A multi-scale analysis method and system for ecosystem function change

Abstract

This invention discloses a multi-scale analysis method and system for ecosystem function changes, relating to the field of ecological monitoring technology. The method includes collecting multi-source remote sensing data, topographic data, meteorological data, and soil data within the study area, preprocessing them, and generating a standardized multi-source data cube. Using the standardized multi-source data cube, an index reflecting water conservation capacity is calculated to generate a spatial distribution map characterizing ecological functions. Based on this spatial distribution map, historical statistical distribution characteristics are analyzed, and quantile statistics are used to set thresholds for source patch and sink patch functions, identifying source patches for functional supply and sink patches for functional consumption. This invention achieves a breakthrough from functional stock assessment to source-sink flow process simulation, providing technical support for watershed ecological compensation and precise spatial governance.

Description

Technical Field

[0001] This invention relates to the field of ecological monitoring technology, and in particular to a multi-scale analysis method and system for changes in ecosystem function. Background Technology

[0002] With the deepening of ecosystem service management, the precise quantification of the spatial pattern and dynamic changes of ecosystem functions has become a research hotspot. Among existing technologies, assessment methods represented by the InVEST model have been widely used. This type of method integrates spatial data such as land use, soil, and topography, calculates the functional quantity of each grid cell based on hydrological or ecological process principles, and summarizes the data at preset scales such as watersheds or regions to achieve static assessment and mapping of ecosystem function supply, providing important support for macro-planning.

[0003] However, existing methods mainly focus on the static assessment of functional stock, and the analysis units mostly rely on regular grids or administrative boundaries, failing to fully reflect the spatial connections formed by the ecological processes themselves. In water conservation assessment, traditional methods quantify upstream functional quantities, but it is difficult to simulate the process of water volume being transported downstream along natural paths and the changes caused by landscape resistance. They cannot effectively reveal the functional dependence between sources and sinks, limiting their application in management practices such as watershed ecological compensation where the functional transfer path needs to be clearly defined. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a multi-scale analysis method for ecosystem function changes to solve the technical problem that existing technologies are unable to dynamically simulate the spatial transmission of ecosystem functions along natural paths and quantify their flow process.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a multi-scale analysis method for ecosystem function changes, which includes collecting multi-source remote sensing data, topographic data, meteorological data and soil data in the study area, and performing preprocessing to generate a standardized multi-source data cube; By utilizing standardized multi-source data cubes, spatial distribution maps representing ecological functions are generated by calculating indicators reflecting water conservation capacity. Based on the spatial distribution map representing the ecological function quantity, by analyzing the historical statistical distribution characteristics of the spatial distribution map representing the ecological function quantity, and by combining the quantile statistical method to set the functional quantity thresholds of source patches and sink patches, the source patches of functional supply sources and sink patches of functional consumption areas are identified. Based on the surface water flow movement law and land use type, the connectivity resistance is obtained, and a graph theory network model of ecological function flow is constructed. Based on the graph theory network model of ecological function flow, the function values ​​in the spatial distribution map representing the ecological function quantity are assigned to the source patches. The path simulation algorithm is used to dynamically simulate the flow of ecological function from the source patch to the sink patch along the weighted network edges, and generate the ecological function flow flux results. Based on the results of ecological function flow flux, corridors and nodes of functional flow connectivity are identified, ecological protection priorities are obtained, and visualization results of the ecological function flow network and areas requiring priority protection are output.

[0007] As a preferred embodiment of the multi-scale analysis method for ecosystem function changes described in this invention, the method includes: collecting multi-source remote sensing data, topographic data, meteorological data, and soil data within the study area, and performing preprocessing to generate a standardized multi-source data cube, comprising the following steps: Acquire multi-source remote sensing data, topographic data, meteorological data, and soil data within the boundary of the study area. Perform geometric fine correction processing on the multi-source remote sensing data, topographic data, meteorological data, and soil data to obtain a unified geographic coordinate framework for the multi-source remote sensing data, topographic data, meteorological data, and soil data. Radiometric calibration and atmospheric correction were performed on multi-source remote sensing data, topographic data, meteorological data, and soil data within a unified geographic coordinate framework. Multi-source remote sensing data, topographic data, meteorological data, and soil data that have undergone radiometric calibration and atmospheric correction are cropped using the study area boundary to obtain multi-source remote sensing data, topographic data, meteorological data, and soil data covering the study area. The cropped multi-source remote sensing data, topographic data, meteorological data, and soil data are then integrated to generate a standardized multi-source data cube.

[0008] As a preferred embodiment of the multi-scale analysis method for ecosystem function changes described in this invention, the method includes the following steps: Utilizing a standardized multi-source data cube, a spatial distribution map representing ecological function quantities is generated by calculating indicators reflecting water conservation capacity. Precipitation data, actual evapotranspiration data, soil saturated hydraulic conductivity data, and land use type data are extracted from a standardized multi-source data cube. The water balance components of pixels in the study area were calculated based on precipitation data and actual evapotranspiration data. The water balance component of the pixel, the soil saturated hydraulic conductivity data and the land use type data are used as input parameters and substituted into the water conservation capacity calculation formula to calculate the index value reflecting the water conservation capacity pixel by pixel. By combining the index values ​​reflecting water conservation capacity into a spatial distribution map, a spatial distribution map representing the ecological function quantity is generated.

