基于Geodetector和MGWR的贵州工业碳排放效率时空演化及影响因素分析

探究工业碳排放效率的时空演化及影响因素对区域产业绿色发展具有重要意义。基于2010—2020年贵州省9个市州的面板数据,利用super-SBM模型与Malmquist指数对工业碳排放效率进行静态和动态分析,并采用探索性时空数据分析方法揭示时空交互特征;基于此结合地理探测器和多尺度地理加权回归模型研究其影响因素。结果表明:①贵州工业碳排放效率整体呈上升趋势,年均增长率为8.45%。②技术进步是贵州工业碳排放效率提升的主要内动力。③工业碳排放效率空间自相关的时间路径长度呈现由东部市州向中、西部增大的趋势;贵州各市州的工业碳排放效率随时间演变呈现出较强的空间依赖关系。④对外开放程度、城市化水平、能源消耗强度、产业结构、重工业水平、生产力水平6个因素是影响工业碳排放效率的主导因子,且影响显著性出现不同程度的提高;对外开放程度、能源消耗强度与工业碳排放效率存在负相关,其余主导因子与工业碳排放效率呈正相关。

耦合InVEST与Geodetector模型的银川市生境质量时空演变特征及影响因子研究

银川市作为黄河流域重要的水源涵养区及国家重点生态功能区,其生境质量关乎区域生态安全和可持续发展。然而,针对银川市生境质量及其影响因子仍需深入研究。基于2000、2010年和2020年3期土地利用数据,耦合InVEST和Geodetector模型估算银川市20 a间生境质量,对研究区生境质量空间分布特征及影响因子进行分析。结果表明:(1)2000、2010年和2020年银川市平均生境质量指数分别为0.337、0.332和0.322,生境质量指数较低且20 a间略有下降,生境质量整体呈“南北高、中部低”的空间分布特征。(2)银川市生境质量在空间上存在着较强的空间集聚效应,莫兰指数呈下降趋势,集聚程度逐渐下降。(3)高程和土地利用类型是银川市生境质量的主要影响因子。各影响因子交互作用都存在增强效应,对生境质量的影响力随之增强,且以双因子增强为主,其次为非线性增强,其中高程与各因子的交互作用最为显著。研究结果可为银川市生态保护与高质量、可持续发展提供科学依据和决策支持。

宁夏水贫困地域分异的WPI-Geodetector测度与分析

水贫困是长期以来备受西北地区关注的焦点问题。以宁夏为典型案例,通过建立水贫困评价指标体系,采用WPI模型计算2011—2015年区域水贫困水平,并运用地理探测器诊断出水贫困分异的主导因素,揭示水贫困动力机制,总结出水贫困地域类型。结果表明:(1)水贫困指数时空分异显著。宁夏WPI值由2011年的0.393上升到2015年的0.404,水贫困呈现缓慢改善态势,但地区差距在扩大。(2)水贫困主导因素时空变化明显。从目标层看,资源系统对宁夏水贫困影响最大,但其决定力在变小;环境系统的决定力在增大;使用系统的决定力最小。从指标层看,年降水量等10个指标q值始终在0.8以上,而年径流深等7个指标对宁夏水贫困决定力在下降。从分地区看,银川市水贫困指数分异决定力q值>0.5的有6个指标,需要提高用水效率、降低用水压力和合理配置生态用水;石嘴山市水贫困指数分异决定力q值>0.5的有11个指标,客观表明作为资源型地区发展转型过程中的矛盾所在;吴忠市水贫困指数分异决定力q值>0.5的有13个指标,今后着力要加强用水效率、用水管理和水资源高效利用水平等的统筹协调;固原市水贫困指数分异决定力q值>0.5的有4个指标,重点要提高设施水平和污染治理能力;中卫市水贫困指数分异决定力q值>0.5的有15个指标,要突出提高用水效率和设施水平。(3)水贫困地域类型划分。在水资源共同约束背景下将研究区划分为途径(设施水平)约束型区域、能力—使用(用水管理—用水效率)约束型区域、环境(用水压力—生态环境)约束型区域等3种水贫困地域类型。

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.

