Evaluation method of resource and environment carrying capacity of island city based on ecosystem service function

Abstract

This invention relates to the field of resource and environmental protection technology, and in particular to a method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions. The comprehensive evaluation index of ecosystem services obtained through the assessment and calculation of ecosystem service functions is applied to the rapid assessment of resource and environmental carrying capacity, providing decision support for regional ecosystem protection and sustainable development.

Description

Technical Field

[0001] This invention relates to the field of resource and environmental protection technology, and in particular to a method, apparatus, computer equipment, and storage medium for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions. Background Technology

[0002] Intense human activities have had numerous negative impacts on ecosystems, making ecological and environmental issues one of the most pressing problems to address. Urbanization, through the rapid expansion of built-up land and increased intensity of human activity, has a profound impact on regional land structure and ecological landscapes, thereby altering the original ecological landscape pattern and ultimately hindering the sustainable supply of ecosystem services. Ecosystem services provide the basic raw materials and material foundation for human survival, maintain the normal operation of humans and the entire global ecosystem, and provide the most important guarantee for the development of human society. Therefore, with the deepening of global urbanization and the continuous deterioration of ecosystems, the scientific and accurate assessment of the value of ecosystem services and its application to sustainable development management has become a hot topic of concern for governments and academia worldwide. Summary of the Invention

[0003] Based on this, the purpose of this invention is to provide a method, apparatus, computer equipment, and storage medium for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions. The comprehensive evaluation index of ecosystem services obtained through the assessment and calculation of ecosystem service functions is applied to the rapid assessment of resource and environmental carrying capacity, providing decision support for regional ecosystem protection and sustainable development.

[0004] In a first aspect, embodiments of this application provide a method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions, comprising the following steps:

[0005] Obtain basic data of the island city to be processed; wherein, the area to be processed includes several raster cells, and the basic data includes basic data of several raster cells; Based on the basic data of the island city to be processed, the ecosystem service function indicators are calculated to obtain several types of ecosystem service function indicators for each grid cell. The ecosystem service function indicators include ecosystem carbon sequestration function indicators, soil conservation function indicators, water conservation function indicators, and biodiversity conservation function indicators. The comprehensive evaluation index of ecosystem services is calculated based on several types of ecosystem service function indicators for each grid cell to obtain the comprehensive evaluation index of ecosystem services for each grid cell. Based on the comprehensive evaluation index of ecosystem services of each grid cell and the preset threshold range of resource and environmental carrying capacity, the resource and environmental carrying capacity level is divided into levels, and the resource and environmental carrying capacity level of each grid cell is obtained as the evaluation result of the resource and environmental carrying capacity of the island city to be processed.

[0006] Secondly, embodiments of this application provide a device for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions, comprising: The data acquisition module is used to obtain basic data of the island city to be processed; wherein, the area to be processed includes several raster units, and the basic data includes basic data of several raster units; The first indicator calculation module is used to calculate the ecosystem service function indicators based on the basic data of the island city to be processed, and to obtain several types of ecosystem service function indicators for each grid cell. The ecosystem service function indicators include ecosystem carbon sequestration function indicators, soil conservation function indicators, water conservation function indicators, and biodiversity conservation function indicators. The second indicator calculation module is used to calculate the comprehensive evaluation index of ecosystem services based on several types of ecosystem service function indicators of each grid cell, and obtain the comprehensive evaluation index of ecosystem services of each grid cell. The resource and environmental carrying capacity evaluation module is used to classify the resource and environmental carrying capacity levels based on the comprehensive evaluation index of ecosystem services of each grid unit and the preset resource and environmental carrying capacity threshold range, and obtain the resource and environmental carrying capacity level of each grid unit as the resource and environmental carrying capacity evaluation result of the island city to be processed.

[0007] Thirdly, embodiments of this application provide a computer device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the island city resource and environmental carrying capacity evaluation method based on ecosystem service functions as described in the first aspect.

[0008] Fourthly, embodiments of this application provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the island city resource and environmental carrying capacity evaluation method based on ecosystem service functions as described in the first aspect.

[0009] In this application embodiment, a method, apparatus, computer equipment, and storage medium for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions are provided. The comprehensive evaluation index of ecosystem services obtained through the assessment and calculation of ecosystem service functions is applied to the rapid assessment of resource and environmental carrying capacity, providing decision support for regional ecosystem protection and sustainable development.

