Landscape ecological risk assessment and its driving factors in the Weihe River basin,China

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development.Based on land use data of the Weihe River basin in 2000,2010,and 2020,with the support of Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS),GeoDa,and other technologies,this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk.Results showed that land use structure of the Weihe River basin has changed significantly,with the decrease of cropland and the increase of forest land and construction land.In the past 20 a,cropland has decreased by 7347.70 km2,and cropland was mainly converted into forest land,grassland,and construction land.The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved,and land use pattern became more concentrated.Meanwhile,landscape ecological risk of the Weihe River basin has been improved.Severe landscape ecological risk area decreased by 19,177.87 km2,high landscape ecological risk area decreased by 3904.35 km2,and moderate and low landscape ecological risk areas continued to increase.It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious,especially in the contiguous areas of high ecological risk,such as Tianshui,Pingliang,Dingxi areas and some areas of Ningxia Hui Autonomous Region.Landscape ecological risk showed obvious spatial dependence,and high ecological risk area was concentrated.Among the driving factors,population density,precipitation,normalized difference vegetation index(NDVI),and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin.The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin,providing crucial insights for sustainable management in the region.

Uphill or downhill?Cropland use change and its drivers from the perspective of slope spectrum

The continuous decrease of low-slope cropland resources caused by construction land crowding poses huge threat to regional sustainable development and food security.Slope spectrum analysis of topographic and geomorphic features is considered as a digital terrain analysis method which reflects the macro-topographic features by using micro-topographic factors.However,pieces of studies have extended the concept of slope spectrum in the field of geoscience to construction land to explore its expansion law,while research on the slope trend of cropland from that perspective remains rare.To address the gap,in virtue of spatial analysis and geographically weighted regression(GWR)model,the cropland use change in the Yangtze River Basin(YRB)from 2000 to 2020 was analyzed and the driving factors were explored from the perspective of slope spectrum.Results showed that the slope spectrum curves of cropland area-frequency in the YRB showed a first upward then a downward trend.The change curve of the slope spectrum of cropland in each province(municipality)exhibited various distribution patterns.Quantitative analysis of morphological parameters of cropland slope spectrum revealed that the further down the YRB,the stronger the flattening characteristics,the more obvious the concentration.The province experienced the greatest downhill cropland climbing(CLC)was Shannxi,while province experienced the highest uphill CLC was Zhejiang.The most common cropland use change type in the YRB was horizontal expansion type.The factors affecting average cropland climbing index(ACCI)were quite stable in different periods,while population density(POP)changed from negative to positive during the study period.This research is of practical significance for the rational utilization of cropland at the watershed scale.

Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Multi-scenario Simulation for 2060 and Driving Factors of the Eco-spatial Carbon Sink in the Beibu Gulf Urban Agglomeration, China

Since China announced its goal of becoming carbon-neutral by 2060, carbon neutrality has become a major target in the development of China's urban agglomerations. This study applied the Future Land Use Simulation(FLUS) model to predict the land use pattern of the ecological space of the Beibu Gulf urban agglomeration, in 2060 under ecological priority, agricultural priority and urbanized priority scenarios. The Integrated Valuation of Ecosystem Services and Trade-offs(In VEST) model was employed to analyse the spatial changes in ecological space carbon storage in each scenario from 2020 to 2060. Then, this study used a Geographically Weighted Regression(GWR) model to determine the main driving factors that influence the changes in land carbon sinking capacity. The results of the study can be summarised as follows: firstly, the agricultural and ecological priority scenarios will achieve balanced urban expansion and environmental protection of resources in an ecological space. The urbanized priority scenario will reduce the carbon sinking capacity. Among the simulation scenarios for 2060, carbon storage in the urbanized priority scenario will decrease by 112.26 × 10^(6) t compared with that for 2020 and the average carbon density will decrease by 0.96 kg/m^(2) compared with that for 2020. Carbon storage in the agricultural priority scenario will increase by 84.11 × 10^(6) t, and the average carbon density will decrease by 0.72 kg/m^(2). Carbon storage in the ecological priority scenario will increase by 3.03 × 10^(6) t, and the average carbon density will increase by 0.03 kg/m^(2). Under the premise that the population of the town will increases continuously, the ecological priority development approach may be a wise choice.Secondly, slope, distance to river and elevation are the most important factors that influence the carbon sink pattern of the ecological space in the Beibu Gulf urban agglomeration, followed by GDP, population density, slope direction and distance to traffic infrastructure.At the same time, urban space expansion is the main cause of the changes of this natural factors. Thirdly, the decreasing trend of ecological space is difficult to reverse, so reasonable land use policy to curb the spatial expansion of cities need to be made.