Quantification of Effects of Natural Geographical Factors and Land-scape Patterns on Non-point Source Pollution in Watershed Based on Geodetector:Burhatong River Basin,Northeast China as An Example

Changes in natural geographic features and landscape patterns directly influence the hydrology and non-point source pollution processes in the watershed;however,to slow down non-point source pollution,it is necessary to distinguish their effects.But the non-point source pollution process is interactional as a result of multiple factors,and the collinearity between multiple independent variables limits our ability of reason diagnosis.Thus,taking the Burhatong River Basin,Northeast China as an example,the methods of hydrological simulation,geographic detectors,and redundancy analysis have been combined to determine the impact of natural geographic features and landscape patterns on non-point source pollution in the watershed.The Soil&Water Assessment Tool(SWAT)has been adopted to simulate the spatial and temporal distribution characteristics of total nitrogen and total phosphorus in the watershed.The results show that the proportions of agricultural land and forest area and the location-weighted landscape contrast index(LWLI)are the main indicators influencing the rivers total nitrogen and total phosphorus.The interaction of these indicators with natural geographic features and landscape configuration indicators also significantly influences the changes in total nitrogen(TN)and total phosphorus(TP).Natural geographical features and landscape patterns have different comprehensive effects on non-point source pollution in the dry and wet seasons.TN and TP loads are affected mainly by the change in landscape pattern,especially in the wet season.Although the ecological restoration program has improved forest coverage,the purification effect of increased forest coverage on the water quality in the watershed may be offset by the negative impact of increased forest fragmentation.The high concentration and complexity of farmland patches increase the risk of non-point source pollution spread to a certain extent.

基于PS-InSAR和GeoDetector的兰州主城区地表变形监测与驱动力分析

采用PS-InSAR技术,基于2014年12月-2017年1月的32幅Sentinel-1A卫星TOPS模式影像,获得兰州主城区地表的形变速率和时间序列形变量信息,利用GeoDetector对建成区、道路网密度、降水、气温、土地覆盖类型以及地质类型进行单因素和多因素交互的驱动力分析.结果表明,兰州市主城区地表形变速率介于-53.17~37.66 mm/a,总体较为稳定,多数区域(95.25%)的形变速率介于-5~5 mm/a.主城区地表变形是各影响因素的交互驱动,而不是单一因素的驱动,建成区∩道路网密度是主城区地表形变的主要影响因素.将GeoDetector方法应用于各驱动力对城市地表形变的解释力,为研究城市地表形变影响因素分析提供了新的思路.

Application of geodetector in sensitivity analysis of reference crop evapotranspiration spatial changes in Northwest China

Reference crop evapotranspiration(ET0)is an important parameter in the research of farmland irrigation management,crop water demand estimation and water balance in scarce data areas,therefore,it is very important to study the factors affecting the spatial variation of ET0.In this paper,the Penman-Monteith formula was used to calculate ET0 which is the dependent variable of elevation(Elev),daily maximum temperature(T_(max)),daily minimum temperature(Tmin),daily average temperature(T_(mean)),wind speed(U_(2)),sunshine duration(SD)and relative humidity(RH).The sensitivity analysis of ET0 was performed using a Geodetector method based on spatial stratified heterogeneity.The applicability of Geodetector in sensitivity analysis of ET0 was verified by comparing it with existing research results.Results show that RH,Tmax,SD,and Tmean are the main factors affecting ET0 in Northwest China,and RH has the best explanatory power for the spatial distribu‐tion of ET0.Geodetector has a unique advantage in sensitivity analysis,because it can analyze the synergistic effect of two factors on the change of ET0.The interactive detector of Geodetector revealed that the synergistic effect of RH and Tmean on ET0 is very significant,and can explain 89%of the spatial variation of ET0.This research provides a new method for sensitivity analysis of ET0 changes.

