中国地理科学 ›› 2020, Vol. 30 ›› Issue (3): 397-409.doi: 10.1007/s11769-020-1119-y

• 论文 • 上一篇    下一篇

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

ZENG Hongwei1,2, WU Bingfang1,2, WANG Shuai3, MUSAKWA Walter4, TIAN Fuyou1,2, MASHIMBYE Zama Eric5, POONA Nitesh5, SYNDEY Mavengahama6   

  1. 1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
    4. Department of Town and Regional Planning, University of Johannesburg, Johannesburg 2028, South Africa;
    5. Department of Geography and Environmental Studies, Stellenbosch University, Stellenbosch 7600, South Africa;
    6. Department of Crop Science, Faculty of Natural and Agricultural Sciences, North West University, Mmabatho 2745, South Africa
  • 收稿日期:2019-04-22 修回日期:2019-09-04 出版日期:2020-05-20 发布日期:2020-04-28
  • 通讯作者: WU Bingfang.E-mail:wubf@radi.ac.cn E-mail:wubf@radi.ac.cn
  • 基金资助:
    Under the auspices of National Natural Science Foundation of China (No. 4171101213, 41561144013, 41991232), National Key R&D Program of China (No. 2016YFC0503401, 2016YFA0600304), International Partnership Program of Chinese Academy of Sciences (No. 121311KYSB20170004)

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

ZENG Hongwei1,2, WU Bingfang1,2, WANG Shuai3, MUSAKWA Walter4, TIAN Fuyou1,2, MASHIMBYE Zama Eric5, POONA Nitesh5, SYNDEY Mavengahama6   

  1. 1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China;
    3. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;
    4. Department of Town and Regional Planning, University of Johannesburg, Johannesburg 2028, South Africa;
    5. Department of Geography and Environmental Studies, Stellenbosch University, Stellenbosch 7600, South Africa;
    6. Department of Crop Science, Faculty of Natural and Agricultural Sciences, North West University, Mmabatho 2745, South Africa
  • Received:2019-04-22 Revised:2019-09-04 Online:2020-05-20 Published:2020-04-28
  • Contact: WU Bingfang.E-mail:wubf@radi.ac.cn E-mail:wubf@radi.ac.cn
  • Supported by:
    Under the auspices of National Natural Science Foundation of China (No. 4171101213, 41561144013, 41991232), National Key R&D Program of China (No. 2016YFC0503401, 2016YFA0600304), International Partnership Program of Chinese Academy of Sciences (No. 121311KYSB20170004)

摘要: 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)

Abstract: 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.

Key words: land-cover classification, random forest, percentile composite, Landsat 8, Sentinel-1, Google Earth Engine (GEE)