A synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africa

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dc.contributor.authorZeng, Hongweien_ZA
dc.contributor.authorWu, Bingfangen_ZA
dc.contributor.authorWang, Shuaien_ZA
dc.contributor.authorMusakwa, Walteren_ZA
dc.contributor.authorTian, Fuyouen_ZA
dc.contributor.authorMashimbye, Zama Ericen_ZA
dc.contributor.authorPoona, Niteshen_ZA
dc.contributor.authorSyndey, Mavengahamaen_ZA
dc.date.accessioned2022-05-31T13:27:48Z
dc.date.available2022-05-31T13:27:48Z
dc.date.issued2020-07-07
dc.descriptionCITATION: Zeng, H. et al. 2020. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science. 30: 397–409. doi:10.1007/s11769-020-1119-y
dc.descriptionThe original publication is available at https://www.springer.com/journal/11769
dc.description.abstractThis 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.en_ZA
dc.description.urihttps://link.springer.com/article/10.1007/s11769-020-1119-y
dc.description.versionPublishers version
dc.format.extent13 pages : illustrations
dc.identifier.citationZeng, H. et al. 2020. A Synthesizing Land-cover Classification Method Based on Google Earth Engine: A Case Study in Nzhelele and Levhuvu Catchments, South Africa. Chinese Geographical Science. 30: 397–409. doi:10.1007/s11769-020-1119-y
dc.identifier.issn1993-064X (online)
dc.identifier.issn1002-0063 (print)
dc.identifier.otherdoi:10.1007/s11769-020-1119-y
dc.identifier.urihttp://hdl.handle.net/10019.1/125283
dc.language.isoen_ZAen_ZA
dc.publisherSpringer
dc.rights.holderScience Press
dc.subjectLand-cover -- Classificationen_ZA
dc.subjectLand-use -- Classificationen_ZA
dc.subjectPercentile compositeen_ZA
dc.subjectLandsat 8en_ZA
dc.subjectLandsat satellitesen_ZA
dc.subjectVegetation classificationen_ZA
dc.subjectForest managementen_ZA
dc.subjectForests and forestry -- Remote sensingen_ZA
dc.subjectGoogle Earth Engine (GEE)en_ZA
dc.subjectImage processing -- Digital techniquesen_ZA
dc.subjectSentinel -1en_ZA
dc.subjectSyntehetic Aperture Radaren_ZA
dc.subjectSAR (Synthetic aperture radar)en_ZA
dc.subjectLimpopo (South Africa)en_ZA
dc.subjectNzhele and Levhuvu Catchmentsen_ZA
dc.titleA synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africaen_ZA
dc.typeArticleen_ZA
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