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Stratified even sampling method for accuracy assessment of land use/land cover classification: a case study of Beijing, China

文献类型： 外文期刊

作者： Dong, Shiwei 1 ; Chen, Ziyue 3 ; Gao, Bingbo 2 ; Guo, Hui 4 ; Sun, Danfeng 2 ; Pan, Yuchun 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China

3.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, Beijing, Peoples R China

4.Chinese Acad Forestry, Forestry Expt Ctr North China, Beijing, Peoples R China

期刊名称：INTERNATIONAL JOURNAL OF REMOTE SENSING （ 影响因子：3.151； 五年影响因子：3.266 ）

ISSN： 0143-1161

年卷期： 2020 年 41 卷 16 期

页码：

收录情况： SCI

摘要： Selecting samples with high representativeness is very important in the accuracy assessment of land use and land cover (LULC) classification. By optimizing sampling sites in both feature space and geographical space, this paper presented a stratified even sampling method for accuracy assessment of the GlobeLand30 dataset for Beijing, China. In this method, spatial stratification and area-weighted proportion sampling assignment method were adopted to achieve optimal coverage in feature space; spatial-simulated annealing (SSA) and the minimization of the mean of the shortest distances (MMSD) criterion were used to optimize sampling sites in geographical space. Six sample sets with sizes of 150, 300, 450, 600, 750 and 900 were drawn using the proposed method, spatial even sampling method, stratified random sampling method and simple random sampling method, and their overall accuracy (OA), root-mean-square error (RMSE) and standard deviation (STDEV) values were evaluated. The results suggested that the OA, RMSE and STDEV results of the proposed method were 71.36%-73.91% (Mean, 72.26%), 0.90% and 0.96%, respectively. Compared with the other sampling methods, the average OA of the proposed method was much closer to the true OA and corresponding RMSE and STDEV were much lower than the other three sampling methods, respectively. It can improve the representativeness of both feature and geographical space, and provide a robust operational tool for the validation of LULC datasets.

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