Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data
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DOI：
10.1080/01431161.2012.748992
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发表时间：
2013-04-10
影响因子：
3.4
通讯作者：
Chen, Jun
中科院分区：
文献类型：
--
作者：
Gong, Peng;Wang, Jie;Chen, Jun
我们已经产生了第一个30米分辨率的全球土地覆盖图使用陆地卫星专题制图仪（TM）和增强型专题制图仪（ETM+）数据。2006年以后的Landsat TM数据有6600多幅，2006年以前的Landsat TM和ETM+数据有2300多幅，都是从绿色季节选择的。这些图像覆盖了除南极洲和格陵兰岛以外的世界大部分陆地表面。这些图像中的大多数来自美国地质调查局的L1 T级（正射校正）。四个分类器是免费提供的，包括传统的最大似然分类器（MLC），J4.8决策树分类器，随机森林（RF）分类器和支持向量机（SVM）分类器。通过遍历每个场景并找到最具代表性和均匀的样本，总共收集了91，433个训练样本。根据全球系统性非对齐采样策略，在预设的固定位置共采集了38，664份测试样本。通过参考2010年中分辨率成像分光仪增强植被指数时间序列和谷歌地球的高分辨率图像，使用了通过扩展谷歌地球功能而开发的两个软件工具Global Analyst和Global Mapper来开发培训和测试样本数据库。开发了一个独特的土地覆盖物分类系统，可与现有的联合国粮食及农业组织（粮农组织）土地覆盖物分类系统以及国际地圈-生物圈方案（地圈-生物圈方案）系统交叉使用。使用这四种分类算法，我们获得了初始的全球土地覆盖图。SVM产生了最高的总体分类准确率（OCA），为64.9%，使用我们的测试样本进行评估，RF（59.8%），J4.8（57.9%）和MLC（53.9%）排名第二至第四。我们还使用我们的测试样本（8629）的子集估计了OCA，每个样本代表大于500 mx500 m的均匀区域。使用这个子集，我们发现SVM的OCA为71.5%。作为估计全球土地覆盖类型覆盖率的一致来源，测试样本的估计表明，世界上只有6.90%的土地用于农业生产。如果加上未开垦的耕地，耕地占总面积的11.51%。森林、草地和灌丛分别占世界总面积的28.35%、13.37%和11.49%。不透水的地表只覆盖了世界的0.66%。内陆水体、不毛之地和冰雪分别占世界的3.56%、16.51%和12.81%。
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 mx500 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.