A method for characterizing and measuring the resilience of an urban ecological ontology
Patent Information
- Application Number
- CN202610894674.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-22
- Publication Date
- 2026-09-25
AI Technical Summary
第一，计算静态指标难以反映全天逐小时高温演化过程中蓝绿空间降温能力的动态变化；第二，多数方法分别计算降温强度、降温距离和降温面积，缺乏将降温幅度与作用范围统一起来的综合服务能力指标；第三，现有方法缺少面向高温扰动的城市韧性评估，难以有效判断蓝绿空间在高温时段相对于低热应力基准时段的功能维持、衰退或增强状态；第四，现有技术往往依赖低空间分辨率或单源数据，难以提升对城市蓝绿空间斑块尺度识别的精度，不利于城市空间分级
1、本发明通过构建单位面积降温服务指数，将降温强度与降温作用范围统一为综合降温服务能力指标，根据气象观测数据或逐小时温度变化特征，能够对蓝绿空间斑块进行韧性等级划分和空间识别，从而反映全天逐小时高温演化过程中蓝绿空间降温能力的动态变化。
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Figure CN122819976A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of urban ecological resilience assessment technology, specifically a method for characterizing and measuring the resilience of urban ecological entities. Background Technology
[0002] Urban blue-green spaces include parks, street green spaces, water bodies, woodlands and their surrounding buffer spaces. They are important natural infrastructures for mitigating the urban heat island effect, regulating the local thermal environment and enhancing the city's ability to adapt to high temperatures.
[0003] Current assessments of the cooling effect of blue-green spaces are mostly based on single-moment remote sensing images. These assessments calculate static indicators such as cooling intensity, cooling distance, and cooling area to characterize the cooling capacity of blue-green spaces at a given instant. However, high-temperature disturbances exhibit significant intra-diurnal dynamic variations, and the cooling service capacity of blue-green spaces fluctuates with changes in air temperature, surface thermal environment, vegetation status, spatial morphology, and surrounding built environment. Static assessments at a single-moment or daily average scale are insufficient to reflect whether blue-green spaces can sustain their cooling function under high-temperature stress.
[0004] The existing technology has the following main shortcomings: First, calculating static indicators is insufficient to reflect the dynamic changes in the cooling capacity of blue-green spaces during the hourly evolution of high temperatures throughout the day. Second, most methods calculate cooling intensity, cooling distance, and cooling area separately, lacking a comprehensive service capacity indicator that unifies the cooling magnitude and the scope of effect. Third, existing methods lack urban resilience assessments oriented towards high-temperature disturbances, making it difficult to effectively determine whether the function of blue-green spaces is maintained, deteriorated, or enhanced during high-temperature periods relative to low-thermal-stress baseline periods. Fourth, existing technologies often rely on low spatial resolution or single-source data, making it difficult to improve the accuracy of patch-scale identification of urban blue-green spaces, which is detrimental to urban spatial classification. Summary of the Invention
[0005] The purpose of this invention is to provide a method for characterizing and measuring the resilience of urban ecological entities. By constructing a cooling service index per unit area, the cooling intensity and cooling range are unified into a comprehensive cooling service capacity index. Based on meteorological observation data or hourly temperature change characteristics, the integral mean of the cooling service index for each time period is calculated. By constructing an ecological resilience index, the resilience level of blue-green space patches can be classified and spatially identified, thereby reflecting the dynamic changes in the cooling capacity of blue-green spaces during the hourly high-temperature evolution throughout the day. This method can effectively determine whether the function of blue-green spaces is maintained, deteriorated, or enhanced during high-temperature periods relative to low-thermal-stress baseline periods, which is beneficial to improving the accuracy of urban blue-green space patch scale identification and facilitating urban spatial classification.
