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Nurlina Nurlina
Physics Study Program, Faculty of Mathematics and Natural Sciences, Universitas Lambung Mangkurat, Indonesia

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Spatial Assessment of Environmental Quality in Hulu Sungai Tengah Regency Using the Remote Sensing Ecological Index (RSEI) Abraham Prakoso; Ichsan Ridwan; Mufida Asyari; Nurlina Nurlina
Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat Vol 23, No 2 (2026): Jurnal Fisika Flux: Jurnal Ilmiah Fisika FMIPA Universitas Lambung Mangkurat
Publisher : Lambung Mangkurat University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/flux.v23i2.25129

Abstract

Increasing ecological pressures in Hulu Sungai Tengah Regency (HST), South Kalimantan — driven by deforestation, built-up land expansion, and land cover change — necessitate an objective, spatially-based environmental quality monitoring approach. This study evaluates the ecological quality of HST Regency using the Remote Sensing Ecological Index (RSEI) derived from Sentinel-2 imagery, integrating NDVI, NDMI, and NDBSI through Principal Component Analysis (PCA), replacing the conventional Land Surface Temperature (LST) component with NDMI since Sentinel-2 lacks thermal bands. Annual median composite Sentinel-2 Level-2A imagery for 2019 and 2023 was processed via Google Earth Engine. PC1 explained over 90% of data variance in both years, validating its use as the RSEI basis. PC1 was dominated by NDMI and NDVI in 2019, reflecting natural factors, but shifted toward NDBSI in 2023, indicating growing anthropogenic pressure. Spatially, most of the area was classified as Good–Very Good in both years, with the Very Good class expanding in 2023. This aggregate improvement is more likely attributable to the La Niña climate phase, which temporarily elevated moisture levels rather than reflecting permanent vegetation recovery — as evidenced by a concurrent decline in tree cover. Field validation across all RSEI classes confirmed strong agreement between image classification and actual biophysical conditions. This study affirms that LST-independent Sentinel-2-based RSEI is a reliable ecological monitoring tool for highly heterogeneous tropical regions and provides a scientific basis for sustainable regional development planning.