International Journal of Advance Computational Engineering and Networking (IJACEN)
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Statistics report
Jun. 2024
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 134
Paper Published : 1567
No. of Authors : 4088
  Journal Paper

Paper Title :
Land Use/Land Cover Classification in The Mekong River Basin, Thailand Using Google Earth Engine

Author :Perayatantianuparp

Article Citation :Perayatantianuparp , (2024 ) " Land Use/Land Cover Classification in The Mekong River Basin, Thailand Using Google Earth Engine " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 47-52, Volume-12,Issue-1

Abstract : Land use/ land cover (LULC) analysis has been greatly encouraged effective management of water resources, especially water-related disaster monitoring and water budget planning. The exploitation of Earth observation satellite images has been applied to support LULC classification in large areas or multi temporal assessment. Several techniques and tools have been developed to produce satellite based LULC mapping, howeverhigh-performance computing and specific software are the basic requirements for these processes. The Google Earth Engine (GEE), cloud computing platform offersmulti-propose processing fromlarge satellite image archives and libraries toenhanced opportunitiesfor earth observation studies including satellite based LULC mapping. This work presents land use analysis of the Mekong RiverBasin, Thailand on1 January to 31 March 2019 applying Earth observation satellite images acquired by optical and Synthetic Aperture Radar (SAR) instruments including Sentinel-1 and Sentinel-2. Advanced machine learning LULC classification algorithmsincludeSupport Vector Machine (SVM), Random Forest (RF) and Classification And Regression Trees (CART) are compared their results. The study has been carried out to identify the active and accurate algorithm for LULC mapping using GEE.The results show thathigher accuracies were produced when using integration of optical and SAR satellite images, twoaccurate LULC maps processed RFClassifierproduces an equally high accuracy with overall accuracy64.29%. The auxiliary datasets calculating from Sentinel-2 are not derived higher accuracy results in all applied classification algorithms. Keywords - Land Use/Land Cover (LULC) Classification, Google Earth Engine (GEE), Machine Learning Classification Algorithm

Type : Research paper

Published : Volume-12,Issue-1


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