Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India.
Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Nanjing, 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, Jiangsu 210023, China.
Sci Total Environ. 2018 Jun 15;627:1264-1275. doi: 10.1016/j.scitotenv.2018.01.290. Epub 2018 Feb 7.
This study aimed to model deforestation susceptibility in forest ecosystem of Rudraprayag district, India. For this purpose, site-specific physical (slope angle, slope aspect, altitude, annual average rainfall, soil texture, soil depth), and anthropogenic (population distribution, distance from road, distance from settlement, proximity to agricultural land) deforestation conditioning factors were chosen. Landsat TM and OLI images for 1990 and 2015 were utilized to evaluate the changes in forest cover. The frequency ratio model was used for deforestation susceptibility mapping. The extent of deforestation was examined by overlaying forest fragmentation map and deforestation susceptibility map. The results showed that about 112.5km forest area has been deforested over the last 25years. Of the total existing forest, nearly 10% area falls under very high, 17% under high and 30% under moderate deforestation susceptibility categories. Patch, edge and perforated have influenced high (64%) and very high (81%) deforestation susceptibility zones. The integrated methodology involving frequency ratio model, fragmentation approach and remote sensing and GIS techniques has proved useful in analyzing deforestation susceptibility and identifying its causative factors. Thus, the methodology adopted in this study can best be utilized for effective planning and management of forest ecosystem.
本研究旨在对印度鲁德拉普拉亚格地区的森林生态系统的森林砍伐易感性进行建模。为此，选择了特定地点的物理因素（坡度角、坡度方向、海拔、年平均降雨量、土壤质地、土壤深度）和人为因素（人口分布、与道路的距离、与定居点的距离、与农田的接近度）作为森林砍伐的诱发因素。利用 1990 年和 2015 年的 Landsat TM 和 OLI 图像评估森林覆盖的变化。利用频率比模型进行森林砍伐易感性制图。通过叠加森林破碎化图和森林砍伐易感性图来检查森林砍伐的程度。结果表明，在过去的 25 年里，大约有 112.5 公里的森林面积被砍伐。在现有的森林中，近 10%的面积属于极高、17%的面积属于高和 30%的面积属于中等等级的森林砍伐易感性。斑块、边缘和穿孔对高（64%）和极高（81%）森林砍伐易感性区域有影响。综合使用频率比模型、破碎化方法以及遥感和 GIS 技术的方法，已被证明对分析森林砍伐易感性和确定其成因因素非常有用。因此，本研究采用的方法最适合于森林生态系统的有效规划和管理。