Predicting the Seasonal NDVI Change by GIS Geostatistical Analyst and Study on Driver Factors of NDVI Change in Hainan Island, China

As Hainan Island belonged to tropical monsoon influenced region, vegetation coverage was high. It is accessible to acquire the vegetation index information from remote sensing images, but predicting the average vegetation index in spatial distributing trend is not available. Under the condition that the average vegetation index values of observed stations in different seasons were known, it was possible to qualify the vegetation index values in study area and predict the NDVI (Normal Different Vegetation Index) change trend. In order to learn the variance trend of NDVI and the relationships between NDVI and temperature, precipitation, and land cover in Hainan Island, in this paper, the average seasonal NDVI values of 18 representative stations in Hainan Island were derived by a standard 10-day composite NDVI generated from MODIS imagery. ArcGIS Geostatistical Analyst was applied to predict the seasonal NDVI change trend by the Kriging method in Hainan Island. The correlation of temperature, precipitation, and land cover with NDVI change was analyzed by correlation analysis method. The results showed that the Kriging method of ARCGIS Geostatistical Analyst was a good way to predict the NDVI change trend. Temperature has the primary influence on NDVI, followed by precipitation and land-cover in Hainan Island.

Characteristics and driving factors of abandoned cultivated land in the hilly regions of southern China:A case study in Longnan,Jiangxi Province

The abandonment of cultivated land in southern China was gradually obvious.This research aims to provide a reference for solving the abandonment of cultivated land in hilly regions and promote rural development in China.We examined Longnan county located in the hilly regions of southern China as an example,where abandoned cultivated land is very common.We analyzed its land use data with a field survey to identify the abandoned cultivated land and geospatial characteristics.From the two aspects of social and natural factors,we analyzed the factors driving cultivated land abandonment with the help of Geodetector.The results showed that in 2019,the total area of the abandoned cultivated land in Longnan county was 4,962.35 hm^(2),covering 39.51% of this region.Among the topographic factors,the abandonment rate is positively correlated with elevation and slope gradient,but not with slope direction.Among the land parcel conditions,the abandonment rate is positively correlated with the access to road network and cultivation distance from settlement.At the county level,the abandonment of cultivated land in study area was affected by multiple factors,among which,the direct factor was the reduction in the labor force,such as the decrease of farming laborers and the increase of female population,which made farming unsustainable.Changes in production factors also promoted transformations in farmers’motivation to engage in production,such as the decrease of grain crops and the increase of cash crops,which was the indirect factor affecting cultivated land abandonment.The development of the rural nonagricultural industry affected farmers’enthusiasm,such as the decrease of farming households,which was the fundamental factor leading to cultivated land abandonment in this area.

Spatiotemporal variations of drought and driving factors based on multiple remote sensing drought indices:A case study in karst areas of southwest China

Droughts are recurrent in southwest China due to the fragility and sensitivity of the karst environment.These events have serious impacts on local agricultural output,ecological diversity,and social stability.Understanding spatiotemporal variations and driving factors of drought in this area is of extreme importance for effective mitigation measures.The karst areas situated in southwest China were spatially divided into seven sub-regions according to the topography and degree of karst development.Drought indices,including vegetation condition index(VCI),temperature condition index(TCI),vegetation health index(VHI),normalized vegetation water supply index(NVSWI),and temperature vegetation drought index(TVDI),were calculated from MODIS data during 2000 and 2018for each sub-region,and drought patterns were examined.The results show that droughts were found to be concentrated in sub-regions such as karst basin,karst plateau,karst gorge,and karst depression areas.Furthermore,there were more drought conditions in karst areas than in non-karst areas.In addition,improvements to drought situation in the study period are significant(p<0.05),and mitigation areas respectively account for 80.1%(NVSWI),74.2%(VCI),74.2%(VHI),30.1%(TCI)and 33.2%(TVDI)of the study area,while drought expands slightly(<3.4%)in areas undergoing urban construction.Pearson's correlation coefficients between drought indices and temperature are generally above 0.5 in all sub-regions.However,the correlation coefficients between drought indices and precipitation mostly fall within the range of 0.3-0.4,indicating a weaker correlation.Our explanation for the spatiotemporal patterns of drought is that karst phenomena are the natural basis of drought and agricultural production is one of important driving forces.Positive changes of drought conditions have benefited from efforts to control rocky desertification and restore ecosystems over the past years.