[0009] As a preferred embodiment of the multi-scale analysis method for ecosystem function changes described in this invention, the method includes the following steps: Based on a spatial distribution map representing ecological function quantities, by analyzing the historical statistical distribution characteristics of the spatial distribution map representing ecological function quantities, and combining quantile statistics to set source patch function quantity thresholds and sink patch function quantity thresholds, the source patches of function supply sources and sink patches of function consumption areas are identified. Collect multiple periods of data on the spatial distribution map representing ecological functions over historical periods to form a historical dataset of the spatial distribution map representing ecological functions. Statistical analysis was performed on historical datasets of spatial distribution maps representing ecological functions, and the historical statistical distribution characteristics of spatial distribution maps representing ecological functions were calculated. Based on the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, the quantile statistical method is used to set the thresholds for source patch functions and sink patch functions. In the spatial distribution map characterizing ecological function, the pixel regions with function values ​​higher than the source patch function value threshold are used to identify the source patches of function supply sources. In the spatial distribution map representing ecological function, pixel regions with function values ​​lower than the sink patch function threshold are identified as sink patches in the function depletion zone.

[0010] As a preferred embodiment of the multi-scale analysis method for ecosystem function changes described in this invention, the method involves: obtaining connectivity resistance based on surface water flow patterns and land use types, and constructing a graph theory network model for ecological function flow, including the following steps: Based on topographic data in a standardized multi-source data cube, surface water flow patterns are extracted through hydrological analysis to generate natural water flow paths; An ecological resistance lookup table based on material flow resistance is used to establish a correspondence between land use type data and surface resistance coefficients. By overlaying natural water flow paths with land use type data, and assigning a surface resistance coefficient to each segment of the natural water flow path according to the corresponding relationship table, the connectivity resistance value is obtained. The source patches of functional supply and the sink patches of functional consumption are defined as network nodes, the natural water flow paths connecting the network nodes are defined as network edges, and the connectivity resistance value is used as the weight of the network edges. By integrating the network nodes, network edges and the weight of the network edges, a graph theory network model of ecological function flow is constructed.

[0011] As a preferred embodiment of the multi-scale analysis method for ecosystem function changes described in this invention, the method includes: based on a graph theory network model of ecological function flow, assigning the function values ​​representing the spatial distribution map of ecological function quantities to source patches; employing a path simulation algorithm to dynamically simulate the flow of ecological function along weighted network edges from source patches to sink patches, generating ecological function flow flux results, including the following steps: In the graph theory network model of ecological function flow, the function values ​​within the range of each source patch in the spatial distribution map representing the ecological function quantity are summed to form the initial flow value assigned to the corresponding source patch; Population density data of the area where the sink patch is located is used as the functional consumption intensity factor and assigned to the sink patch. A path simulation algorithm is used, with the connectivity resistance value on the weighted network edge as the conduction resistance value and the functional consumption intensity factor of the sink patch as the attraction value, to drive the initial flow value from the source patch to the sink patch along the weighted network edge. The path simulation algorithm runs up to the flow allocation stage, recording the functional throughput value of each network edge in the network and the total functional throughput value received by each sink block; By integrating the functional flux values ​​of network edges, the total functional flux values ​​received by sink patches, and the contribution ratio of source patches, ecological functional flow flux results are generated.

[0012] As a preferred embodiment of the multi-scale analysis method for ecosystem function changes described in this invention, the method includes the following steps: based on the results of ecological function flow fluxes, identifying corridors and nodes of functional flow connectivity, obtaining ecological protection priorities, and outputting visualization results of the ecological function flow network and areas requiring priority protection: By analyzing the results of ecological function flow fluxes, network edges whose function flux values ​​are higher than the function flux threshold set by analyzing the statistical distribution characteristics of the function flux values ​​of network edges in the results of ecological function flow fluxes are identified, and these network edges are defined as functional flow connectivity corridors. By analyzing the results of ecological functional flow fluxes, we identified functional fluxes that were higher than the functional flux thresholds for both source and sink patches, and defined source and sink patches as nodes of functional flow connectivity. Ecological protection priorities are obtained by ranking the functional flow connectivity corridors and the size of the functional flow connectivity nodes. The graph theory network model of ecological function flow, the results of ecological function flow flux, and the priority of ecological protection are integrated into the geographic information module. The visualization results of the ecological function flow network and the areas requiring priority protection are then rendered in the geographic information module.

[0013] Secondly, the present invention provides a multi-scale analysis system for ecosystem function changes, including a data acquisition module that collects multi-source remote sensing data, topographic data, meteorological data and soil data within the study area, and performs preprocessing to generate a standardized multi-source data cube. The quantification module utilizes a standardized multi-source data cube to calculate indicators reflecting water conservation capacity and generate a spatial distribution map representing ecological function quantities. A graph theory network model module is constructed based on the spatial distribution map representing ecological functions. By analyzing the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, and combining the quantile statistical method to set the functional quantity thresholds of source patches and sink patches, the source patches of functional supply sources and sink patches of functional consumption areas are identified. Based on the surface water flow movement law and land use type, the connectivity resistance is obtained, and a graph theory network model of ecological function flow is constructed. The simulation module, based on a graph theory network model of ecological function flow, assigns the function values ​​in the spatial distribution map representing the ecological function quantity to the source patches. It uses a path simulation algorithm to dynamically simulate the flow of ecological function from the source patches to the sink patches along the weighted network edges, generating ecological function flow flux results. The visualization module identifies corridors and nodes of functional flow connectivity based on the results of ecological function flow flux, obtains ecological protection priorities, and outputs visualization results of the ecological function flow network and areas requiring priority protection.

