随着城市化进程的加快，人类活动对景观格局空间分布的作用逐渐增强。频繁的人类活动对景观生态风险构成一定的威胁，景观生态风险的科学评价可为区域生态风险预测与防范、景观格局监管与优化提供科学依据，同时也可以为生态环境可持续发展提供一定的参考依据。研究选取植被净初级生产力、产水深度、土壤保持能力、生境质量来改进景观脆弱度，并结合景观格局指数，评价2000-2020年陕北地区景观生态风险空间分布及其时空变化特征。运用地理探测器分析影响陕北地区景观生态风险空间异质性的主要驱动力和影响因素，研究结论如下：

从土地利用时空变化分析来看：草地在陕北地区土地利用类型中占主体位置，占研究区总面积的64.7%，林地主要分布在延安南部地区，面积占研究区总面积的18.21%。2000-2020年间耕地、未利用地面积呈减少趋势。林地、草地和建设用地面积呈增加趋势。在土地利用变化中，净转出量较高的是耕地和林地，净转入量较高的是草地和林地。建设用地变化动态度最高为6.69%，说明陕北地区城镇化速度较高。

从生态系统服务时空变化分析来看：(1)陕北地区植被净初级生产力空间分布南北差异较大，总体呈现南高北低的特点，低值区面积约占总面积的40.3%，主要分布在榆林地区的风沙滩地，高值区主要分布在延安南部的黄陵县和黄龙县；时间变化上来看，陕北地区大部分地区植被NPP呈显著增加，显著增加区域占总面积的98.2%。(2)陕北地区多年产水深度为251 mm，低值区主要集中在定边县的西部以及府谷县；高值区主要分布在研究区的最南部洛川县。近年来陕北地区产水深度增长速度较快，呈波动式变化。(3)研究区的南部黄龙县和西南部富县为土壤保持量高值区，榆阳区和定边县等地是土壤保持量低值区；从时间尺度方面来看，陕北地区土壤保持能力一直处于不断增加的趋势。(4)生境质量分布较高的地区主要分布在黄龙县和黄陵县，低值区分布在榆林地区的横山区和榆阳区，生境质量指数呈波动增长趋势。(5)陕北地区各生态系统相关性较高，尤其是植被NPP、产水深度以及土壤保持量这三者处于强相关性，相关性均大于0.55。说明陕北地区在实施退耕还林还草后，各生态系统均处于逐步协同增强的趋势。

从景观生态风险时空变化分析来看：2000-2020年，陕北景观生态风险在空间上呈现出南高北低的特征，中等生态风险等级的面积较大，整体风险指数由2000年的0.3247下降至2020年的0.3040。高、较高生态风险区的面积持续减少，中等生态风险区变化最大，主要以风险降低为主，低生态风险区相对稳定，变化较少。总体来看，研究区生态风险呈现向良性发展趋势。从空间相关分析方面来讲，五期Moran’s I分别为0.888、0.891、0.884、0.892和0.893，景观生态风险呈现空间正相关；从局部自相关结果来看景观生态风险空间分布主要以“高-高”和“低-低”两种聚集方式为主，其中“高-高”聚集分布在榆林地区西北部，“低-低”聚集分布在延安地区南部。

从景观生态风险驱动因素分析来看：自然因素和人为因素对景观生态风险造成的影响不一，自然因子对生态风险起到主要影响作用，人为因子起到控制作用。其中降水和土壤类型对景观生态风险的解释力最大，分别为53.25%和25.76%，夜间灯光和人口密度对生态风险的解释力最小。从交互探测结果可以看出，任意两种因子交互作用对生态风险的解释力起到增强作用。年均降水量与人为干扰度两者的交互解释力达到最大值，为65.97%。研究表明年均降水量是解释生态风险动态变化的关键因素，而人为干扰是影响景观生态风险的重要因素，两者之间的相互作用是对生态风险的影响最大。

With the acceleration of urbanization, the effect of human activities on the spatial distribution of landscape pattern is gradually enhanced, and the frequent human activities pose a certain threat to the landscape ecological risk. The scientific assessment of landscape ecological risk can provide a scientific basis for the prediction and prevention of regional ecological risk, as well as the regulation and optimization of landscape pattern. In this study, vegetation net primary productivity, water yield depth, soil conservation ability and habitat quality were selected to improve the landscape vulnerability. Combined with landscape pattern index, the spatial distribution and spatio-temporal changes of landscape ecological risks in northern Shaanxi were evaluated during 2000-2020. The main driving forces and influencing factors affecting spatial heterogeneity of landscape ecological risk in northern Shaanxi were analyzed by using geographic detector. The research conclusions were as follows:

