4.7 Article

Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 627, 期 -, 页码 1264-1275

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2018.01.290

关键词

Deforestation susceptibility; Frequency ratio model; Forest fragmentation; Remote sensing and GIS; Rudraprayag District

资金

  1. National Natural Science Foundation of China [41431177, 41601413]
  2. Natural Science Research Program of Jiangsu [BK20150975, 14KJA170001]
  3. National Basic Research Program of China [2015CB954102]
  4. Vilas Associate Award
  5. Hammel Faculty Fellow Award
  6. Manasse Chair Professorship from the University of Wisconsin-Madison
  7. One-Thousand Talents Program of China

向作者/读者索取更多资源

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 112.5 km(2) forest area has been deforested over the last 25 years. 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 ratiomodel, 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. (c) 2018 Elsevier B.V. All rights reserved.

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