Ecosystem service simulation method and system based on spatial gradient analysis

Abstract

The application provides an ecosystem service simulation method and system based on spatial gradient analysis, and belongs to the technical field of ecosystem service evaluation. The method comprises the following steps: determining a service supply unit and a non-supply unit of a target ecosystem service type; performing spatial gradient analysis on each supply unit to determine a service supply area of the supply unit; in the service supply area, determining the nearest non-supply grid of the ecosystem service as a reference unit for each supply grid; taking the value of the environmental parameter of the reference unit as a background value, calculating the difference between the actual value and the background value of the environmental parameter of the supply grid, and obtaining the supply amount of the ecosystem service. The scheme does not require experience parameters and complex preliminary data preparation, the required data can be obtained from public channels, the service supply range is quantified, the nearest reference unit is found, the local regional background climate effect is stripped, the net service supply amount of the ecosystem is accurately reflected, and the simulation accuracy is improved.

Description

Technical Field

[0001] This application relates to the field of ecosystem service assessment technology, and in particular to an ecosystem service simulation method and system based on spatial gradient analysis. Background Technology

[0002] Ecosystem services, especially cooling and humidifying services with local microclimate regulation functions, are of great significance for mitigating the heat island effect, improving the thermal comfort of human living environments, and enhancing regional climate resilience.

[0003] In existing technologies, simulation methods for cooling and humidification services mainly fall into two categories: one is physical theoretical models based on energy balance or hydrothermal coupling processes, and the other is simplified models represented by the InVEST urban cooling model. This model mainly evaluates the cooling effect of vegetation based on the urban heat island effect (UHI) theory, and the evaluation result is the cooling capacity (CC) index, thus indirectly characterizing the cooling effect. However, although the theoretical mechanisms of traditional methods are clear, they rely on a large number of empirical parameters and parameter tuning, which has a high technical threshold and relatively complex requirements for input data (for example, the InVEST urban cooling model requires biophysical tables), making it difficult to promote and use in areas with relatively scarce data, such as towns and neighborhoods.

[0004] In addition, cooling and humidification services are local microclimate regulation services, characterized by rapid response, small spatial scale (street and township level), and spatiotemporal heterogeneity. Existing technologies handle spatial heterogeneity in a relatively simplified way. For example, the InVEST model only considers the spatial attenuation effect of large green spaces (>2 ha) and artificially sets its service distance to 450m, resulting in a large bias in the assessment of ecosystem services at a fine scale.

[0005] Therefore, there is a need to provide an improved technical solution that addresses the shortcomings of the existing technology. Summary of the Invention

[0006] The purpose of this application is to provide an ecosystem service simulation method and system based on spatial gradient analysis to solve or alleviate the problems existing in the prior art.

[0007] To achieve the above objectives, this application provides the following technical solution: Firstly, this application provides an ecosystem service simulation method based on spatial gradient analysis, comprising: Identify the service-providing and non-providing units for the target ecosystem service type, and acquire the environmental parameter raster data corresponding to the target ecosystem service type; For each supply unit, perform spatial gradient analysis to determine the service supply area of ​​the supply unit; Within the service supply area, for each supply grid, the nearest non-supply grid of the ecosystem service is determined as a reference unit; Using the environmental parameter values ​​of the reference unit as background values, the difference between the actual values ​​of the environmental parameters of the supply grid and the background values ​​is calculated to obtain the supply of ecosystem services. The spatial gradient analysis includes: generating a series of annular buffer zones with increasing radii from the vector boundary of the supply unit; performing pixel statistics on supply units of the same land use type according to the same buffer radius interval, and calculating the average value of environmental parameters; analyzing the trend of the average value of the environmental parameters relative to the buffer radius, and defining the outer boundary radius of the previous buffer zone corresponding to the stable trend as the effective service distance of the corresponding type of service supply unit, and then delineating the spatial area with a radius less than or equal to the effective service distance as the service supply area of ​​that type of service supply unit.

[0008] The aforementioned technical solution does not rely on the accurate setting of empirical parameters for physical processes, nor does it require complex regional data. It only needs to acquire basic data and environmental parameters, such as remote sensing and meteorological data (temperature, humidity), which are relatively easy to obtain. By mapping buffer zones and analyzing their gradient changes, the service supply range (i.e., the service supply area) is quantified. The difference between the actual values ​​of environmental parameters and background values ​​(from reference units) is used to characterize the ecosystem service supply, thereby effectively capturing spatial heterogeneity at the local scale and improving the simulation capability and applicability of local regulation services at fine scales such as street blocks and towns. Secondly, by calculating the average values ​​of environmental parameters within each buffer zone for different land use types and using the stabilization of the change trend as a criterion, the effective service distance of each type of supply unit is objectively identified, achieving data-driven, type-differentiated, and spatially adaptive delineation of the service supply area. Furthermore, the use of adjacent non-supply grids as a background value mechanism effectively isolates the background climate effects in local areas, accurately reflecting the net ecosystem service supply.

