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ZENG Hongwei, WU Bingfang, WANG Shuai, MUSAKWA Walter, TIAN Fuyou, MASHIMBYE Zama Eric, POONA Nitesh, SYNDEY Mavengahama. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa[J]. Chinese Geographical Science, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
Citation: ZENG Hongwei, WU Bingfang, WANG Shuai, MUSAKWA Walter, TIAN Fuyou, MASHIMBYE Zama Eric, POONA Nitesh, SYNDEY Mavengahama. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa[J]. Chinese Geographical Science, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
shu

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

doi: 10.1007/s11769-020-1119-y

  • 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

Funds:

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)

  • Received Date: 2019-04-22
  • Rev Recd Date: 2019-09-04
  • 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.
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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

doi: 10.1007/s11769-020-1119-y
  • 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
Funds:

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)

  • Received Date: 2019-04-22
  • Rev Recd Date: 2019-09-04

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.

ZENG Hongwei, WU Bingfang, WANG Shuai, MUSAKWA Walter, TIAN Fuyou, MASHIMBYE Zama Eric, POONA Nitesh, SYNDEY Mavengahama. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa[J]. Chinese Geographical Science, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
Citation: ZENG Hongwei, WU Bingfang, WANG Shuai, MUSAKWA Walter, TIAN Fuyou, MASHIMBYE Zama Eric, POONA Nitesh, SYNDEY Mavengahama. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa[J]. Chinese Geographical Science, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
shu

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