Changes in Remotely Sensed Vegetation Growth Trend in the Heihe Basin of Arid Northwestern China

PLoS One. 2015 Aug 18;10(8):e0135376. doi: 10.1371/journal.pone.0135376. eCollection 2015.

Abstract

The Heihe River Basin (HRB) is the second largest inland river basin in China, characterized by high diversity in geomorphology and irrigated agriculture in middle reaches. To improve the knowledge about the relationship between biotic and hydrological processes, this study used Global Inventory Modeling and Mapping Studies Normalized Difference Vegetation Index (NDVI) data (1982-2006) to analyze spatiotemporal variations in vegetation growth by using the Mann-Kendall test together with Sen's slope estimator. The results indicate that 10.1% and 1.6% of basin area exhibit statistically significant (p < 0.05) upward and downward trends, and maximum magnitude is 0.066/10a and 0.026/10a, respectively. More specifically, an increasing trend was observed in the Qilian Mountains and Hexi Corridor and a decreasing trend detected in the transitional region between them. Increases in precipitation and temperature may be one possible reason for the changes of vegetation growth in the Qilian Mountains. And decreasing trend in transitional region may be driven by the changes in precipitation. Increases of irrigation contribute to the upward trend of NDVI for cropland in the Hexi Corridor, reflecting that agricultural development becomes more intensive. Our study demonstrates the complexity of the response of vegetation growth in the HRB to climate change and anthropogenic activities and correspondingly adopting mechanistic ecological models capable of describing both factors is favorable for reasonable predictions of future vegetation growth. It is also indicated that improving irrigation water use efficiency is one practical strategy to balance water demand between human and natural ecosystems in the HRB.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture*
  • China
  • Climate Change*
  • Ecosystem*
  • Environmental Monitoring
  • Humans
  • Models, Theoretical*
  • Seasons
  • Temperature
  • Water Resources*

Grants and funding

This study was supported by the National Natural Science Foundation of China (Grant No. 91125015, 41201018), National Key Technology R&D Program of China (Grant No. 2013BAB05B04), and Fundamental Research Funds for the Central Universities. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.