[0014] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the multi-scale analysis method for ecosystem function changes as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the multi-scale analysis method for ecosystem function changes as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By integrating and standardizing multi-source remote sensing, topographic, meteorological, and soil data, a unified multi-source data cube is generated. This cube is then used to calculate water conservation capacity indicators and generate a spatial distribution map of ecological functions. Based on historical statistical characteristics and quantile methods, source and sink patches are identified. A graph theory network model of ecological function flow is constructed by combining water flow patterns and land use resistance. The function flow process is dynamically simulated through path simulation algorithms to generate flow flux results. Based on the flux results, key corridors and nodes are identified, protection priorities are determined, and visualization output is achieved. This invention solves the problem that traditional static assessment methods cannot quantify the dynamic spatial transmission of ecological functions. It represents a breakthrough from functional stock assessment to source-sink flow process simulation, providing technical support for watershed ecological compensation and precise spatial governance. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for a multi-scale analysis method for ecosystem function changes.

[0019] Figure 2 A schematic diagram illustrating multi-scale analysis of ecosystem function changes.

[0020] Figure 3 This is a flowchart for identifying source and sink patches. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0024] Reference Figures 1-3As one embodiment of the present invention, this embodiment provides a multi-scale analysis method for ecosystem function changes, including the following steps: S1. Collect multi-source remote sensing data, topographic data, meteorological data and soil data within the study area, and preprocess them to generate a standardized multi-source data cube.

[0025] S1.1 Acquire multi-source remote sensing data, topographic data, meteorological data, and soil data within the boundary of the study area. Perform geometric fine correction processing on the multi-source remote sensing data, topographic data, meteorological data, and soil data to obtain a unified geographic coordinate framework for the multi-source remote sensing data, topographic data, meteorological data, and soil data.

[0026] Furthermore, the process involves acquiring vector files of the pre-defined study area boundaries, collecting multi-source remote sensing data, topographic data, meteorological data, and soil data of the covered area from a local database, and using a unified projection coordinate system and geographic coordinate system for the collected multi-source remote sensing data, topographic data, meteorological data, and soil data. Using image registration tools and with high-precision reference images as a reference, geometric fine correction processing is performed to ensure that the multi-source remote sensing data, topographic data, meteorological data, and soil data are strictly aligned in spatial location, thereby obtaining a unified geographic coordinate framework for the multi-source remote sensing data, topographic data, meteorological data, and soil data.

[0027] S1.2 Perform radiometric calibration and atmospheric correction on multi-source remote sensing data, topographic data, meteorological data and soil data within a unified geographic coordinate framework.

[0028] Furthermore, radiometric calibration and atmospheric correction are performed on multi-source remote sensing data, topographic data, meteorological data, and soil data that have undergone geometric calibration and have a unified geographic coordinate framework. For multi-source remote sensing data, sensor-specific radiometric calibration coefficients are applied to convert the raw digital quantization values ​​into apparent reflectance or radiance, and atmospheric correction models are used to eliminate the effects of atmospheric scattering and absorption. For topographic, meteorological, and soil data, necessary unit unification and format standardization are performed to ensure that all data values ​​have clear physical meaning and comparability.

[0029] S1.3. The multi-source remote sensing data, topographic data, meteorological data, and soil data that have undergone radiometric calibration and atmospheric correction are cropped using the boundary of the study area to obtain multi-source remote sensing data, topographic data, meteorological data, and soil data covering the study area. The cropped multi-source remote sensing data, topographic data, meteorological data, and soil data are then integrated to generate a standardized multi-source data cube.

[0030] Furthermore, the multi-source remote sensing data, topographic data, meteorological data, and soil data, after radiometric calibration and atmospheric correction, are uniformly resampled to the same spatial resolution. Using the boundary vector file of the study area as a mask, the resampled multi-source remote sensing data, topographic data, meteorological data, and soil data are precisely cropped, and data outside the boundaries are removed to obtain multi-source remote sensing data, topographic data, meteorological data, and soil data that strictly cover the study area. The cropped multi-source remote sensing data, topographic data, meteorological data, and soil data are then integrated according to the corresponding pixels to generate a standardized multi-source data cube with a completely consistent spatiotemporal reference.

[0031] S2. Using a standardized multi-source data cube, an index reflecting water conservation capacity is calculated to generate a spatial distribution map representing ecological function quantities.

[0032] S2.1 Extract precipitation data, actual evapotranspiration data, soil saturated hydraulic conductivity data, and land use type data from the standardized multi-source data cube.

[0033] Furthermore, based on the standardized multi-source data cube, precipitation data, actual evapotranspiration data, soil saturated hydraulic conductivity data, and land use type data stored within it can be extracted through the data reading interface. The standardized multi-source data cube already has a unified spatial reference and cell alignment relationship.

[0034] S2.2 Calculate the water balance component of pixels in the study area based on precipitation data and actual evapotranspiration data.

[0035] The formula for calculating the water balance components is: ; in, For precipitation, This is the actual evaporation rate. It is the sum of surface runoff and groundwater runoff. This represents the change in soil water storage.

