AUTHOR=Zhou Peng , Zhao Dongxue , Liu Xiao , Duo Linghua , He Bao-Jie 

TITLE=Dynamic Change of Vegetation Index and Its Influencing Factors in Alxa League in the Arid Area

JOURNAL=Frontiers in Ecology and Evolution

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.922739

DOI=10.3389/fevo.2022.922739

ISSN=2296-701X

ABSTRACT=Multiple factors are critical to influencing patterns of vegetation distribution, and studying their interactions can help improve our understanding of future vegetation dynamics, especially in arid areas. Using the normalized vegetation index (NDVI) data set (2000-2019), combined with land cover types, DEM, air temperature, precipitation, soil moisture, total evaporation, statistical data, and air quality data, we analyzed the dynamic changes of vegetation in Alxa League in an arid area; We conducted correlation analysis and research combined with multiple driving factors. The results show that: before 2012, the NDVI value fluctuated; after 2012, the NDVI value dropped sharply. High NDVI values are mainly concentrated infrequent human activities (city centers). The NDVI values in the region’s northwest especially showed a slight degradation trend, and the southeast showed a slight improvement trend. In terrain analysis, the NDVI value is most affected by elevation. NDVI is negatively correlated with air temperature, precipitation, soil moisture, and total evaporation in space, and only soil moisture is positively correlated in time. NDVI and absorbable particulate matter (PM10) are negatively correlated with monthly variation. The seasonal variation of NDVI is: Summer > autumn > spring > winter. The seasonal variation of PM10 is: spring > winter > summer > autumn. NDVI and PM10 are positively correlated in interannual variation. This paper can provide a theoretical basis for the dynamic changes of vegetation in arid areas, improve the ecological environment through human intervention for related factors, and provide new ideas for satellite remote sensing observations that may underestimate vegetation growth and changes.