[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for characterizing and measuring the resilience of urban ecological entities, comprising: S1: Based on the urban research area, obtain hourly surface temperature data and blue-green space patch distribution data within the day of high temperature disturbance; S2: Calculate the cooling intensity, cooling distance and cooling area of each blue-green space patch on an hourly scale to obtain the dynamic sequence of the hourly cooling effect of the blue-green space patch; S3: Based on the dynamic sequence of cooling effect, construct the cooling service index of the unit area cooling service capacity of blue-green space patches; S4: Based on meteorological observation data of the study area, the days of high temperature disturbance are divided into the baseline period, the warming transition period, the high temperature stress period, and the evening relief period; S5: Calculate the integral mean of the cooling service index of each blue-green space patch during the baseline period, warming transition period, high temperature stress period, and evening relief period, and construct the ecological resilience index based on the comparison of cooling service capacity between the high temperature stress period and the baseline period. S6: Based on the ecological resilience index and the preset index classification rules, the ecological resilience level of blue-green spatial patches is classified and spatially identified to obtain the ecological resilience level and spatial distribution of the ecological body.
[0007] As a further aspect of the present invention: the high-temperature disturbance day is defined as three consecutive days in which the temperature in the study area exceeds 32°C.
[0008] As a further aspect of the present invention: hourly surface temperature data are generated using a data fusion method, wherein the data fusion method includes: The hourly surface temperature was used as the time-driven sequence, and the instantaneous surface temperature obtained by high spatial resolution thermal infrared remote sensing was used as the spatial template. The spatial detail residuals are calculated and superimposed onto the data at each time point to generate hourly high spatial resolution surface temperature data for the study area.
[0009] As a further aspect of the present invention: In S2, the cooling intensity, cooling distance, and cooling area of each blue-green space patch on an hourly scale are calculated, including: Extract the boundaries of the blue-green spatial patches. Starting from these boundaries, construct multiple buffer zones outwards with fixed step sizes to obtain the maximum buffer distance: The sequence number of the multiple buffers is... , For fixed step size; Obtain the average surface temperature of all pixels within the blue-green space patch. Based on multiple buffer zones, k buffer rings are constructed, and the average surface temperature of all pixels within the k buffer rings is obtained. ; Obtain the center distance of each buffer ring , with center distance With the mean surface temperature as the independent variable and the mean surface temperature as the dependent variable, a distance-temperature dataset is constructed. Curve fitting was performed on the distance-temperature dataset to obtain a continuous function. ,in, The distance from the patch boundary is represented by the cooling distance PCD and the cooling intensity PCI are determined according to the three-level solution order: After constructing a cubic polynomial function to fit the dataset, the zero point of the first derivative is found to obtain the cooling distance PCD; Construct the formula for cooling intensity: The cooling intensity was obtained; Based on the area of the planar region formed by buffering the PCD distance outward from the boundary of the blue-green space patch and the body area of each blue-green space patch, a cooling area formula is constructed: The cooling area was calculated, where, Blue-green spatial patches The area of the planar region formed after the vector boundary is buffered outward by the PCD distance. Blue-green spatial patches The body area is determined by setting PCA to zero when PCD is invalid or PCI ≤ 0.
[0010] As a further aspect of the present invention: if finding the zero point of the first derivative is ineffective, then the PCD is searched using the method of minimizing the first derivative; if searching the PCD is also ineffective, then the distance of the farthest valid data point is taken as the PCD.
[0011] As a further aspect of the present invention: the cubic polynomial function is: ; The first derivative of a cubic polynomial function is: ; The zeros of the first derivative are: Take the satisfied and The smallest real root is taken as PCD. When the discriminant is less than or equal to zero, the roots are not in the valid interval, or the cooling intensity PCI calculated based on the zero point is less than or equal to zero, an alternative method is used, which uses the first derivative within the preset search interval. A bounded search is performed on the objective function, and the position where the first derivative reaches its minimum value is selected as the candidate cooling distance. If the cooling intensity corresponding to the candidate cooling distance meets the preset condition, it is determined as PCD. Otherwise, a fallback method is adopted, in which the center distance of the farthest effective buffer ring is determined as PCD, and the PCI is determined by the difference between the average temperature of the ring and the temperature of the patch body.
