Spatiotemporal variation and driving mechanisms of vegetation net primary productivity in Hunan Province from 2001 to 2023

Understanding the spatiotemporal variation in vegetation net primary productivity (NPP) and its response to natural and anthropogenic factors is essential for advancing regional ecological conservation and restoration. Therefore, this study employed the Mann–Kendall test, Sen's slope estimator, and partial correlation analysis to identify the trends and relationships between NPP and climatic factors across Hunan Province, China. Additionally, Random Forest and Geodetector models were used to evaluate the explanatory power of natural and anthropogenic factors on NPP. The following results were obtained: (1) During the study period from 2001 to 2023, the average annual NPP showed an increasing trend, with a growth rate of 2.66 gC/m 2 /a; (2) in high-altitude areas and the Dongting Lake Plain, average NPP was lower during 2019–2023 than that during 2001–2005, but higher in other regions. (3) in Hunan Province, NPP was more sensitive to temperature than to precipitation. Considering the lag effect of climatic factors, the temperature from the previous year showed a significant positive impact on NPP; (4) elevation had the strongest explanatory power for NPP and exhibited notable bivariate enhancement when interacting with other factors. Thus, our study provides a systematic analysis of the temporal and spatial variations in NPP, offering a scientific basis for the sustainable management of regional ecosystems. • NPP spatiotemporal variation analysis in Hunan from 2001 to 2023. • Mann–Kendall and Sen's slope utilised to detect temporal NPP trend. • Partial correlation showed temperature and precipitation effects on NPP. • Random Forest revealed marginal effects of natural environmental factors on NPP. • Geodetector identified elevation as the key NPP driver.

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Publication Details

Journal
Science of Remote Sensing
Published
2025-08-08
DOI
https://doi.org/10.1016/j.srs.2025.100269
Citations
2
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
2.21

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article

Spatiotemporal variation and driving mechanisms of vegetation net primary productivity in Hunan Province from 2001 to 2023

Xingjia Yin, Dan Cao
2 citations
Science of Remote Sensing
Land Use and Ecosystem Services
2.21
article

Spatiotemporal variation and driving mechanisms of vegetation net primary productivity in Hunan Province from 2001 to 2023

Xingjia Yin, Dan Cao
article en
2 citations

Abstract

Understanding the spatiotemporal variation in vegetation net primary productivity (NPP) and its response to natural and anthropogenic factors is essential for advancing regional ecological conservation and restoration. Therefore, this study employed the Mann–Kendall test, Sen's slope estimator, and partial correlation analysis to identify the trends and relationships between NPP and climatic factors across Hunan Province, China. Additionally, Random Forest and Geodetector models were used to evaluate the explanatory power of natural and anthropogenic factors on NPP. The following results were obtained: (1) During the study period from 2001 to 2023, the average annual NPP showed an increasing trend, with a growth rate of 2.66 gC/m 2 /a; (2) in high-altitude areas and the Dongting Lake Plain, average NPP was lower during 2019–2023 than that during 2001–2005, but higher in other regions. (3) in Hunan Province, NPP was more sensitive to temperature than to precipitation. Considering the lag effect of climatic factors, the temperature from the previous year showed a significant positive impact on NPP; (4) elevation had the strongest explanatory power for NPP and exhibited notable bivariate enhancement when interacting with other factors. Thus, our study provides a systematic analysis of the temporal and spatial variations in NPP, offering a scientific basis for the sustainable management of regional ecosystems. • NPP spatiotemporal variation analysis in Hunan from 2001 to 2023. • Mann–Kendall and Sen's slope utilised to detect temporal NPP trend. • Partial correlation showed temperature and precipitation effects on NPP. • Random Forest revealed marginal effects of natural environmental factors on NPP. • Geodetector identified elevation as the key NPP driver.

Science of Remote SensingVol. 12
Central South University of Forestry and Technology (CN), Central South University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Hunan Province, Scientific Research Foundation of Hunan Provincial Education Department, National Key Research and Development Program of China
Openalex Percentile: Top 12%
Land Use and Ecosystem Services
2.21
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