Abstract

This paper focuses on forest health risk (FHR) assessment and prediction in the mining-affected forest region using AHP model based on multi-criteria analysis in a GIS platform. We considered a total twenty-eight (twenty two present and six predicted) causative parameters including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The assessment results of FHR show that of the total existing forest area, 2.85% area under very high, 13.63% high, 31.98% moderate, 32.68% low, and 18.87% are under very low categories. According to the assessment and prediction FHR results, the very high-risk classes were found at mines surrounding forest compartments. The sensitivity analysis showed that some factors were more sensitive to FHR. The correlation results showed a negative relationship between FHR and distance from mines and foliar dust concentration. This work will provide a basic guideline for effective planning and management in forestry studies for the mining-affected region.

摘要

本文重点研究了在 GIS 平台上基于多准则分析的 AHP 模型对采矿影响林区的森林健康风险 (FHR) 进行评估和预测。我们考虑了总共 28 个（现有 22 个和预测的 6 个）致病参数，包括气候、自然或地貌、林业、地形、环境和人为因素。FHR 评估结果表明，在现有森林总面积中，2.85% 的面积属于非常高，13.63% 属于高，31.98% 属于中等，32.68% 属于低，18.87% 属于非常低。根据评估和预测 FHR 结果，在森林隔间周围的矿井中发现了极高风险等级。敏感性分析显示，部分因素对FHR更为敏感。相关结果表明，FHR与矿井距离和叶面粉尘浓度呈负相关。这项工作将为受采矿影响地区的林业研究的有效规划和管理提供基本指南。