[0009] Preferably, the environmental parameters include surface temperature and relative humidity, and correspondingly, the target ecosystem service types include cooling services and humidifying services.

[0010] In the above scheme, surface temperature directly reflects the surface thermal state and is the core indicator for measuring the cooling capacity of vegetation and other ecosystems; relative humidity characterizes atmospheric water vapor content and is a key variable for assessing humidification effects such as vegetation transpiration and water evaporation. Both are typical microclimate variables with spatial gradient attenuation characteristics, and their data sources are extensive, such as remote sensing inversion and meteorological station data inversion. Establishing a one-to-one correspondence between surface temperature and cooling services, and between relative humidity and humidification services, gives the environmental parameters in the technical scheme clear physical and ecological connotations.

[0011] Preferably, the radius of the annular buffer zone increases at equal intervals, with an increment of 50 meters.

[0012] The above scheme further defines the generation method of the ring buffer, specifying that its radius increases at equal intervals with an increment of 50 meters. This can effectively distinguish the typical effective service distance range of local scales (such as urban parks, small woodlands, and wetlands), ensuring that there are enough data points for identifying trends that tend to stabilize. Furthermore, the equal-interval increment method simplifies the buffer construction and statistical calculation process, making it easier for the algorithm to be implemented.

[0013] Preferably, the trend of the average value of the environmental parameters relative to the buffer radius is analyzed, and the outer boundary radius of the previous buffer zone corresponding to the point where the trend stabilizes is defined as the effective service distance of the corresponding type of service provisioning unit, including: Plot a line graph showing the average value of the environmental parameters as a function of the buffer radius; Analyzing the line graph, if the average value of the environmental parameters tends to stabilize under a certain buffer radius, the outer boundary radius of the previous buffer zone when it stabilizes is determined as the effective service distance.

[0014] In the above scheme, by drawing a line graph showing the change of the average environmental parameters with the buffer radius, the buffer sequence and the corresponding average parameter values ​​are presented graphically, intuitively reflecting the decay process of service effect with distance. Based on the line graph, a stabilization trend is identified: that is, within a certain buffer radius range, the fluctuation range of the average parameter value decreases significantly and enters a plateau period. The outer boundary radius of the buffer before stabilization is determined as the effective service distance, clearly using the trend inflection point as the criterion, avoiding misjudging background noise as the service impact area.

[0015] Preferably, acquiring environmental parameter raster data corresponding to the target ecosystem service type includes: obtaining surface temperature data by inverting remote sensing images, or obtaining relative humidity data by spatial interpolation using meteorological station data; and filling in missing values ​​in the environmental parameter raster data.

[0016] The above scheme clarifies the differentiated acquisition paths for two types of data: surface temperature is obtained through remote sensing inversion, and relative humidity is obtained through station data plus spatial interpolation (such as Kriging and inverse distance weighting). This enables continuous regional representation under limited station conditions, improves the accessibility, spatial integrity, and usability of environmental parameter data, and provides high-quality basic data for subsequent analysis steps.

[0017] Secondly, this embodiment provides an ecosystem service simulation system based on spatial gradient analysis, which is used to perform the steps of the method described in any of the above embodiments, including: The data acquisition and segmentation unit is configured as a service supply unit and a non-supply unit to determine the target ecosystem service type, and acquires environmental parameter raster data corresponding to the target ecosystem service type; The supply distance determination unit is configured to perform spatial gradient analysis for each supply unit to determine the service supply area of ​​the supply unit. The proximity analysis unit is configured to, within the service supply area, for each supply grid, determine the nearest non-supply grid of the ecosystem service as a reference unit. The supply calculation unit is configured to use the environmental parameter values ​​of the reference unit as background values, calculate the difference between the actual values ​​of the environmental parameters of the supply grid and the background values, and obtain the supply of ecosystem services. The spatial gradient analysis includes: generating a series of annular buffer zones with increasing radii from the vector boundary of the supply unit; performing pixel statistics on supply units of the same land use type according to the same buffer radius interval, and calculating the average value of environmental parameters; analyzing the trend of the average value of the environmental parameters relative to the buffer radius, and defining the outer boundary radius of the previous buffer zone corresponding to the stable trend as the effective service distance of the corresponding type of service supply unit, and then delineating the spatial area with a radius less than or equal to the effective service distance as the service supply area of ​​that type of service supply unit.