[0010] To better understand and implement this invention, the following detailed description is provided in conjunction with the accompanying drawings. Attached Figure Description

[0011] Figure 1 A flowchart illustrating a method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions, provided in one embodiment of this application; Figure 2 A schematic diagram of step S2 in the process of an island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in one embodiment of this application; Figure 3 A schematic diagram of step S2 in the process of an island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in one embodiment of this application; Figure 4 A schematic diagram of step S2 in the process of an island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in one embodiment of this application; Figure 5 A schematic diagram of step S3 in the process of an island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in one embodiment of this application; Figure 6 A schematic diagram of step S4 in the process of an island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in one embodiment of this application; Figure 7 A schematic diagram of step S4 in the process of an island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in one embodiment of this application; Figure 8 A schematic diagram of the structure of an island city resource and environmental carrying capacity assessment device based on ecosystem service functions provided in one embodiment of this application; Figure 9 This is a schematic diagram of the structure of a computer device provided in one embodiment of this application. Detailed Implementation

[0012] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0013] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0014] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0015] Please see Figure 1 , Figure 1 The flowchart of the resource and environmental carrying capacity assessment method for island cities based on ecosystem service functions provided in the first embodiment of this application is shown. The method includes the following steps: S1: Obtain basic data of the island cities to be processed.

[0016] The implementing entity of the evaluation method for the resource and environmental carrying capacity of island cities based on ecosystem service functions is the evaluation equipment (hereinafter referred to as the evaluation equipment). In an optional embodiment, the evaluation equipment may be a computer device, a server, or a server cluster composed of multiple computer devices.

[0017] In this embodiment, the evaluation device obtains basic data of the island city to be processed; wherein, the area to be processed includes a number of raster cells, and the basic data includes basic data of the number of raster cells.

[0018] S2: Calculate ecosystem service function indicators based on the basic data of the island city to be processed, and obtain several types of ecosystem service function indicators for each grid cell.

[0019] In this embodiment, the evaluation device calculates ecosystem service function indicators based on the basic data of the island city to be processed using an ecosystem service assessment model, obtaining several types of ecosystem service function indicators for each grid cell. The ecosystem service assessment model employs the InVEST (Integrated Valuation of Ecosystem Services and Trade-offs) model, which integrates various biophysical and socioeconomic data, providing a range of ecosystem service assessment capabilities. The ecosystem service function indicators include indicators for ecosystem carbon sequestration, soil conservation, water conservation, and biodiversity conservation.

[0020] The basic data includes land use data, which reflects the status, characteristics, dynamic changes, and distribution of land use systems and elements, as well as data on human activities related to land development, utilization, management, protection, and land use planning. Specifically, the land use data uses the annual land use classification data for China from 1985 to 2024, with a spatial resolution of 30 meters, compiled by Yang Jie and Huang Xin's team. This data is divided into nine categories at the first level. In accordance with the requirements and referring to the "Classification of Current Land Use" (GB / T 21010-2007) standard, and combined with the actual characteristics of land use in island cities, it has been reclassified into six categories using ArcMap 10.8: cultivated land, forest land, grassland, water area, construction land, and unused land.

[0021] For the aforementioned ecosystem carbon sequestration function indicators, please refer to Figure 2 , Figure 2 A schematic diagram of step S2 in the process of the island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in the first embodiment of this application, including steps S201 to S202, as follows: S201: Obtain the carbon density data of each grid cell based on the land use data of each grid cell and the preset land use type and carbon density mapping table.

[0022] In this embodiment, the evaluation device obtains the carbon density data of each grid cell based on the land use data of each grid cell and a preset land use type-carbon density mapping table. The carbon density data includes aboveground biomass carbon storage, belowground biomass carbon storage, soil carbon storage, and dead organic matter carbon storage. The land use type-carbon density mapping table is as follows:

[0023] S201: The carbon storage of aboveground biomass, belowground biomass, soil carbon, and dead organic matter in the carbon density data of the same grid cell are summed to obtain the total carbon storage of each grid cell, which is used as an indicator of the carbon sequestration function of the ecosystem.

[0024] In this embodiment, the evaluation device sums the aboveground biomass carbon storage, belowground biomass carbon storage, soil carbon storage, and dead organic matter carbon storage from the carbon density data of the same grid cell to obtain the total carbon storage of each grid cell, which serves as an indicator of the ecosystem's carbon sequestration function. The total carbon storage is: C tot = C above + C below + C soil + C dead In the formula, C tot For total carbon storage, C above For aboveground biomass carbon storage, C below For aboveground biomass carbon storage, C soil For soil carbon storage, C dead This refers to the carbon storage of dead organic matter.

[0025] For the aforementioned soil conservation function indicators, please refer to Figure 3 , Figure 3 A schematic diagram of step S2 in the process of the island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in the second embodiment of this application, including steps S211 to S212, as follows: S211: Factors are constructed based on the basic data of each grid cell to obtain the rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measures factor for each grid cell.

[0026] In this embodiment, the evaluation device constructs factors based on the basic data of each grid cell to obtain the rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measure factor for each grid cell.

[0027] Specifically, the vegetation cover factor reflects the combined effect of vegetation cover and human management on soil erosion. The soil and water conservation measures factor assesses soil erosion before and after soil conservation measures to reflect the extent of soil loss. The determination of the vegetation cover factor and soil and water conservation measures factor referenced model manuals and relevant literature. Furthermore, the vegetation cover factor was improved based on Cai Chongfa's research methods, ultimately determining the soil conservation biophysical coefficients for different land use types, as shown in the table below:

[0028] The improved biophysical coefficient table provides a scientific basis for studying the changes and distribution of soil conservation in island cities.