基于RFFLUS-InVEST-Geodetector耦合模型的平朔矿区生境质量时空演变及其影响因素

将土地利用模型(RFFLUS)、生态系统服务功能模型(InVEST)和地理探测器模型(Geodetector)耦合,分析平朔大型露采矿区1985—2025年土地利用变化,探索其生境质量的时空演变规律,并揭示影响生境质量的自然和人为因素。结果表明:(1)平朔矿区的主要土地利用类型为耕地、草地和建设用地;1985—2025年建设用地、草地面积增加,耕地大面积减少,耕地主要转为林地和草地,林地和水域减少较小。(2)生境质量等级分布与土地利用分布具有一致性,高质量生境主要分布于林地,低质量生境分布于建设用地;1985—2015年生境质量总体略有下降,煤矿开采是生境质量下降的主要原因;2025年仍存在生境下降风险,需要坚持生态复垦。(3)影响生境质量变化的主导影响因子是NDVI,其次为煤炭生产能力和GDP;在交互作用探测中,因子之间的交互作用均为增强,其中NDVI与GDP的交互作用最大。

基于贝叶斯时空层次模型(BSTHM)和地理探测器法(GeoDetector)对细菌性痢疾的环境风险评估

在全世界范围,细菌性痢疾是一个得到广泛关注的健康问题,具有显著的时空异质性,但大多数研究只是在某一区域单独实施了时间或空间层面的分析,其在不同地区的时空关联尚不清楚。考虑到山东省社会经济发展不平衡,东、中、西部差异明显,因此本研究首先利用贝叶斯时空层次模型(BSTHM)呈现山东省细菌性痢疾的时空异质性,其次利用地理探测器法(GeoDetector)分别从不同的区域(东、中、西部)来研究气象要素对此疾病的影响,且针对热点地区,量化社会经济要素及其两两的交互作用对其的解释力。结果表明,时间上,发病高峰出现在夏季。空间上,高风险地区主要分布在山东省东部和北部区县,且气象因子在不同地区对该病的影响也存在显著差异。山东省东部沿海地区最重要的两个主导因子是风速和日照时数,其解释力分别为0.28和0.22。山东省西部内陆地区前两个主导因子为平均温度和降水量,其解释力分别为0.47和0.32。山东省中部地区前两个主导因子为平均温度和风速,其解释力分别为0.66和0.48。本研究通过对山东省各个地区细菌性痢疾发病情况的时空异质性以及影响因素的分析发现,湿热的环境有利于细菌性痢疾的传播。另外,在热点地区,起主导作用的社会经济因子是第三产业比重,表明社会经济状况对细菌性痢疾的发病具有一定的影响作用,为本地区细菌性痢疾的控制和预防政策的制定提供理论依据,针对不同地区需要更具体的方案来预防、控制和分配医疗资源,以提高其疾病应对能力,减少该疾病造成的潜在损失。

基于Geodetector的山区耕地种植生产空间优化--以昭通市为例

为研究面向种植业生产适宜性的区域差异及其定量化评价。针对西南山区种植业生产适宜性的特殊问题,引入了地理探测器空间分析方法科学量化耕地种植生产适宜性评价指标权重,探索了提高山区耕地种植生产适宜性定量评价的科学性和准确性。以云南省昭通市为例,基于地理探测器模型科学量化各评价指标对耕地种植生产适宜性的贡献度,确定了其评价权重,结合加权叠加模型对昭通市山区耕地种植生产适宜性进行了评价,并对山区种植业生产空间进行了优化分析。结果表明:①通过地理探测可以科学定量评价指标对耕地种植生产适宜性的贡献度依次为:积温>年降水量>土壤厚度>坡度>土壤有机质>高程>地形起伏度,且能实现耕地种植生产适宜性准确定量评价;②昭通市现状耕地面积是4651.9 km^(2),耕地种植生产适宜区面积为4550.31 km^(2),通过优化调整后耕地生产空间面积为4741.18 km^(2),既能提出耕地空间分布优化方案,又能有效挖掘耕地生产空间潜力;③昭通市现状耕地中不适宜区主要受生态、坡度、土壤环境容量以及建设占用等因素的影响。研究结果可为山区农业生产合理开发利用和规划管理提供科学支撑。