[0012] As a further aspect of the present invention: In S3, a cooling service index is constructed to measure the cooling service capacity per unit area of blue-green space patches, including: The cooling service index function based on blue-green space patches is as follows: ,in, For hourly serial numbers, For blue-green space patches, PCI ( ) is a plaque exist The cooling intensity at any given time, PCA ( ) is a plaque exist Effective cooling area at any given time, Area( ) is a plaque The cooling service index is used to characterize the comprehensive cooling service capacity of a unit area of blue-green space patch at a corresponding time. This invention selects blue-green patches with effective PCI positive cooling as the analysis object.
[0013] As a further aspect of the present invention: the baseline period is the basic cooling service capacity of the blue-green space from midnight to sunrise on the day of high temperature disturbance; The warming transition period is the process of changes in the cooling service capacity of blue-green spaces from after sunrise to the high-temperature stress period. The high-temperature stress period refers to the ability of blue-green spaces to maintain their cooling service function during high-temperature heat wave disturbances; The evening relief period represents the recovery or continuation of the cooling service capacity of blue-green spaces after the high-temperature stress has weakened.
[0014] As a further aspect of the present invention: In S5, the integral mean of the cooling service index of each blue-green space patch during the baseline period, the warming transition period, the high-temperature stress period, and the evening relief period is calculated, including: Construct the integral mean function of the cooling service index: ,in, For different time periods, This represents the duration of the corresponding time period s. and These are the start and end times for the corresponding time periods.
[0015] As a further aspect of this invention: the ecological resilience index is constructed as follows: ,in, This represents the average integral value of the cooling service index during periods of high-temperature stress. The average value of the cooling service index integral for the baseline period; when ≤ε or When the threshold is less than or equal to the preset minimum threshold ε, a special judgment rule is adopted.
[0016] As a further aspect of the present invention: special determination rules, including: like ≤ε and If ≤ε, it is determined that there is no effective cooling service type; if >ε and If ≤ε, it is determined to be a service loss type cooling service patch; if ≤ε and If the value is greater than ε, it is determined to be a high-temperature activated cooling service patch.
[0017] As a further aspect of the present invention, constructing the ecological ontology resilience index also includes: when >ε and When >ε, use / As a ratio-based ecological resilience index; when ≤ε and When the value is greater than ε, it is not considered as infinite. Instead, a zero-benchmark activation-type alternative representation value is constructed based on the relationship between the mean of the positive service integral during the high-temperature period and the positive median of the sample.
[0018] As a further aspect of the present invention: in S6, the resilience level of the ecological ontology includes: Ecological resilience is classified into five levels: no effective cooling service, extremely low resilience, low resilience, relatively high resilience, and high resilience. The type with no effective cooling service is directly classified as having no effective cooling service; patches with lost cooling service are classified as having extremely low resilience; for ratio-based ecological resilience indices, when 0 < When the value is less than 1, the natural breakage method is used to classify it into extremely low toughness and low toughness grades; when... When the value is ≥1, the natural break point method is used to classify the toughness into higher and higher toughness grades; high-temperature activated cooling service patches are classified into higher and higher toughness grades based on the zero-baseline activated substitution characterization value.
[0019] As a further aspect of the present invention, it also includes a result verification step: using measured air temperature data from meteorological stations in the study area and fused surface temperature data for accuracy verification, and combining high-resolution instantaneous surface temperature products for spatial consistency verification, so as to improve the reliability of the ecological resilience measurement results.
[0020] As a further aspect of the present invention: the high-temperature disturbance scenario is a typical high-temperature day or a continuous high-temperature heat wave process, and the transformation from a static cooling effect evaluation at a single moment to a dynamic resilience evaluation over all weather and multiple time periods is achieved through continuous hourly analysis.
[0021] As a further aspect of the present invention, the ecological resilience index is used to characterize the maintenance, decline or enhancement of the cooling service function of blue-green space patches under high-temperature disturbances. Its core is to unify the cooling intensity, cooling range and temporal change process of blue-green space into a quantifiable dynamic resilience index.
[0022] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention constructs a unit area cooling service index, unifying cooling intensity and cooling range into a comprehensive cooling service capacity index. Based on meteorological observation data or hourly temperature change characteristics, it can classify and spatially identify blue-green space patches according to their resilience level, thereby reflecting the dynamic changes in the cooling capacity of blue-green space during the hourly high temperature evolution throughout the day.