[0018] Thirdly, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in any of the above embodiments.

[0019] Fourthly, this embodiment provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the method described in any of the above embodiments.

[0020] Fifthly, this embodiment provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in any of the above embodiments.

[0021] The beneficial effects of the second, third, fourth, and fifth aspects of this application can be referred to the description of the first aspect, and will not be repeated here. Attached Figure Description

[0022] Figure 1 This is a line graph showing the variation of surface temperature with buffer distance in different months of 2020.

[0023] Figure 2 A schematic diagram showing the multi-year average cooling service supply for each month.

[0024] Figure 3 The diagram shows the structure of an electronic device provided in some embodiments of this application. Detailed Implementation

[0025] The embodiments of this application will now be described with reference to the accompanying drawings.

[0026] This embodiment provides an ecosystem service simulation method based on spatial gradient analysis, which includes the following steps: S1. Identify the service-providing and non-providing units of the target ecosystem service type and acquire the environmental parameter raster data corresponding to the target ecosystem service type.

[0027] Among them, the target ecosystem service type refers to ecosystem services with local microclimate regulation function, such as cooling services and humidification services. These services are usually provided by specific land cover / land use types, such as forests, grasslands, wetlands, and water bodies, which regulate the local thermal and humid environment through ecological processes such as evapotranspiration, shading, and reflectivity regulation.

[0028] A service provision unit refers to a land use / cover patch that can provide target ecosystem services in space. The definition of each service provision unit can be based on high-resolution land use / cover data (such as GlobeLand30, local planning patches, or remote sensing classification results) and combined with ecological function criteria.

[0029] Non-supply units refer to areas that provide little or no service, typically including built-up areas, bare soil, and hardened surfaces. In this embodiment, non-supply units are used as a reference background for service effects.

[0030] Environmental parameter raster data refers to remote sensing or meteorological derived variables that can characterize the physical effects of target ecosystem services, and their types correspond to the target service types. For example, for cooling services, the environmental parameter could be land surface temperature (LST); for humidification services, the environmental parameter could be relative humidity (RH) or specific humidity. Furthermore, environmental parameter raster data should have a spatial resolution matching the study area (e.g., 0.5m, 1m, 5m, 10m, 30m) and cover the service provisioning units and their surrounding potential impact zones.

[0031] In a preferred embodiment, environmental parameter raster data can be obtained by: retrieving surface temperature data from remote sensing images, or obtaining relative humidity data through spatial interpolation using meteorological station data; and filling in missing values ​​in the environmental parameter raster data. Specifically, surface temperature can be retrieved from multispectral or thermal infrared remote sensing images; or a spatially continuous relative humidity raster can be generated based on ground meteorological observation station data using spatial interpolation methods (such as Kriging interpolation or inverse distance weighting); and missing values ​​(such as LST null values ​​caused by cloud pollution) in the obtained raster can be filled in to ensure the integrity and stability of subsequent spatial analysis.

[0032] In step S1, by clearly distinguishing between service supply units and non-supply units, the main body providing ecosystem services is objectively defined. On the one hand, this avoids misclassifying non-ecological areas as supply units, and on the other hand, non-supply units serve as a source of background values, thereby improving the ecological rationality of the assessment.

[0033] S2. For each supply unit, perform spatial gradient analysis to determine the service supply area of ​​the supply unit. This spatial gradient analysis includes: generating a series of annular buffer zones with increasing radii outwards from the vector boundary of the supply unit; calculating the average environmental parameters within each buffer zone for different land use types; analyzing the trend of the average environmental parameters relative to the buffer radius; defining the outer boundary radius of the previous buffer zone corresponding to the point where the trend stabilizes as the effective service distance for the corresponding type of service supply unit; and then delineating the spatial area with a radius less than or equal to the effective service distance as the service supply area for that type of service supply unit.

[0034] Here, spatial gradient refers to the change in the average value of environmental parameters between adjacent buffer zones.

[0035] An annular buffer zone is a spatial analysis zone (buffer sequence) that expands continuously from the inside out, with equidistant increments at preset intervals (e.g., 50 meters). This buffer sequence is used to detect the decay of ecosystem service effects with distance. Furthermore, the annular buffer zone is generated by gradually expanding outward from the vector boundary of the service supply unit. Therefore, before generating the buffer zone, a vectorization step is included, that is, converting the patches identified as service supply units in the land use / cover data into vector format for storage. This can be performed using existing GIS software and will not be elaborated here.

