Nationwide classification of forest types of India using remote sensing and GIS

Environ Monit Assess. 2015 Dec;187(12):777. doi: 10.1007/s10661-015-4990-8. Epub 2015 Nov 28.

Abstract

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.

Keywords: Classification; Forest type; India; Land cover; Mapping; Remote sensing; Vegetation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Carbon
  • Climate
  • Conservation of Natural Resources / methods*
  • Environmental Monitoring / methods*
  • Forests*
  • Geographic Information Systems*
  • India
  • Remote Sensing Technology*
  • Seasons
  • Trees

Substances

  • Carbon