[0029] The basic data also includes daily rainfall data, soil data, and elevation data; the soil data reflects the content of sand, silt, clay, soil organic carbon, and plant root depth; elevation data (DEM) is the abbreviation for Digital Elevation Model, which can reflect local topographic features at a certain resolution and is an important source of raw data for studying and analyzing topography, watersheds, and land feature identification.

[0030] Please see Figure 4 , Figure 4 A schematic diagram of step S211 in the process of the island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in the second embodiment of this application, including steps S2111 to S2114, as follows: S2111: Based on the daily rainfall data of each grid cell, determine the daily erosive rainfall, the number of days with erosive precipitation in a half-month, the annual average erosive rainfall, and the average of the annual average erosive rainfall over many years for each grid cell.

[0031] In this embodiment, the evaluation device determines the daily erosive rainfall, the number of days with erosive precipitation in a half-month, the annual average erosive rainfall, and the average of the annual average erosive rainfall over many years for each grid cell based on the daily rainfall data of each grid cell.

[0032] S2112: Based on the daily erosive rainfall, the number of days with erosive precipitation in a half-month, the annual average erosive rainfall, the average of the annual average erosive rainfall over many years, and the preset half-month rainfall erosivity calculation algorithm, obtain the half-month rainfall erosivity data for each grid cell; construct the rainfall erosivity factor based on the half-month rainfall erosivity data for each grid cell to obtain the rainfall erosivity factor for each grid cell.

[0033] In this embodiment, the evaluation device employs a bi-monthly rainfall erosivity model based on daily rainfall data. This model reflects the seasonal distribution of erosivity over a bi-monthly period, with rainfall and erosivity exhibiting a power function relationship. The annual rainfall erosivity is the cumulative value of the bi-monthly rainfall erosivity. The evaluation device obtains the bi-monthly rainfall erosivity data for each grid cell based on the daily erosive rainfall, the number of days with erosive precipitation in the bi-monthly period, the annual average erosive rainfall, the average of the annual average erosive rainfall over many years, and the pre-set bi-monthly rainfall erosivity calculation algorithm in the bi-monthly rainfall erosivity model. The evaluation device then constructs rainfall erosivity factors based on the bi-monthly rainfall erosivity data for each grid cell to obtain the rainfall erosivity factors for each grid cell. The bi-monthly rainfall erosivity calculation algorithm is as follows:

[0034]

[0035] In the formula, For the first j Half-month rainfall erosion; For the first i Year d Daily erosive rainfall, M This indicates the number of days in a half-month period where erosive precipitation occurs; and These are the regression coefficients; This refers to the annual average erosive rainfall. This is the average annual erosive rainfall over many years. N To indicate the year.

[0036] S2113: Based on the sand content, silt content, clay content, soil organic carbon content, and the preset soil erodibility factor calculation algorithm in the soil data of each grid cell, obtain the soil erodibility factor of each grid cell.

[0037] Soil erosibility factors reflect the soil's sensitivity to erosion and the magnitude of runoff and runoff rate generated by precipitation. In this embodiment, the evaluation device obtains the soil erosibility factor for each grid cell based on the sand content, silt content, clay content, soil organic carbon content, and a preset soil erosibility factor calculation algorithm in the soil data of each grid cell. The soil erosibility factor calculation algorithm is as follows:

[0038] In the formula, SAN Sand content, SIL Particle content, CLA Clay content; C Soil organic carbon content; SNI This represents the percentage of soil particles other than sand, calculated from the sand content: SNI = 1- SAN / 100; S2114: Based on the slope length, percentage slope, and preset slope length and slope factor calculation algorithms in the elevation data of each grid cell, obtain the slope length factor and slope factor of each grid cell.

[0039] In this embodiment, the evaluation device obtains the slope length factor and slope factor of each grid cell based on the slope length, percentage slope, and a preset slope length and slope factor calculation algorithm in the elevation data of each grid cell. The slope length and slope factor calculation algorithm is as follows:

[0040]

[0041] In the formula, S For slope factor, L For slope length factor, γ The slope length; θ Percentage slope; m It is a dimensionless constant that depends on the percentage slope value.

[0042] S212: Based on the rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, soil and water conservation measures factor, and the preset soil conservation per unit area calculation algorithm of each grid cell, the soil conservation per unit area of ​​each grid cell is obtained as the soil conservation function index.

[0043] Soil retention capacity refers to the difference between potential and actual soil erosion, that is, the difference between soil erosion under conditions of no vegetation cover and conditions of vegetation cover.