[0036] It is also important to note that since the target ecosystem services are provided by specific land cover / land use types (land use types), the supply units and land use types are naturally linked, and thus the annular buffer also records different land use type attributes (i.e., land use types). In this embodiment, when performing spatial gradient analysis, instead of calculating the average environmental parameters for each buffer as a whole, all supply units in the study area are grouped according to their own land use types, i.e., supply units of the same land use type are grouped together; then, for the same land use type (e.g., "forest land") in the study area, all supply units belonging to forest land are aggregated, and within the same buffer distance range (e.g., 0 to 50 m), pixels are uniformly extracted and the average environmental parameters (e.g., surface temperature) are statistically calculated.

[0037] Similarly, the mean environmental parameters for this type are calculated sequentially for each buffer distance range, such as 50 to 100 m, 150 to 200 m, etc. By using a statistical method that aggregates across spatial units and by type, the trend of change between the mean environmental parameters and the buffer radius is constructed. Aggregating by land use type ensures that the analysis of service attenuation patterns can be carried out according to the differences in land use background, avoiding trend ambiguity caused by heterogeneous mixing, and laying the foundation for achieving effective service distance identification based on type differences.

[0038] Furthermore, the trend of the average environmental parameters changing with the buffer radius under each land use type is analyzed, and the effective service distance is determined. As an example, the effective service distance can be obtained by the following sub-steps: drawing a line graph of the average environmental parameters changing with the buffer radius; analyzing the line graph; if the trend of the average environmental parameters changing with the buffer radius tends to stabilize under a certain buffer radius, the outer boundary radius of the previous buffer zone at which the stabilization occurs is determined as the effective service distance.

[0039] In other words, the trend of environmental parameter changes can be analyzed by plotting the relationship between the average value of environmental parameters and the buffer radius, and the endpoint of service impact attenuation can be identified using a trend stability criterion. Specifically, if the average fluctuation of environmental parameters within two or more consecutive buffer radii is lower than a preset threshold (e.g., difference <0.3℃ or change rate <1%, or judged by the difference between non-supply units, such as the building temperature difference between adjacent buffers ≤0.3℃), it can be determined that the average value of environmental parameters tends to stabilize within a certain buffer radius. If this trend tends to stabilize, it indicates that the service effect has attenuated to a level that is not significantly different from the regional background level. Accordingly, the outer boundary radius of the buffer before the trend tends to stabilize (i.e., the previous buffer radius) is defined as the effective service distance of this type of service supply unit. For example, if the average surface temperature in the 300m~350m annular buffer zone tends to be stable, and the fluctuation range between the average surface temperature of the annular buffer zone with a radius of more than 350m and the average surface temperature of the adjacent buffer zone is less than a preset threshold, then the outer boundary radius (i.e., 300m) of the previous buffer zone (with a radius of 250m~300m) is taken as the effective service distance. This distance is also the inner boundary radius of the annular buffer zone (i.e., 300m~350m) when the average surface temperature tends to be stable.

[0040] Finally, using the effective service distance as the radius and the boundary of each supply unit as the starting point, a circular or annular area is delineated outward. All spatial locations with a radius less than or equal to the effective service distance are uniformly designated as the service supply area of ​​the supply unit. This area is the actual range of influence of the ecosystem service that has a significant spatial moderating effect.

[0041] It should be noted that the above process can be performed separately for different types of ecosystem services (such as cooling and humidification) and different types of supply units (i.e., the land use types corresponding to the supply units, such as forests, water bodies, and grasslands), thereby achieving differentiated and spatially adaptive delineation of service supply areas. For example, the cooling effect of forest land may stabilize at 300 m, while the humidification effect of water bodies may stabilize at 500 m. In other words, this embodiment can differentiate the actual service supply area range for both, thus overcoming the errors caused by existing models that use a fixed service range (such as artificially setting 450 m). By calculating the mean values ​​of environmental parameters separately according to land use type, the differences in service attenuation of different supply units under different underlying surface backgrounds can be identified, enhancing the ability to characterize spatial heterogeneity. In addition, different types of supply units (such as forest land and cultivated land) can obtain their respective effective service distances, providing differentiated scientific basis for ecological space planning, green space configuration optimization, and ecological zoning management.

[0042] S3. Within the service supply area, for each supply grid, determine the nearest non-supply grid of the ecosystem service as the reference unit.

[0043] In this embodiment, after determining the service supply area of ​​each type of service supply unit, within the service supply area, for each supply grid, its nearest non-supply grid for ecosystem services is determined as a reference unit. A supply grid refers to a grid cell located within the service supply area whose original land use / cover type belongs to a service supply unit (such as woodland, canals, grassland, water bodies, etc.). Correspondingly, a non-supply grid refers to a grid cell whose land use / cover type is defined as an area that basically does not provide the target ecosystem services (such as urban and rural areas, industrial and mining areas, residential land, etc.).