Factors driving surface deformations in plain area of eastern Zhengzhou City,China

With the rapid socio-economic development and urban expansion,land subsidence has emerged as a major environmental issue,impeding the high-quality development of the plain area in eastern Zhengzhou City,Henan Province,China.However,effective prevention and control of land subsidence in this region have been challenging due to the lack of comprehensive surface deformations monitoring and the quantitative analysis of the factors driving these deformations.In order to accurately identify the dominant factor driving surface deformations in the study area,this study utilized the Persistent Scattered Interferometric Synthetic Aperture Radar(PS-InSAR)technique to acquire the spatio-temporal distribution of surface deformations from January 2018 to March 2020.The acquired data was verified using leveling data.Subsequently,GIS spatial analysis was employed to investigate the responses of surface deformations to the driving factors.The findings are as follows:Finally,the geographical detector model was utilized to quantify the contributions of the driving factors and reveal the mechanisms of their interactions.The findings are as follows:(1)Surface deformations in the study area are dominated by land subsidence,concentrated mainly in Zhongmu County,with a deformation rate of−12.5–−37.1 mm/a.In contrast,areas experiencing surface uplift are primarily located downtown,with deformation rates ranging from 0 mm to 8 mm;(2)Groundwater level,lithology,and urban construction exhibit strong spatial correlations with cumulative deformation amplitude;(3)Groundwater level of the second aquifer group is the primary driver of spatially stratified heterogeneity in surface deformations,with a contributive degree of 0.5328.The contributive degrees of driving factors are significantly enhanced through interactions.Groundwater level and the cohesive soil thickness in the second aquifer group show the strongest interactions in the study area.Their total contributive degree increases to 0.5722 after interactions,establishing them as the primary factors influencing surface deformation patterns in the study area.The results of this study can provide a theoretical basis and scientific support for precise prevention and control measures against land subsidence in the study area,as well as contributing to research on the underlying mechanisms.

中国四大城市群碳排放驱动因素时空分解研究

城市群是中国经济发展和能源消耗的集聚区域,也是碳排放的主要来源。研究中国典型城市群碳排放的时空演变特征及其影响因素对实现“双碳”目标具有重要意义。文中应用ST-IDA模型(时空指数分解分析法)和LMDI(对数平均迪氏指数法)分解法,分析2000—2019年京津冀、长三角、珠三角和成渝城市群的碳排放驱动因素(人口规模、经济水平、产业结构、能源强度和能源结构)。研究发现:2000—2019年间,四大城市群能源活动碳排放总体趋势均由高速增长阶段步入平稳增长阶段,其中成渝城市群已基本实现碳达峰;能源强度效应是影响碳排放空间差异的主要因素;人口规模扩张、经济发展水平提高和能源强度上升是促进碳排放增长的主要因素,产业结构和能源消费结构优化起到抑制作用;四大城市群碳排放的时空演变主要取决于工业部门。鉴于四大城市群呈现出不同的碳排放特征,未来应探索差异化、多元化的城市群减排路径,促进城市群碳减排。

中国县域数字乡村发展的空间格局及驱动因素研究

基于北京大学新农村发展研究院公布的2018年中国1880个县域数字乡村指数数据,采用探索性空间数据分析、冷热点分析和地理探测器等方法,考察中国县域数字乡村发展的空间格局及其驱动因素。结果表明:1)中国县域数字乡村发展水平呈现明显的“梯度化”特征,数字乡村发展水平由东部向西部逐渐递减,数字乡村在长三角地区呈“片状分布”,而在西部地区和东北地区呈“点状分布”。2)中国县域数字乡村发展呈现明显的空间集聚特征,在经济发展水平较高、网络基础设施较为完善的长三角地区形成“高-高”集聚。数字乡村发展呈现次热点区和热点区围绕高热点区集聚的“中心-外围”结构形态。在热点区与冷点区之间形成“T”字形狭长地带,将热点区与冷点区分隔开。3)中国县域数字乡村发展的总体基尼系数为0.0359,其中数字乡村的区域间差距是数字乡村发展总体差异的主要来源。4)产业经济、人口教育、财政金融和基础设施的空间分异特征对数字乡村空间分布具有较强的影响。