[0023] 2. This invention calculates the integral mean of the cooling service index of each blue-green space patch during the baseline period, the warming transition period, the high-temperature stress period, and the evening relief period. Based on the comparison of cooling service capacity between the high-temperature stress period and the baseline period, it constructs an ecological resilience index, which can evaluate the dynamic resilience of the cooling function of the blue-green space itself. This is beneficial to improving the accuracy of the scale identification of urban blue-green space patches and facilitates urban spatial classification.
[0024] 3. By comparing the baseline period with the high-temperature stress period, this invention can identify the maintenance, decline or enhancement status of the blue-green space cooling service function under high-temperature disturbance.
[0025] 4. This invention can generate a layer of ecological resilience levels and statistical results for blue-green spaces, providing quantitative data for optimizing the layout of urban blue-green spaces, adapting to high-temperature risks, and building resilient cities. Attached Figure Description
[0026] Figure 1 This is a flowchart illustrating the implementation of the method for characterizing and measuring the resilience of urban ecological entities under high-temperature disturbance in this invention. Figure 2 Spatial distribution map of urban ecological resilience levels provided in embodiments of the present invention; Figure 3This is a statistical chart showing the number and area of patches with different ecological resilience levels provided in an embodiment of the present invention. Figure 4 This is a diagram illustrating the method steps of the present invention. Detailed Implementation
[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0028] Example: This embodiment uses the area within the Sixth Ring Road of a certain city as the study area, July 19, 2023 (a typical high-temperature day) as the high-temperature disturbance day, and urban blue-green space patches with an area of 4900 square meters or more within the study area as evaluation objects to measure the ecological resilience of urban blue-green spaces. All spatial data are uniformly implemented using the WGS84 / UTMzone50N projection coordinate system. The calculation process is implemented using Python language in conjunction with open-source scientific computing libraries such as GeoPandas, Rasterio, Shapely, SciPy, NumPy, Pandas, and Matplotlib. On July 19, 2023, the city experienced a typical summer high-temperature event, with temperatures exceeding 32°C for three consecutive days prior, and temperatures remaining above 35°C from 12:00 to 18:00, fully meeting the selection criteria for a high-temperature event.
[0029] Please see Figures 1-4 This embodiment provides a method for characterizing and measuring the resilience of urban ecological entities, including the following steps: S1: Based on the urban research area, obtain hourly surface temperature data and blue-green space patch distribution data within the day of high temperature disturbance.
[0030] In this embodiment, a high-temperature disturbance day is defined as three consecutive days in which the temperature in the study area exceeds 32°C. The hourly surface temperature data is generated using a data fusion method, which includes: using the hourly surface temperature as a time-driven sequence and the instantaneous surface temperature obtained by high spatial resolution thermal infrared remote sensing as a spatial template; calculating the spatial detail residuals and superimposing them onto the data at each time point to generate hourly high spatial resolution surface temperature data for the study area.
[0031] Specifically, the acquisition and fusion process of hourly surface temperature data is as follows: First, hourly coarse-resolution surface temperature data covering the study area is acquired as the time-driven sequence for temperature changes throughout the day. Second, high spatial resolution thermal infrared remote sensing surface temperature products are acquired as spatial templates for spatial differences in surface temperature within the study area. The coarse-resolution surface temperature data is resampled to the spatial resolution and projected coordinate system of the high-resolution remote sensing products. The satellite transit time is selected, and the spatial residual between the high-resolution remote sensing surface temperature and the resampled coarse-resolution surface temperature is calculated. This spatial residual is used as a spatial detail correction term for the entire day and superimposed onto the hourly reanalysis surface temperature data to generate hourly high spatial resolution surface temperature data for the entire study area. Missing value interpolation and Gaussian filtering are applied to the fusion results to remove outlier temperature values, resulting in a surface temperature dataset that can be used for hourly cooling effect identification. The accuracy of the fused surface temperature results is verified using measured air temperature data from meteorological stations in the study area, and spatial consistency is verified in conjunction with high-resolution instantaneous surface temperature products.
