Assessment of land cover changes using sentinel-2 satellite image data: A case study of Thanh Hoa coastal area, Viet Nam

Abstract

Abstract Land use and management decisions heavily rely on the evaluating changes in land cover. Remote sensing techniques are becoming increasingly reliable and applicable, making them particularly useful for assessing and monitoring changes in land cover. In this study, the authors used Sentinel-2 MSI optical satellite image data and machine learning algorithms to classify and evaluate land cover changes in Thanh Hoa province’s coastal area between 2015 and 2023. Our research findings showed that Sentinel-2 MSI satellite image data can accurately interpret and classify land cover, with Kappa values ranging from 0.892 to 0.907. Furthermore, our findings indicated an increase in the area covered by build-up class. Meanwhile, vegetation cover and water surface class tend to decrease, especially the sharp decline of surface water. Research results help local policymakers develop land use plans in the direction of sustainable economic and social development.