Spatiotemporal dynamics of vegetation density in moramo district protected forest: A remote sensing approach - Gandri, La; Fatahuddin, Muhammad; Bana, Sahindomi; Hasani, Umar Ode; Sakti, Abdul; Fitriani, Dewi; Ahmaliun, La De; Muhsimin; Fitriani, Vivi (Institute for Advanced Science, Social, and Sustainable Future, 2025)
Background: Protected forests in the Moramo District play a critical role in maintaining ecosystem balance, but they are increasingly threatened by human activities such as illegal logging and land use change. Vegetation density shifts can disrupt ecosystem functions, particularly the hydrological cycle. This study aims to analyze spatial and temporal changes in vegetation density in the Moramo District Protected Forest using remote sensing. Methods: To detect vegetation density changes, the NDVI (Normalized Difference Vegetation Index) algorithm was employed using satellite imagery from Landsat OLI 8 (2013 and 2018) and Landsat OLI 9 (2023), processed with GIS software. NDVI values range from -1 to 1, allowing for vegetation condition assessment based on spectral reflectance. Findings: Results show a degradation trend in dense vegetation, with a decrease of 67.25 ha (2.86%) during 2013–2018 and 289.11 ha (12.31%) during 2018–2023. Conversely, moderately dense vegetation increased by 68.45 ha (2.91%) and 300.21 ha (12.78%) over the same periods, indicating signs of vegetation regeneration. Conclusion: Despite some vegetation recovery, forest ecosystems continue to face high pressure. More adaptive conservation strategies supported by spatial monitoring are needed to reduce degradation and support long-term sustainability. Novelty/Originality of this article: This study uniquely integrates a multi-temporal NDVI-based approach with socio-ecological analysis and GIS tools to monitor vegetation dynamics. It offers valuable insights for adaptive forest management in the Moramo District Protected Forest, an area previously lacking detailed environmental change analysis.