[0032] S2: Calculate the cooling intensity, cooling distance, and cooling area of each blue-green space patch on an hourly scale to obtain the dynamic sequence of the hourly cooling effect of the blue-green space patch.
[0033] In this embodiment, the hourly cooling service effect sequence is calculated. This step involves performing a complete calculation process for the cooling index for each of the 3928 blue-green space patches, hourly.
[0034] Vector data of urban blue-green spatial patches in the study area were read, and patches with an area of no less than 4900 square meters were selected and uniformly projected onto a coordinate system consistent with the surface temperature data (UTMZone50N). For each blue-green spatial patch... Extract its vector boundary and body geometry.
[0035] In S2, the cooling intensity, cooling distance, and cooling area of each blue-green spatial patch on an hourly scale are calculated. This includes: extracting the boundaries of the blue-green spatial patches; using the boundaries of the blue-green spatial patches as the starting point, constructing multiple buffer zones outward with a fixed step size; and obtaining the maximum buffer distance as follows: Starting from the patch boundary, according to a fixed step size: Multiple buffer zones are constructed outwards, with buffer numbers k=1,2,...,20 and a maximum buffer distance Dmax=600 m. Non-overlapping, equally spaced concentric buffer rings are formed through geometric difference operations. For each buffer ring and the interior of the patch itself, the mean temperature of all effective pixels within it is calculated using the fused surface temperature raster corresponding to the hour. Average internal temperature of the plaque (reference temperature); The average temperature of the k-th buffer ring.
[0036] If the number of effective pixels in a buffer ring is less than 4, the data for that ring is discarded; if the total number of effective buffer rings is less than 4, the cooling capacity calculation for that patch for that hour is deemed to have failed.
[0037] Calculation of hourly cooling intensity, cooling distance, and cooling area; For each patch j and each hour h, the average surface temperature of all valid pixels within the patch is extracted and denoted as T. patch (h,j); Extract the average surface temperature of all valid pixels within the k-th buffer ring, denoted as T. k (h,j). The distance dk from the center of the buffer ring is the independent variable, and T is the variable... k With (h,j) as the dependent variable, construct a distance-temperature dataset.
[0038] The cubic polynomial function is: ; The first derivative of a cubic polynomial function is: ; The zeros of the first derivative are: Take the satisfied and The smallest real root is taken as PCD. When the discriminant is less than or equal to zero, the roots are not in the valid interval, or the cooling intensity PCI calculated based on the zero point is less than or equal to zero, an alternative method is used, which uses the first derivative within the preset search interval. A bounded search is performed on the objective function, and the location where the first derivative reaches its minimum value is selected as the candidate cooling distance. If the cooling intensity corresponding to the candidate cooling distance meets the preset condition, it is determined as the PCD; otherwise, a fallback method is used, in which the center distance of the farthest effective buffer ring is determined as the PCD, and the PCI is determined by the difference between the average temperature of this ring and the temperature of the patch body.
[0039] When the priority method is ineffective, the first derivative minimum method is used to determine the candidate PCD within the search interval; When the backup method is still ineffective, the furthest effective buffer zone distance is taken as PCD, and the temperature difference between that zone and the patch body is taken as PCI. If PCI ≤ 0, it is considered that there is no effective cooling effect at that moment, and PCD and PCA are set to zero; if PCI is greater than the preset abnormal threshold, the hourly result is marked as abnormal and not included in subsequent effective calculations. The cooling area PCA is calculated by the following formula: ; Among them, S buffer (j,PCD(h,j)) represents the area of the areal region formed after buffering the vector boundary of patch j outwards by a distance of PCD, S patch (j) represents the body area of patch j. Through the above process, the PCI, PCD, and PCA sequences of each blue-green space patch are obtained on an hourly scale throughout the day.
[0040] S3: Based on the dynamic sequence of cooling effect, construct a cooling service index for the cooling service capacity per unit area of blue-green space patches.
[0041] The cooling service index construction in this embodiment includes: Based on hourly cooling intensity and cooling area, a cooling service index per unit area is constructed:
[0042] Where Area(j) is the body area of patch j. This index unifies the cooling magnitude and cooling range to a unit area scale, and can characterize the comprehensive cooling service capacity of blue-green space patches in each hour. CS(h,j) is written field by field into the blue-green space patch attribute table to form a daily hourly cooling service index sequence. This invention selects blue-green patches with effective PCI positive cooling as the analysis object.
