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 Vege...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℃–0.67℃,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.展开更多
基金funded by the Key Science and Technology Research Projects of Henan Province(252102320172).
文摘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℃–0.67℃,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.