[0044] In this embodiment, the evaluation device obtains the soil retention per unit area of ​​each grid cell based on the rainfall erosivity factor, soil erodibility factor, slope length factor, slope gradient factor, vegetation cover factor, soil and water conservation measures factor, and a preset algorithm for calculating soil retention per unit area. This soil retention is used as the soil conservation function index. The algorithm for calculating soil retention per unit area is as follows:

[0045] In the formula, for, R As the erosivity factor of rainfall, K As a soil erodibility factor, L For vegetation cover factor, S For soil and water conservation measures, CFor slope length factor, P This is the slope factor.

[0046] The basic data also includes average annual precipitation and average annual evapotranspiration; for the water conservation function indicators, please refer to [link / reference needed]. Figure 5 , Figure 5 A schematic diagram of step S2 in the process of the island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in the third embodiment of this application includes steps S221 to S222, as follows: S221: Based on the sand content, silt content, clay content, soil organic carbon content, and the preset plant available water content calculation algorithm in the soil data of each grid cell, obtain the plant available water content of each grid cell.

[0047] In this embodiment, the evaluation device obtains the plant-available water content of each grid cell based on the sand content, silt content, clay content, soil organic carbon content, and a preset algorithm for calculating plant-available water content in the soil data of each grid cell. The algorithm for calculating plant-available water content is as follows: AWC (%) = 54.509 0.132× SAND %-0.003×( SAND %) 2 0.055× SLIT %-0.006×( SLIT %) 2 0.738× CLAY %+0.007×( CLAY %) 2 2.688× C %+0.501×( C %) 2 In the formula, AWC is the plant-available water content.

[0048] S222: Based on the land use data, average annual precipitation data, average annual evapotranspiration data, plant root depth data, plant available water content, and a preset annual water yield calculation algorithm for each grid cell, the annual water yield of each grid cell is obtained as the water conservation function indicator.

[0049] The assessment of water conservation function mainly refers to the assessment of water yield function. In this embodiment, the evaluation equipment obtains the annual water yield of each grid cell based on land use data, average annual precipitation data, average annual evapotranspiration data, plant root depth data, available water content of plants, and a preset annual water yield calculation algorithm. This annual water yield is used as the water conservation function index. The annual water yield calculation algorithm is as follows:

[0050]

[0051]

[0052]

[0053] In the formula, Y xj For the first j Land use type in the first x Annual water production per grid cell; AET xj For the first j Land use type in the first x The actual annual evapotranspiration of each grid cell; P x For the first x The average annual precipitation per grid cell; ω x This is the ratio of annual available water for vegetation to average annual precipitation. R xj For the first j Land use category in x Budyko Dryness Index for each grid cell; Z Zhang coefficient represents parameters indicating rainfall distribution and depth; PAWC x For the first x The effective soil moisture content of each grid cell; k ij For the first j Class 1 Land Use Type i Evapotranspiration coefficient of the vegetation type; ET 0 represents potential evaporation.

[0054] Specifically, the annual average precipitation data comes from the China 1km resolution monthly precipitation dataset (1901-2024) from the National Tibetan Plateau Scientific Data Center. The data format is NETCDF, i.e., .nc format, with units of 0.1mm. In ArcGIS, a raster layer is constructed from the nc format data. The 12 monthly raster data periods are summed to generate the annual average precipitation data. After the unit conversion from 0.1mm to 1mm is completed using the raster calculator, the annual average precipitation data over many years is averaged to obtain the annual average precipitation data.

[0055] The annual average evapotranspiration data were obtained from the China 1km Monthly Potential Evapotranspiration Dataset (1901-2024) from the National Tibetan Plateau Scientific Data Center. The data format is NETCDF, i.e., .nc format, with units of 0.1 mm. In ArcGIS, a raster layer was constructed from the nc format data. The 12 monthly raster data were summed to generate the annual evapotranspiration data. After converting the units from 0.1 mm to 1 mm using the raster calculator, the annual evapotranspiration data over many years was averaged to obtain the annual average evapotranspiration.

[0056] The ecosystem service function indicators also include landscape aesthetic function indicators. Landscape aesthetics (LA), as a form of cultural service, has made a significant contribution to human well-being. Please refer to [link / reference]. Figure 6 , Figure 6 A schematic diagram of step S2 in the process of the island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in the fourth embodiment of this application includes steps S231 to S232, as follows: S231: Obtain the ecological disturbance index and landscape diversity index of each grid cell.

[0057] In this embodiment, the evaluation device obtains the ecological disturbance index and landscape diversity index of each grid cell.

[0058] S232: Based on the ecological disturbance index, landscape diversity index and preset landscape aesthetic function index calculation algorithm of each grid cell, obtain the landscape aesthetic function index of each grid cell.

[0059] In this embodiment, the evaluation device obtains the landscape aesthetic function index of each grid cell based on the ecological disturbance index, landscape diversity index, and a preset landscape aesthetic function index calculation algorithm. The landscape aesthetic function index calculation algorithm is as follows:

[0060] In the formula, For the first x The landscape aesthetic function index of each grid represents the overall landscape aesthetic value of the study area; For the standardized first x Ecological disturbance index of each grid; For the standardized first x Landscape diversity index of each grid.

