Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming

Environ Monit Assess. 2016 Mar;188(3):186. doi: 10.1007/s10661-016-5196-4. Epub 2016 Feb 23.

Abstract

The eastern Himalayas, especially the Yarlung Zangbo Grand Canyon Nature Reserve (YNR), is a global hotspot of biodiversity because of a wide variety of climatic conditions and elevations ranging from 500 to > 7000 m above sea level (a.s.l.). The mountain ecosystems at different elevations are vulnerable to climate change; however, there has been little research into the patterns of vegetation greening and their response to global warming. The objective of this paper is to examine the pattern of vegetation greening in different altitudinal zones in the YNR and its relationship with vegetation types and climatic factors. Specifically, the inter-annual change of the normalized difference vegetation index (NDVI) and its variation along altitudinal gradient between 1999 and 2013 was investigated using SPOT-VGT NDVI data and ASTER global digital elevation model (GDEM) data. We found that annual NDVI increased by 17.58% in the YNR from 1999 to 2013, especially in regions dominated by broad-leaved and coniferous forests at lower elevations. The vegetation greening rate decreased significantly as elevation increased, with a threshold elevation of approximately 3000 m. Rising temperature played a dominant role in driving the increase in NDVI, while precipitation has no statistical relationship with changes in NDVI in this region. This study provides useful information to develop an integrated management and conservation plan for climate change adaptation and promote biodiversity conservation in the YNR.

Keywords: Climate change; Elevation; Mountain ecosystem; Normalized difference vegetation index (NDVI); Southeastern Tibet; Vegetation greening.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biodiversity
  • China
  • Climate Change
  • Ecosystem
  • Environmental Monitoring*
  • Global Warming*
  • Plants*
  • Satellite Imagery*
  • Temperature