu, Zhiliang, Chen, Jin, Chen, Jun
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 10.04.2013
Subjects:	algorithms Animal, plant and microbial ecology Antarctica Applied geophysics Areal geology. Maps Biological and medical sciences computer software cropland decision support systems Earth sciences Earth, ocean, space Exact sciences and technology Food and Agriculture Organization forests Fundamental and applied biological sciences. Psychology General aspects. Techniques Geologic maps, cartography grasslands Greenland ice Internal geophysics land cover Landsat moderate resolution imaging spectroradiometer monitoring shrublands snow surveys Teledetection and vegetation maps time series analysis United States polar regions Antarctica Arctic region Greenland United States Thematic map Thematic Mapper land cover maps Space remote sensing Landsat Map(geography) cartography Classifier Support vector machine Earth surface monitoring Decision tree Landsat satellite Ecological map maximum likelihood Plant cover land cover
ISSN:	1366-5901, 0143-1161, 1366-5901
Online Access:	Get full text
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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.
AbstractList	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.
Author	Chen, Yanlei Giri, Chandra Zhu, Menghua Guo, Jianhong Zhao, Yuanyuan Zhang, Yawen Shi, Tingting Yao, Wenbo Yang, Guangwen Liu, Caixia Cheng, Qu Ji, Luyan Zheng, Yaomin Wang, Xiaoyi Clinton, Nicholas Zhao, Ziying Tang, Ping Chen, Jin Liang, Lu Liu, Xiaojun Gong, Peng Zhang, Han Chen, Jun Zhu, Peng Xu, Bing Li, Xueyan Yu, Liang Yu, Le Hu, Luanyun Zhu, Zhiliang Niu, Zhenguo Wang, Jie Fu, Wei Li, Congcong Yan, An Zhao, Yongchao Huang, Xiaomeng Wang, Lei Chen, Han Fu, Haohuan Zhang, Haiying Liu, Shuang Xu, Yue
Author_xml	– sequence: 1 fullname: Gong, Peng – sequence: 2 fullname: Wang, Jie – sequence: 3 fullname: Yu, Le – sequence: 4 fullname: Zhao, Yongchao – sequence: 5 fullname: Zhao, Yuanyuan – sequence: 6 fullname: Liang, Lu – sequence: 7 fullname: Niu, Zhenguo – sequence: 8 fullname: Huang, Xiaomeng – sequence: 9 fullname: Fu, Haohuan – sequence: 10 fullname: Liu, Shuang – sequence: 11 fullname: Li, Congcong – sequence: 12 fullname: Li, Xueyan – sequence: 13 fullname: Fu, Wei – sequence: 14 fullname: Liu, Caixia – sequence: 15 fullname: Xu, Yue – sequence: 16 fullname: Wang, Xiaoyi – sequence: 17 fullname: Cheng, Qu – sequence: 18 fullname: Hu, Luanyun – sequence: 19 fullname: Yao, Wenbo – sequence: 20 fullname: Zhang, Han – sequence: 21 fullname: Zhu, Peng – sequence: 22 fullname: Zhao, Ziying – sequence: 23 fullname: Zhang, Haiying – sequence: 24 fullname: Zheng, Yaomin – sequence: 25 fullname: Ji, Luyan – sequence: 26 fullname: Zhang, Yawen – sequence: 27 fullname: Chen, Han – sequence: 28 fullname: Yan, An – sequence: 29 fullname: Guo, Jianhong – sequence: 30 fullname: Yu, Liang – sequence: 31 fullname: Wang, Lei – sequence: 32 fullname: Liu, Xiaojun – sequence: 33 fullname: Shi, Tingting – sequence: 34 fullname: Zhu, Menghua – sequence: 35 fullname: Chen, Yanlei – sequence: 36 fullname: Yang, Guangwen – sequence: 37 fullname: Tang, Ping – sequence: 38 fullname: Xu, Bing – sequence: 39 fullname: Giri, Chandra – sequence: 40 fullname: Clinton, Nicholas – sequence: 41 fullname: Zhu, Zhiliang – sequence: 42 fullname: Chen, Jin – sequence: 43 fullname: Chen, Jun
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Snippet	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...
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SubjectTerms	algorithms Animal, plant and microbial ecology Antarctica Applied geophysics Areal geology. Maps Biological and medical sciences computer software cropland decision support systems Earth sciences Earth, ocean, space Exact sciences and technology Food and Agriculture Organization forests Fundamental and applied biological sciences. Psychology General aspects. Techniques Geologic maps, cartography grasslands Greenland ice Internal geophysics land cover Landsat moderate resolution imaging spectroradiometer monitoring shrublands snow surveys Teledetection and vegetation maps time series analysis United States
Title	Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
URI	https://www.tandfonline.com/doi/abs/10.1080/01431161.2012.748992 https://www.proquest.com/docview/1663569024
Volume	34
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