[0043] S4: This embodiment uses hourly or three-hourly measured temperature data from meteorological stations in the study area to perform smooth interpolation and feature identification on the temperature change process during days of high temperature disturbance. Based on the daily temperature change curve, the time of occurrence of the highest temperature, and the high temperature threshold, the whole day is divided into a baseline period, a warming transition period, a high temperature stress period, and an evening relief period.
[0044] Based on meteorological observation data or hourly surface temperature data of the study area, the days of high-temperature disturbance are divided into the baseline period, the warming transition period, the high-temperature stress period, and the evening relief period.
[0045] In this embodiment, the baseline period can be set to 0:00-6:00 to characterize the basic cooling service capacity of blue-green spaces under low thermal stress conditions; the warming transition period can be set to 6:00-12:00; the high-temperature stress period can be set to 12:00-18:00 to characterize the cooling service maintenance capacity during the period of strongest daytime thermal stress; and the evening relief period can be set to 18:00-23:00 to characterize the continuation of the cooling function after the high-temperature stress weakens. These periods can be adjusted according to different cities, different climate zones, and different characteristics of high-temperature events.
[0046] S5: Calculate the integral mean of the cooling service index for each blue-green space patch during the baseline period, warming transition period, high temperature stress period, and evening relief period, and construct the ecological resilience index based on the comparison of cooling service capacity between the high temperature stress period and the baseline period.
[0047] In this embodiment, the baseline time period is the basic cooling service capacity of the blue-green space from midnight to sunrise on the day of high temperature disturbance. The warming transition period is the process of changes in the cooling service capacity of blue-green spaces from after sunrise to the high-temperature stress period. The high-temperature stress period refers to the ability of blue-green spaces to maintain their cooling service function during high-temperature heat wave disturbances; The evening relief period represents the recovery or continuation of the cooling service capacity of blue-green spaces after the high-temperature stress has weakened.
[0048] Calculate the integral mean of the cooling service index for each blue-green space patch during the baseline period and the high-temperature stress period, including: Constructing the integral mean function of the cooling service index: Calculating the integral of cooling service index for different time periods. For each blue-green space patch j, the trapezoidal integral method is used to calculate the integral mean of the cooling service index for each time period.
[0049] Where s is the time period type, T s This represents the duration of the corresponding time period. The mean integral value of the cooling service index during the high-temperature stress period was calculated. and the average cooling service index integral CS for the baseline period avg (base,j), when or When the value is less than or equal to a preset minimum threshold ε, a special judgment rule is applied. The ecological resilience index is defined as:
[0050] Among them, CS avg (hot,j) represents the integral mean of the cooling service index during the high-temperature stress period, CS avg (base,j) represents the mean integral value of the cooling service index for the baseline period. ≤ε and If the value is ≤ε, then it is determined that there is no effective cooling service; when >ε and If the value is less than or equal to ε, then the service is considered lost; when... >ε and When >ε, use / As a ratio-based ecological resilience value; when ≤ε and If the value is greater than ε, it is determined to be high-temperature activation, and an alternative characterization value is constructed according to the zero-baseline activation rule.
[0051] S6: Based on the ecological resilience index and the preset index classification rules, the ecological resilience level of blue-green spatial patches is classified and spatially identified to obtain the ecological resilience level and spatial distribution of the ecological body.
[0052] In this embodiment, in S6, the resilience level of the ecological ontology includes: Ecological resilience is classified into five levels: no effective cooling service, extremely low resilience, low resilience, relatively high resilience, and high resilience. The type with no effective cooling service is directly classified as having no effective cooling service; patches with lost cooling service are classified as having extremely low resilience; for ratio-based ecological resilience indices, when 0 < When the value is less than 1, the natural breakage method is used to classify it into extremely low toughness and low toughness grades; when... When the value is ≥1, the natural break point method is used to classify the toughness into higher and higher toughness grades; high-temperature activated cooling service patches are classified into higher and higher toughness grades based on the zero-baseline activated substitution characterization value.
