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
探究土地利用演变及其对碳储量的影响,对于减缓都市圈气候变化、促进绿色低碳发展具有重要意义。该研究在“双碳”目标背景下,结合兴趣点(point of interest,POI)数据并顾及斑块生成土地利用模拟模型(patch-generating land use simulat...探究土地利用演变及其对碳储量的影响,对于减缓都市圈气候变化、促进绿色低碳发展具有重要意义。该研究在“双碳”目标背景下,结合兴趣点(point of interest,POI)数据并顾及斑块生成土地利用模拟模型(patch-generating land use simulation model,PLUS)进行双约束转移矩阵优化,耦合生态系统服务与权衡的综合评估(integrated valuation of ecosystem services and trade-offs,InVEST)模型分析山东省济南都市圈2000—2020年土地利用演变规律及其对生态系统碳储量的影响,模拟预测了自然发展、城镇发展和生态保护3种情景下济南都市圈2030年和2060年土地利用变化并估算其生态系统碳储量,分析其碳储量重心迁移情况,并利用参数最优地理探测器探究碳储量空间分异驱动因素。结果表明:①2000—2020年,济南都市圈耕地、草地和未利用地面积持续减少,林地面积呈波动增加状态,水域、建设用地面积增长迅速;②2000—2020年,济南都市圈碳储量及土地利用空间格局相似,以黄河主脉为分界线,呈现“东南高,西北低”的分布特征,耕地类型碳储量为研究区碳储量的主要来源,占总碳储量的80%以上;③多情景模拟下的碳储量均有所降低,主要原因为高碳密度区域耕地转换为低碳密度区域建设用地,其中生态保护情景碳储量最高,2030年总碳储量为4226.86×10^(6) t,2060年总碳储量为3967.94×10^(6) t;④不同发展时期和情景下的济南都市圈碳储量重心均发生一定偏移,发展趋势受土地利用变化影响,重心地带一直处于山东大学历城区,说明济南都市圈发展较为全面均衡;⑤各驱动因子对济南都市圈碳储量空间分布具有明显影响,其中人口密度对碳储量空间分异解释力最大,交互作用下各因子均呈现对碳储量解释力增强的结果。展开更多
Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the ...Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the effects of various factors on urban flooding have been extensively evaluated,few studies consider both interregional flood connection and interactions between driving factors.In this study,driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector(OPGD).An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding.Furthermore,interregional flood volume connection was analyzed based on simulation results.Subsequently,driving force of urban flooding factors and interactions between them were quantified using the OPGD model.Taking Haidian Island in Hainan Province,China as an example,the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area.The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force(0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force(0.968).In addition,the interactions between driving factors have bivariable or nonlinear enhancement effects.The interactions between two factors strengthen the influence of each factor on urban flooding.This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.展开更多
协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特...协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特性,提出将地理模拟与优化(Geographical Simulation and Optimization Systems,简写为GeoSOS)等复杂GIS空间分析技术引入规划决策分析。通过利用最小累积阻力模型获取生态敏感区保护压力格局,并利用元胞自动机模型进行城市扩张模拟,分析城市惯性扩张模式对生态敏感区的潜在影响;然后根据生态敏感区保护和城市空间扩张的协调性发展目标进行生态适宜性评价,进而利用蚁群智能空间优化配置模型产生一种优化的城市空间布局方案。研究以我国珠江三角洲地区的广州市为案例,详细分析了基于GeoSOS的城市扩张与生态保护的协调决策过程。结果表明,整合了城市发展惯性与生态敏感区保护双重目标的空间优化布局方案,比单纯基于地理模拟进行规划布局更符合生态型城市建设需求,研究所提出的城市与生态二元空间协调分析框架可为城市规划提供可靠的定量决策支撑。展开更多
A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the...A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.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.