[0036] Furthermore, by utilizing readily available precipitation data and actual evapotranspiration data from a standardized multi-source data cube, the difference between precipitation and actual evapotranspiration in each pixel is calculated as a water balance component. This enables an efficient and rapid assessment of the potential for clean water supply in the study area. It avoids the problems of high data requirements, complex models, and large uncertainties associated with directly measuring or simulating complex runoff processes and soil moisture changes. The complex water balance equation is simplified to the difference calculation of core elements, greatly improving computational efficiency and feasibility.

[0037] It should be noted that the formula for calculating water balance components is a specific application of the classical water balance equation describing the terrestrial water cycle at the pixel scale. Its physical significance lies in revealing the conservation relationship of water quantity acting on a closed pixel unit over a certain period: precipitation... It is a moisture input item, actual evapotranspiration. It is the moisture output item, and the difference between the two is ( - This represents the net amount of water available for surface runoff generation, infiltration, and replenishment of soil water storage after deducting evapotranspiration losses. This reflects the final destination of the purified water, which is converted into the sum of surface runoff and groundwater runoff. and the amount that causes changes in soil water storage. .

[0038] S2.3. Using the water balance component of the pixel, the soil saturated hydraulic conductivity data, and the land use type data as input parameters, and substituting them into the water conservation capacity calculation formula, the index value reflecting the water conservation capacity is calculated pixel by pixel.

[0039] The formula for calculating water conservation capacity is: ; in, To characterize the spatial distribution of ecological functions, the first... The value of each pixel reflects the water conservation capacity. For the first Precipitation in the water balance component of each pixel For the first The actual evapotranspiration in the water balance component of each pixel. For the first Soil saturated hydraulic conductivity data for each pixel, For the first The runoff coefficient corresponding to the land use type data of each pixel.

[0040] Furthermore, by coupling key parameters representing water balance, soil saturated hydraulic conductivity, and surface runoff characteristics of pixels with land use type data, and substituting them into a unified water conservation capacity calculation formula for pixel-by-pixel calculation, a refined assessment of the ecosystem's water conservation capacity from potential to actual quantity is achieved. This comprehensively considers three core physical processes: climate supply (precipitation and evapotranspiration), soil matrix regulation (hydraulic conductivity), and surface cover impact (land use), avoiding the limitations of single-factor assessment. This allows the final index value reflecting water conservation capacity to more realistically and comprehensively reflect the comprehensive capacity of each pixel to intercept, store, and supply water.

[0041] It should be noted that the formula for calculating water conservation capacity describes the formation mechanism of water conservation capacity within a pixel from a physical perspective. Its physical significance lies in its quantitative characterization of the potential for clean water resource supply. Actual conversion efficiency under soil and surface cover conditions: soil saturated hydraulic conductivity This represents the soil medium's ability to allow water to infiltrate rapidly and be stored in soil pores and groundwater, serving as the matrix conditions for water conservation; while the runoff coefficient corresponding to land use type... This reflects the water retention effect of surface cover (such as vegetation canopy and litter layer) and the delay effect of surface roughness on runoff. It is a surface regulation factor for water conservation, representing the effective retention of the net water volume of a pixel after soil infiltration and surface regulation, that is, the actual water conservation volume.

[0042] S2.4 Combine the index values ​​reflecting water conservation capacity into a spatial distribution map to generate a spatial distribution map representing the ecological function quantity.

[0043] Furthermore, the index values ​​reflecting water conservation capacity are arranged according to the spatial position of the pixels in the original standardized multi-source data cube to generate a raster image with the same spatial range and pixel size as the input data. The raster image is a spatial distribution map representing the ecological function quantity, where the value of each pixel represents the water conservation capacity of the location.

[0044] S3. Based on the spatial distribution map representing the ecological function quantity, by analyzing the historical statistical distribution characteristics of the spatial distribution map representing the ecological function quantity, and combining the quantile statistical method to set the functional quantity thresholds of source patches and sink patches, the source patches of the functional supply source and the sink patches of the functional consumption area are identified.

[0045] S3.1 Collect multiple periods of data representing the spatial distribution of ecological functions over historical periods to form a historical dataset of spatial distribution maps representing ecological functions.

[0046] Furthermore, spatial distribution maps representing ecological functions generated annually or periodically within a preset historical period are collected. These spatial distribution maps representing ecological functions from multiple periods are then organized and compiled to form a complete dataset containing the functional values ​​of all pixels at all points in the historical period, i.e., a historical dataset of spatial distribution maps representing ecological functions.

[0047] S3.2 Statistically analyze the historical dataset of spatial distribution maps representing ecological functions, and calculate the historical statistical distribution characteristics of spatial distribution maps representing ecological functions.

[0048] The formula for the average water conservation capacity is: ; in, This represents the average water conservation capacity value. The time period number in the historical dataset used to characterize the spatial distribution of ecological functions. For time period index, The total number of samples in the historical dataset. The total number of pixels on the spatial distribution map characterizing ecological functions. For cell index, For the first Spatial distribution map of ecological function quantities in the first period The value of each pixel reflects the water conservation capacity.

[0049] The formula for standard deviation is: ; in, This represents the fluctuation value of water conservation capacity over historical periods.

[0050] Furthermore, by integrating historical data from multiple periods into a unified sample set and calculating its average water conservation capacity value and standard deviation, background characteristics and steady-state information of ecosystem functions over long-term time series were extracted. This avoids the randomness and bias that may result from setting thresholds based solely on data from a single year or short period. As a result, the functional thresholds used to identify source / sink patches can be based on the long-term patterns of regional eco-hydrological processes, thus improving the robustness and reliability of patch identification.

