Integrating Remote Sensing and Spatial Data for Ecological Sustainability through Spatio-temporal Analysis - Singgalen, Yerik Afrianto (STMIK Budi Darma, 2024)
This research underscores the pivotal role of integrating spatial data and remote sensing technologies within a spatio-temporal analysis framework for regional development planning. Analyzing NDVI, NDBI, and SAVI values from 2013, 2018, and 2024 provided significant insights into vegetation health, urbanization, and soil conditions on Kumo Island. The NDVI values exhibited changes from a minimum of -0.0549, a mid-value of 0.1782, and a maximum of 0.4690 in 2013 to a minimum of 0.2456, a mid-value of 0.8296, and a maximum of 0.9416 in 2024. Similarly, the NDBI values shifted from a minimum of -0.8734, a mid-value of -0.5779, and a maximum of 0.0009 in 2013 to a minimum of -0.6561, a mid-value of -0.4304, and a maximum of 0.0247 in 2024. The SAVI values showed notable changes from a minimum of -0.0365, a mid-value of 0.1245, and a maximum of 0.3814 in 2013 to a minimum of 0.1138, a mid-value of 0.4953, and a maximum of 0.6160 in 2024. These findings highlight the importance of ecological sustainability in decision-making processes, demonstrating how advanced spatial analysis within a spatio-temporal framework can effectively monitor and manage land use changes. The urgency of this research lies in addressing rapid environmental changes and escalating human activities, necessitating timely and accurate monitoring techniques. The study reveals the utility of the NDVI, NDBI, and SAVI indices in assessing vegetation health, urbanization, and soil conditions, which are instrumental in identifying trends and informing sustainable development strategies. The research advocates for the continued use of remote sensing and spatial data to ensure balanced and informed regional development, emphasizing the necessity of sustainable practices to preserve ecological integrity while supporting socio-economic growth. Integrating remote sensing into the decision-making process enhances the accuracy and reliability of spatial data, leading to more effective and responsible regional development