Quantitative attribution analysis of soil erosion in different geomorphological types in karst areas: Based on the geodetector method

The formation mechanism and influencing factors identification of soil erosion are the core and frontier issues of current research. However, studies on the multi-factor synthesis are still relatively lacked. In this study, the simulation of soil erosion and its quantitative attribution analysis have been conducted in different geomorphological types in a typical karst basin based on the RUSLE model and the geodetector method. The influencing factors, such as land use type, slope, rainfall, elevation, lithology and vegetation cover, have been taken into consideration. Results show that the strength of association between the six influencing factors and soil erosion was notably different in diverse geomorphological types. Land use type and slope were the dominant factors of soil erosion in the Sancha River Basin, especially for land use type whose power of determinant(q value) for soil erosion was much higher than other factors. The q value of slope declined with the increase of relief in mountainous areas, namely it was ranked as follows: middle elevation hill> small relief mountain> middle relief mountain. Multi-factors interactions were proven to significantly strengthen soil erosion, particularly for the combination of land use type with slope, which can explain 70% of soil erosion distribution. It can be found that soil erosion in the same land use type with different slopes(such as dry land with slopes of 5° and above 25°) or in the diverse land use types with the same slope(such as dry land and forest with a slope of 5°), varied much. These indicate that prohibiting steep slope cultivation and Grain for Green Project are reasonable measures to control soil erosion in karst areas. Based on statistics of soil erosion difference between diverse stratifications of each influencing factor, results of risk detector suggest that the amount of stratification combinations with significant difference accounted for 55% at least in small relief mountain and middle relief mountainous areas. Therefore, the spatial heterogeneity of soil erosion and its influencing factors in different geomorphological types should be investigated to control karst soil loss more effectively.

Driving forces and prediction of urban land use change based on the geodetector and CA-Markov model:a case study of Zhengzhou,China

Exploring urban land use change is a classical problem in urban geography.Taking Zhengzhou as an example,this paper analyzes the spatial and temporal characteristics and driving factors of urban land use change,and simulates the spatial pattern of urban land use in the future.The results of the study show that the land use types in Zhengzhou city were mainly farmland and construction land,the area of forestland,grassland,water area,and unused land was smaller,and the main land use change was the transformation of farmland into construction land.The accuracy check of the simulated land use type data in 2020 showed that the kappa coefficient reached 0.9445,which met the accuracy requirement.Then,according to the predicted land use data in 2025,it was found that the area of grassland,construction land,and water area may have decreased,and the area of farmland,forestland and unused land may have increased.Based on the driving force analysis of land use changes,its prediction results can provide an important reference basis for the formulation and planning of policies related to urban construction.

基于Geodetector模型的鄂北岗地土壤有机质空间格局及影响因素分析——以枣阳市为例

为探明鄂北岗地耕地土壤质量及影响因素情况,以湖北省枣阳市为研究区,选择样点240个,以土壤有机质土壤有机质(SOM)含量为研究对象,运用经典统计学、地统计学对耕地土壤有机质含量空间格局进行分析,引入地理探测器Geodetector模型结合佩尔森(Pearson)相关系数法,得出影响枣阳市土壤有机质含量空间格局的显著性因子。结果表明:(1)枣阳市耕地土壤有机质含量分布空间格局呈弱变异,空间变异模型为球状模型拟合,分布格局呈现四周高,中心向东北向偏低的空间格局;(2)对土壤有机质含量单因子影响最强的5种因子均为降水量、土壤类型、NDVI、地表起伏度、公路距离;(3)各因子交互作用解释力均大于单因子作用解释力,表现为非线性协同作用。该研究将地理信息科学与遥感、统计学以及地理探测器等多学科技术相结合,为探索控制鄂北岗地土壤养分的空间格局及其因素提供方法途径,尝试为水稻种植区及盆地地区进行农业管理、施肥管控及土壤养分空间预测等研究工作提供基础。

