Spatial Analysis Function Research Articles

Remote Sensing and GIS in Natural Resource Management: Comparing Tools and Emphasizing the Importance of In-Situ Data

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Analysis of the Spatial Constraints of Urban Underpass Channels Based on BIM

The construction of urban underpasses faces various spatial constraints, which will lead to deformation or failure of the structures if not satisfied. The introduction of BIM (Building Information Modeling) and other modern information technology to achieve rapid and accurate spatial constraint analysis of the design scheme is of great significance to ensure the quality and safety of the underpass and adjacent structures. Firstly, through extensive literature collection and survey, the key spatial constraint indicators of underpasses in dense urban spaces and the evaluation rules of each indicator are determined. Then, an SQL (Structured Query Language)-based database of spatial constraint rules for underpasses in dense urban space areas is constructed, and the spatial constraint analysis process for underpasses in dense urban space areas is proposed. Finally, based on Building Information Modeling, programming support, and a development environment provided by C# and Visual Studio, a spatial constraint analysis system for underpasses in dense urban space areas is developed to realize complex spatial constraint analysis functions. The application practice of the system is carried out by taking a city underpass project as an example. The results show that in the design of urban underpass engineering, the spatial constraint analysis system designed in this paper and the calculation method adopted can simplify the model transmission process, save on labor costs, and reduce the error of the calculation results.

Mapping World: A Comparative Analysis of Functionality and Usability between QGIS and ArcGIS

The current review conducts a comprehensive comparison of Quantum GIS (QGIS) and ArcGIS in the context of healthcare applications, aiming to assist users in selecting the most appropriate GIS platform for their specific needs. Geographic Information Systems (GIS) are essential tools in healthcare, providing capabilities for spatial analysis, disease mapping, and resource allocation. Driven by the need to improve healthcare delivery and public health strategies, this study compares the functionalities, costs, and support systems of QGIS and ArcGIS. The major objective is to determine which GIS platform offers the best balance of cost-efficiency and performance across various healthcare scenarios. The methodology encompasses a detailed comparative analysis, incorporating quantitative data from user satisfaction surveys, performance evaluations, and real-world case studies. Findings reveal that QGIS, as an open-source GIS software, is highly cost-effective and customizable, making it particularly advantageous for budget-limited projects and smaller organizations requiring flexibility. Its extensibility via plugins and robust user community fosters continuous innovation and improvement. QGIS proves exceptionally effective in healthcare applications such as disease mapping and accessibility analysis, where customization and cost-efficiency are critical. Conversely, ArcGIS is distinguished by its advanced analytical capabilities and strong performance in handling complex, large-scale studies. Its sophisticated tools and comprehensive spatial analysis functionalities are essential for thorough healthcare research and planning. Although ArcGIS incurs higher costs, its extensive feature set, professional support, and exhaustive documentation justify the investment for large organizations and research institutions engaged in detailed GIS projects. In conclusion, both QGIS and ArcGIS exhibit significant strengths in key healthcare applications. QGIS excels in affordability and flexibility, while ArcGIS provides superior analytical power and support for extensive projects. Future research should focus on integrating real-time data and enhancing user experience to further optimize GIS applications in healthcare.

Teaching Reform of BIM Modeling and Realistic 3D Integration Application

BIM technology is based on three digit technology to achieve information integration and sharing throughout the entire life cycle of buildings, which can effectively improve design and construction efficiency. Realistic 3D technology is a technology that digitally presents real-world objects and scenes in high-precision, high-efficiency, and high fidelity 3D form, which can objectively and realistically reflect the real world and provide rich spatial analysis functions. This article integrates real-life 3D and BIM modeling technology and applies it to practical teaching of civil engineering majors in vocational colleges, explores its teaching practice methods, and proposes reform measures such as deepening course design, innovating teaching methods, deepening school enterprise cooperation, and improving assessment and evaluation systems. In order to enhance students' competitiveness in employment and continuously promote the teaching reform of BIM modeling courses.

Visibility graph-based covariance functions for scalable spatial analysis in non-convex partially Euclidean domains.