[0051] It should be noted that the average water conservation capacity value The physical significance lies in the fact that it quantifies the overall average level of water conservation capacity of the entire study area over a historical period, representing the average hydrological regulation background or baseline state of the regional ecosystem, with a standard deviation of [missing information]. The physical meaning lies in the fact that it measures the functional values ​​of all pixels over a historical period relative to the average level. The degree of dispersion, A larger value indicates greater fluctuations in functional quantities at different locations and in different years, and stronger spatial heterogeneity and interannual variability of ecosystem functions; conversely, a smaller value indicates a relatively uniform and stable distribution of functional quantities, which together characterize the long-term and overall statistical behavior of ecosystem functions.

[0052] S3.3 Based on the historical statistical distribution characteristics of the spatial distribution map representing ecological function quantities, the quantile statistical method is used to set the threshold for source patch function quantities and sink patch function quantities.

[0053] Furthermore, based on the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, the quantile statistical method is applied to determine specific thresholds. The index values ​​reflecting water conservation capacity of all pixels in the historical dataset at all time points are taken as a statistical population, and a specific high percentile (e.g., the 70th percentile) is taken as the source patch function threshold, and a specific low percentile (e.g., the 30th percentile) is taken as the sink patch function threshold.

[0054] S3.4. In the spatial distribution map representing the ecological function quantity, the pixel region with the function quantity value higher than the function quantity threshold of the source patch is identified as the source patch of the function supply source.

[0055] Furthermore, the functional quantity value of each pixel in the spatial distribution map representing the ecological functional quantity of the current period to be analyzed is compared with the functional quantity threshold of the source patch. All pixels with functional quantity values ​​higher than the functional quantity threshold of the source patch are identified, and the continuous spatial region formed by the pixels is defined as the source patch of the functional supply source.

[0056] S3.5. Pixel regions in the spatial distribution map characterizing ecological function quantities whose function quantity values ​​are lower than the sink patch function quantity threshold are identified as sink patches in the function depletion zone.

[0057] Furthermore, the functional value of each pixel in the spatial distribution map representing the ecological functional quantity of the current period to be analyzed is compared with the functional quantity threshold of the sinking patch. All pixels with functional values ​​lower than the functional quantity threshold of the sinking patch are identified, and the continuous spatial region formed by the pixels is defined as the sinking patch of the functional consumption zone.

[0058] S4. Based on the surface water flow patterns and land use types, obtain connectivity resistance and construct a graph theory network model for ecological function flow.

[0059] S4.1 Based on the terrain data in the standardized multi-source data cube, the surface water flow movement pattern is extracted through hydrological analysis to generate natural water flow paths.

[0060] Furthermore, topographic data is extracted from standardized multi-source data cubes, and the functions of water flow direction calculation and runoff accumulation calculation in hydrological analysis tools are used to generate natural water flow paths connected by continuous pixels based on the natural law that surface runoff follows the flow from high altitude to low altitude. The path network represents the potential movement channels of surface water flow.

[0061] S4.2. An ecological resistance lookup table based on material flow resistance is used to establish a correspondence between land use type data and surface resistance coefficients.

[0062] Furthermore, referencing existing research findings in landscape ecology regarding resistance to species migration or material flow, a pre-defined ecological resistance lookup table is obtained. This table defines the standard surface resistance coefficient for each land use type (e.g., forest, grassland, cultivated land, and construction land). By consulting the ecological resistance lookup table, a correspondence table is established between each land use type in the study area and the specific surface resistance coefficient value.

[0063] S4.3 Overlay natural water flow paths with land use type data, and assign a surface resistance coefficient to each segment of the natural water flow path according to the corresponding relationship table to obtain the connectivity resistance value.

[0064] Furthermore, the natural water flow path is spatially overlaid with the land use type data in the standardized multi-source data cube. For each pixel location covered by the natural water flow path, the corresponding relationship table is queried according to its land use type to obtain the surface resistance coefficient of the pixel location. The surface resistance coefficients of all pixels on the natural water flow path are accumulated or averaged to obtain the overall connectivity resistance value of the natural water flow path.

[0065] S4.4 Define the source patches of functional supply and the sink patches of functional consumption as network nodes, define the natural water flow paths connecting the network nodes as network edges, and use the connectivity resistance value as the weight of the network edges. Integrate the network nodes, network edges and the weight of the network edges to construct a graph theory network model of ecological function flow.

[0066] Furthermore, the spatial centroid or representative location of the source patch of the functional supply source and the sink patch of the functional consumption area are defined as network nodes. The natural water flow path with the assigned connectivity resistance value is defined as the network edge connecting the network nodes. The connectivity resistance value is directly used as the weight of the corresponding network edge. Finally, all network nodes, network edges and the weights of the network edges are integrated to construct a graph theory network model of ecological function flow.

[0067] S5. Based on the graph theory network model of ecological function flow, the function values ​​in the spatial distribution map representing the ecological function quantity are assigned to the source patches. The path simulation algorithm is used to dynamically simulate the flow of ecological function from the source patch to the sink patch along the weighted network edges, and generate the ecological function flow flux results.

[0068] S5.1 In the graph theory network model of ecological function flow, the function values ​​within the range of each source patch in the spatial distribution map representing the ecological function quantity are summed to form the initial flow value and assigned to the corresponding source patch.

[0069] Initial flow rate formula: ; in, For the first The initial flow value of each source patch, for The set of all pixels covered by a source patch. For source patch indexing.

