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Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (10): 1341-1360    DOI: 10.1007/s40333-025-0110-y     CSTR: 32276.14.JAL.0250110y
Research article     
Driving mechanism and nonlinear threshold identification of vegetation in China: Based on causal inference and machine learning
ZHANG Houtian1, WANG Shidong2,*(), DING Junjie3
1School of Intelligent Construction and Civil Engineering, Luoyang Institute of Science and Technology, Luoyang 471023, China
2School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China
3Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China
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Abstract  

Climate change significantly affects vegetation dynamics. Thus, understanding interactions between vegetation and climatic factors is essential for ecological management. This study used kernel Normalized Difference Vegetation Index (kNDVI) and climatic data (temperature, precipitation, humidity, and vapor pressure deficit (VPD)) of China from 2000 to 2022, integrating Geographic Convergent Cross Mapping (GCCM) causal modeling, Extreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP) nonlinear threshold identification, and Geographical Simulation and Optimization Systems-Future Land Use Simulation (GeoSOS-FLUS) spatial prediction modeling to investigate vegetation spatiotemporal characteristics, driving mechanisms, nonlinear thresholds, and future spatial patterns. Results indicated that from 2000 to 2022, China's kNDVI showed an overall increasing trend (annual average ranging from 0.29 to 0.33) with distinct spatial differentiation: 52.77% of areas locating in agricultural and ecological restoration regions in the central-eastern plain) experienced vegetation improvement, whereas 2.68% of areas locating in the southeastern coastal urbanized regions and the Yangtze River Delta experience vegetation degradation. The coefficient of variation (CV) of kNDVI at 0.30-0.40 (accounting for 10.61%) was significantly higher than that of NDVI (accounting for 1.80%). Climate-driven mechanisms exhibited notable library length (L) dependence. At short-term scales (L<50), vegetation-driven transpiration regulated local microclimate, with a causal strength from kNDVI to temperature of 0.04-0.15; at long-term scales (L>100), cumulative temperature effects dominated vegetation dynamics, with a causal strength from temperature to kNDVI of 0.33. Humidity and kNDVI formed bidirectional positive feedback at long-term scales (L=210, causal strength>0.70), whereas the long-term suppressive effect of VPD was particularly pronounced (causal strength=0.21) in arid areas. The optimal threshold intervals identified were temperature at -12.18°C-0.67°C, precipitation at 24.00-159.74 mm, humidity of lower than 22.00%, and VPD of <0.07, 0.17-0.24, and >0.30 kPa; notably, the lower precipitation threshold (24.00 mm) represented the minimum water requirements for vegetation recovery in arid areas. Future kNDVI spatial patterns are projected to continue the trend of "southeastern optimization and northwestern delay" from 2025 to 2040: the area proportion of high kNDVI value (>0.50) will rise from 40.43% to 41.85%, concentrated in the Sichuan Basin and the southern hills; meanwhile, the proportion of low-value areas of kNDVI (0.00-0.10) in the arid northwestern areas will decline by only 1.25%, constrained by sustained temperature and VPD stress. This study provides a scientific basis for vegetation dynamic regulation and sustainable development under climate change.

Key words： kernel Normalized Difference Vegetation Index (kNDVI)      climate drivers      machine learning      Geographic Convergent Cross Mapping (GCCM)      Extreme Gradient Boosting-Shapley Additive Explanations (XGBoost-SHAP)      Geographical Simulation and Optimization Systems-Future Land Use Simulation (GeoSOS-FLUS) model     
Received: 31 May 2025      Published: 31 October 2025
Corresponding Authors: *WANG Shidong (E-mail: wsd0908@hpu.edu.cn)
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ZHANG Houtian
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Cite this article:

ZHANG Houtian, WANG Shidong, DING Junjie. Driving mechanism and nonlinear threshold identification of vegetation in China: Based on causal inference and machine learning. Journal of Arid Land, 2025, 17(10): 1341-1360.

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http://jal.xjegi.com/10.1007/s40333-025-0110-y     OR     http://jal.xjegi.com/Y2025/V17/I10/1341

Fig. 1 Demarcation of seven geographic regions in China. Note that the map is based on the standard map (GS(2022)0438) of the Map Service System (http://bzdt.ch.mnr.gov.cn/) marked by the Ministry of Natural Resources of the People's Republic of China, and the boundary of base map has not been modified. The demarcation line of the seven geographic regions was referred to Tong et al. (2022).
Fig. 2 Temporal trends of kernel Normalized Difference Vegetation Index (kNDVI) in China from 2000 to 2022. Shaded area represents the 95.00% confidence interval of the regression.
Fig. 3 Spatial distribution of kNDVI value (a) and change trend (b) in China from 2000 to 2022
Fig. 4 Comparative spatial distribution of vegetation variability in China between Normalized Difference Vegetation Index (NDVI; a) and kNDVI (b) from 2000 to 2022
Fig. 5 Spatial distribution of average annual temperature (a), precipitation (b), humidity (c), and vapor pressure deficit (VPD; d) in China from 2000 to 2022
Fig. 6 Geographic Convergent Cross Mapping (GCCM)-based causal trend between kNDVI and temperature (a) precipitation (b), humidity (c), and VPD (d) in China. The orange line represents kNDVI xmap climatic factor, and the blue line represents climatic factor xmap kNDVI. ρ denotes the cross-mapping correlation coefficient, and L is the library length. The x-axis shows the characteristic values of each climatic factor, while the y-axis indicates the probability trade-off of kNDVI change.
Fig. 7 Nonlinear response curve and threshold of kNDVI in relation to dominant climatic factor in China. (a), temperature; (b), precipitation; (c), humidity; (d), VPD. The scatter points represent observed data, the black solid line indicates the fitted trend, the shaded areas show the Probability of Comprehensive Synergy (PCS) range, and the dashed lines denote intersection points.
Fig. 8 Validation result of kNDVI simulation accuracy in China from 2000 to 2010. (a), original kNDVI value; (b), forecasted kNDVI value from the Geographical Simulation and Optimization Systems-Future Land Use Simulation (GeoSOS-FLUS) model.
Fig. 9 Projected spatial distribution of kNDVI in China in 2025 (a), 2030 (b), 2035 (c), and 2040 (d) based on GeoSOS-FLUS model
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