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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (11): 2027-2050    DOI: 10.1016/j.jaridl.2026.11.009    
Research article     
Evaluation of vegetation resilience in mining areas and impact of mining disturbance based on improved critical slowdown model and long time series leaf area index
LIU Menglin1, WANG Shidong1,2,*(), WANG Pengfei1, JIAO Yiheng1, ZHANG Hebing1,2, HE Shi1, DU Zebing1, CHEN Zhichao1
1 School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
2 Observation and Research Station of Land Ecology and Land Use in the Grain-Producing Area of Central China, Ministry of Natural Resources, Xinzheng 451100, China
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Abstract  

Mining activities often lead to drastic alterations in surface structure, and the subsequent vegetation recovery process exhibits significant spatiotemporal heterogeneity. The Changping mining area in Shanxi Province, China was selected as the study region. Leaf area index (LAI) data derived from Sentinel-2A/B multispectral imagery were subjected to time series decomposition. Based on the decomposed series, we applied an improved critical slowing down (CSD) model to calculate two indicators—the first-order autoregressive (AR(1)) coefficient and the detrended fluctuation analysis (DFA) scaling exponent—to assess vegetation resilience. The spatiotemporal variation of vegetation resilience and its correlation with mining-induced disturbances were subsequently analyzed in the six panels in the Changping mining area from 2016 to 2024. The results indicated the following: (1) Overall, LAI in the Changping mining area exhibited a spatial pattern of higher values in the west and lower values in the east. Within naturally vegetated areas, the annual mean LAI increased from 0.891 to 1.172, showing a gradual upward trend over time; (2) Overall vegetation resilience in the Changping mining area exhibited a declining trend. Compared with 2016-2020, the proportion of areas with enhanced resilience increased by 33.97% during 2021-2024, with the most pronounced changes observed in the panels 4 and 5, indicating significant impacts of mining activities. At the working face scale, the negative effects of mining on vegetation resilience were particularly evident, as resilience during the mining period was substantially lower than that in the corresponding control areas; And (3) mining activities exerted significant disturbances on vegetation resilience in the Changping mining area, with both temporal and spatial gradients. Owing to differences in mining intensity and operational schedules among zones, the responses of vegetation resilience were spatially heterogeneous. Vegetation resilience tended to increase with distance from the actively mined areas, and this spatial difference diminished after mining ceased, indicating a delayed and partially reversible disturbance effect. These findings provide a theoretical basis and a decision-making reference for the protection and restoration of vegetation ecosystems in mining areas.

Key words： Changping mining area      vegetation resilience      leaf area index (LAI)      first-order autoregressive (AR(1)) model      detrended fluctuation analysis (DFA)      improved critical slowing down (CSD) model      Seasonal and Trend decomposition using Loess (STL) decomposition     
Received: 22 March 2026      Published: 30 November 2026
Corresponding Authors: *WANG Shidong (E-mail: wsd0908@hpu.edu.cn)
About author: Author contributions

LIU Menglin: conceptualization, formal analysis, writing - original draft preparation, project administration; WANG Shidong: methodology, investigation, writing - review and editing; WANG Pengfei: investigation, methodology; JIAO Yiheng: methodology, project administration; ZHANG Hebing: investigation; HE Shi: methodology, formal analysis; DU Zebing: project administration, methodology; CHEN Zhichao: data curation, investigation. All authors approved the manuscript.

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LIU Menglin
WANG Shidong
WANG Pengfei
JIAO Yiheng
ZHANG Hebing
HE Shi
DU Zebing
CHEN Zhichao
Cite this article:

LIU Menglin, WANG Shidong, WANG Pengfei, JIAO Yiheng, ZHANG Hebing, HE Shi, DU Zebing, CHEN Zhichao. Evaluation of vegetation resilience in mining areas and impact of mining disturbance based on improved critical slowdown model and long time series leaf area index. Journal of Arid Land, 2026, 18(11): 2027-2050.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.11.009     OR     http://jal.xjegi.com/Y2026/V18/I11/2027