[0044] Nearest neighbor refers to starting from the current supply grid, searching for all non-supply grids around it, and selecting the non-supply grid with the shortest distance as its corresponding reference cell. The distance can be Euclidean or Mahalanobis distance; this embodiment does not limit this. Furthermore, the search range can be limited to the service supply area or its adjacent extended area to ensure that the reference cells have comparable regional climate backgrounds in space, while avoiding large-scale climate gradient interference introduced by long-distance matching.

[0045] Optionally, Euclidean distance transformation or nearest neighbor search algorithms can be used to match each feed grid cell across the entire map to its nearest non-feed grid cell, generating a reference cell mapping map that corresponds one-to-one with each feed grid cell. The environmental parameter values ​​(such as surface temperature and relative humidity) corresponding to the reference cells are used as background values ​​for subsequent calculations of the ecosystem service provision of the current feed grid cell.

[0046] Traditional methods often use regional averages, fixed control areas, or model-simulated background fields as benchmarks, which are insufficient to reflect the microclimate background differences caused by complex underlying surface combinations at the local scale. In this embodiment, the nearest non-feeding grid is used as a reference to ensure that the background value and the feeding grid are under highly similar local regional climate and geographical conditions (such as the same street block or the same micro-topographic unit). This effectively isolates large-scale climate disturbances and accurately reflects the net regulatory effect of the ecosystem relative to the adjacent no-service state, conforming to the scientific principles of ecosystem service increment assessment. The effective range of services such as cooling and humidification is typically only a few hundred meters, and their benefits are highly dependent on the thermal and moisture characteristics of the surrounding non-ecological underlying surfaces (such as buildings and roads). Using the nearest non-feeding grid as a reference can dynamically capture the intensity of local environmental stresses faced by different feeding locations (such as adjacent high-density building clusters or adjacent low-density residences), making service supply assessment more spatially sensitive and realistic. Furthermore, the reference unit is automatically determined by the algorithm, eliminating the need for manual delineation of control areas or reliance on expert experience, thus eliminating assessment uncertainties caused by control selection bias.

[0047] S4. Using the environmental parameter values ​​of the reference cell as background values, calculate the difference between the actual value of the environmental parameter of the supply grid (i.e., the current pixel) and the background value to obtain the supply of ecosystem services.

[0048] Specifically, assume that the actual value of the environmental parameters currently supplied to the raster p is V. actual (p), such as the surface temperature is Or, with a relative humidity of 65%, the environmental parameter value of the corresponding reference cell (i.e., the nearest non-supply grid) is V. bk (p), for example, the surface temperature is If the relative humidity is 58%, then the ecosystem service provision of this grid is calculated as follows: For cooling services, since the service is manifested as a reduction in temperature, the supply is defined as: (1) For humidification services, since the service is characterized by increased humidity, the supply is defined as follows: (2) In the formula, , These refer to the supply of cooling and humidifying services. The higher the value, the stronger the cooling effect; The larger the value, the more significant the humidification effect.

[0049] The above calculation method can be summarized as follows: Ecosystem service supply = Difference between actual value and background value in the service-favorable direction. The service-favorable direction is determined by the physical mechanisms of the target ecosystem service type, and has been clarified in step S1 through the correspondence between environmental parameters and ecosystem service types.

[0050] By performing this interpolation operation on all supply grids within the service supply area, a continuous, pixel-level ecosystem service supply raster map can be generated, which intuitively reflects the spatial distribution pattern of service intensity.

[0051] Ecosystem services are not absolute state quantities, but rather additional benefits relative to a state of no services or baseline. This embodiment uses the interpolation method to explicitly isolate the background state, ensuring that service provision reflects the net improvement brought about by the ecosystem itself. This aligns with the definition of moderating services under the IPBES and MA (Millennium Ecosystem Assessment) framework, significantly enhancing the scientific rigor and comparability of the assessment. Secondly, the thermal and humid environment in urban and rural areas is influenced by multiple factors such as topography, wind fields, solar radiation, and anthropogenic heat emissions, exhibiting strong spatial heterogeneity. Using regional averages or fixed controls makes it difficult to eliminate these confounding factors. This embodiment uses the spatially nearest non-providing grid as the background, which is spatially close to the providing grid, sharing similar local climate and geographical background, differing only in underlying surface ecological attributes. This maximizes control over confounding variables, allowing the interpolation to more accurately reflect the ecosystem's own moderating capacity.

[0052] The method provided in this embodiment will be illustrated with examples below.

