Integrating Statistical Analysis for Standardized Remote Sensing Eco-Environment Index Comparisons Under Land Use Land Cover Constraints: A Case Study of Blida Province, Algeria

The Remote Sensing Ecological Index (RSEI) is a crucial tool for assessing ecological environmental quality and supporting sustainable land management. Different weighting methods influence RSEI results, yet existing research primarily compares techniques like Principal Component Analysis (PCA), the Analytical Hierarchy Process (AHP), and entropy-based methods without fully considering Land Use and Land Cover (LULC) variations. This study investigates whether RSEI differences across weighting techniques are statistically significant and how LULC types influence RSEI results. We compare objective (PCA-based RSEI), subjective (RSEI using AHP), and combined methods, such as the entropy-based Knowledge Granulation Entropy (RSEI_KGE) and a hybrid PCA-AHP approach (RSEI_PCA-AHP). These techniques are applied to four LULC types: urban areas, bare land, farmland, and forests in Blida, Algeria. By applying statistical analyses, we assess the reliability of different techniques and their sensitivity to LULC variations. Findings will contribute to improving RSEI-based assessments by clarifying the impact of weighting methods and land cover types on ecological evaluations, ultimately enhancing ecological monitoring and decision-making for better environmental management.