9 March 2018 Characterizing the responses of vegetation to climate change in the Tibet Plateau using remote sensing data
Author Affiliations +
It is of great significance to investigate the changes in vegetation and its response to climate change in Tibet due to the sensitivity and vulnerability of the area to climate change. The spatiotemporal pattern of the normalized difference vegetation index (NDVI) and its trends between 2001 and 2015 were depicted using NDVI from the moderate resolution imaging spectroradiometer (MODIS). The responses of vegetation to climatic variables were analyzed through linear regression and correlation analysis with tropical rainfall measuring mission precipitation data and MODIS land surface temperature (LST) data. The results showed that (1) the average annual NDVI gradually decreased from the southeast to the northwest in accordance with the variations in LST and precipitation, (2) the annual NDVI increased from 2001 to 2015 at a rate of 0.3  ×  10  −  3  per year. The LST exhibited an average annual increase of 0.05°C while precipitation remained relatively stable, (3) the correlation between NDVI and precipitation was positive in the central region, whereas it became negative in the southeast and northeast. The correlation between NDVI and LST was opposite of that between NDVI and precipitation, and (4) the increases in NDVI in the tropical monsoon rain forest and rain forest, subalpine coniferous forest, and alpine meadow vegetation types in the southeast depended more on LST than precipitation. In contrast, the increases in NDVI responded strongly to precipitation in the alpine bush and meadow, alpine grassland, alpine desert, and alpine desert steppe vegetation types in the northwest.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Chunchun An, Chunchun An, Jianrong Fan, Jianrong Fan, Yanfen Zhang, Yanfen Zhang, Dong Yan, Dong Yan, } "Characterizing the responses of vegetation to climate change in the Tibet Plateau using remote sensing data," Journal of Applied Remote Sensing 12(1), 016035 (9 March 2018). https://doi.org/10.1117/1.JRS.12.016035 . Submission: Received: 30 September 2017; Accepted: 30 January 2018
Received: 30 September 2017; Accepted: 30 January 2018; Published: 9 March 2018

Back to Top