2000-2020年陕西省植被NDVI时空变化及气候因子探测

[目的]揭示陕西省不同生态系统植被时空变化,厘清不同气候因子及相互作用对植被变化的影响机制,为区域生态环境保护提供理论依据。[方法]基于MODIS NDVI及年均高温、年均低温、年均温、年总降水数据,采用Theil-Sen Median趋势、偏相关、地理探测器等方法,分析了2000—2020年各地貌分区植被NDVI时空变化特征,结合变量分离探究了植被NDVI变化与降水、气温的内部关联和响应机制。[结果]2000—2020年陕西植被NDVI波动增加,速率为5.9%/10 a,速率大小为陕北>关中>陕南;全省多年植被NDVI值为0.71,南高北低;植被NDVI显著改善区域占比67%,分区占比为陕北>陕南>关中。2000—2020年陕西气候因子随时间波动变化,速率大小为陕南>关中>陕北,空间上呈现年均高温降低、年均低温上升、年均温降低、降水增加。2000—2020年陕西及各分区植被NDVI与年均高温整体呈负相关,与年均低温、年均温、年总降水量呈正相关;全省及陕北年总降水贡献最大,关中和陕南年均高温贡献最大;年均高温与年总降水交互主导全省、陕北及陕南植被NDVI变化,年均温与年总降水的交互主导关中植被NDVI变化。[结论]研究期陕西及各分区植被整体变好,各分区植被对气候的响应关系、各因子的贡献及其相互作用不同,降水和年均高温、年均温的交互显著影响植被NDVI变化。

黄河水源涵养区土地利用变化的区域分异特征及未来格局

解析黄河水源涵养区历史和未来的土地利用/覆盖变化(LUCC)对于保护黄河流域水土资源和维系生态系统服务功能等具有重要意义。在研究区及其三大子区(Ⅰ黄河上游水源区、Ⅱ渭河南山区、Ⅲ伊洛河区),基于1990—2020年的土地覆盖产品数据,采用转移矩阵、地理探测器、CA-Markov模型等方法,分析LUCC特征、探测驱动因素和预测未来期(2025年)空间格局。结果表明:自2000s以来,研究区及分区地类呈现退耕还林还草和建设用地快速增长的2条主线变化趋势,其中,全区LUCC主要受到粮食产量、高程和人口密度驱动,Ⅰ区、Ⅱ区和Ⅲ区LUCC的主导驱动因素分别是高程、人口密度和粮食产量。未来生态保护情景相对于自然发展情景,耕地和建设用地扩张将受到约束,能更好地保障生态用地面积以支撑水源涵养等生态服务功能。

长白山地区乡村聚落空间格局及影响因素

[目的]探究长白山地区乡村聚落空间分布特征和影响因素,理解近代大量移民背景下中高纬度山区乡村居民点空间格局的形成过程和机制。[方法]综合运用核密度分析、空间自相关分析、空间回归分析和地理探测器等方法,解析长白山地区乡村聚落的空间格局并揭示其影响因素。[结果]长白山地区的乡村聚落呈现出明显的集聚分布特征,存在东北和西南两个聚集区域;各类空间回归方法中,空间杜宾误差模型的拟合效果最佳,结果表明耕地资源和高程是影响长白山地区乡村聚落格局最重要的因素,且对于邻域居民点的布局存在“推拉”效应,潜在未知变量(如土壤质量、文化因素等)也是导致乡村聚落表现出空间自相关的因素;地理探测器分析的结果进一步证实了耕地资源和高程的重要性,同时也表明与县级以上城市、建制镇、主要交通线、主要河流的距离等区位因素以及地形坡度同样对乡村聚落分布有显著影响,且彼此间均呈现出交互增强的特点。[结论]主要由移民形成的中高纬度山区乡村聚落的空间格局是多因素综合作用的结果,其中耕地资源和高程的影响更为显著;空间回归杜宾(误差)模型能够同时探测自变量和因变量的空间自相关问题,在乡村聚落空间格局的研究中具有一定的适用性。