Fig. 1 Geographical location (a) and overview (b) of the Changping mining area in Jincheng City, Shanxi Province, China. The image in Figure 1b is a Sentinel-2 satellite image, obtained from the European Space Agency (ESA) Copernicus Open Access Hub.
Panel Mining period Mining depth (m) Coal seam thickness (m) Dominant land use type
1 2003-2015 220.00-340.00 4.24-5.92 Construction land, road, and farmland
2 2005-2013 213.00-389.00 3.77-4.80 Construction land, road, and farmland
3 - - - -
4 2008-2020 350.00-580.00 4.00-6.00 Construction land, road, and farmland
5 2019-2025 355.00-750.00 5.50-6.00 Natural vegetation (woodland and shrubland)
6 2022-2024 510.00-607.00 5.00-5.50 Natural vegetation (woodland and shrubland)
Table 1 Mining parameters and land use characteristics of the six panels in the Changping mining area
Fig. 2 Changes in leaf area index (LAI) in the Changping mining area from 2016 to 2024. (a), 2016; (b), 2017; (c), 2018; (d), 2019; (e), 2020; (f), 2021; (g), 2022; (h), 2023; (i), 2024.
Fig. 3 Changes in LAI (a) and natural vegetation (b) in the Changping mining area from 2016 to 2024. The white areas denote the non-target areas including water bodies, buildings, and bare ground.
Fig. 4 Spatial distribution of first-order autoregressive (AR(1); a) and detrended fluctuation analysis (DFA; b) Kendall's Tau correlation coefficient (τ), monthly average time series of AR(1) and DFA (c), and histogram of AR(1) Kendall's τ values (d) in the Changping mining area from 2016 to 2024. Data points in Figure 4c are recorded at a monthly time scale, and frequency in Figure 4d represents the proportion of pixels falling within each interval. Note that the time series in Figure 4c begins in December 2017, as it corresponds to the end of the first complete 24-month rolling window (starting from January 2016). The white areas in Figure 4a and b denote the non-target areas including water bodies, buildings, and bare ground.
Fig. 5 Spatial distribution of statistically significant (P<0.05) Kendall's τ trends for the vegetation resilience indicators AR(1) (a-c) and DFA (d-f) in the Changping mining area for the periods 2016-2020, 2021-2024, and 2016-2024. The white areas denote the non-target areas including water bodies, buildings, and bare ground.
Fig. 6 Spatial distribution of vegetation resilience trends based on the AR(1) and DFA series for the periods 2016-2020 (a) and 2021-2024 (b). The white areas in Figure 4a and b denote the non-target areas including water bodies, buildings, and bare ground.
Fig. 7 Temporal changes in LAI for working faces 4308 (a1), 5302 (b1), and their corresponding control groups (a2 and b2) from 2016 to 2024
Fig. 8 Trends of AR(1) and DFA at working faces 4308 (a1 and a2) and 5302 (b1 and b2) from 2017 to 2024. AR(1) and DFA α were calculated using a 24-month sliding window with a 1-month step size.
Fig. 9 Trends of AR(1) and DFA in different panels of the Changping mining area from 2017 to 2024. (a), Panel 1; (b), Panel 2; (c), Panel 3; (d) Panel 4; (e), Panel 5; (f), Panel 6. Mining periods and geological parameters for each panel are provided in Table 1.
Fig. 10 Trends of AR(1) and DFA α across nine 50.00 m wide distance bands within a 450.00-m radius from each working face center from 2017 to 2024. (a1 and a2), working face 4308; (b1 and b2), working face 5302. Mean values were calculated for natural vegetation grid cells in each band.
Fig. 11 Robustness assessment of AR(1) under different time window lengths. (a), average AR(1) series under each t.window choice; (b), Kendall's τ values of the average AR(1) time series for each t.window; (c), trends removed from the LAI time series of a typical grid cell under each t.window; (d), circumplex of Kendall's τ values under different t.window choices.
Fig. 12 Robustness of AR(1) (a) and DFA (b) to sliding window lengths ranging from 24 to 60 months
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