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Application of Cloud-Based Remote Sensing With Google Earth Engine in Land Cover Change Analysis
Abstract
This chapter reviews research on the Google Earth Engine (GEE) platform and includes a case study in Vietnam. The literature highlights GEE's transformative role in geo-big data analytics, with its scalable and cost-effective processing of large datasets, despite some limitations in spatial functions and algorithms. The study employed GIS and remote sensing with GEE to generate land cover maps for 1985, 1995, 2005, 2015, and 2022, analyzing changes over this period. Using Landsat imagery and the random forest algorithm, the study achieved high classification accuracy, with overall accuracies above 96% and Kappa coefficients over 0.95. Findings show a shift from bare land and forest to increased crop areas and reduced forest cover due to socio-economic factors. Water bodies initially shrank but later expanded with irrigation development. The study provides insights for land management, while noting limitations like satellite resolution and weather conditions, and suggests future research should incorporate high-resolution data and additional factors affecting land cover changes.
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