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A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa

引用

Chinese Geographical Science 2020年第3期30卷 397-409页

作者： ZENG Hongwei WU Bingfang WANG Shuai MUSAKWA Walter TIAN Fuyou MASHIMBYE Zama Eric POONA Nitesh syndey mavengahama State Key Laboratory of Remote Sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Earth Surface Proc-esses and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China Department of Town and Regional Planning University of JohannesburgJohannesburg 2028South Africa Department of Geography and Envi-ronmental Studies Stellenbosch UniversityStellenbosch 7600South Africa Department of Crop Science Faculty of Natural and Agricultural SciencesNorth West UniversityMmabatho 2745South Africa

This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission(SRTM) Digital Elevation Model(DEM), and the Google Earth Engine(GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index(NDVI), Enhanced Vegetation Index(EVI), Soil Adjusted Vegetation Index(SAVI), Normalized Difference Built-up Index(NDBI) have little effect on final land-cover classification result.

关键词： land-cover classification random forest percentile composite Landsat 8 Sentinel-1 Google Earth Engine(GEE)

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