[0053] We selected a city as the study area and simulated the supply of ecosystem services at the city block and town levels.

[0054] (1) Obtain data.

[0055] The data required for the experiment include: basic geographic data, remote sensing data, meteorological data, land use data, socio-economic data, and other data. The acquisition methods for each type of data are shown in Table 1. Table 1 is as follows: Table 1. Data Sources and Descriptions

[0056] (2) Cooling service supply.

[0057] Surface temperature is the temperature condition at the interface between the atmosphere and the Earth's surface, and it has a significant linear correlation with near-surface atmospheric temperature. In this application, the supply of cooling services is calculated based on the difference in surface temperature between supplying units (e.g., vegetation) and non-supplying units (e.g., urban built-up areas). The calculation method is as follows: The first step was to retrieve land surface temperature. Landsat 7 and Landsat 8 remote sensing data were processed on the GEE (Google Earth Engine) platform. The 2010 land surface temperature data came from the LANDSAT / LE07 / C02 / T1_L2 dataset, specifically its sixth band (ST_B6), which is an atmospherically corrected LST (Land Surface Temperature, LST) band. The 2015 and 2020 land surface temperatures came from the tenth band (SR_B10) of the LANDSAT / LC08 / C02 / T1_L2 dataset. The corresponding bands were processed using a cloud removal function, and monthly averages were synthesized to obtain the monthly land surface temperature for the study area (city level). For raster cells with missing values, linear regression was used for filling. Specifically, the average LST for the same month over the previous four years was used as the independent variable, and the LST for the current month was used as the dependent variable. A linear regression model was established, and the regression coefficients and offsets were extracted to fill in the missing LST for the current year.

[0058] To further verify the accuracy of the land surface temperature (LST) retrieval results, a comparative method was used to validate the accuracy. The average LST of a 3×3 pixel area surrounding a meteorological station was calculated using ArcGIS's focus statistics tool and used as the station's LST value. The LST value was then compared with the observed ground surface temperature (GST) at the meteorological station. Considering the differences in results across different land use types, a different number of stations were selected for validation based on each land use type. Using the land use types of 2020 as a baseline, 25 stations were ultimately selected for validating the LST retrieval results.

[0059] Furthermore, the ground temperature observations from meteorological stations in 2010, 2015, and 2020 were compared with the retrieved surface temperature values. The results showed that the retrieved surface temperature was in good agreement with the actual situation and had high accuracy, making it suitable as basic data for further research.

[0060] The second step is to calculate the supply of cooling services in the ecosystem. Ecological spaces such as woodlands, grasslands, and wetlands have a significant cooling effect. Therefore, woodlands, grasslands, water bodies, and cultivated land are defined as cooling service supply units, while urban and rural areas, industrial and mining areas, residential land, and unused land are defined as cooling service non-supply units.

[0061] 30m land use raster data was converted into vector data. Based on land use data from different time periods, for each type of vector data—forest, grassland, water area, and cultivated land—GIS software (such as ArcGIS) was used to establish a series of buffer zones with radii of 50 m, 100 m, 150 m, 200 m, 250 m, 300 m, 350 m, 400 m, 450 m, and 500 m, respectively. Then, the intersection of adjacent buffer zones was inverted, resulting in a series of annular buffer zones with radii ranging from 0 to 50 m, 50 to 100 m, 100 to 150 m, 150 to 200 m, 200 to 250 m, 250 to 300 m, 300 to 350 m, 350 to 400 m, 400 to 450 m, and 450 to 500 m. All pixels within the same buffer radius range and supply unit of the same land use type were collected and statistically analyzed to calculate the 0 to 50 m, 50 to 100 m, and 50 to 100 m radius ranges. Average surface temperatures within buffer zones of 100-150 m, 150-200 m, 200-250 m, 250-300 m, 300-350 m, 350-400 m, 400-450 m, and 450-500 m. Plot line graphs showing the variation of surface temperature within the buffer zones of each type of supply unit as a function of buffer distance at different times. For example, Figure 1 The graph shows the variation of surface temperature with buffer distance in different months of 2020. Other years (such as 2010 and 2015) are plotted in the same way and are not shown here.

