This study underscores that the sensitivity of vegetation cover to climate change is spatially differentiated at the regional scale. The central river valley dominated by herbaceous swamp is more sensitive to temperature-vegetation dryness index. Relatively, the northern part of this area is more affected by a combination of precipitation and temperature, while the southern plains dominated by desert steppe are more sensitive to precipitation. Temperate steppe in the northern mountain and herbaceous swamp and broadleaf forest in the river valley, where the normalized difference vegetation index is the highest, show the strongest sensitivity, while the desert steppe in the northern plain, where the NDVI is the lowest, shows the strongest memory effect (or the strongest resilience). Sensitivity of vegetation increases with the increase of coverage. The results show that 88.09% of vegetated pixels show an increasing trend in vegetation coverage, and the sensitivity of vegetation to climate change presents spatial heterogeneity. It is available at and LST datasets can be downloaded at. The selected time range is Januto December 31, 2018. The selected product is MOD13Q1 (16-day NDVI synthetic data). Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI product (MOD13Q1) covering the period of 2000-2018 were used to determine the variation of vegetation cover.
#Swamp song mvc registration#
The selected time range is Januto December 31, 2018.ĭue to copyright restrictions, the CMDC requires registration to access the dataset. Monthly precipitation and air temperature datasets of 72 meteorological stations in the territory of Xinjiang province of China, where the study area located, were collected from the China Meteorological Data Service Center (CMDC). The following information was supplied regarding data availability:
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