Open AccessLand Cover Classification Based on Cloud Computing Platform
Author(s) -
Nghia Viet Nguyen,
Thu Hoai Thi Trinh,
H.T.M. Pham,
Trang Thu Thi Tran,
Thi Lan Pham,
Cuc Nguyen
Publication year - 2020
Publication title -
xi'nan jiaotong daxue xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 21
ISSN - 0258-2724
DOI - 10.35741/issn.0258-2724.55.2.61
Subject(s) - land cover , cloud computing , cloud cover , cover (algebra) , remote sensing , software , computer science , residence , field (mathematics) , land use , database , environmental science , hydrology (agriculture) , geography , engineering , operating system , civil engineering , mathematics , mechanical engineering , demography , sociology , pure mathematics , geotechnical engineering
Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.