This work mainly focused on deforestation susceptibility (DS) assessment and its prediction based on statistical models (FR, LR & AHP) in the Saranda forest, India. Also, efforts had been made to quantify the effect of mining on deforestation. We had considered twenty-five (twenty present and five predicted) causative variables of deforestation, including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The predicted variables have been generated from different simulation models. Also, very high-resolution, Google Earth imagery have been used in time series analysis for deforestation from 1987 to 2020 data and generated dependent variable. On deforestation analysis, it was observed that a total of 4197.84 ha forest areas were lost in the study region due to illegal mining, agricultural and tribal people allied activities. The DS results have shown that of total existing forest area, 11.22% area were under very high, 16.08% under high, 16.18% under moderate, 24.25% under low, and 32.27% falls very low categories. According to the DS assessment and predicted results, the very high susceptibility classes were found at and close to mines, agricultural, roads and settlement's surrounding sites. The sensitivity analysis results also shown that some causative variables (maximum temperature (2.95%), minimum temperature (0.51%), rainfall (2.69%), LST (4.56%), hot spot (7.36%), aspect (1.14%), NDVI (2.64%), forest density (3.78%), lithology (3.26%), geomorphology (3.00%), distance from agricultural (19.40%), soil type (2.05%), solar radiation (5.97%), LULC (3.26%), drought (3.16%), altitude (2.85%), slope (5.97%), distance from mines (18.05%), roads (2.17%), and settlements (5.18%)) were more sensitive to deforestation. Most of the sensitive parameters showed a positive correlation with DS. The AUC values of the ROC curve had shown a better fit for AHP (0.72) than (0.69) FR and LR (0.68) models for present DS results. The correlation results had shown a good inverse relationship between DS and distance from mines and foliar dust concentration. This work will espouse the future work in the effective planning and management of the mining-affected forest region and predicted deforestation susceptibility would be helpful for forest ecosystem study and policymaking.

这项工作主要集中在对印度萨兰达森林的森林砍伐敏感性（DS）评估及其预测基于统计模型（FR，LR和AHP）的基础上。此外，还努力量化采矿对毁林的影响。我们已经考虑了25种（目前有20种，预计有5种）毁林的因果变量，包括气候，自然或地貌，林业，地形，环境和人为因素。预测变量已从不同的仿真模型生成。同样，从1987年到2020年的数据中，非常高分辨率的Google Earth影像已用于时间序列分析中，用于森林砍伐数据并生成因变量。在森林砍伐分析中，发现由于非法采矿，研究区域总共损失了4197.84公顷森林，农业和部落人民的联合活动。DS的结果表明，在现有森林总面积中，极高类别的森林面积为11.22％，极高森林为16.08％，中度森林为16.18％，低度森林为24.25％，极低森林率为32.27％。根据DS评估和预测结果，在矿山，农业，道路和居民点周围的地点及其附近发现了很高的敏感性等级。敏感性分析结果还显示出一些致因变量（最高温度（2.95％），最低温度（0.51％），降雨（2.69％），LST（4.56％），热点（7.36％），长宽比（1.14％）， NDVI（2.64％），森林密度（3.78％），岩性（3.26％），地貌（3.00％），与农业的距离（19.40％），土壤类型（2.05％），太阳辐射（5.97％），LULC（3.26） ％），干旱（3.16％），海拔（2.85％），坡度（5.97％），距矿山（18.05％），道路（2.17％）和居民区（5.18％）的距离对森林砍伐更敏感。大多数敏感参数与DS呈正相关。对于目前的DS结果，ROC曲线的AUC值显示的AHP（0.72）比（0.69）FR和LR（0.68）模型更好。相关结果表明，DS与距矿山的距离与叶面粉尘浓度之间具有良好的反比关系。这项工作将支持在受矿业影响的森林地区的有效规划和管理方面的未来工作，并且预测的毁林敏感性将有助于森林生态系统的研究和决策。对于目前的DS结果，ROC曲线的AUC值显示的AHP（0.72）比（0.69）FR和LR（0.68）模型更好。相关结果表明，DS与距矿山的距离与叶面粉尘浓度之间具有良好的反比关系。这项工作将支持在受矿业影响的森林地区的有效规划和管理方面的未来工作，并且预测的毁林敏感性将有助于森林生态系统的研究和决策。对于目前的DS结果，ROC曲线的AUC值显示的AHP（0.72）比（0.69）FR和LR（0.68）模型更好。相关结果表明，DS与距矿山的距离与叶面粉尘浓度之间具有良好的反比关系。这项工作将支持在受矿业影响的森林地区的有效规划和管理方面的未来工作，并且预测的毁林敏感性将有助于森林生态系统的研究和决策。