Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.
Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.
J Environ Manage. 2021 Jul 1;289:112504. doi: 10.1016/j.jenvman.2021.112504. Epub 2021 Apr 8.
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印度萨兰达森林的基于统计模型（FR、LR 和 AHP）的森林砍伐易感性（DS）评估及其预测，同时努力量化采矿对森林砍伐的影响。我们考虑了二十五个（二十个现有和五个预测）森林砍伐的致因变量，包括气候、自然或地貌、林业、地形、环境和人为因素。预测变量是由不同的模拟模型生成的。此外，还使用了非常高分辨率的谷歌地球图像进行时间序列分析，以获取 1987 年至 2020 年数据的森林砍伐和生成的因变量。在森林砍伐分析中，观察到由于非法采矿、农业和部落人民的联合活动，研究区域共有 4197.84 公顷的森林面积流失。DS 结果表明，在总现有森林面积中，11.22%的面积属于极高、16.08%属于高、16.18%属于中、24.25%属于低、32.27%属于极低类别。根据 DS 评估和预测结果，极高易感性类别出现在矿区、农业、道路和定居点周围地区。敏感性分析结果还表明，一些致因变量（最高温度（2.95%）、最低温度（0.51%）、降雨量（2.69%）、LST（4.56%）、热点（7.36%）、方位（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%）、土地利用/土地覆盖（3.26%）、干旱（3.16%）、海拔（2.85%）、坡度（5.97%）、与矿区的距离（18.05%）、道路（2.17%）和定居点（5.18%））对森林砍伐更为敏感。大多数敏感参数与 DS 呈正相关。ROC 曲线的 AUC 值表明，AHP（0.72）模型比 FR（0.69）和 LR（0.68）模型更适合当前 DS 结果。相关性结果表明，DS 与矿区和叶面尘浓度之间存在良好的负相关关系。这项工作将为未来受采矿影响的森林区域的有效规划和管理提供支持，预测森林砍伐易感性将有助于森林生态系统研究和决策制定。
