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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
Chinese Geographical Science ( IF 3.4 ) Pub Date : 2020-06-01 , DOI: 10.1007/s11769-020-1119-y
Hongwei Zeng , Bingfang Wu , Shuai Wang , Walter Musakwa , Fuyou Tian , Zama Eric Mashimbye , Nitesh Poona , Mavengahama Syndey

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.

中文翻译:

基于谷歌地球引擎的综合土地覆盖分类方法:以南非 Nzhelele 和 Levhuvu 流域为例

本研究设计了一种在地面样本不足的情况下获取南非土地覆盖的方法,并在南非的 Nzhelele 和 Levhuvu 流域进行了案例演示。该方法是基于 Landsat 8、Sentinel-1 和航天飞机雷达地形任务 (SRTM) 数字高程模型 (DEM) 以及谷歌地球引擎 (GEE) 平台的集成而开发的。采用 300 棵树的随机森林分类器作为土地覆盖分类模型。为了克服地面数据不足的缺陷,采用分层抽样的方法从现有的土地覆盖产品中生成训练和验证样本。同样,为了识别不同的土地覆盖类别,采用百分位数和月中位数复合来扩展随机森林分类器的输入指标。结果表明,2017-2018年南非Nzhelele和Levhuvu流域的土地覆盖总体准确率达到76.43%。从我们的研究中可以得出三个重要的结果。1)Sentinel-1数据的参与可以略微提高土地覆盖的整体精度,但其对土地覆盖分类的贡献因土地类型而异。2)在使用随机抽样训练非优势土地覆盖类别时,存在欠拟合问题,建议采用分层抽样的方法来保证非优势类别的分类精度。3)当相关反射带参与训练过程时,个体归一化植被指数(NDVI)、增强植被指数(EVI)、土壤调整植被指数(SAVI)、
更新日期:2020-06-01
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