[0062] Data from different time periods are summarized according to land use type to obtain, as follows: Figure 2 The summary data is shown in the line chart. According to the line chart, when the surface temperature change within a certain buffer zone relative to the previous buffer zone tends to stabilize (i.e., the surface temperature fluctuation of adjacent buffer zones is lower than a preset threshold), the distance of the preceding buffer zone is taken as the supply distance (called the effective service distance) to the application land type supply unit, and the buffer zone where the supply distance is located is the cooling service supply area. The supply distances for different land use types are shown in Table 2, as follows: Table 2 Supply Distance for Different Land Use Types

[0063] As can be seen from Table 2, the effective service distances are not the same for different land use types, and even for the same land use type, the effective service distances are different in different months. Traditional methods use a fixed service distance (such as setting it directly to 450m). This setting can meet the needs of simulating ecosystem service supply at large spatial scales (such as the whole country or spanning multiple provinces) and annual time scales, but it will have a large deviation at fine scales. The method provided in this embodiment can identify the effective service distance based on type differences and use the spatial nearest non-supply grid to remove the influence of local background factors, providing technical support for solving the simulation of ecosystem services at fine spatiotemporal scales.

[0064] After determining the service supply area, for each supply grid within the service supply area, the cooling service supply is calculated by establishing nearest neighbor spatial connections with non-supply unit grids and using the surface temperature corresponding to the nearest non-supply unit grid as the background surface temperature value for that supply grid. (3) In the formula, To increase the supply of cooling services, To supply the surface temperature (°C) of the cell grid, The surface temperature (°C) is the temperature of the non-supply unit grid. Formula (3) is essentially the result of replacing the environmental parameters as a whole with the surface temperature based on Formula (1), that is... Equivalent to formula (1) , Equivalent to , Equivalent to .

[0065] Finally, the supply of cooling services in different townships in different months was statistically analyzed, and the multi-year average supply of cooling services on a monthly scale was calculated. The results are as follows: Figure 2 As shown in the diagram. The above steps enable the assessment of ecosystem services at the urban and rural levels, providing a basis for ecological management and ecological engineering.

[0066] (3) Humidification service supply.

[0067] The supply of humidification services can be characterized as the humidity difference between the service supply area and the building space. First, the average humidity of 68 stations around the study area was interpolated using the professional meteorological data interpolation software Anusplin. Second, similar to the calculation process for cooling service supply, buffer zones of 50 m, 100 m, 150 m, 200 m, 250 m, 300 m, 350 m, 400 m, 450 m, and 500 m were established for supply units such as woodland, grassland, water area, and cultivated land. The average relative humidity within the buffer zones of 0-50 m, 50-100 m, 100-150 m, 150-200 m, 200-250 m, 250-300 m, 300-350 m, 350-400 m, 400-450 m, and 450-500 m was calculated. The analysis examines the variation of relative humidity within the buffer zone of each supply unit with the buffer distance. When the relative humidity within a certain buffer zone no longer increases (i.e., the change tends to stabilize), the buffer distance in front of it is taken as the supply distance for the corresponding type of supply unit, and the buffer zone containing this supply distance is designated as the humidification service supply area. Finally, the difference between the average relative humidity of the service supply area and the humidity of the nearest building space is used as the supply volume of the humidification service.

[0068] The embodiments of this application can be applied to Figure 3 The electronic devices (computer devices) shown may be, but are not limited to, mobile terminals such as mobile phones, tablets, handheld computers, and personal digital assistants (PDAs), smart home devices such as smart TVs and smart cameras, wearable devices such as smart bracelets, smartwatches, and smart glasses, or other desktop, laptop, notebook, ultra-mobile personal computer (UMPC), netbook, and smart screen computer devices.

[0069] like Figure 3 As shown, the electronic device 200 may include one or more of the following components: a processor 201, a memory 203, a communication interface 202, and a communication bus 204. The memory 203 can be connected to the processor 201 via the bus 204. The bus can transfer data between the processor 201 and the memory 203. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0070] Processor 201 may include one or more processing cores. Processor 201 can connect to various parts within the electronic device 200 using various interfaces and lines. It performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 203, and by calling data stored in memory 203. For example, processor 201 may include an application processor (AP), a modem processor, a CPU, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA), and / or a neural network processing unit (NPU). The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content to be displayed; the NPU implements artificial intelligence (AI) functions; and the modem handles wireless communication. Different processing units can be independent devices or integrated into one or more processors. For example, the multiple processing units shown above are all integrated into a single SoC, or the AP is a separate semiconductor chip, while other processing units are integrated into a single SoC. This application does not limit this to any particular type.

[0071] The memory 203 may include random access memory (RAM), read-only memory (ROM), or non-transitory computer-readable storage medium. The memory 203 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 203 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system or instructions for at least one function, such as an ecosystem service simulation method based on spatial gradient analysis. The data storage area may store data created based on the use of the electronic device 200, such as basic geographic data, meteorological data, and simulation results.