[0053] The natural breakpoint method was used to determine the cutoff value J for functionally deteriorating plaques and functionally maintained plaques, respectively. low and J high Based on this, the resilience levels are categorized as follows: no effective cooling service, extremely low resilience, low resilience, relatively high resilience, and high resilience. An ecological resilience level field is generated based on the categorization results, a spatial distribution map of ecological resilience is plotted, and the number, area, spatial distribution, and contribution characteristics of patches at different levels are statistically analyzed.
[0054] The final output includes: hourly PCI, PCD, and PCA index sequences, hourly cooling service index sequences, average integral values of cooling service index for different time periods, ecological resilience index, ecological resilience level layer, and corresponding statistical reports.
[0055] This embodiment fully demonstrates the entire process from hourly surface temperature fusion, identification of cooling effects of blue-green space patches, construction of cooling service indices, time-segmented integral calculation, to ecological resilience grading and mapping. This method can effectively identify the dynamic maintenance capacity of the cooling function of blue-green spaces under high-temperature disturbances, providing quantifiable, comparable, and spatially expressible technical basis for urban blue-green space protection, renewal and optimization, and high-temperature adaptation planning. The method is data source independent; temperature data can be replaced with other hourly products, and population data can be replaced with other location data without affecting the applicability of the method framework. It can be extended to the assessment of blue-green space resilience in other cities, different climate zones, and different types of heat wave events, providing standardized and comparable quantitative data for urban climate adaptation planning.
[0056] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.
Claims
1. A method for characterizing and measuring the resilience of urban ecological entities, characterized in that, include: S1: Based on the urban research area, obtain hourly surface temperature data and blue-green space patch distribution data within the day of high temperature disturbance; S2: Calculate the cooling intensity, cooling distance and cooling area of each blue-green space patch on an hourly scale to obtain the dynamic sequence of the hourly cooling effect of the blue-green space patch; S3: Based on the dynamic sequence of cooling effect, construct the cooling service index of the unit area cooling service capacity of blue-green space patches; S4: Based on meteorological observation data or hourly surface temperature data of the study area, the days of high temperature disturbance are divided into the baseline period, the warming transition period, the high temperature stress period, and the evening relief period; S5: Calculate the integral mean of the cooling service index of each blue-green space patch during the baseline period, warming transition period, high temperature stress period, and evening relief period, and construct the ecological resilience index based on the comparison of cooling service capacity between the high temperature stress period and the baseline period. S6: Based on the ecological resilience index and the preset index classification rules, the ecological resilience level of blue-green spatial patches is classified and spatially identified to obtain the ecological resilience level and spatial distribution of the ecological body.
2. The method for characterizing and measuring the resilience of urban ecological entities according to claim 1, characterized in that, Hourly surface temperature data were generated using a data fusion method, which includes: Using hourly surface temperature as the time-driven sequence and high spatial resolution instantaneous surface temperature as the spatial template, spatial detail residuals are calculated and superimposed onto the data at each time point to generate hourly high spatial resolution surface temperature data for the study area.
3. The method for characterizing and measuring the resilience of urban ecological entities according to claim 2, characterized in that, In S2, the cooling intensity, cooling distance, and cooling area of each blue-green space patch on an hourly scale are calculated, including: Extract the boundaries of the blue-green spatial patches. Starting from these boundaries, construct multiple buffer zones outwards with fixed step sizes to obtain the maximum buffer distance: The sequence number of the multiple buffers is... , For fixed step size; Obtain the average surface temperature of all pixels within the blue-green space patch. Based on multiple buffer zones, k buffer rings are constructed, and the average surface temperature of all pixels within the k buffer rings is obtained. ; Obtain the center distance of each buffer ring , with center distance With the mean surface temperature as the independent variable and the mean surface temperature as the dependent variable, a distance-temperature dataset is constructed. Curve fitting was performed on the distance-temperature dataset to obtain a continuous function. ,in, The distance from the patch boundary is represented by the cooling distance PCD and the cooling intensity PCI are determined according to the three-level solution order: After constructing a cubic polynomial function to fit the dataset, the zero point of the first derivative is found to obtain the cooling distance PCD; Construct the formula for cooling intensity: The cooling intensity was obtained; Based on the area of the planar region formed by buffering the PCD distance outward from the boundary of the blue-green space patch and the body area of each blue-green space patch, a cooling area formula is constructed: The cooling area was calculated, where, Blue-green spatial patches The area of the planar region formed after the vector boundary is buffered outward by the PCD distance. Blue-green spatial patches The body area is determined by setting PCA to zero when PCD is invalid or PCI ≤ 0.
