Exploratory of Ecological Quality from Remote-Sensing Ecological Index and Drought Hazard in Pekalongan Regency, Indonesia
DOI:
https://doi.org/10.25299/jgeet.2025.10.3.22396Keywords:
RSEI, Ecological quality, Remote sensing, Drought hazard, Chi-square, Climate changeAbstract
Climate change has intensified environmental hazards, including floods, landslides, and droughts, with Pekalongan Regency, Indonesia, emerging as a vulnerable region facing these multifaceted challenges. While flood-related studies dominate existing study, drought impacts remain understudied, despite their growing prevalence. Current climate hazard assessments in Pekalongan's adaptation plans rely heavily on historical data, limiting their predictive accuracy. This study addresses these gaps by developing a Remote Sensing Ecological Index (RSEI) model to evaluate ecological quality and its association with drought hazards, aligning with climate-resilient development objectives. The study employs Landsat imagery to construct RSEI using four key indicators: NDVI (greenness), WET (wetness), NDBSI (dryness), and LST (heat). Drought hazard data were derived from 2023 disaster records provided by Pekalongan's Regional Disaster Management Agency (BPBD). Statistical analysis using chi-square tests examined the relationship between RSEI components and drought hazard classes.Results demonstrate that RSEI's first principal component (PC1) effectively captures spatial ecological patterns, with southern regions (notably Petungkriyono's tropical rainforest) exhibiting "good" to "excellent" conditions, while northern urbanized areas score lower ("fair" to "poor"). PC1 shows a statistically significant association with drought hazard, unlike PC2 or PC3, suggesting its utility as a drought vulnerability indicator. However, the chi-square approach only identifies categorical relationships without quantifying effect strength or direction, highlighting methodological limitations.
This study contributes to climate adaptation science by validating RSEI's applicability for drought assessment in tropical coastal regions. Future study should incorporate ordinal regression or spatial modeling to enhance predictive capability. The findings support evidence-based policymaking for targeted mitigation in Pekalongan Regency and similar vulnerable regions, emphasizing the integration of ecological monitoring into climate adaptation frameworks.
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