We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water. Standard covariance functions based on geodesic distances are not guaranteed to be positive definite on such domains, while existing non-Euclidean approaches fail to respect the partially Euclidean nature of these domains where the geodesic distance agrees with the Euclidean distances for some pairs of points. Using a visibility graph on the domain, we propose a class of covariance functions that preserve Euclidean-based covariances between points that are connected in the domain while incorporating the non-convex geometry of the domain via conditional independence relationships. We show that the proposed method preserves the partially Euclidean nature of the intrinsic geometry on the domain while maintaining validity (positive definiteness) and marginal stationarity of the covariance function over the entire parameter space, properties which are not always fulfilled by existing approaches to construct covariance functions on non-convex domains. We provide useful approximations to improve computational efficiency, resulting in a scalable algorithm. We compare the performance of our method with those of competing state-of-the-art methods using simulation studies on synthetic non-convex domains. The method is applied to data regarding acidity levels in the Chesapeake Bay, showing its potential for ecological monitoring in real-world spatial applications on irregular domains.

Using Geospatial Analysis to Identify Priority Communities for Cervical Cancer Prevention in Texas

Despite being almost entirely preventable, cervical cancer is the fourth most frequently diagnosed cancer among women worldwide. Cervical cancer incidence suggests missed opportunities for prevention. Geospatial analysis could strategically guide public health interventions. This study aimed to identify geographic clusters of cervical cancer incidence in Texas, a state with higher than national rates of cervical cancer incidence and mortality. In this population-based cross-sectional study, the authors analyzed incident cervical cancer data among Texas women aged 30-64 years, from 2014 to 2018. The authors conducted a purely spatial Poisson-based analysis function in SaTScan to examine geographic clusters of higher-than-expected proportions of cervical cancer incidence (i.e., hot spots) and adjusted for age. A total of 5,060 women aged 30-64 years with incident cervical cancer diagnosis (mean age: 45.7 years, SD=9.6), including 1,840 (36.4%) Hispanic, 591 (11.7%) non-Hispanic Black, 2,397 (47.4%) non-Hispanic White, and 232 (4.6%) other races, were analyzed. Spatial scan analysis detected 7 significant hot spots of cervical cancer incidence. Hot spots were identified in the South Texas Plains (near Mexico border), Gulf Coast (Houston), Prairies and Lakes (North Texas), Panhandle Plains (Northwest Texas), and Piney Woods (Southeast Texas) regions of Texas. Hot spots, compared with the rest of Texas, had higher proportions of Hispanic population and individuals with socioeconomic disadvantages. This study found spatial variation in cervical cancer incidence in Texas. The hot spot areas can benefit from targeted, novel, scalable, and cost-effective interventions to increase human papillomavirus vaccination and screening and early detection and treatment of precancerous cervical lesions.

Principal causes of water damage in mining roofs under giant thick topsoil–lilou coal mine

The roof water inrush disaster induced by coal mining is becoming a vital bottleneck restricting mine safety. To accurately predict the water inrush position of the coal seam roof sandstone aquifer and accurately prevent and control it, this paper takes Lilou Coal Mine in Juye Coalfield as an example. Based on the comprehensive analysis of hydrogeological data, drilling data, and geophysiological data, this paper examines the water richness of the roof aquifer, the water insulation and geological structure, and the fracture development characteristics of the roof aquifer. Starting from the general side, nine factors, including fault strength index, tectonic intersection point, tip extinguishing point, development height of water conduction crack zone, roof sandstone aquifer thickness, aquifer drilling unit inrush, geophysical prospecting water-rich anomaly area, roof key layer thickness, roof aquifer thickness, brittle plastic rock thickness ratio, etc., form the roof plate roof. The main control factors for water inrush are deeply discussed. Through the spatial analysis function of GIS, unique drawings of different evaluation indicators are drawn, and the data are normalized. Combined with the AHP hierarchical analysis method, the corresponding weight is determined. Finally, a comprehensive water inrush risk assessment map of the roof of coal seam 3 in Lilou Coal Mine is obtained. Through the verification of the coal mine water inrush survey ledger over the years, it has been found that the evaluation results of the coal seam roof water inrush model are consistent with the actual situation. The evaluation results are reasonable and accurate, which can provide a reference basis for coal seam mining and water damage prevention and control in the future.

The Spatial and Temporal Distribution, Flow Network and its Characteristics of Population in Henan Province

Based on the WorldPop center population data set and the dynamic monitoring data of China 's floating population, this paper analyzes the spatial and temporal changes of regional population and the characteristics of flow network and its influencing factors with the help of ArcGIS spatial analysis and mapping function and geographic detector model. It shows that: The regional difference of population spatial distribution in Henan Province is obvious and extremely uneven, and the population agglomeration speed of Zhengzhou metropolitan zone is obviously accelerated. The regional differentiation of population change in Henan Province is serious, and the population attraction of Zhengzhou is increasing, The population of Undertake industrial transfer zone such as Shangqiu and Zhoukou continued contraction. The population growth area and the population contraction area are more concentrated in space, showing the characteristics of spatial agglomeration. In addition, economic factors are still the main driving factors of population change in Henan Province. The influence of service industry on population change is increasing, and the explanatory power of traditional agriculture, forestry, animal husbandry and fishery and manufacturing industry on population change is decreasing. The integrated development of secondary and tertiary industries will further affect the future population distribution pattern of each region.

