Decadal land cover change dynamics in Bhutan

J Environ Manage. 2015 Jan 15:148:91-100. doi: 10.1016/j.jenvman.2014.02.014. Epub 2014 Mar 26.

Abstract

Land cover (LC) is one of the most important and easily detectable indicators of change in ecosystem services and livelihood support systems. This paper describes the decadal dynamics in LC changes at national and sub-national level in Bhutan derived by applying object-based image analysis (OBIA) techniques to 1990, 2000, and 2010 Landsat (30 m spatial resolution) data. Ten LC classes were defined in order to give a harmonized legend land cover classification system (LCCS). An accuracy of 83% was achieved for LC-2010 as determined from spot analysis using very high resolution satellite data from Google Earth Pro and limited field verification. At the national level, overall forest increased from 25,558 to 26,732 km(2) between 1990 and 2010, equivalent to an average annual growth rate of 59 km(2)/year (0.22%). There was an overall reduction in grassland, shrubland, and barren area, but the observations were highly dependent on time of acquisition of the satellite data and climatic conditions. The greatest change from non-forest to forest (277 km(2)) was in Bumthang district, followed by Wangdue Phodrang and Trashigang, with the least (1 km(2)) in Tsirang. Forest and scrub forest covers close to 75% of the land area of Bhutan, and just over half of the total area (51%) has some form of conservation status. This study indicates that numerous applications and analyses can be carried out to support improved land cover and land use (LCLU) management. It will be possible to replicate this study in the future as comparable new satellite data is scheduled to become available.

Keywords: Harmonized legend; LCLU management; Land cover; Landsat; Satellite data.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Bhutan
  • Conservation of Natural Resources*
  • Ecosystem
  • Environmental Monitoring / methods*
  • Geographic Information Systems
  • Humans
  • Remote Sensing Technology
  • Trees*
  • Urbanization / trends*