[0070] Furthermore, the spatial distribution map representing the ecological function quantity is spatially superimposed with the source patches defined in the graph theory network model of ecological function flow. For each source patch, the sum of the index values ​​reflecting the water conservation capacity of all pixels within the patch boundary is calculated and assigned as the initial flow value to the network node corresponding to the source patch.

[0071] S5.2. Use the population density data of the area where the sink patch is located as the functional consumption intensity factor and assign it to the sink patch.

[0072] Furthermore, population density data corresponding to the spatial range of sink patches in the graph theory network model of ecological function flow is obtained. The average population density data within the range of each sink patch is used as the functional consumption intensity factor and assigned to the network node corresponding to the sink patch.

[0073] S5.3. The path simulation algorithm is adopted, with the connectivity resistance value on the weighted network edge as the conduction resistance value and the functional consumption intensity factor of the sink patch as the attraction value, to drive the initial flow value from the source patch to the sink patch along the weighted network edge.

[0074] Furthermore, by employing path simulation algorithms such as circuit theory or random walks, the connectivity resistance value on each network edge in the graph theory network model of ecological function flow is used as the conduction resistance, and the functional consumption intensity factor assigned to the sink patch is used as the attraction. This drives the initial flow value assigned to the source patch to flow from the source patch node along the weighted network edge towards the sink patch node.

[0075] S5.4 The path simulation algorithm runs to the flow allocation stage, recording the functional throughput value of each network edge in the network and the total functional throughput value received by each sink block.

[0076] Furthermore, the path simulation algorithm continues to run iteratively until the flow distribution in the graph theory network model of the entire ecological function flow reaches a stable state. It records the functional flux value of each network edge in the network under the stable state, as well as the total functional flux value received by each sink patch node from all source patch nodes.

[0077] S5.5. Integrate the functional flux values ​​of network edges, the total functional flux value received by sink patches, and the contribution ratio of source patches to generate ecological functional flow flux results.

[0078] Furthermore, the functional flux values ​​of network edges recorded by the path simulation algorithm, the total functional flux value received by sink patches, and the contribution ratio of each source patch to each sink patch obtained through backtracking calculation are integrated to generate ecological functional flow flux results that include spatial flow path flux, total amount received by sink patches, and source-sink contribution relationship.

[0079] S6. Based on the results of ecological function flow flux, identify corridors and nodes of functional flow connectivity, obtain ecological protection priorities, and output the visualization results of the ecological function flow network and areas requiring priority protection.

[0080] S6.1 Analyze the results of ecological function flow fluxes, identify network edges whose functional flux values ​​are higher than the functional flux threshold set by analyzing the statistical distribution characteristics of the functional flux values ​​of network edges in the results of ecological function flow fluxes, and define the network edges as functional flow connectivity corridors.

[0081] Furthermore, the functional flux values ​​of all network edges included in the ecological function flow flux results are analyzed. Based on the statistical distribution characteristics of the functional flux values, a functional flux threshold is set. Network edges with functional flux values ​​higher than the functional flux threshold in the ecological function flow flux results are identified, and these network edges are defined as functional flow connectivity corridors that have a core impact on functional flow connectivity.

[0082] S6.2 Analyze the results of ecological function flow fluxes, identify those whose function flux values ​​are higher than the function quantity thresholds of patch and sink patch, and define the source patch and sink patch as nodes of functional flow connectivity.

[0083] Furthermore, the total functional flux value output by each source patch and the total functional flux value received by each sink patch are recorded in the ecological functional flow flux results. The flux values ​​are compared with the set source patch functional flux thresholds and sink patch functional flux thresholds. Source patches whose output total functional flux value is higher than the source patch functional flux threshold, and sink patches whose received total functional flux value is higher than the sink patch functional flux threshold, are collectively defined as nodes of functional flow connectivity.

[0084] S6.3. Sort the functional flow connectivity corridors and functional flow connectivity nodes according to their size to obtain the priority of ecological protection.

[0085] Furthermore, functional flow connectivity corridors are sorted from high to low based on their functional flux values, and functional flow connectivity nodes are sorted from high to low based on their total functional flux value or total received functional flux value. Based on the sorting results, the ecological protection priority of functional flow connectivity corridors and functional flow connectivity nodes is determined, with corridors or nodes having higher flux values ​​receiving higher protection priority.

[0086] S6.4 Integrate the graph theory network model of ecological function flow, the results of ecological function flow flux, and the priority of ecological protection into the geographic information module, and render and generate the visualization results of the ecological function flow network and the areas that need priority protection in the geographic information module.

[0087] Furthermore, the spatial structure data of the graph theory network model of ecological function flow, the flux data in the ecological function flow flux results, and the ecological protection priority data are all imported into the geographic information system software. In the geographic information system software, different symbols, colors, and line widths are used to visualize and render the functional flow connectivity corridors, functional flow connectivity nodes, and their corresponding ecological protection priorities. Finally, a visualization result map containing the spatial layout of the ecological function flow network and the level information of areas requiring priority protection is generated.