Neural Network Based Determination of the Degree of Fiber Mixing in Hybrid Yarns and Composites

A deep understanding on the intermixing of components in hybrid yarn or composite structures is decisive in order to develop hybrid structures with desired properties. This paper presents the development of a versatile procedure for the determination of the degree of fiber mixing in yarns and composites based on microscopy images auto-segmented by a neural network. The procedure is based on the quantification of blend irregularity values and blend homogeneity. For this purpose, functions of spatial point patterns analysis have been used to investigate the blend uniformity of yarn and composite cross sectional areas. The results show that the trained neural network model for segmentation of images has an accuracy of 92 %, indicating that the method is capable of accurately assessing the location of fibers in hybrid struc-tures. The results of the spatial point patterns analysis reveals a correlation between the blend value and the properties of yarns and composites. The proposed method provides a fast and reliable way to evaluate the hybrid structures, which could be used as a tool for quality control and process optimization.

Risk Assessment of Water Inrush from Coal Seam Roof Based on the Combined Weighting of the Geographic Information System and Game Theory: A Case Study of Dananhu Coal Mine No. 7, China

Coal mines’ water inrush is one of the five major disasters that affect the safety of coal mine production. The assessment of coal mines’ water inrush is a prerequisite for preventing and controlling coal mines’ water inrush. To objectively and effectively evaluate the risk of water inrush in the coal seam roof and overcome the shortage of single assignment methods, two methods, the analytic hierarchy process and the entropy method, are used in this paper to determine each evaluation factor’s subjective and objective consequences. Game theory is applied to obtain the combined weights of each influencing factor to make up for the lack of a single assignment method. Taking the roof of Coal Seam No. 7 in mining Areas I and II of Dananhu Coal Mine No. 7 as an example, six primary evaluation indexes are created to control water inrush in the coal seam roof. The comprehensive weights of each index is determined; a vulnerability index evaluation model is established; and the results of the water inrush risk zone in the coal seam roof of Dananhu Coal Mine No. 7 are obtained using the GIS spatial analysis function. The results show that the discriminatory effects of the zoning model have a high accuracy and can provide a reference basis for future coal seam mining control work in this mine.

A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis.

Extended correlation functions for spatial analysis of multiplex imaging data.

Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate.

Comprehensive Evaluation of Adaptability Zoning for Shallow Geothermal Energy Development and Utilization: Case of Nantong City, East Coast of China

The development of green energy sources, such as shallow geothermal energy, is one of the most important measures to reduce the environmental pollution caused by the use of traditional energy sources. At present, coastal cities in the east of the Yangtze River Delta, China, have favorable conditions for the development of shallow geothermal energy because of their abundant geothermal resources. However, the development and utilization of shallow geothermal energy are still in their infancy, and adaptability zoning is yet to be fully carried out. The study area is Nantong City, which is in the eastern coast of the Yangtze River Delta. A comprehensive analysis of the conditions of shallow geothermal energy is carried out based on the analysis of the regional geological, hydrogeological, and environmental geological conditions. The adaptability zoning index system of the groundwater source heat pump and buried pipe ground source heat pump system is then constructed, and the weight of evaluation factors is determined according to analytic hierarchy process. Finally, the adaptability zoning of shallow geothermal energy development and utilization mode is obtained by using the spatial analysis function of geographic information. The results showed that the areas with good suitability and better suitability account for 98.24% of the study area and those with good economy and better economy account for 99.67% of the study area. This method can provide reference suggestions for the orderly development and sustainable utilization of shallow geothermal energy in coastal cities in plain areas.