中国式现代化的形态建构、动力要素与实践进路——以人与自然和谐共生为分析视角

中国式现代化是中国共产党立足人类文明和现代化发展规律,坚持把马克思主义基本原理同中国具体实际相结合、同中华优秀传统文化相结合的重大成果,超越了西方现代化道路与模式。文本以人与自然和谐共生为视角展开对中国式现代化的形态建构,从政治领导力量、文化因素、经济因素、制度因素和技术因素等方面探讨实现中国式现代化的动力要素;指出中国式现代化的推进始终坚持中国共产党领导,坚持中国特色社会主义,坚持以人民为中心的发展思想,坚持人与自然和谐共生,走生态文明发展的现代化新道路,追求“五个文明”相协调和全体人民共同富裕的价值目标,既为后发国家独立自主实现现代化提供了全新选择,又创造出人类文明新形态。

工业互联网使用如何促进中小企业智能化转型:驱动因素与赋能机制

工业互联网是中小制造企业智能化转型的重要载体。基于TOE框架与企业能力理论,构建工业互联网使用驱动因素及赋能机制综合研究模型。结果表明:第一,工业互联网使用的重要驱动因素包括资源就绪、数字能力、业务复杂度与政府支持。资源就绪是中小制造企业使用工业互联网的前提,数字能力是中小制造企业使用工业互联网的技术基础,业务复杂度可以反映中小制造企业使用工业互联网的内在需求,政府支持可为中小制造企业使用工业互联网提供保障。第二,工业互联网通过培育两种核心能力赋能企业转型升级。工业互联网使用对中小制造企业智能化转型具有显著赋能效应,集成互联能力通过促进企业互联互通实现智能化转型,协同融合能力通过促进企业内外协同实现智能化转型。结论可为中小企业“上云上平台”和智能化转型提供理论依据与实践启示。

Evaluation of Postoperative Psychological Distress and Its Driving Factors in Patients with Oral and Maxillofacial Malignant Tumors

Objective:To explore and analyze the evaluation and driving factors of postoperative psychological pain inpatients with oral and maxillofacial malignant tumors.Methods:Relevant data were collected from 80 patients with oral and maxillofacial malignant tumors who attended the outpatient clinic for follow-up consultations between May 2021 to May 2023.The patients used the psychological distress thermometer(DT)to circle words that best described their experiences in the past week,assigning a numerical value(0-10)to indicate their pain level on each day.The scoring results were employed to assess the psychological pain in these patients.A self-developed patient basic information questionnaire was utilized to record demographic details.Logistic regression analysis was employed to evaluate patients two weeks after surgery,focusing on the assessment of psychological distress and the identification and location of driving factors.Results:Following evaluation,the results revealed that the average postoperative DT score for the 80 patients with oral and maxillofacial malignant tumors was 4.53±1.98 points.Scores<4 points indicated no psychological pain(Group N)in 48 cases,while scores≥4 points indicated psychological pain(Group Y)in 32 cases.The differences in postoperative DT scores among patients with varying educational levels,fears and worries about disease progression,economic problems,sleep problems,level of hope,and oral pain were statistically significant(P<0.05).Multiple linear regression analysis results indicated that education level,fear and worry about disease progression,economic problems,sleep problems,level of hope,and oral pain are driving factors of postoperative psychological pain in patients with oral and maxillofacial malignant tumors(P<0.05).Conclusion:The postoperative psychological pain level in patients with oral and maxillofacial malignant tumors is at a moderate level.Educational level,fear and worry about disease progression,economic problems,sleep problems,level of hope,and oral pain were identified as driving factors for postoperative psychological pain in these patients.