[0088] This embodiment also provides a multi-scale analysis system for ecosystem function changes, including: a data acquisition module that collects multi-source remote sensing data, topographic data, meteorological data and soil data within the study area, and performs preprocessing to generate a standardized multi-source data cube; The quantification module utilizes a standardized multi-source data cube to calculate indicators reflecting water conservation capacity and generate a spatial distribution map representing ecological function quantities. A graph theory network model module is constructed based on the spatial distribution map representing ecological functions. By analyzing the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, and combining the quantile statistical method to set the functional quantity thresholds of source patches and sink patches, the source patches of functional supply sources and sink patches of functional consumption areas are identified. Based on the surface water flow movement law and land use type, the connectivity resistance is obtained, and a graph theory network model of ecological function flow is constructed. The simulation module, based on a graph theory network model of ecological function flow, assigns the function values ​​in the spatial distribution map representing the ecological function quantity to the source patches. It uses a path simulation algorithm to dynamically simulate the flow of ecological function from the source patches to the sink patches along the weighted network edges, generating ecological function flow flux results. The visualization module identifies corridors and nodes of functional flow connectivity based on the results of ecological function flow flux, obtains ecological protection priorities, and outputs visualization results of the ecological function flow network and areas requiring priority protection.

[0089] This embodiment also provides a computer device suitable for multi-scale analysis methods of ecosystem function changes, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the multi-scale analysis method of ecosystem function changes as proposed in the above embodiment.

[0090] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0091] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the multi-scale analysis method for realizing ecosystem function changes as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0092] In summary, this invention integrates and standardizes multi-source remote sensing, topographic, meteorological, and soil data to generate a unified multi-source data cube. This cube then calculates water conservation capacity indicators and generates a spatial distribution map of ecological functions. Based on historical statistical characteristics and quantile methods, it identifies source and sink patches. Combining water flow patterns and land use resistance, it constructs a graph theory network model of ecological function flow. Through path simulation algorithms, it dynamically simulates the function transfer process, generating flow flux results. Based on these flux results, it identifies key corridors and nodes, determines protection priorities, and provides visualized output. This invention solves the problem that traditional static assessment methods cannot quantify the dynamic spatial transmission of ecological functions, achieving a breakthrough from functional stock assessment to source-sink flow process simulation. It provides technical support for watershed ecological compensation and precise spatial governance.

[0093] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A multi-scale analysis method for ecosystem function changes, characterized in that: This includes collecting multi-source remote sensing data, topographic data, meteorological data, and soil data within the study area, and preprocessing them to generate standardized multi-source data cubes; By utilizing standardized multi-source data cubes, spatial distribution maps representing ecological functions are generated by calculating indicators reflecting water conservation capacity. Based on the spatial distribution map representing the ecological function quantity, by analyzing the historical statistical distribution characteristics of the spatial distribution map representing the ecological function quantity, and by combining the quantile statistical method to set the functional quantity thresholds of source patches and sink patches, the source patches of functional supply sources and sink patches of functional consumption areas are identified. Based on the surface water flow movement law and land use type, the connectivity resistance is obtained, and a graph theory network model of ecological function flow is constructed. Based on the graph theory network model of ecological function flow, the function values ​​in the spatial distribution map representing the ecological function quantity are assigned to the source patches. The path simulation algorithm is used to dynamically simulate the flow of ecological function from the source patch to the sink patch along the weighted network edges, and generate the ecological function flow flux results. Based on the results of ecological function flow flux, corridors and nodes of functional flow connectivity are identified, ecological protection priorities are obtained, and visualization results of the ecological function flow network and areas requiring priority protection are output.

2. The multi-scale analysis method for ecosystem function changes as described in claim 1, characterized in that: Collect multi-source remote sensing data, topographic data, meteorological data, and soil data within the study area, and preprocess them to generate a standardized multi-source data cube, including the following steps: Acquire multi-source remote sensing data, topographic data, meteorological data, and soil data within the boundary of the study area. Perform geometric fine correction processing on the multi-source remote sensing data, topographic data, meteorological data, and soil data to obtain a unified geographic coordinate framework for the multi-source remote sensing data, topographic data, meteorological data, and soil data. Radiometric calibration and atmospheric correction were performed on multi-source remote sensing data, topographic data, meteorological data, and soil data within a unified geographic coordinate framework. Multi-source remote sensing data, topographic data, meteorological data, and soil data that have undergone radiometric calibration and atmospheric correction are cropped using the study area boundary to obtain multi-source remote sensing data, topographic data, meteorological data, and soil data covering the study area. The cropped multi-source remote sensing data, topographic data, meteorological data, and soil data are then integrated to generate a standardized multi-source data cube.

3. The multi-scale analysis method for ecosystem function changes as described in claim 2, characterized in that: Using a standardized multi-source data cube, an index reflecting water conservation capacity is calculated to generate a spatial distribution map characterizing ecological functions, including the following steps: Precipitation data, actual evapotranspiration data, soil saturated hydraulic conductivity data, and land use type data are extracted from a standardized multi-source data cube. The water balance components of pixels in the study area were calculated based on precipitation data and actual evapotranspiration data. The water balance component of the pixel, the soil saturated hydraulic conductivity data and the land use type data are used as input parameters and substituted into the water conservation capacity calculation formula to calculate the index value reflecting the water conservation capacity pixel by pixel. By combining the index values ​​reflecting water conservation capacity into a spatial distribution map, a spatial distribution map representing the ecological function quantity is generated.