[0061] The basic data also includes threat factor data, which comprises several threat factors and their respective weights. These threat factors include those for paddy fields, dry land, construction land, rural areas, and industrial land. For the biodiversity conservation function indicators, please refer to [link to relevant documentation]. Figure 7 , Figure 7 A schematic diagram of step S2 in the process of the island city resource and environmental carrying capacity assessment method based on ecosystem service functions provided in the fifth embodiment of this application includes steps S241 to S243, as follows: S241: Obtain habitat degradation risk index data for each grid cell based on land use data, threat factor data, and a preset habitat degradation risk index calculation algorithm.

[0062] In this embodiment, the evaluation device obtains habitat degradation risk index data for each grid cell based on land use data, threat factor data, and a preset habitat degradation risk index calculation algorithm. The habitat degradation risk index data includes habitat degradation risk indices for several land use types. The habitat degradation risk index calculation algorithm is as follows:

[0063] In the formula, D xj For the first x The first grid j Habitat degradation risk index for land use type; R The number of threat factors; W r For the first r The weights of each threat factor; Y r For all grid cells, the first r The number of threat factors; r y The number of threat factors for each grid cell in the land use type; S jr For the first j Land use type for the first r Sensitivity coefficient of each threat factor.

[0064] S242: Obtain the ecological suitability index data of each grid cell, wherein the ecological suitability index data includes the ecological suitability index corresponding to each land use type; based on the habitat degradation risk index data, ecological suitability index data and preset habitat quality index calculation algorithm of each grid cell, obtain the habitat quality index of each land use type of each grid cell.

[0065] In this embodiment, the evaluation device obtains ecological suitability index data for each grid cell, wherein the ecological suitability index data includes the ecological suitability index corresponding to each land use type.

[0066] The evaluation equipment obtains the habitat quality index for each land use type in each grid cell based on the habitat degradation risk index data, ecological suitability index data, and a preset habitat quality index calculation algorithm. The habitat quality index calculation algorithm is as follows:

[0067] In the formula, Indicates the first x The first grid j Habitat quality index for land use type The ecological suitability index for the first type of land use; k It is the half-saturation constant.

[0068] S243: Combine the habitat quality indices of various land use types in the same grid cell to obtain the biodiversity conservation function index of each grid cell.

[0069] Biodiversity conservation function assessment mainly refers to habitat quality evaluation, which quantifies the health status and threat level of biological habitats, helps identify areas of high ecological value, and assesses their changes. In this embodiment, the evaluation device combines the habitat quality indices of various land use types within the same grid cell to obtain biodiversity conservation function indicators for each grid cell. This allows for an overall evaluation of habitat quality at a regional scale, revealing the potential changes in ecological function and quality caused by land use change.

[0070] S3: Calculate the comprehensive evaluation index of ecosystem services based on several types of ecosystem service function indicators for each grid cell to obtain the comprehensive evaluation index of ecosystem services for each grid cell.

[0071] In this embodiment, the evaluation device calculates the comprehensive ecosystem service evaluation index based on several types of ecosystem service function indicators for each grid cell, thereby obtaining the comprehensive ecosystem service evaluation index for each grid cell. The comprehensive ecosystem service evaluation index is as follows:

[0072] In the formula, Let L be the comprehensive evaluation index of ecosystem services for the i-th grid cell, and L be the number of types of ecosystem service function indicators. This is the ecosystem service function indicator of the l-th type for the i-th grid cell.

[0073] S4: Based on the comprehensive evaluation index of ecosystem services of each grid cell and the preset threshold range of resource and environmental carrying capacity, the resource and environmental carrying capacity level is divided to obtain the resource and environmental carrying capacity level of each grid cell, which is used as the evaluation result of the resource and environmental carrying capacity of the island city to be processed.

[0074] In this embodiment, the evaluation device classifies the resource and environmental carrying capacity levels based on the comprehensive evaluation index of ecosystem services of each grid cell and the preset resource and environmental carrying capacity threshold range, and obtains the resource and environmental carrying capacity level of each grid cell as the resource and environmental carrying capacity evaluation result of the island city to be processed, as follows:

[0075] The comprehensive evaluation index of ecosystem services, obtained through the assessment and calculation of ecosystem service functions, is applied to the rapid assessment of resource and environmental carrying capacity, providing decision support for regional ecosystem protection and sustainable development.

[0076] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of the island city resource and environmental carrying capacity assessment device based on ecosystem service functions provided in the sixth embodiment of this application. This device can be implemented in whole or in part through software, hardware, or a combination of both. The device 8 includes: The data acquisition module 81 is used to obtain basic data of the island city to be processed; wherein, the area to be processed includes a number of raster units, and the basic data includes basic data of the number of raster units; The first indicator calculation module 82 is used to calculate the ecosystem service function indicators based on the basic data of the island city to be processed, and to obtain several types of ecosystem service function indicators for each grid cell. The ecosystem service function indicators include ecosystem carbon sequestration function indicators, soil conservation function indicators, water conservation function indicators, and biodiversity conservation function indicators. The second indicator calculation module 83 is used to calculate the comprehensive evaluation index of ecosystem services based on several types of ecosystem service function indicators of each grid cell, and obtain the comprehensive evaluation index of ecosystem services of each grid cell. The resource and environmental carrying capacity evaluation module 84 is used to classify the resource and environmental carrying capacity level according to the comprehensive evaluation index of ecosystem services of each grid unit and the preset resource and environmental carrying capacity threshold range, and obtain the resource and environmental carrying capacity level of each grid unit as the resource and environmental carrying capacity evaluation result of the island city to be processed.

