@ARTICLE{Thien_Bui_Bao_Multi-temporal_2024,
 author={Thien, Bui Bao and Phuong, Vu Thi},
 volume={vol. 73},
 number={No. 2},
 journal={Advances in Geodesy and Geoinformation},
 pages={e54},
 howpublished={online},
 year={2024},
 publisher={Polska Akademia Nauk/ Komitet Geodezji Polskiej Akademii Nauk; Polish Academy of Sciences / Commitee on Geodesy Polish Academy of Sciences},
 abstract={Understanding changes in land use and land cover (LULC) is crucial for effective land management, environmental planning, and decision-making. It helps identify areas of environmental concern, assess the impacts of human activities on ecosystems, and develop strategies for conservation efforts and sustainable land use. In this study, remote sensing and geographic information systems (GIS) were used to monitor LULC changes in Binh Duong province, Vietnam from 1988 to 2023. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1988 and 2004, and Landsat 9-OLI/TIRS for 2023) to detect and classify five main LULC types: arable land, barren land, built-up areas, forests and waterbodies. The classification accuracy was evaluated using kappa coefficients, which were 0.877, 0.894 and 0.908 for 1988, 2004, and 2023, respectively. During the period of 1988–2023, the forest, barren land, and waterbodies class areas decreased by 560.55 km2, 200.04 km2, and 19.68 km2, respectively. Meanwhile, the arable land and built-up areas classes increased by 343.80 km2 and 436.47 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) were used to quickly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose significant threats to the environment and the findings of this study can serve as valuable resources for future land management and planning in the region.},
 title={Multi-temporal analysis of land use and land cover change detection in Binh Duong province, Vietnam using geospatial techniques},
 type={Article},
 URL={http://czasopisma.pan.pl/Content/133647/PDF/e54_FINAL.pdf},
 doi={10.24425/agg.2024.150683},
 keywords={Landsat, land management, vegetation index, remote sensing, maximum likelihood classification},
}