Analysis of Spatial and Temporal Evolution Characteristics and Driving Forces of NDVI in Gansu Province from 2000 to 2022

The synergistic effects of climate change and human activities have profoundly influenced the spatiotemporal dynamics of vegetation in arid and semi-arid regions. In this study, MODIS NDVI data and an integrated methodological approach, including trend analysis, partial correlation, residual regression, and geographical detector modeling, were used to analyze the variations in NDVI in Gansu Province from 2000 to 2022. The results showed the following: (1) The growing-season NDVI in Gansu Province exhibited a significant increasing trend overall (0.0029 per year, p < 0.05). (2) Both the NDVI values and their increasing rates presented a spatial pattern of “higher in the southeast and lower in the northwest”; although low vegetation coverage dominated the entire province, 49.47% of the area showed an extremely significant increasing trend in NDVI (p < 0.01). (3) In the future, the area ratio of regions with improved NDVI to those with degraded NDVI in Gansu Province will be approximately 45.5%:54.5%. (4) The contribution rate of human activities to the spatiotemporal variations in NDVI was higher than that of climate change; however, the synergistic effect of the two factors was greater than their individual effects. (5) Precipitation and solar radiation were the two primary climatic factors affecting NDVI variations in Gansu Province, while human activities played a regulatory role in mediating climate–vegetation interactions. Therefore, we suggest implementing more proactive ecological management and restoration measures to mitigate the impacts of future climate change, particularly in regions where NDVI may degrade in the future.

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Publication Details

Journal
Land
Published
2025-11-03
DOI
https://doi.org/10.3390/land14112184
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Analysis of Spatial and Temporal Evolution Characteristics and Driving Forces of NDVI in Gansu Province from 2000 to 2022

Ming Fan, Xiaolei Zhou, Mingpeng Liu, Jianlong Fu et al.
Land
Remote Sensing in Agriculture
article

Analysis of Spatial and Temporal Evolution Characteristics and Driving Forces of NDVI in Gansu Province from 2000 to 2022

Ming Fan, Xiaolei Zhou, Mingpeng Liu, Jianlong Fu, Xuhu Wang, Zhang Xiao-wei, Songsong Lu, Weibo Du
article en

Abstract

The synergistic effects of climate change and human activities have profoundly influenced the spatiotemporal dynamics of vegetation in arid and semi-arid regions. In this study, MODIS NDVI data and an integrated methodological approach, including trend analysis, partial correlation, residual regression, and geographical detector modeling, were used to analyze the variations in NDVI in Gansu Province from 2000 to 2022. The results showed the following: (1) The growing-season NDVI in Gansu Province exhibited a significant increasing trend overall (0.0029 per year, p < 0.05). (2) Both the NDVI values and their increasing rates presented a spatial pattern of “higher in the southeast and lower in the northwest”; although low vegetation coverage dominated the entire province, 49.47% of the area showed an extremely significant increasing trend in NDVI (p < 0.01). (3) In the future, the area ratio of regions with improved NDVI to those with degraded NDVI in Gansu Province will be approximately 45.5%:54.5%. (4) The contribution rate of human activities to the spatiotemporal variations in NDVI was higher than that of climate change; however, the synergistic effect of the two factors was greater than their individual effects. (5) Precipitation and solar radiation were the two primary climatic factors affecting NDVI variations in Gansu Province, while human activities played a regulatory role in mediating climate–vegetation interactions. Therefore, we suggest implementing more proactive ecological management and restoration measures to mitigate the impacts of future climate change, particularly in regions where NDVI may degrade in the future.

LandVol. 14(11)
Gansu Agricultural University (CN), Beijing Municipal Ecological and Environmental Monitoring Center (CN), Guangdong Province Environmental Monitoring Center (CN)
Gansu Agricultural University
Openalex Percentile: Top 69%
Remote Sensing in Agriculture
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