From the analysis of temporal and spatial changes of land use, grassland occupies the main position of land use types in northern Shaanxi, accounting for 64.7% of the total area of the study area, while woodland is mainly distributed in southern Yan 'an, accounting for 18.21% of the total area of the study area. The arable land and unused land area showed a decreasing trend from 2000 to 2020. The area of forest land, grassland and construction land showed an increasing trend. In the land use change, the net transfer amount is higher in cultivated land and forest land, while the net transfer amount is higher in grassland and forest land. The highest change attitude of construction land is 6.69%, indicating that the urbanization speed is relatively high in northern Shaanxi.

From the analysis of temporal and spatial changes of ecosystem services: (1) The spatial distribution of net primary productivity of vegetation in northern Shaanxi has a great difference between north and south, with the overall characteristics of high in the south and low in the north. The low-value area accounts for about 40.3% of the total area, and the low-value area is mainly distributed in the sandy beach in Yulin, while the high-value area is mainly distributed in Huangling and Huanglong counties in the south of Yan 'an. NPP of vegetation increased significantly in most areas of northern Shaanxi, accounting for 98.2% of the total area. (2) The annual water depth in northern Shaanxi is 251mm, and the low value areas are mainly concentrated in the west of Dingbian County and Fugu County; The high value area of water production depth is mainly distributed in Luochuan County, the southernmost part of the study area. In recent years, the water yield depth in northern Shaanxi increased rapidly and in the form of fluctuation growth. (3) Huanglong County in the south and Fuxian County in the southwest of the study area were the areas with high soil conservation values, while Yuyang District and Dingbian County were the areas with low soil conservation values. From the perspective of time scale, soil conservation ability in northern Shaanxi has been increasing. (4) The areas with high habitat quality distribution were mainly distributed in Huanglong County and Huangling County, while the areas with low habitat quality were distributed in Hengshan District and Yuyang district of Yulin area, and the habitat quality index showed a trend of fluctuating growth. (5) The ecosystems in northern Shaanxi are highly correlated, especially the NPP of vegetation, water yield depth and soil conservation, which are strongly correlated, and the correlation is all greater than 0.55. After the implementation of the conversion of farmland to forest and grassland in northern Shaanxi, all the ecosystems are in a trend of gradual synergistic enhancement.

From the analysis of spatial and temporal changes of landscape ecological risk, from 2000 to 2020, the landscape ecological risk in the study area presented the characteristics of high in the south and low in the north, and the area of medium ecological risk grade was large, and the overall risk index decreased from 0.3247 in 2000 to 0.3040 in 2020. The area of high and high ecological risk areas continued to decrease, the medium ecological risk areas changed the most, mainly with risk reduction, and the low ecological risk areas were relatively stable with little change. In general, the ecological risk in the study area presents a benign development trend. In terms of spatial correlation analysis, Moran's I in the five phases were 0.888, 0.891, 0.884, 0.892 and 0.893, respectively, showing a spatial positive correlation between landscape ecological risks. According to the local autocorrelation results, the spatial distribution of landscape ecological risk is mainly divided into "high-high" and "low-low" clustering modes, among which "high-high" clustering is distributed in the northwest of Yulin region, and "low-low" clustering is distributed in the south of Yan 'an region.

From the analysis of driving factors of landscape ecological risk, natural factors and human factors have different impacts on landscape ecological risk, in which natural factors play a major role in influencing ecological risk, while human factors play a controlling role. Precipitation and soil type have the greatest explanatory power to landscape ecological risk, which are 53.25% and 25.76% respectively, while night light and population density have the least explanatory power to landscape ecological risk. It can be seen from the interactive detection results that the interaction of any two factors enhances the explanatory power of ecological risk. The interactive explanatory power of average annual precipitation and human disturbance degree reached the maximum, which was 65.97%. It is found that average annual precipitation is the key factor to explain the dynamic change of ecological risk, while human disturbance is the important factor to affect the ecological risk of landscape, and the interaction between the two is the biggest influence on ecological risk

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