Journal Article

Examining the drivers of forest cover change and deforestation susceptibility in Northeast India using multicriteria decision-making models

Baraj, Biswaranjan, Guria, Rajkumar, Santos, Celso Augusto Guimarães, da Silva, Richarde Marques, Bhutia, Karma Detsen Ongmu, Mishra, Manoranjan, Goswami, Shreerup

2024

Overview

The increasing rates of forest cover change and heightened vulnerability to deforestation present significant environmental challenges in Northeast India. This study investigates the dynamics of forest cover change and susceptibility to deforestation in this region from 2001 to 2021, utilizing data from the Hansen Global Forest Change (HGFC) product on the Google Earth Engine (GEE) platform. A suite of multicriteria decision-making (MCDM) models—including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), Simple Additive Weighting (SAW), Evaluation Based on Distance from Average Solution (EDAS), and Weighted Aggregates Sum Product Assessment (WASPAS)—was employed to assess changes in forest cover and deforestation susceptibility across varied zones. Multicollinearity tests confirmed the relevance of the factors influencing deforestation. Statistical validations, such as the Wilcoxon Signed Ranks Test, underscored the models' robustness, revealing statistically significant outcomes. Additionally, Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analysis demonstrated the superior fit of the VIKOR model (AUC = 0.938) compared to SAW (AUC = 0.901), EDAS (AUC = 0.895), and WASPAS (AUC = 0.864) in predicting current deforestation susceptibility. Validation affirmed the reliability of all MCDM methods, with VIKOR displaying high sensitivity (True Positive Rate, TPR = 0.878) and optimal AUC (0.938). Correlation analyses among the models identified significant inter-relationships, notably a positive correlation between EDAS and SAW, and a negative correlation between VIKOR and SAW. The regions of Assam, Nagaland, Mizoram, and Arunachal Pradesh were identified as experiencing significant forest cover loss, indicating a pronounced susceptibility to future deforestation. These findings underscore the need for immediate intervention to address this critical environmental issue. Graphical Abstract

• Publisher: Springer International Publishing,Springer Nature B.V
• Subject: Atmospheric Protection/Air Quality Control/Air Pollution / Conservation of Natural Resources / Correlation analysis / Decision Making / Deforestation / Earth and Environmental Science / Ecology / Ecotoxicology / Environment / Environmental Management / Environmental Monitoring - methods / Forestry / Forests / India / Internet / Models, Theoretical / Monitoring/Environmental Analysis / multi-criteria decision making / Multiple criteria decision making / Multiple criterion / Statistical analysis / Statistical models / Susceptibility