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大尺度土地覆盖数据集在中国及周边区域的精度评价
引用本文:杨永可,肖鹏峰,冯学智,李海星,常潇,冯威丁.大尺度土地覆盖数据集在中国及周边区域的精度评价[J].遥感学报,2014,18(2):453-475.
作者姓名:杨永可  肖鹏峰  冯学智  李海星  常潇  冯威丁
作者单位:1. 南京大学 地理信息科学系, 江苏 南京 210023;2. 南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023;1. 南京大学 地理信息科学系, 江苏 南京 210023;2. 南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023;1. 南京大学 地理信息科学系, 江苏 南京 210023;2. 南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023;1. 南京大学 地理信息科学系, 江苏 南京 210023;2. 南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023;1. 南京大学 地理信息科学系, 江苏 南京 210023;2. 南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023;1. 南京大学 地理信息科学系, 江苏 南京 210023;2. 南京大学 江苏省地理信息技术重点实验室, 江苏 南京 210023
基金项目:国家重点基础研究发展计划(973计划)(编号:2011CB952001)
摘    要:大尺度土地覆盖数据是全球陆地表层过程研究、生态系统评估、环境建模等科学研究的重要基础,研究现有数据集的特点对数据使用者及生产新的数据集都具有指导意义。本研究以中国及周边区域为研究区,根据不同分类体系对地物的定义,研究不同分类体系中对应地物的相关系数,并将所有分类体系转换为IGBP分类体系;然后,从定性和定量两方面分析现有5种土地覆盖数据集(IGBP DISCover、UMD、GLC2000、MOD12Q1和GlobCover 2005)的空间一致性;并利用Google Earth高分影像选取两期验证样本评价5种土地覆盖数据集的精度。结果表明:同种地物在不同土地覆盖数据集之间的空间分布格局差异较大,且不同土地覆盖数据集之间的总体一致性系数较低;5种土地覆盖数据集中,GLC2000的总体精度和Kappa系数均最高,GlobCover 2005的总体精度和Kappa系数均最低。

关 键 词:土地覆盖数据集  比较分析  精度评价  Google  Earth
收稿时间:2013/3/18 0:00:00
修稿时间:2013/9/30 0:00:00

Comparison and assessment of large-scale land cover datasets in China and adjacent regions
YANG Yongke,XIAO Pengfeng,FENG Xuezhi,LI Haixing,CHANG Xiao and FENG Weiding.Comparison and assessment of large-scale land cover datasets in China and adjacent regions[J].Journal of Remote Sensing,2014,18(2):453-475.
Authors:YANG Yongke  XIAO Pengfeng  FENG Xuezhi  LI Haixing  CHANG Xiao and FENG Weiding
Institution:1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;1. Department of Geographic Information Science, Nanjing University, Nanjing 210023, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
Abstract:Large-scale land cover datasets comprise an important foundation of the research on land surface processes, ecosystem assessment, and environmental modeling. The evaluation of existing land cover datasets provides a guide to dataset use and new dataset production. Five kinds of global land cover datasets (IGBP DISCover, UMD, GLC2000, MOD12Q1, and GlobCover 2005) over China and adjacent regions are evaluated in this paper. First, the categories of five land cover datasets are translated into the International Geosphere-Biosphere Programme-IGBP scheme based on the correlation coefficients of the corresponding classes, which is computed according to the class definition in each land cover dataset. Second, the spatial agreements of the five land cover datasets are analyzed using visual comparison and per-pixel comparison. Finally, the classification accuracy of the five land cover datasets is evaluated based on validation samples collected through Google Earth high-resolution satellite images. The results show large areas of disagreement among the five land cover datasets, and the overall consistency among them is low. GLC2000 has the highest overall accuracy and Kappa coefficient, whereas GlobCover 2005 has the lowest overall accuracy and Kappa coefficient.
Keywords:Land cover datasets  comparison analysis  accuracy assessment  Google Earth
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