4. The method for characterizing and measuring the resilience of urban ecological entities according to claim 3, characterized in that, In S3, a cooling service index is constructed to measure the cooling service capacity per unit area of blue-green space patches, including: The cooling service index function based on blue-green space patches is as follows: ,in, For hourly serial numbers, For blue-green space patches, PCI ( ) is a plaque exist The cooling intensity at any given time, PCA ( ) is a plaque exist Effective cooling area at any given time, Area( ) is a plaque The body area is used to characterize the comprehensive cooling service capacity of a unit area of blue-green space patch at a corresponding time. Among them, blue-green patches with effective PCI positive cooling are selected as the analysis objects.
5. The method for characterizing and measuring the resilience of urban ecological entities according to claim 4, characterized in that, The baseline period is the basic cooling service capacity of blue-green spaces in a lower state from midnight to sunrise on the day of high temperature disturbance. The warming transition period is the process of changes in the cooling service capacity of blue-green spaces from after sunrise to the high-temperature stress period. The high-temperature stress period refers to the ability of blue-green spaces to maintain their cooling service function during high-temperature heat wave disturbances; The evening relief period represents the recovery or continuation of the cooling service capacity of blue-green spaces after the high-temperature stress has weakened.
6. The method for characterizing and measuring the resilience of urban ecological entities according to claim 5, characterized in that, In S5, the integral mean of the cooling service index for each blue-green space patch during the baseline period, the warming transition period, the high-temperature stress period, and the evening relief period is calculated, including: Construct the integral mean function of the cooling service index: ,in, For different time periods, This represents the duration of the corresponding time period s. and These are the start and end times for the corresponding time periods.
7. The method for characterizing and measuring the resilience of urban ecological entities according to claim 6, characterized in that, The ecological resilience index is constructed as follows: ,in, This represents the average integral value of the cooling service index during periods of high-temperature stress. The average value of the cooling service index integral for the baseline period; when or When the threshold is less than or equal to the preset minimum threshold ε, a special judgment rule is adopted.
8. The method for characterizing and measuring the resilience of urban ecological entities according to claim 7, characterized in that, Special judgment rules include: like ≤ε and If ≤ε, it is determined that there is no effective cooling service type; if >ε and If ≤ε, it is determined to be a service loss type cooling service patch; if ≤ε and If the value is greater than ε, it is determined to be a high-temperature activated cooling service patch.
9. The method for characterizing and measuring the resilience of urban ecological entities according to claim 8, characterized in that, The construction of the ecological resilience index also includes: when >ε and When >ε, use / As a ratio-based ecological resilience index; when ≤ε and When the value is greater than ε, it is not considered as infinite. Instead, a zero-benchmark activation-type alternative representation value is constructed based on the relationship between the mean of the positive service integral during the high-temperature period and the positive median of the sample.
10. The method for characterizing and measuring the resilience of urban ecological entities according to claim 9, characterized in that, In S6, the resilience levels of the ecosystem ontology include: Ecological resilience is classified into five levels: no effective cooling service, extremely low resilience, low resilience, relatively high resilience, and high resilience. The type of no effective cooling service is directly classified as having no effective cooling service; patches with lost cooling service are classified as having extremely low resilience. For ratio-based ecological resilience indices, when 0 < When the value is less than 1, the natural breakage method is used to classify it into extremely low toughness and low toughness grades; when... When the value is ≥1, the natural break point method is used to classify the toughness into higher and higher toughness grades; high-temperature activated cooling service patches are classified into higher and higher toughness grades based on the zero-baseline activated substitution characterization value.