文章基本信息

标题：Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine
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作者：Trida Ridho Fariz ; Ely Nurhidayati
期刊名称：Journal of Applied Geospatial Information
电子版ISSN：2579-3608
出版年度：2020
卷号：4
期号：2
页码：390-395
DOI：10.30871/jagi.v4i2.2256
语种：English
出版社：Politeknik Negeri Batam
摘要：Land coverage information is essential data in the management of watersheds. The challenge in providing land coverage information in the Kapuas watershed is the cloud cover and its significant area coverage,thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land coverage in the Kapuas watershed using machine learningbased classification on GEE. The process of mapping land coverage in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019,with mapped land coverage classes consisting of water bodies, vegetation,non-vegetated (barren land),and built-up area. Machine learning that tested included CART,Random Forest,GMO Max Entropy,SVM Voting,and SVM Margin. The results of this study indicate that the best machine learning in mapping land coverage in the Kapuas watershed is GMO Max Entropy,then CART. This research still has many limitations,especially mapped the covering land classes. So that research needs to be developed with more detailed land coverage classes, more diverse and multi-time input data.
关键词：Land coverage;Supervised classification;Machine learning;Google Earth Engine