[0077] In this embodiment, a data acquisition module obtains basic data of the island city to be processed. The area to be processed comprises several grid cells, and the basic data includes basic data for each grid cell. A first indicator calculation module calculates ecosystem service function indicators based on the basic data of the island city to be processed, obtaining several types of ecosystem service function indicators for each grid cell. These ecosystem service function indicators include ecosystem carbon sequestration function indicators, soil conservation function indicators, water conservation function indicators, and biodiversity conservation function indicators. A second indicator calculation module calculates a comprehensive ecosystem service evaluation indicator based on the several types of ecosystem service function indicators for each grid cell, obtaining a comprehensive ecosystem service evaluation indicator for each grid cell. A resource and environmental carrying capacity evaluation module classifies resource and environmental carrying capacity levels based on the comprehensive ecosystem service evaluation indicators of each grid cell and a preset resource and environmental carrying capacity threshold range, obtaining the resource and environmental carrying capacity level of each grid cell, which serves as the resource and environmental carrying capacity evaluation result for the island city to be processed. The comprehensive ecosystem service evaluation indicator obtained through the assessment and calculation of ecosystem service functions is applied to the rapid assessment of resource and environmental carrying capacity, providing decision support for regional ecosystem protection and sustainable development.

[0078] Please refer to Figure 9 , Figure 9 This is a schematic diagram of the structure of a computer device provided in the seventh embodiment of this application. The computer device 9 includes: a processor 91, a memory 92, and a computer program 93 stored in the memory 92 and executable on the processor 91. The computer device can store multiple instructions, which are applicable to the method steps of the first to fifth embodiments described above being loaded and executed by the processor 91. For the specific execution process, please refer to the specific description of the first to fifth embodiments, which will not be repeated here.

[0079] The processor 91 may include one or more processing cores. The processor 91 connects to various parts of the server using various interfaces and lines. It executes instructions, programs, code sets, or instruction sets stored in the memory 92, and retrieves data from the memory 92 to perform various functions and process data in the island city resource and environmental carrying capacity assessment device 8 based on ecosystem service functions. Optionally, the processor 91 may be implemented using at least one of the following hardware forms: Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 91 may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content displayed on the touch screen; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 91.

[0080] The memory 92 may include random access memory (RAM) or read-only memory. Optionally, the memory 92 may include a non-transitory computer-readable storage medium. The memory 92 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 92 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch instructions), instructions for implementing the various method embodiments described above, etc.; the data storage area may store data involved in the various method embodiments described above, etc. Optionally, the memory 92 may also be at least one storage device located remotely from the aforementioned processor 91.

[0081] This application also provides a storage medium that can store multiple instructions. These instructions are applicable to being loaded and executed by a processor using the specific steps of the first to fifth embodiments described above. For details of the execution process, please refer to the specific descriptions of the first to fifth embodiments, which will not be repeated here.

[0082] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional grid units and modules is used as an example. In practical applications, the above functions can be assigned to different functional grid units and modules as needed, that is, the internal structure of the device can be divided into different functional grid units or modules to complete all or part of the functions described above. The functional grid units and modules in the embodiments can be integrated into one processing grid unit, or each grid unit can exist physically separately, or two or more grid units can be integrated into one grid unit. The integrated grid unit can be implemented in hardware or software. Furthermore, the specific names of the functional grid units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the grid units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0083] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0084] Those skilled in the art will recognize that the grid cells and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the algorithm. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0085] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or grid units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple grid units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between apparatuses or grid units may be electrical, mechanical, or other forms.

[0086] The grid cells described as separate components may or may not be physically separate. The components shown as grid cells may or may not be physical grid cells; that is, they may be located in one place or distributed across multiple network grid cells. Some or all of the grid cells can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0087] Furthermore, in the various embodiments of the present invention, the functional grid units can be integrated into one processing grid unit, or each grid unit can exist physically separately, or two or more grid units can be integrated into one grid unit. The integrated grid unit described above can be implemented in hardware or in the form of a software functional grid unit.

[0088] If the integrated module / grid unit is implemented as a software functional grid unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms.

[0089] This invention is not limited to the above-described embodiments. If any modifications or variations to this invention do not depart from the spirit and scope of this invention, and if such modifications and variations fall within the scope of the claims and equivalent technologies of this invention, then this invention also intends to include such modifications and variations.