东北三省农业碳排放时空分异特征及其关键驱动因素

推动农业低碳发展是应对气候威胁和农业面源污染的有效途径。本文基于IPCC和农用物资投入数据核算2000—2019年东北三省农业碳排放,利用空间自相关等方法分析其时空分异特征,通过LMDI指数分解模型和地理探测器探究农业碳排放驱动因素及其交互作用关系。结果表明:1)东北三省2015年农业碳排放总量达到峰值,约为1759.66万t,较2000年(1048.19万t)增加67.88%,年均递增4.53%;研究期整体呈现“先上升后下降”态势,碳排放增量变动可划分为“波动上升期(2000—2009年)—过渡期(2010—2015年)—平稳下降期(2016—2019年)”3个阶段。化肥施用是主要碳源,占比75.12%。2)分解模型测算结果表明,农业生产效率、农业产业结构和农业劳动力规模对碳排放具有抑制作用,其碳减排比例分别为207.31%、21.56%、20.72%;农业经济发展水平对碳排放表现出较强的推动作用,实现349.59%的碳增量。3)相较于单因子来说,农业经济发展水平、农业生产效率与农业产业结构之间交互结果对农业碳排放的影响呈非线性增强特征,农业劳动力规模与其他因素叠加均呈现出双因子增强的作用效果。以上研究结果表明东北三省农业碳排放受周边地区影响且影响程度不断加强,同时碳排放关键驱动因素之间存在协同作用。本研究成果为推动农业低碳发展提供理论基础与政策依据。

地理探测器方法下长江经济带(江苏段)土地城镇化时空演变及驱动因子分析

定量分析土地城镇化时空演变驱动力可为城市可持续发展提供科学依据。本文将对地观测数据与人口、经济数据相结合,以长江经济带(江苏段)沿江8市为例,基于2000—2020年5期Landsat影像、夜间灯光影像、年末人口总数和GDP,采用GeDetector对其近20年间空间、人口及经济维度的演化及多尺度分化的进程进行探讨,分析土地城镇化时空演变规律及驱动因子。研究结果表明:(1)城镇化过程直接驱动地表类型的变化,且人口数量和GDP总量能够作为城镇化过程强度的定量指标;(2)夜间灯光(NTL)数据耦合人口或经济统计数据能够较好解释区域城镇化过程中的时空差异;(3)长江经济带(江苏段)沿江8市城镇化过程经历了由数量向质量的转变。研究结果为引导城镇建设用地合理配置提供科学依据,为城市可持续发展提供决策参考。

新疆亚洲玉米螟暴发成灾驱动机制分析及其治理对策

亚洲玉米螟Ostrinia furnacalis(Guenée)为新疆玉米生产中的主要害虫,2010年以来呈整体暴发态势,并造成了极为严重的产量损失,已成为制约当前新疆玉米高产稳产和粮食产能提升最重要的害虫,然而有关其暴发成灾的驱动机制尚不清楚。为此,本文通过分别概述近年来新疆气候、作物种植结构、栽培耕作措施以及亚洲玉米螟田间发生世代的变化情况,综合分析了上述变化对亚洲玉米螟越冬幼虫存活、个体生长发育、取食偏好和种群增殖扩张等方面造成的具体影响。在此基础上,结合已有大量研究成果,系统解析了新疆绿洲生态区亚洲玉米螟种群暴发成灾的驱动机制。此外,本文还介绍了新疆植保科技工作者在亚洲玉米螟综合治理技术研究与应用方面取得的相关成果,简述了田间治理采取的措施和取得的防治成效,最终为亚洲玉米螟的持续有效治理提供一定参考。

四川省大豆生产格局变化及驱动因素研究

四川省是我国13个粮食主产区之一,也是我国大豆种植的新兴地区和西南产区的重要组成,研究大豆生产格局对四川省落实粮食安全战略、推动西南地区大豆产业发展具有重大意义。文章基于2000—2020年四川省183个区市县的面板数据,运用空间基尼指数、地理集中度系数、空间转移系数、探索性空间数据分析、最优地理探测器分析了大豆生产的时空格局变化及驱动因素。研究结果发现:1)2000—2020年,四川省大豆产能波动上升,空间分布极不均衡,聚集水平逐步上升,并逐步向川中丘陵区集中;2)大豆生产存在较强的正向空间相关性,总体表现为高-高聚集和低-低聚集;3)资源要素、比较收益、地理气候、经济社会等因素对大豆生产格局变化的影响均高度显著,且呈现非线性增强、双因子增强的交互效应。资源要素投入、比较收益、海拔高程长期以来对大豆生产格局的影响较为显著且呈波动上升趋势,气温、乡村家庭规模的影响力提升较快,交通条件、地区GDP的影响力则总体呈下降趋势,耕作制度长期以来驱动力最弱。基于此,四川省大豆生产应着力破解耕地资源细碎化与劳动力短缺等资源环境约束,大力发展生产性服务业,全面提升大豆生产机械化水平。通过强化科技创新提升川豆单产,并进一步优化大豆生产、农机、服务、保险等环节的政策保障。同时,应重点关注气候变化引发的干旱等自然风险,健全农业领域自然灾害风险预警与防范机制,以进一步强化大豆产业的综合风险抵御能力。