文摘探究土地利用演变及其对碳储量的影响,对于减缓都市圈气候变化、促进绿色低碳发展具有重要意义。该研究在“双碳”目标背景下,结合兴趣点(point of interest,POI)数据并顾及斑块生成土地利用模拟模型(patch-generating land use simulation model,PLUS)进行双约束转移矩阵优化,耦合生态系统服务与权衡的综合评估(integrated valuation of ecosystem services and trade-offs,InVEST)模型分析山东省济南都市圈2000—2020年土地利用演变规律及其对生态系统碳储量的影响,模拟预测了自然发展、城镇发展和生态保护3种情景下济南都市圈2030年和2060年土地利用变化并估算其生态系统碳储量,分析其碳储量重心迁移情况,并利用参数最优地理探测器探究碳储量空间分异驱动因素。结果表明:①2000—2020年,济南都市圈耕地、草地和未利用地面积持续减少,林地面积呈波动增加状态,水域、建设用地面积增长迅速;②2000—2020年,济南都市圈碳储量及土地利用空间格局相似,以黄河主脉为分界线,呈现“东南高,西北低”的分布特征,耕地类型碳储量为研究区碳储量的主要来源,占总碳储量的80%以上;③多情景模拟下的碳储量均有所降低,主要原因为高碳密度区域耕地转换为低碳密度区域建设用地,其中生态保护情景碳储量最高,2030年总碳储量为4226.86×10^(6) t,2060年总碳储量为3967.94×10^(6) t;④不同发展时期和情景下的济南都市圈碳储量重心均发生一定偏移,发展趋势受土地利用变化影响,重心地带一直处于山东大学历城区,说明济南都市圈发展较为全面均衡;⑤各驱动因子对济南都市圈碳储量空间分布具有明显影响,其中人口密度对碳储量空间分异解释力最大,交互作用下各因子均呈现对碳储量解释力增强的结果。
基金supported by the National Natural Science Foundation of China(Grant No.52379019,42477501)the Key Research and Development Program of Ningxia Hui Autonomous Region(Grant No.2022BEG02020).
文摘Urban flooding is caused by multiple factors,which seriously restricts the sustainable development of society.Understanding the driving factors of urban flooding is pivotal to alleviating flood disasters.Although the effects of various factors on urban flooding have been extensively evaluated,few studies consider both interregional flood connection and interactions between driving factors.In this study,driving factors of urban flooding were analyzed based on the water tracer method and the optimal parameters-based geographical detector(OPGD).An urban flood simulation model coupled with the water tracer method was constructed to simulate flooding.Furthermore,interregional flood volume connection was analyzed based on simulation results.Subsequently,driving force of urban flooding factors and interactions between them were quantified using the OPGD model.Taking Haidian Island in Hainan Province,China as an example,the coupled model simulation results show that sub-catchment H6 is the region experiencing the most severe flooding and sub-catchment H9 contributes the most to overall flooding in the study area.The results of subsequent driving effect analysis show that elevation is the factor with the maximum single-factor driving force(0.772) and elevation ∩ percentage of building area is the pair of factors with the maximum two-factor driving force(0.968).In addition,the interactions between driving factors have bivariable or nonlinear enhancement effects.The interactions between two factors strengthen the influence of each factor on urban flooding.This study contributes to understanding the cause of urban flooding and provides a reference for urban flood risk mitigation.
文摘协调城市扩张与生态敏感区保护之间的矛盾是当前我国新型城镇化建设中的一项重要任务,但基于传统供需平衡模式或历史惯性驱动模拟的城市规划布局可能导致一系列潜在的生态环境问题。根据城市发展具有历史惯性驱动和空间规划引导的双重特性,提出将地理模拟与优化(Geographical Simulation and Optimization Systems,简写为GeoSOS)等复杂GIS空间分析技术引入规划决策分析。通过利用最小累积阻力模型获取生态敏感区保护压力格局,并利用元胞自动机模型进行城市扩张模拟,分析城市惯性扩张模式对生态敏感区的潜在影响;然后根据生态敏感区保护和城市空间扩张的协调性发展目标进行生态适宜性评价,进而利用蚁群智能空间优化配置模型产生一种优化的城市空间布局方案。研究以我国珠江三角洲地区的广州市为案例,详细分析了基于GeoSOS的城市扩张与生态保护的协调决策过程。结果表明,整合了城市发展惯性与生态敏感区保护双重目标的空间优化布局方案,比单纯基于地理模拟进行规划布局更符合生态型城市建设需求,研究所提出的城市与生态二元空间协调分析框架可为城市规划提供可靠的定量决策支撑。
基金National Outstanding Youth Foundation of China (Grant No. 40525002)the National 863 Project of China (2006AA12Z206)+1 种基金 the National Natu-ral Science Foundation of China (Grant No. 40471105)the "985 Project" of GIS and Remote Sensing for Geosciences from the Ministry of Education of China (Grant No. 105203200400006)
文摘A new intelligent algorithm of geographical cellular automata (CA) based on ant colony optimization (ACO) is proposed in this paper. CA is capable of simulating the evolution of complex geographical phenomena, and the core of CA models is how to define transition rules. However, most of the transition rules are defined by mathematical equations, and are hence not explicit. When the study area is complicated, it is much more difficult to extract parameters for geographical CA. As a result, ACO is applied to geographical CA to automatically and intelligently obtain transition rules in this paper. The transition rules extracted by ACO are defined as logical expressions rather than implicit mathematical equations to describe the complex relationships of the nature, and easy for people to understand. The ACO-CA model was applied to simulating rural-urban land conversions in Guangzhou City, China, and appropriate simulation results were generated. Compared with See5.0 decision tree model, ACO-CA is more suitable to discovering transition rules for geographical CA.
