Spatial-Temporal Analysis of Land Cover Change and Flood Risk Detection in North Musi Rawas District, South Sumatra, Indonesia: Approach Gis and Sentinel Imagery
Main Article Content
This study aims to analyze the dynamics of land cover changes in North Musi Rawas Regency in the period 2017–2023 and triggers for flood risk using remote sensing and Geographic Information Systems (GIS) approaches. The research methods include Sentinel-2 satellite imagery classification using supervised classification techniques, accuracy validation, and spatial-temporal analysis to identify land conversion patterns and their relationship to hydrological dynamics. The results of the analysis show significant conversion of agricultural land vegetation (9,769 ha), built-up land (194 ha), shrubs (15,011 ha), and swamps (514.17 ha), which are driven by urbanization factors, expansion of the agricultural sector, and environmental degradation. These changes contribute to increased flood risk, with the area affected by the medium risk category reaching 133,586 ha, especially in Karang Dapo, Rupit, and Rawas Ilir Districts, while the low-risk area covers 31,397 ha. Meanwhile, areas with better vegetation cover show higher flood mitigation capacity. This study confirms that changes in land cover have a close correlation with increased flood risk, so that land management policies based on spatial data and the use of remote sensing technology are needed to support disaster mitigation and adaptation strategies more effectively
Keywords:
Land cover, flood risk, remote sensing, Geographic Information System (GIS), land conversion, Sentinel-2, Musi Rawas Utara
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Junaid, M., Sun, J., Iqbal, A., Sohail, M., Zafar, S., & Khan, A. A. (2023). Mapping LULC Dynamics and Its Potential Implication on Forest Cover in Malam Jabba Region With Landsat Time Series Imagery and Random Forest Classification. Sustainability, 15(3), 1858. https://doi.org/10.3390/su15031858
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Lambin, E. F., & Meyfroidt, P. (2010). Land use transitions: Socio-ecological feedback versus socio-economic change. Land Use Policy, 27(2), 108–118.
Lefebvre, A., Sannier, C., & Corpetti, T. (2016). Monitoring urban areas with Sentinel-2A data: Application to the update of the Copernicus high resolution layer imperviousness degree. Remote Sensing, 8(7), 606.
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Li, J., & Roy, D. P. (2017). A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9(9), 902.
Liping, C., Yujun, S., & Saeed, S. (2018). Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, Jiangle, China. PloS One, 13(7), e0200493.
Liu, D., Zheng, X., Zhang, C., & Wang, H. (2017). A new temporal–spatial dynamics method of simulating land-use change. Ecological Modelling, 350, 1–10.
Liu, Y., Gong, W., Hu, X., & Gong, J. (2018). Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data. Remote Sensing, 10(6), 946.
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