Response of vegetation to water balance conditions at different time scales across the karst area of southwestern China-A remote sensing approach

Sci Total Environ. 2018 Dec 15:645:460-470. doi: 10.1016/j.scitotenv.2018.07.148. Epub 2018 Jul 18.

Abstract

This work identifies the vegetation communities, landform types and seasons in which vegetation is most sensitive to water imbalance in the karst area of southwestern China. The normalized difference vegetation index (NDVI) and standardized precipitation and evapotranspiration index (SPEI) were used to evaluate the effects of water balance conditions on vegetation in different seasons and at different time scales. During the growing seasons from 1982 to 2013, the vegetation growth in 79% of the study area was statistically significantly sensitive to the water balance condition (p < 0.05). The vegetation in the spring and autumn responded more visibly to water imbalances. The SPEI over the last 6 months was statistically significantly correlated with the monthly maximum NDVI during the growing season over the larger areas compared with the SPEI over other time periods. Therefore, the vegetation was most likely sensitive to six months of water imbalance in this area. Among the selected vegetation types, the shrubland and sparse woodland were the most sensitive to water imbalances, whereas grasslands and forests were less sensitive. The maximum correlation coefficient between the NDVI and SPEI for each karst landform type was statistically significantly different (p < 0.01). The vegetation in the peak-cluster depressions was the most sensitive to water imbalances, whereas the vegetation in the middle and high karst mountains was the least sensitive to water imbalances. Overall, although the climate of the karst region of southwestern China is humid and subtropical, the vegetation is still vulnerable to water imbalances in particular regions and soils.

Keywords: China; Correlation; Karst area; SPEI; Vegetation; Water balance.

MeSH terms

  • China
  • Climate
  • Environmental Monitoring / methods*
  • Forests
  • Remote Sensing Technology*