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

TitleFiner resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data
Authors
Issue Date2013
Citation
International Journal of Remote Sensing, 2013, v. 34, n. 7, p. 2607-2654 How to Cite?
AbstractWe 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. © 2013 Copyright Taylor and Francis Group, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/296713
ISSN
2020 Impact Factor: 3.151
2020 SCImago Journal Rankings: 0.918
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGong, Peng-
dc.contributor.authorWang, Jie-
dc.contributor.authorYu, L.-
dc.contributor.authorZhao, Yongchao-
dc.contributor.authorZhao, Yuanyuan-
dc.contributor.authorLiang, L.-
dc.contributor.authorNiu, Zhenguo-
dc.contributor.authorHuang, Xiaomeng-
dc.contributor.authorFu, Haohuan-
dc.contributor.authorLiu, Shuang-
dc.contributor.authorLi, Congcong-
dc.contributor.authorLi, Xueyan-
dc.contributor.authorFu, Wei-
dc.contributor.authorLiu, Caixia-
dc.contributor.authorXu, Yue-
dc.contributor.authorWang, Xiaoyi-
dc.contributor.authorCheng, Q.-
dc.contributor.authorHu, Luanyun-
dc.contributor.authorYao, Wenbo-
dc.contributor.authorZhang, Han-
dc.contributor.authorZhu, Peng-
dc.contributor.authorZhao, Ziying-
dc.contributor.authorZhang, Haiying-
dc.contributor.authorZheng, Yaomin-
dc.contributor.authorJi, Luyan-
dc.contributor.authorZhang, Yawen-
dc.contributor.authorChen, Han-
dc.contributor.authorYan, A.-
dc.contributor.authorGuo, Jianhong-
dc.contributor.authorYu, Liang-
dc.contributor.authorWang, Lei-
dc.contributor.authorLiu, Xiaojun-
dc.contributor.authorShi, Tingting-
dc.contributor.authorZhu, Menghua-
dc.contributor.authorChen, Yanlei-
dc.contributor.authorYang, Guangwen-
dc.contributor.authorTang, Ping-
dc.contributor.authorXu, Bing-
dc.contributor.authorGiri, Chandra-
dc.contributor.authorClinton, Nicholas-
dc.contributor.authorZhu, Zhiliang-
dc.contributor.authorChen, Jin-
dc.contributor.authorChen, Jun-
dc.date.accessioned2021-02-25T15:16:30Z-
dc.date.available2021-02-25T15:16:30Z-
dc.date.issued2013-
dc.identifier.citationInternational Journal of Remote Sensing, 2013, v. 34, n. 7, p. 2607-2654-
dc.identifier.issn0143-1161-
dc.identifier.urihttp://hdl.handle.net/10722/296713-
dc.description.abstractWe 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. © 2013 Copyright Taylor and Francis Group, LLC.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Remote Sensing-
dc.titleFiner resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data-
dc.typeArticle-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1080/01431161.2012.748992-
dc.identifier.scopuseid_2-s2.0-84872359932-
dc.identifier.volume34-
dc.identifier.issue7-
dc.identifier.spage2607-
dc.identifier.epage2654-
dc.identifier.eissn1366-5901-
dc.identifier.isiWOS:000315722200021-
dc.identifier.issnl0143-1161-

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