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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

ZENG Hongwei WU Bingfang WANG Shuai MUSAKWA Walter TIAN Fuyou MASHIMBYE Zama Eric POONA Nitesh SYNDEY Mavengahama

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]. 中国地理科学, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
引用本文: 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]. 中国地理科学, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
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

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
基金项目: 

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)

详细信息
    通讯作者:

    WU Bingfang.E-mail:wubf@radi.ac.cn

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

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  • 被引次数: 0
出版历程
  • 收稿日期:  2019-04-22
  • 修回日期:  2019-09-04

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
    基金项目:

    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)

    通讯作者: WU Bingfang.E-mail:wubf@radi.ac.cn

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.

English Abstract

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]. 中国地理科学, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
引用本文: 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]. 中国地理科学, 2020, 30(3): 397-409. doi: 10.1007/s11769-020-1119-y
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
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