[Response of Vegetation Dynamics to Climate Change and Human Activities in Shaanxi Province from 2000 to 2020].

Clarifying the impacts of climate changes and human activities on vegetation dynamics is of significant importance to environmental resource managements, as vegetation undergoes notable changes under the dual influences of the two factors. Based on MODIS NDVI data, meteorological data, and human activity intensity (HAI) characterized by land cover data from 2000 to 2020, this study used methods such as trend analysis, Hurst index, partial correlation analysis, bivariate spatial autocorrelation, and random forest modeling. The impact mechanisms and contributions of climate changes and human activities on vegetation dynamics in Shaanxi Province as a whole and in its three regions (northern Shaanxi, central Shaanxi, and southern Shaanxi) were investigated. The results showed as follows: ① From 2000 to 2020, the average NDVI value in Shaanxi Province was 0.71, with a higher value in southern Shaanxi, followed by those in central and northern Shaanxi. During the study period, the overall NDVI of Shaanxi Province and its three regions showed an increasing trend, with a significant improvement in vegetation covering 87.1% of the area, of which 32.7% had a sustainable improvement trend. The most noticeable improvement in vegetation was observed in northern Shaanxi. ② The NDVI responded differently to various climatic factors. Precipitation and average temperature primarily promoted vegetation, while solar radiation had an inhibitory effect. ③ From 2000 to 2020, the average HAI value in Shaanxi Province was 0.06, with a higher value in central Shaanxi, followed by those in northern and southern Shaanxi, and exhibited an increasing trend over time. The cluster patterns were mainly described as high-low, low-high, and low-low in the northern, central, and southern Shaanxi regions, respectively. ④ The NDVI change rates under climate change and human activities were 0.005 4 a<sup>-1</sup> and 0.000 5 a<sup>-1</sup>, respectively, with their contribution rate being 91.5% and 8.5%, in Shaanxi Province. In the three regions, climate change contributed positively to vegetation change, with the highest contribution observed in northern Shaanxi. Human activities contributed positively in northern and southern Shaanxi, with the highest contribution in northern Shaanxi, while in the central region, human activities showed a negative contribution. The vegetation dynamics in Shaanxi Province were influenced by both climate change and human activities, but the impact mechanisms varied across different regions. It is essential to develop scientifically tailored ecological protection plans based on the specific conditions of each region.

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Publication Details

Journal
PubMed
Published
2025-06-08
DOI
https://doi.org/10.13227/j.hjkx.202405140
Primary Topic
Remote Sensing and Land Use
Type
article
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[Response of Vegetation Dynamics to Climate Change and Human Activities in Shaanxi Province from 2000 to 2020].

Meng Zhang, Pei Li, Yajun Zhang, Fan Yang
PubMed
Remote Sensing and Land Use
article

[Response of Vegetation Dynamics to Climate Change and Human Activities in Shaanxi Province from 2000 to 2020].

Meng Zhang, Pei Li, Yajun Zhang, Fan Yang
article en

Abstract

Clarifying the impacts of climate changes and human activities on vegetation dynamics is of significant importance to environmental resource managements, as vegetation undergoes notable changes under the dual influences of the two factors. Based on MODIS NDVI data, meteorological data, and human activity intensity (HAI) characterized by land cover data from 2000 to 2020, this study used methods such as trend analysis, Hurst index, partial correlation analysis, bivariate spatial autocorrelation, and random forest modeling. The impact mechanisms and contributions of climate changes and human activities on vegetation dynamics in Shaanxi Province as a whole and in its three regions (northern Shaanxi, central Shaanxi, and southern Shaanxi) were investigated. The results showed as follows: ① From 2000 to 2020, the average NDVI value in Shaanxi Province was 0.71, with a higher value in southern Shaanxi, followed by those in central and northern Shaanxi. During the study period, the overall NDVI of Shaanxi Province and its three regions showed an increasing trend, with a significant improvement in vegetation covering 87.1% of the area, of which 32.7% had a sustainable improvement trend. The most noticeable improvement in vegetation was observed in northern Shaanxi. ② The NDVI responded differently to various climatic factors. Precipitation and average temperature primarily promoted vegetation, while solar radiation had an inhibitory effect. ③ From 2000 to 2020, the average HAI value in Shaanxi Province was 0.06, with a higher value in central Shaanxi, followed by those in northern and southern Shaanxi, and exhibited an increasing trend over time. The cluster patterns were mainly described as high-low, low-high, and low-low in the northern, central, and southern Shaanxi regions, respectively. ④ The NDVI change rates under climate change and human activities were 0.005 4 a<sup>-1</sup> and 0.000 5 a<sup>-1</sup>, respectively, with their contribution rate being 91.5% and 8.5%, in Shaanxi Province. In the three regions, climate change contributed positively to vegetation change, with the highest contribution observed in northern Shaanxi. Human activities contributed positively in northern and southern Shaanxi, with the highest contribution in northern Shaanxi, while in the central region, human activities showed a negative contribution. The vegetation dynamics in Shaanxi Province were influenced by both climate change and human activities, but the impact mechanisms varied across different regions. It is essential to develop scientifically tailored ecological protection plans based on the specific conditions of each region.

PubMedVol. 46(6)
Hebei Agricultural University (CN), Chang'an University (CN), PowerChina (China) (CN), China Power Engineering Consulting Group (China) (CN)
Remote Sensing and Land Use
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