4. The multi-scale analysis method for ecosystem function changes as described in claim 3, characterized in that: Based on the spatial distribution map representing ecological functions, by analyzing the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, and combining the quantile statistical method to set source patch function thresholds and sink patch function thresholds, source patches of function supply and sink patches of function consumption areas are identified, including the following steps: Collect multiple periods of data on the spatial distribution map representing ecological functions over historical periods to form a historical dataset of the spatial distribution map representing ecological functions. Statistical analysis was performed on historical datasets of spatial distribution maps representing ecological functions, and the historical statistical distribution characteristics of spatial distribution maps representing ecological functions were calculated. Based on the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, the quantile statistical method is used to set the thresholds for source patch functions and sink patch functions. In the spatial distribution map characterizing ecological function, the pixel regions with function values ​​higher than the source patch function value threshold are used to identify the source patches of function supply sources. In the spatial distribution map representing ecological function, pixel regions with function values ​​lower than the sink patch function threshold are identified as sink patches in the function depletion zone.

5. The multi-scale analysis method for ecosystem function changes as described in claim 4, characterized in that: Based on the patterns of surface water flow and land use types, connectivity resistance is obtained, and a graph theory network model of ecological function flow is constructed, including the following steps: Based on topographic data in a standardized multi-source data cube, surface water flow patterns are extracted through hydrological analysis to generate natural water flow paths; An ecological resistance lookup table based on material flow resistance is used to establish a correspondence between land use type data and surface resistance coefficients. By overlaying natural water flow paths with land use type data, and assigning a surface resistance coefficient to each segment of the natural water flow path according to the corresponding relationship table, the connectivity resistance value is obtained. The source patches of functional supply and the sink patches of functional consumption are defined as network nodes, the natural water flow paths connecting the network nodes are defined as network edges, and the connectivity resistance value is used as the weight of the network edges. By integrating the network nodes, network edges and the weight of the network edges, a graph theory network model of ecological function flow is constructed.

6. The multi-scale analysis method for ecosystem function changes as described in claim 5, characterized in that: Based on a graph theory network model of ecological function flow, the functional values ​​representing ecological function quantities in the spatial distribution map are assigned to source patches. A path simulation algorithm is then used to dynamically simulate the flow of ecological function along weighted network edges from source patches to sink patches, generating ecological function flow flux results. The process includes the following steps: In the graph theory network model of ecological function flow, the function values ​​within the range of each source patch in the spatial distribution map representing the ecological function quantity are summed to form the initial flow value assigned to the corresponding source patch; Population density data of the area where the sink patch is located is used as the functional consumption intensity factor and assigned to the sink patch. A path simulation algorithm is used, with the connectivity resistance value on the weighted network edge as the conduction resistance value and the functional consumption intensity factor of the sink patch as the attraction value, to drive the initial flow value from the source patch to the sink patch along the weighted network edge. The path simulation algorithm runs up to the flow allocation stage, recording the functional throughput value of each network edge in the network and the total functional throughput value received by each sink block; By integrating the functional flux values ​​of network edges, the total functional flux values ​​received by sink patches, and the contribution ratio of source patches, ecological functional flow flux results are generated.

7. The multi-scale analysis method for ecosystem function changes as described in claim 6, characterized in that: Based on the results of ecological function flow fluxes, corridors and nodes of functional flow connectivity are identified, ecological protection priorities are obtained, and a visualization of the ecological function flow network and areas requiring priority protection is output, including the following steps: By analyzing the results of ecological function flow fluxes, network edges whose function flux values ​​are higher than the function flux threshold set by analyzing the statistical distribution characteristics of the function flux values ​​of network edges in the results of ecological function flow fluxes are identified, and these network edges are defined as functional flow connectivity corridors. By analyzing the results of ecological functional flow fluxes, we identified functional fluxes that were higher than the functional flux thresholds for both source and sink patches, and defined source and sink patches as nodes of functional flow connectivity. Ecological protection priorities are obtained by ranking the functional flow connectivity corridors and the size of the functional flow connectivity nodes. The graph theory network model of ecological function flow, the results of ecological function flow flux, and the priority of ecological protection are integrated into the geographic information module. The visualization results of the ecological function flow network and the areas requiring priority protection are then rendered in the geographic information module.

8. A multi-scale analysis system for ecosystem function change, based on the multi-scale analysis method for ecosystem function change according to any one of claims 1 to 7, characterized in that: This includes a data acquisition module that collects multi-source remote sensing data, topographic data, meteorological data, and soil data within the study area, and performs preprocessing to generate a standardized multi-source data cube; The quantification module utilizes a standardized multi-source data cube to calculate indicators reflecting water conservation capacity and generate a spatial distribution map representing ecological function quantities. A graph theory network model module is constructed based on the spatial distribution map representing ecological functions. By analyzing the historical statistical distribution characteristics of the spatial distribution map representing ecological functions, and combining the quantile statistical method to set the functional quantity thresholds of source patches and sink patches, the source patches of functional supply sources and sink patches of functional consumption areas are identified. Based on the surface water flow movement law and land use type, the connectivity resistance is obtained, and a graph theory network model of ecological function flow is constructed. The simulation module, based on a graph theory network model of ecological function flow, assigns the function values ​​in the spatial distribution map representing the ecological function quantity to the source patches. It uses a path simulation algorithm to dynamically simulate the flow of ecological function from the source patches to the sink patches along the weighted network edges, generating ecological function flow flux results. The visualization module identifies corridors and nodes of functional flow connectivity based on the results of ecological function flow flux, obtains ecological protection priorities, and outputs visualization results of the ecological function flow network and areas requiring priority protection.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the multi-scale analysis method for ecosystem function changes as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-scale analysis method for ecosystem function changes as described in any one of claims 1 to 7.

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