基于DPSIR模型的秦岭北麓陕西城市群水安全评估及驱动因子识别

水安全评估是水资源开发利用与区域发展的基础。为了加强秦岭北麓陕西城市群可持续建设,助力秦岭“中央水塔”绿色发展,基于DPSIR模型构建秦岭北麓陕西城市群水安全评估体系,结合主客观综合赋权法与模糊综合评价法对区域2011~2021年水安全进行评估分级,并利用灰色关联度法分析其水安全的驱动因子。结果表明:秦岭北麓陕西城市群2011~2021年水安全状态变化具有一致性,波动中稳步提升。西安市水安全等级相较宝鸡市、渭南市上升趋势显著,于2021年转为无警状态;宝鸡市一直保持低警状态,而渭南市长期处于中警状态。陕西城市群的水安全状态及发展趋势与人均GDP、年降水量、人均用水量等10个驱动因子密切相关,其中经济因子是水安全等级呈现一致性的根本保证,社会和生态因子则是造成水安全差异性的主要原因。

中国省域交通碳排放空间关联格局的驱动特征

利用我国2003~2020年省级交通碳排放数据,基于模体结构分析和指数随机图模型对中国交通碳排放空间关联网络的宏观格局、微观连通和驱动特征进行研究.结果表明,中国交通碳排放空间关联网络呈现周期波动特征,等级均衡化发展和核心虹吸的空间分布,演化出少核心—多半核心—边缘新格局,碳转移路径表现出明显的空间惰性与时间惯性以及区域集中性;经纪属性在推动跨区域交通协同减排的经济活动中发挥重要作用;内生网络的互惠性、连通性、集聚性对交通碳排放网络形成具有重要驱动作用,行为者属性中发出、接收、抑制、互惠的驱动关系明显,外部网络的影响具有明显的地理邻近效应,呈现出地理距离衰减规律特征.由此提出短期重大项目驱动,推动绿色交通转型联动;中期提升协同水平,加强跨区域交通碳减排协调机制;长期顶层零碳设计,工业化-产业化-市场化全面协同等政策建议.

不同草原防火政策下内蒙古草原火灾发生风险及其驱动因素的研究

草原火灾是草原生态系统中重要的干扰因子之一,不同时期的草原防火政策可能会导致草原火灾发生概率及驱动因素发生变化。本研究基于内蒙古1981~2020年草原火灾数据,以新旧《草原防火条例》实施时间(旧《草原防火条例》1993年10月5日颁布并实施,新《草原防火条例》2008年11月19日颁布并于2009年1月1日起实施)为界线,通过随机森林模型分4个时期(1981~2020年、1981~1993年、1994~2008年、2009~2020年)对内蒙古草原火灾发生概率与驱动因素进行比较与分析,并绘制草原火灾风险等级区划图。结果表明:(1)4个时期建模的全样本AUC在0.930~0.940之间,精度优异。(2)在不同时期,气象因素(日平均相对湿度、气温日较差等)始终是影响草原火灾的主导因素,海拔、距火点最近公路距离等因素也是内蒙古草原火灾发生的重要驱动因素;(3)1981~1993年和1981~2020年草原火灾风险区基本相似,中、高、极高草原火灾风险区主要集中在呼伦贝尔市大部分地区和兴安盟北部,1994~2008年中、高、极高草原火灾风险区主要集中在呼伦贝尔市,而2009~2020年中、高、极高草原火灾风险区主要集中在呼伦贝尔市西部、锡林郭勒盟北部、阿拉善盟东南部、乌海市和鄂尔多斯市东部。