Long-term vegetation dynamics and its drivers in the north of China

doi:10.1007/s40333-025-0085-8

Journal of Arid Land

2025, Vol. 17

Issue (8): 1064-1083 DOI: 10.1007/s40333-025-0085-8

Research article

Long-term vegetation dynamics and its drivers in the north of China

MA Junyao¹^,², YANG Kun¹^,², ZHANG Xuyang¹^,², WANG Leiyu¹^,², XUE Yayong¹^,²^,^*(

)

¹Geography Postdoctoral Research Station, Xinjiang University, Urumqi 830046, China
²College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830046, China

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Abstract

Vegetation change is the most intuitive and sensitive bioindicator reflecting seasonal and interannual variations in the external environment, and it can directly reflect the rapid response of terrestrial ecosystems to climate change. Using remote sensing and meteorological data, this study revealed the spatiotemporal characteristics of leaf area index (LAI) in the north of China during 1982-2022, clarified the response of LAI change to different meteorological factors, quantified the impacts of climate change and human activities on LAI change, and predicted the future trends in LAI change. From 1982 to 2022, the vegetation in the north of China generally showed a greening trend with a change rate of 0.0071 m²/(m²•a). Temperature was strongly positively correlated with LAI and was the main climate factor driving LAI change. Residual analysis revealed that vegetation improvement occurred in across 74.53% of the study area, and vegetation improvement in about 96.83% of the improved zone was attributed to a combination of climate change and human activities. The regions where anthropogenic contribution exceeded 60.00% covered 36.83% of human-affected areas, while the regions where climatic contribution exceeded 60.00% covered 19.77% of climate-affected areas, demonstrating that human activities influenced the intensity of LAI change more deeply despite the broad spatial impact of climate change. Human activities such as afforestation and the Three-North Protective Forest Program played the dominant role in vegetation greening compared to climate change. Hurst index analysis indicated that 80.30% of vegetation in the north of China is expected to experience a non-sustained improvement in the future. These findings will provide a scientific basis for optimizing the protection strategies of the national ecological barrier areas and evaluating the effectiveness of major ecological projects.

Key words： vegetation change leaf area index, climate change, human activities, residual analysis Hurst index north of China

Received: 07 January 2025 Published: 31 August 2025

Corresponding Authors: ^*XUE Yayong (E-mail: xueyy@xju.edu.cn)

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Cite this article:

MA Junyao, YANG Kun, ZHANG Xuyang, WANG Leiyu, XUE Yayong. Long-term vegetation dynamics and its drivers in the north of China. Journal of Arid Land, 2025, 17(8): 1064-1083.

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http://jal.xjegi.com/10.1007/s40333-025-0085-8 OR http://jal.xjegi.com/Y2025/V17/I8/1064

Fig. 1 Overview of the study area based on the digital elevation model (DEM). Note that the figure is based on the standard map (GS(2024)0650) of the Map Service System (https://cloudcenter.tianditu.gov.cn) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified. The DEM data were generated using Shuttle Radar Topography Mission (SRTM) v4.1 data resampled from the Resource and Environmental Science Data Platform (https://www.resdc.cn/data.aspx?DATAID=123).

Table 1 Partial correlation classification

Table 2 Determination of the relative contributions of climate change and human activities to LAI change

Table 3 Sustainability of LAI trend

Fig. 2 Temporal trend of LAI in the north of China during 1982-2022

Fig. 3 Spatial variations of multi-year average LAI during 1982-2022 (a) and trends of LAI during the periods of 1982-2022 (b), 1982-2001 (c), and 2002-2022 (d) in the north of China. Only areas with significant trend in LAI at P<0.05 level are shown in Figure 3b-d. Note that the maps are based on the standard map (GS(2024)0650) of the Map Service System (https://cloudcenter.tianditu.gov.cn) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified.

Fig. 4 Temporal variations of climate factors in the north of China from 1982 to 2022. (a), temperature; (b), precipitation; (c), solar radiation.

Fig. 5 Spatial variations of trends in temperature (a1-c1), precipitation (a2-c2), and solar radiation (a3-c3) in the north of China during the periods of 1982-2001, 2002-2022, and 1982-2022. Only the areas with significant trend at P<0.05 level are shown in the figures. Note that the maps are based on the standard map (GS(2024)0650) of the Map Service System (https://cloudcenter.tianditu.gov.cn) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified.

Fig. 6 Spatial variations of correlations between LAI and climate factors in the north of China during the periods of 1982-2001, 2002-2022, and 1982-2022. (a1-c1), correlation between LAI and temperature; (a2-c2), correlation between LAI and precipitation; (a3-c3), correlation between LAI and solar radiation. Only the areas with significant correlations at P<0.05 level are shown in the figures. Note that the maps are based on the standard map (GS(2024)0650) of the Map Service System (https://cloudcenter.tianditu.gov.cn) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified.

Fig. 7 Spatial distributions of drivers of vegetation change in the north of China during the periods of 1982-2001 (a), 2002-2022 (b), and 1982-2022 (c). Note that the maps are based on the standard map (GS(2024)0650) of the Map Service System (https://cloudcenter.tianditu.gov.cn) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified.

Fig. 8 Spatial distributions of relative contributions of climate change (a1-c1) and human activities (a2-c2) to LAI change during the periods of 1982-2001, 2002-2022, and 1982-2022. Note that the maps are based on the standard map (GS(2024)0650) of the Map Service System (https://www.tianditu.gov.cn/) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified.

Table 5 Proportions of pixels with relative contributions of climate change and human activities in different ranges during different periods

Fig. 9 Spatial distributions of Hurst index from 1982 to 2022 (a) and sustainability of future vegetation change (b). Note that the maps are based on the standard map (GS(2024)0650) of the Map Service System (https://cloudcenter.tianditu.gov.cn) marked by the National Platform for Common Geospatial Information Services, and the boundary of the standard map has not been modified.


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