[0072] In addition, those skilled in the art will understand that the structure of the electronic device 200 shown in the above figures does not constitute a limitation on the electronic device 200. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements. For example, the electronic device 200 may also include components such as a microphone, speaker, radio frequency circuit, sensor, audio circuit, power supply, and Bluetooth module, which will not be described in detail here.

[0073] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for simulating ecosystem services based on spatial gradient analysis, characterized in that, include: Identify the service-providing units and non-providing units of the target ecosystem service type, and acquire the environmental parameter raster data corresponding to the target ecosystem service type; the service-providing unit refers to the land use / cover patch that can provide the target ecosystem service in space; the non-providing unit refers to the area that basically does not provide the target ecosystem service or has a very weak ability to provide it. For each supply unit, a spatial gradient analysis is performed to determine the service supply area of ​​the supply unit; within the service supply area, for each supply grid, the nearest non-supply grid of the ecosystem service is determined as a reference unit. Using the environmental parameter values ​​of the reference unit as background values, the difference between the actual values ​​of the environmental parameters of the supply grid and the background values ​​is calculated to obtain the supply of ecosystem services. The spatial gradient analysis includes: generating a series of annular buffer zones with increasing radii from the vector boundary of the supply unit; performing pixel statistics on supply units of the same land use type according to the same buffer radius interval, and calculating the average value of environmental parameters; analyzing the trend of the average value of the environmental parameters relative to the buffer radius, and defining the outer boundary radius of the previous buffer zone corresponding to the stable trend as the effective service distance of the corresponding type of service supply unit; and further defining the spatial area with a radius less than or equal to the effective service distance as the service supply area of ​​that type of service supply unit. Among them, the supply grid refers to the grid cell located within the service supply area and whose original land use / cover type belongs to the service supply unit, while the non-supply grid refers to the grid cell in the area whose land use / cover type is defined as not providing the target ecosystem services.

2. The method according to claim 1, characterized in that, The environmental parameters include surface temperature and relative humidity. Accordingly, the target ecosystem service types include cooling services and humidifying services.

3. The method according to claim 1, characterized in that, The radius of the annular buffer zone increases at equal intervals, with each interval being 50 meters.

4. The method according to claim 1, characterized in that, Analyze the trend of the average value of the environmental parameters relative to the buffer radius, and define the outer boundary radius of the previous buffer zone corresponding to the point where the trend stabilizes as the effective service distance of the corresponding type of service provisioning unit, including: Plot a line graph showing the average value of the environmental parameters as a function of the buffer radius; analyze the line graph, and if the average value of the environmental parameters tends to stabilize at a certain buffer radius, determine the outer boundary radius of the previous buffer zone at which the stabilization occurs as the effective service distance.

5. The method according to any one of claims 1 to 4, characterized in that, Obtain raster data of environmental parameters corresponding to the target ecosystem service type, including: Land surface temperature data is obtained by inverting remote sensing images, or relative humidity data is obtained by spatial interpolation using meteorological station data; and missing values ​​in the environmental parameter raster data are filled in.

6. An ecosystem service simulation system based on spatial gradient analysis, the system being used to perform the steps of the method as described in any one of claims 1 to 5, characterized in that, include: The data acquisition and segmentation unit is configured to determine the service supply unit and non-supply unit of the target ecosystem service type, and acquire environmental parameter raster data corresponding to the target ecosystem service type; the service supply unit refers to the land use / cover patch that can provide the target ecosystem service in space; the non-supply unit refers to the area that basically does not provide the target ecosystem service or has a very weak ability to provide it. The supply distance determination unit is configured to perform spatial gradient analysis for each supply unit to determine the service supply area of ​​the supply unit. The proximity analysis unit is configured to, within the service supply area, for each supply grid, determine the nearest non-supply grid of the ecosystem service as a reference unit. The supply calculation unit is configured to use the environmental parameter values ​​of the reference unit as background values, calculate the difference between the actual values ​​of the environmental parameters of the supply grid and the background values, and obtain the supply of ecosystem services. The spatial gradient analysis includes: generating a series of annular buffer zones with increasing radii from the vector boundary of the supply unit; performing pixel statistics on supply units of the same land use type according to the same buffer radius interval, and calculating the average value of environmental parameters; analyzing the trend of the average value of the environmental parameters relative to the buffer radius, and defining the outer boundary radius of the previous buffer zone corresponding to the stable trend as the effective service distance of the corresponding type of service supply unit; and further defining the spatial area with a radius less than or equal to the effective service distance as the service supply area of ​​that type of service supply unit. Among them, the supply grid refers to the grid cell located within the service supply area and whose original land use / cover type belongs to the service supply unit, while the non-supply grid refers to the grid cell in the area whose land use / cover type is defined as not providing the target ecosystem services.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 5.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 5.

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