Claims

1. A method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions, characterized in that, Includes the following steps: Obtain basic data of the island city to be processed; wherein, the area to be processed includes several raster cells, and the basic data includes basic data of several raster cells; Based on the basic data of the island city to be processed, the ecosystem service function indicators are calculated to obtain several types of ecosystem service function indicators for each grid cell. The ecosystem service function indicators include ecosystem carbon sequestration function indicators, soil conservation function indicators, water conservation function indicators, and biodiversity conservation function indicators. The comprehensive evaluation index of ecosystem services is calculated based on several types of ecosystem service function indicators for each grid cell to obtain the comprehensive evaluation index of ecosystem services for each grid cell. Based on the comprehensive evaluation index of ecosystem services of each grid cell and the preset threshold range of resource and environmental carrying capacity, the resource and environmental carrying capacity level is divided into levels, and the resource and environmental carrying capacity level of each grid cell is obtained as the evaluation result of the resource and environmental carrying capacity of the island city to be processed.

2. The method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions according to claim 1, characterized in that: The basic data includes land use data; The step of calculating ecosystem service function indicators based on the basic data of the island city to be processed, and obtaining several types of ecosystem service function indicators for each grid cell, includes the following steps: Based on the land use data of each grid cell and the preset land use type and carbon density mapping table, carbon density data of each grid cell is obtained, wherein the carbon density data includes aboveground biomass carbon storage, belowground biomass carbon storage, soil carbon storage and dead organic matter carbon storage. The carbon storage of aboveground biomass, belowground biomass, soil, and dead organic matter in the carbon density data of the same grid cell is summed to obtain the total carbon storage of each grid cell, which is used as an indicator of the carbon sequestration function of the ecosystem.

3. The method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions according to claim 1, characterized in that, The step of calculating ecosystem service function indicators based on the basic data of the island city to be processed, and obtaining several types of ecosystem service function indicators for each grid cell, includes the following steps: Factors are constructed based on the basic data of each grid cell to obtain the rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measure factor for each grid cell. Based on the rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, soil and water conservation measure factor, and a preset algorithm for calculating soil retention per unit area for each grid cell, the soil retention per unit area for each grid cell is obtained as the soil conservation function index. The algorithm for calculating soil retention per unit area is as follows: In the formula, for, R As the erosivity factor of rainfall, K As a soil erodibility factor, L For vegetation cover factor, S For soil and water conservation measures, C For slope length factor, P This is the slope factor.

4. The method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions according to claim 3, characterized in that: The basic data also includes daily rainfall data, soil data, and elevation data; The process of constructing factors based on the basic data of each grid cell to obtain the rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, vegetation cover factor, and soil and water conservation measure factor for each grid cell includes the following steps: Based on the daily rainfall data of each grid cell, determine the daily erosive rainfall, the number of days with erosive precipitation in a half-month, the annual average erosive rainfall, and the average of the annual average erosive rainfall over many years for each grid cell. Based on the daily erosive rainfall, the number of days with erosive precipitation in a half-month, the annual average erosive rainfall, the average of the annual average erosive rainfall over many years, and a preset algorithm for calculating the erosivity of half-month rainfall for each grid cell, the half-month rainfall erosivity data for each grid cell is obtained. Based on the half-month rainfall erosivity data for each grid cell, a rainfall erosivity factor is constructed to obtain the rainfall erosivity factor for each grid cell. The algorithm for calculating the half-month rainfall erosivity is as follows: In the formula, For the first j Half-month rainfall erosion; For the first i Year d Daily erosive rainfall, M This indicates the number of days in a half-month period where erosive precipitation occurs; and These are the regression coefficients; This refers to the annual average erosive rainfall. This is the average annual erosive rainfall over many years. N To indicate the year; Based on the sand content, silt content, clay content, soil organic carbon content, and a preset soil erosibility factor calculation algorithm in the soil data of each grid cell, the soil erosibility factor of each grid cell is obtained. The soil erosibility factor calculation algorithm is as follows: In the formula, SAN Sand content, SIL Particle content, CLA Clay content; C Soil organic carbon content; SNI This represents the percentage of soil particles other than sand, calculated from the sand content: SNI = 1- SAN / 100; Based on the slope length, percentage slope, and a preset slope length and slope factor calculation algorithm in the elevation data of each grid cell, the slope length factor and slope factor of each grid cell are obtained. The slope length and slope factor calculation algorithm is as follows: In the formula, S For slope factor, L For slope length factor, γ The slope length; θ Percentage slope; m It is a dimensionless constant that depends on the percentage slope value.

