A Novel Fuzzy Forest Health Index (FFHI) for Standardizing Stochastic Forest-Smart Mining

Abstract

The pressing concerns associated with climate change underscore the critical need for environmental conservation and sustainable resource management. As technological and industrial advancements continue to drive an escalating demand for materials, the extraction of which often involves mining, the imperative to explore novel methodologies for assessing and mitigating the environmental impact of such operations becomes evident. This study proposes a novel approach utilizing fuzzy logic to calculate the Forest Health Index (FHI), introducing both a Fuzzy Constructive FHI and a Fuzzy Destructive FHI index, each ranging from 0 to 100. The disparity between these indices, ranging from − 100 to 100, elucidates the overall forest health index. The study employs the Sungun copper mine as a case study, situated within the Arasbaran environmental protected area, which necessitates the application of forest-smart mining regulations and policies. To examine the impact of mining operations on forest health, remote sensing is employed to identify potential porphyry copper mineralization areas and to visualize deforestation trends at the Sungun copper mine from 2008 to 2023. Vegetation indices are utilized to estimate the Forest Health Index (FHI) through remote sensing methodologies, incorporating a combination of expert opinions and guest numbers to assess variables influencing the FHI. Results indicate that the Forest Health Index (FFHI) for Sungun is 2.1, with maximum and minimum FFHIs observed in Merian (37.92) and Nimba Range Mineral Province (NRMP) (-25.7), respectively. The outcomes emphasize the importance of implementing forest-smart mining practices to mitigate the adverse effects of mining activities on the Arasbaran forest and promote conditions conducive to forest health.