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This study analyzed the area of Grebaštica, located in the Šibenik-Knin County, before and after a major fire that occurred on 13 July 2023, with the aim of calculating the extent of the burned area using Sentinel-2 satellite imagery and QGIS software. Three vegetation indices were used for the analysis: the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Normalized Burn Ratio (NBR). Low values of these indices indicate significant damage to vegetation and soil. The analysis revealed that the NBR index was the most effective for detecting burned areas due to the high reflectance of vegetation in the near-infrared and shortwave infrared regions of the electromagnetic spectrum, while the NDVI index proved to be the least effective. To calculate the area affected by the fire, the delta Normalized Burn Ratio (dNBR or ΔNBR) was also computed. Using the ΔNBR index, different levels of vegetation damage and fire hazard were identified. These indices enabled clear differentiation between damaged and undamaged areas, based on the specific reflectance characteristics of materials affected by fire. [ABSTRACT FROM AUTHOR]

• LULC changes of three decades from 1991 to 2021 were examined and predicted. • Multi-spectral remote sensing data and RFT were utilized to generate LULC maps. • ACR were considered to analyze the categorical land use change detection. • Impervious land has expanded 10 times within 30 years and will increase in the future. The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. [ABSTRACT FROM AUTHOR]