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.
The Vu Gia - Thu Bon river basin is one of the largest basins in Vietnam. Research and assessment of this potential basin is a great interest to scientists and regulators. One of the important studies is the evaluation of morphological parameters of the basin. The morphological parameters represent water resources and at the same time are one of the factors that help researchers give a comprehensive view of the basin, assessing the factors related to the direction of the flow, the flow rate or hazards throughout the basin. Therefore, this paper is an attempt to evaluate the morphology of Vu Gia - Thu Bon river basin using DEM SRTM (30 m) data in GIS. This analysis can be achieved through the measurement of linear aspects, aerial aspects and relief aspects of the drainage basin. The results of the study show that stream order ranges from first to sixth order with a total stream length of 1024, a total length of 3183.2 km. Basin was divided into three subregions: upland, midland, and lowland. Those represent 66,9%, 26,0% and 7,1% percent of the region’s total area respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.