5. The method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions according to claim 4, characterized in that: The basic data also includes average annual precipitation and average annual evapotranspiration; the soil data also includes plant root depth data; The step of calculating ecosystem service function indicators based on the basic data of the island city to be processed, and obtaining several types of ecosystem service function indicators for each grid cell, includes the following steps: Based on the sand content, silt content, clay content, and soil organic carbon content in the soil data of each grid cell, and a preset algorithm for calculating the available water content for plants, the available water content for plants in each grid cell is obtained. The algorithm for calculating the available water content for plants is as follows: AWC(%)=54.509 0.132× SAND %-0.003×( SAND %) 2 0.055× SLIT %-0.006×( SLIT %) 2 0.738× CLAY %+0.007×( CLAY %) 2 2.688× C %+0.501×( C %) 2 In the formula, AWC is the plant-available water content. Based on land use data, average annual precipitation data, average annual evapotranspiration data, plant root depth data, available water content of plants, and a preset annual water yield calculation algorithm for each grid cell, the annual water yield of each grid cell is obtained as the water conservation function indicator. The annual water yield calculation algorithm is as follows: In the formula, Y xj For the first j Land use type in the first x Annual water production per grid cell; AET xj For the first j Land use type in the first x The actual annual evapotranspiration of each grid cell; P x For the first x The average annual precipitation per grid cell; ω x This is the ratio of annual available water for vegetation to average annual precipitation. R xj For the first j Land use category in x Budyko Dryness Index for each grid cell; Z Zhang coefficient represents parameters indicating rainfall distribution and depth; PAWC x For the first x The effective soil moisture content of each grid cell; k ij For the first j Class 1 Land Use Type i Evapotranspiration coefficient of the vegetation type; ET 0 represents potential evaporation.

6. The method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions according to claim 4, characterized in that: The ecosystem service function indicators also include landscape aesthetic function indicators; The step of calculating ecosystem service function indicators based on the basic data of the island city to be processed, and obtaining several types of ecosystem service function indicators for each grid cell, includes the following steps: Obtain the ecological disturbance index and landscape diversity index of each grid cell; Based on the ecological disturbance index, landscape diversity index, and preset landscape aesthetic function index calculation algorithm of each grid cell, the landscape aesthetic function index of each grid cell is obtained. The calculation algorithm for the landscape aesthetic function index is as follows: In the formula, For the first x The landscape aesthetic function index of each grid represents the overall landscape aesthetic value of the study area; For the standardized first x Ecological disturbance index of each grid; For the standardized first x Landscape diversity index of each grid.

7. The method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions according to claim 2, characterized in that: The basic data also includes threat factor data, which includes several threat factors and the weight of each threat factor; the threat factors include paddy field threat factors, dry land threat factors, construction land threat factors, rural threat factors, and industrial land threat factors. The step of calculating ecosystem service function indicators based on the basic data of the island city to be processed, and obtaining several types of ecosystem service function indicators for each grid cell, includes the following steps: Based on land use data, threat factor data, and a preset habitat degradation risk index calculation algorithm for each grid cell, habitat degradation risk index data for each grid cell is obtained. The habitat degradation risk index data includes habitat degradation risk indices for several land use types. The habitat degradation risk index calculation algorithm is as follows: In the formula, D xj For the first x The first grid j Habitat degradation risk index for land use type; R The number of threat factors; W r For the first r The weights of each threat factor; Y r For all grid cells, the first r The number of threat factors; r y The number of threat factors for each grid cell in the land use type; S jr For the first j Land use type for the first r Sensitivity coefficient of each threat factor; Obtain ecological suitability index data for each grid cell, including ecological suitability indices corresponding to each land use type; based on habitat degradation risk index data, ecological suitability index data, and a preset habitat quality index calculation algorithm for each grid cell, obtain the habitat quality index for each land use type of each grid cell, wherein the habitat quality index calculation algorithm is as follows: In the formula, Indicates the first x The first grid j Habitat quality index for land use type The ecological suitability index for the first type of land use; k It is the half-saturation constant; By combining the habitat quality indices of various land use types within the same grid cell, the biodiversity conservation function index of each grid cell can be obtained.

8. A device for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions, characterized in that, include: The data acquisition module is used to obtain basic data of the island city to be processed; wherein, the area to be processed includes several raster units, and the basic data includes basic data of several raster units; The first indicator calculation module is used to calculate the ecosystem service function indicators based on the basic data of the island city to be processed, and to obtain several types of ecosystem service function indicators for each grid cell. The ecosystem service function indicators include ecosystem carbon sequestration function indicators, soil conservation function indicators, water conservation function indicators, and biodiversity conservation function indicators. The second indicator calculation module is used to calculate the comprehensive evaluation index of ecosystem services based on several types of ecosystem service function indicators of each grid cell, and obtain the comprehensive evaluation index of ecosystem services of each grid cell. The resource and environmental carrying capacity evaluation module is used to classify the resource and environmental carrying capacity levels based on the comprehensive evaluation index of ecosystem services of each grid unit and the preset resource and environmental carrying capacity threshold range, and obtain the resource and environmental carrying capacity level of each grid unit as the resource and environmental carrying capacity evaluation result of the island city to be processed.

9. A computer device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for evaluating the resource and environmental carrying capacity of island cities based on ecosystem service functions as described in any one of claims 1 to 7.

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