The target selection and quantitative evaluation for deep geothermal resource zoning of typical geothermal fields in central Hebei of North China plain

With the rapid economic development in North China, the demand for geothermal energy is increasing. It is urgent to find favorable deep geothermal resource targets in North China. Although geothermal resources in the North China Plain are widely distributed, in order to develop deep geothermal resources in North China safely, stably and efficiently, it is essential to carry out the target selection and evaluation of geothermal resource zoning. This article takes the typical geothermal fields in the central Hebei region as the research object, and through the comprehensive collation of regional geothermal geological data, constructs an optimization evaluation indicator system for geothermal resource target areas from three aspects: resource conditions, mining potential, and heating demand. On this basis, it establishes a linear relationship between the attribute values and scores of each indicator, and uses the analytic hierarchy process to assign weights to each indicator and calculate the comprehensive weight. Then, the comprehensive evaluation value is obtained by weighted calculation of the scores and comprehensive weights of each block in the grid segmentation of the evaluation area. Finally, through the spatial analysis function of GIS, the comprehensive evaluation values of all blocks in the evaluation area were analyzed using kriging difference analysis, and a comprehensive evaluation map, the geothermal resource prospective target area map, was finally obtained. Using quantitative zoning evaluation methods, the target areas for exploration and development of deep geothermal resources in central Hebei Province have been delineated within a large region. The evaluation results indicate that the Cambrian-Ordovician reservoir target area is relatively large, and there are many favorable target areas with good reservoir conditions in the Middle-Upper Proterozoic. The excellent prospective target area of the Mesoproterozoic geothermal reservoir accounts for 56%, mainly located in the Xingji uplift and Gaoyang low uplift. Xiong’an New Area, Cangzhou, and the eastern part of Hengshui are excellent prospective target areas for the evaluation of the Mesoproterozoic geothermal reservoir in a single area. This evaluation method can provide a reference for the optimization of resource exploration and development target areas in key regions.

Construction of Green Ecological Network in Qingdao (Shandong, China) Based on the Combination of Morphological Spatial Pattern Analysis and Biodiversity Conservation Function Assessment

Building urban green ecological network systems and increasing urban and rural landscape connectivity are effective ways to improve urban biodiversity and landscape sustainability. The ecological sources in the main urban area of Qingdao City (Shandong, China) were identified based on morphological spatial pattern analysis (MSPA) combined with a biodiversity conservation function assessment, with the ecological corridors established and the ecological network structure optimized. The results showed that (1) the study area lacked high-quality patches with strong landscape connectivity; (2) the potential green ecological network of the study area was composed of 38 ecological sources, 703 ecological corridors, and 284 ecological nodes, effectively connecting urban and suburban green spaces; (3) after optimization, the green ecological network contained a total of 223 important corridors and 61 key nodes, with significantly increased network connectivity; (4) the optimal ecological corridor width in Qingdao was determined to be 30 m. Our study provided important guidance for the construction of ecological security patterns and scientific evidence to support urban green space planning and sustainable development in Qingdao.

Karakter Spasial dan Pengembangan Usaha Sarang Burung Walet di Jakarta Utara

Business unit of swallow's birdnest has great potential for economic activity because almost95% of Indonesian swallow's nest is exported. One of the efforts to develop a business is to carryout spatial analysis and production functions. This is necessary to determine the feasibility of aparticular system location and variables that affect a business. This study aims to determine thespatial character of the distribution of business units and several variables that influence theproduction process to develop this business. Analysis of spatial pattern of swallow's nest businessis done by using Average Nearest Neighbor (ANN). Income Analysis is done by calculating thedifference between receipts and costs. Production function analysis is done by using NormalityTest, Multicollinearity Test, Autocorrelation Test, and Heteroscedastic Test. The results showedthat the distribution of swallow's nest business units in North Jakarta was clustered, which wasonly found in 3 sub-districts i.e. Penjaringan, Tanjung Priok and Cilincing. The pattern showedthat the swallow’s nest business unit is profitable for business actors, it can attract the interest ofthe community and make competition more competitive. Production function are influenced by rawmaterials, water filter equipment, and electricity costs. Efforts to develop by reviewing thelicencing of the Swallow’s Birdnest Business units in North Jakarta, thus it remains in accordancewith the city spatial design plan and government policies.

Spatiotemporal Dynamics of the Suitability for Ecological Livability of Green Spaces in the Central Yunnan Urban Agglomeration

