4.5 Article

Identifying the spatiotemporal pattern and driving factors of vegetation dynamics in Shaanxi Province, China

Journal

GEOCARTO INTERNATIONAL
Volume 37, Issue 27, Pages 17890-17916

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/10106049.2022.2136257

Keywords

NDVI; climate change; Mann-Kendall test; Geodetector; Shaanxi Province

Funding

  1. Shaanxi Provincial Department of Education Scientific Research Project [18JS037]
  2. Young Talent fund of University Association for Science and Technology in Shaanxi, China [20180705]
  3. Natural Science Talent Project of Weinan Normal University [18ZRRC04]

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This study analyzed the spatiotemporal distribution characteristics and driving factors of vegetation dynamics in Shaanxi Province using the NDVI. The results showed that solar radiation, relative humidity, and land use were the main factors influencing vegetation changes, which is significant for ecosystem protection and vegetation degradation prevention.
Exploring the long-term spatiotemporal pattern of vegetation changes and its response to natural factors and human activities is vital for making informed decisions regarding ecosystem protection. The spatial-temporal distribution characteristics of vegetation dynamics and its driving factors in Shaanxi Province from 2000 to 2019 were analysed using the normalized difference vegetation index (NDVI). The explanatory power of solar radiation was the highest (63.45%), followed by relative humidity (59.07%), land use (56.73%), precipitation (52.17%), temperature (51.28%), and vegetation type (50.95%). In terms of climatic factors, the influences of solar radiation and relative humidity on vegetation changes were stronger than those of precipitation and temperature. The comprehensive impact of the pairwise factors was higher than those of the independent factors. Our results contribute to a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention remediation of vegetation degradation in fragile ecosystems.

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