Abstract
Land use land cover (LULC) change has become a crucial topic that needs to be addressed when the studying global and local sustainable development. In this research, time-series of Sentinel-2 images from 2019 to 2020 are used to derive LULC change in Mu Cang Chai (MCC) and Van Yen (VY) districts, Yen Bai province, Vietnam. We identified seven main land cover types and collected reference data from visual interpretation using Google Earth. The random forest (RF) classification algorithm is applied to construct the classified LULC map in these regions of Yen Bai province. The classification accuracy of the method is evaluated using producer’s accuracy, user’s accuracy, overall accuracy, and Kappa coefficient. We obtain a high overall accuracy (90.7%) with a corresponding Kappa coefficient of 0.85 for the classification in 2019. In the case of 2020, overall classification accuracy reaches about 91.1% and 0.87 of the Kappa coefficient. Then, the LULC change area in the period 2019–2020 of the study area is evaluated and discussed by using the transition matrix of LULC.
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Acknowledgements
The work is partially funded by the Italian Ministry of Foreign Affairs and International Cooperation within the project “Geoinformatics and Earth Observation for Landslide Monitoring”—CUP D19C21000480001 (Italian side) and partially funded by Ministry of Science and Technology of Vietnam (MOST) (Vietnamese side) by the bilateral scientific research project between Vietnam and Italy, CODE: NĐT/IT/21/14.
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Truong, X.Q. et al. (2023). Random Forest Analysis of Land Use and Land Cover Change Using Sentinel-2 Data in Van Yen, Yen Bai Province, Vietnam. In: Nguyen, L.Q., Bui, L.K., Bui, XN., Tran, H.T. (eds) Advances in Geospatial Technology in Mining and Earth Sciences. GTER 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-20463-0_27
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DOI: https://doi.org/10.1007/978-3-031-20463-0_27
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