Green spaces are an essential aspect of building an eco-livable city and play an important role in building for eco-livability in the central Yunnan urban agglomeration. However, there are relatively few studies evaluating the eco-livability of green spaces. The establishment of a green-space eco-livability assessment system may help researchers to analyze the eco-livability of urban green spaces more effectively. To address this research gap, we constructed an ecological livability-evaluation index system for green spaces that incorporates three determinants—economic development, social life, and the ecological environment—using the green spaces of the urban agglomeration in central Yunnan as a case study. We used the entropy method to calculate the suitability for ecological livability of the green spaces in each district and county in the central Yunnan urban agglomeration for 2010, 2015 and 2020. We used the spatial autocorrelation analysis function of ArcGIS 10.8 software to explore the spatial clustering characteristics of the suitability for ecological livability of green spaces in the central Yunnan urban agglomeration. The results showed that, from 2010 to 2020, the suitability for ecological livability of green spaces of the 49 districts and counties in the central Yunnan urban agglomeration increased in some districts and decreased in others. The spatial characteristics were high in the central districts and counties and low in the peripheral districts and counties. The spatial characteristics of the suitability of the target layers for economic development and ecological-environment target were consistent with the overall suitability. Through a spatial autocorrelation analysis, we observed that the suitability of green spaces for ecological livability had a positive spatial correlation and demonstrated significant spatial clustering. In this study, we propose recommendations to improve the suitability for ecological livability of green spaces from two dimensions of government policy and urban development, using a combination of the three target layers. The results of the study provide a reference for decision-making in the construction of eco-livable cities in the central Yunnan urban agglomeration.

Spatial autocorrelation equation based on Moran’s index

Moran’s index is an important spatial statistical measure used to determine the presence or absence of spatial autocorrelation, thereby determining the selection orientation of spatial statistical methods. However, Moran’s index is chiefly a statistical measurement rather than a mathematical model. This paper is devoted to establishing spatial autocorrelation models by means of linear regression analysis. Using standardized vector as independent variable, and spatial weighted vector as dependent variable, we can obtain a set of normalized linear autocorrelation equations based on quadratic form and vector inner product. The inherent structure of the models’ parameters are revealed by mathematical derivation. The slope of the equation gives Moran’s index, while the intercept indicates the average value of standardized spatial weight variable. The square of the intercept is negatively correlated with the square of Moran’s index, but omitting the intercept does not affect the estimation of the slope value. The datasets of a real urban system are taken as an example to verify the reasoning results. A conclusion can be reached that the inner product equation of spatial autocorrelation based on Moran’s index is effective. The models extend the function of spatial analysis, and help to understand the boundary values of Moran’s index.

Evolution Law of Shallow Water in Multi-Face Mining Based on Partition Characteristics of Catastrophe Theory

It is of great significance for ecological environment protection to clarify the regional evolution characteristics of shallow water under the disturbance of multi-working face mining. In this paper, the catastrophe theory method, GIS spatial analysis function and FEFLOW numerical calculation method were comprehensively used to study the instability risk and evolution law of shallow water systems in the Zhuan Longwan Coal Mine. The results show that: the Zhuan Longwan Coal Mine is divided into five areas (small risk area, light risk area, middle risk area, heavy risk area and special risk area) based on catastrophe theory, among which the middle risk area has the largest area of 16,616,880 m2, and the special risk area has the smallest area of 1,769,488 m2. Based on the results of catastrophe zoning, the evolution law of shallow water under multi-surface disturbance in different zones is expounded. In the middle-risk area, the water level drop at measuring point 4 is the largest, which is 0.525 m, and the water level drop at measuring point 5 is the smallest, which is 0.116 m. The study aims to provide a basis for regional coal development planning and research on the method of water-retaining coal mining.

Evaluation of forest fire risk based on multicriteria decision analysis techniques for Changzhou, China

With the rapid development of China's economy, an increasing number of cities are building forest parks or landscape forests to improve the quality of people's lives and protect the environment. However, forest fires are notoriously difficult to extinguish and have caused tragic losses to cities. Therefore, it is particularly important to conduct a comprehensive assessment of forest fire risk. This study focuses on urban forest fire risk to develop an integrated fire risk model to prevent forest fires. A risk assessment model covering different multicriteria decision analysis (MCDA) techniques was developed to analyze safety vulnerabilities and determine fire risk levels with the spatial analysis functions of geographical information system (GIS) software. Moreover, the accuracy of the model was verified using fire data downloaded from the NASA-FIRMS website. The area under the ROC curve (AUC) was used to evaluate the accuracy of each model. The results show that the AUC value of the combination weighting of game theory (CWGT) method is 0.700. The method has good accuracy and can be used to predict the forest fire risk in Changzhou City. The towns with the highest forest fire risk in Changzhou City were Tianmuhu, Daibu, and Xuebu, with average areas of 8354.77 ha, 5125.92 ha, and 5256.67 ha, respectively, and proportions of 36.20 %, 37.15 %, and 23.43 %, respectively. The conclusion of this study provides a reference and guide for local government management. At the same time, this study considers the emergency rescue capability index and comprehensively evaluates the risk over a time series of pre- and postfire. This is a new attempt and has made new contributions to this field.