1995—2021年榆神府矿区景观生态风险时空异质性研究

煤炭开采作为一种强烈的人类活动影响了矿区生态系统服务功能和经济价值,而相同气候环境不同地貌长时序空间尺度上矿区开发后景观生态风险演化特征对比研究较欠缺。以半干旱榆神府矿区为研究对象,基于1995—2021年Landsat数据,通过构建景观生态风险指数以及空间统计学分析,探究了近30年来矿区黄土丘陵和覆沙地两种不同地貌类型景观生态风险时空演化特征。结果表明:①1995—2000年间榆神府矿区两种地貌生态风险格局整体无明显变化;2000—2010年间黄土丘陵区景观低生态风险区向更高等级生态风险转变,2010年黄土丘陵区中高、中和中低生态风险区占比为70%,而覆沙区高生态风险区域虽增加但不显著,仍以中低生态风险区为主,占比为31%;2010—2021年矿区景观生态格局向着均匀化的方向发展,黄土丘陵区逐渐稳定为以中低、中和中高生态风险等级为主,2021年3个风险等级占比为74%,覆沙区也形成了以低、中低和中生态风险等级为主的分布情况,3个风险等级占比为77%,生态风险等级逐渐趋于稳定,生态风险格局不断优化;②1995—2021年榆神府矿区景观生态风险在空间上有明显的集聚分布特征,局部生态风险具有明显的热点、冷点区域,生态风险热点主要分布于煤炭开发时间长、地质环境损害较严重的东北部和东南部黄土丘陵区,生态风险冷点主要位于目前仍处于资源勘探普查的中部覆沙区;③人为干扰度是对榆神府矿区景观生态风险影响最大的驱动因子,黄土区与覆沙区决定力q值大小分别在0.49~0.72和0.38~0.55,在黄土丘陵区植被覆盖度是第二大影响因子,决定力q值在0.27~0.46之间,气温因子影响程度次之,而覆沙区植被覆盖度与气温的影响程度在1995—2021年间互有高低,两种地貌类型景观生态风险均受高程影响最小,q值均小于0.10;④景观生态风险时空演化相关分析表明,榆神府矿区生态修复过程中,应根据不同地貌类型特征优化景观生态格局,在黄土丘陵区应优化开采方案减小地表生态损害、积极开展损害地主动修复策略,在覆沙区由于开采对该区景观格局影响较小,矿区损害地可实施引导型生态自修复等以保障矿区生态安全。

能源富集区土地利用变化及驱动因素分析——以山西省为例

对山西省土地利用变化及驱动因素研究可以为黄河流域生态保护与高质量发展战略落实提供科学依据。基于1980-2020年7期影像数据,利用土地利用动态度、转移矩阵及地理探测器对山西省土地利用变化特征进行分析并探究土地利用变化影响因素。研究期内最主要的3种土地利用类型为耕地、草地和林地。建设用地动态度最大。耕地、草地及林地三者之间的相互转化最为突出。因子探测结果表明,不同研究时期山西黄土高原土地利用变化的主导因子不同。高程、坡度、土壤类型、人口密度、GDP等5个因子的平均解释力较强。因子间交互作用相对于单因子均有所增强,GDP、人口密度、城镇化等因子与其他因子间交互作用增强最为显著。

A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

Analysis of the spatiotemporal trends and influencing factors of Hyphantria cunea in China

In recent years,the situation of the Hyphantria cunea(Drury)(Lepidoptera:Erebidae),infestation in China has been serious and has a tendency to continue to spread.A comprehensive analysis was carried out to examine the spa-tial distribution trends and influencing factors of H.cunea.This analysis involved integrating administrative division and boundary data,distribution data of H.cunea,and envi-ronmental variables for 2021.GeoDetector and gravity analysis techniques were employed for data processing and interpretation.The results show that H.cunea exhibited high aggregation patterns in 2021 and 2022 concentrated mainly in eastern China.During these years,the focal point of the infestation was in Shandong Province with a spread towards the northeast.Conditions such as high vegetation density in eastern China provided favorable situations for growth and development of H.cunea.In China,the spatial distribution of the moth is primarily influenced by two critical factors:precipitation during the driest month and elevation.These play a pivotal role in determining the spread of the species.Based on these results,suggestions are provided for a mul-tifaceted approach to prevention and control of H.cunea infestation.