Estimating Land Degradation Level Using Remote Sensing Data and Fuzzy AHP Technique

ABSTRACT

Land degradation is a critical global environmental concern that significantly impacts ecosystem services, agricultural productivity, weather patterns, human livelihoods and overall well‐being. Prompt and precise assessments of land degradation status are crucial for environmental governance and sustainable development. Remote sensing data can offer excellent capabilities for monitoring and estimating land degradation over vast areas. This study presents a comprehensive empirical framework for assessing land degradation levels. Landsat imagery, acquired for Spring 2022, in conjunction with supplementary datasets, is utilised by the Fuzzy Analytical Hierarchy Process (FAHP), based on results of a designed expert questionnaire, to compute the Land Degradation Index (LDI). Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), precipitation, slope and drainage density are used within the proposed methodology. The proposed methodology is implemented for Erbil Governorate, Iraq. Also, 80 soil samples collected from the field are used to validate the model results. The findings show that the proposed FAHP‐based model achieved an overall accuracy of 80% with a Kappa coefficient of 0.72. In addition, the proposed approach represents an improvement over two recent published articles that are replicated and implemented on the same case study. The results highlight the relevance and robustness of the FAHP‐based framework for assessing land degradation at regional scales, providing a novel decision‐support tool for transitioning towards sustainable land management and policy.