Coral ecological multi-risk overlay analysis method and system based on GIS

Abstract

The invention relates to the technical field of oceans, in particular to a coral ecology multi-risk overlay analysis method and system based on a GIS (Geographic Information System), and the method comprises the steps: constructing a marine ecology resistance surface which reflects the interference degree of human activities, and carrying out the spatial overlay operation of each standardized risk intensity grid and the marine ecology resistance surface in a GIS platform, generating a coral ecological risk spatial distribution map comprehensively reflecting coral reef stressed space; identifying a potential ecological corridor connected with the risk interval residual suitability degree raw land; according to the evaluation result of the coral ecological risk spatial distribution map and the corridor fracture risk, coral protection supervision core areas and ecological corridor restoration areas with different priorities are delimited, and targeted supervision suggestions are generated, so that a direct and scientific decision basis can be provided for delimiting differentiated protection subareas and formulating targeted restoration strategies, and the method is suitable for popularization and application. And the refinement level and foresight of coral reef protection management are improved.

Description

Technical Field

[0001] This invention relates to the field of marine technology, and in particular to a GIS-based method and system for analyzing multiple risks in coral ecosystems. Background Technology

[0002] Coral reef ecosystems, as vital marine ecological resources, are facing multiple pressures from climate change, human activities in the ocean, and other risks. Traditional methods for assessing coral ecological risks often focus on static analysis of single risk factors (such as seawater warming) or rely on limited field survey data, making it difficult to comprehensively reveal the spatial interactions and synergistic effects of multiple and dynamic risk factors. Particularly in coastal areas with increasingly frequent human activities, changes in marine use such as land reclamation and dredging not only directly encroach on habitats but also exert continuous and complex indirect impacts on coral reefs by altering the hydrological environment and sedimentary patterns. Existing research often lacks effective means to quantitatively superimpose environmental parameter risks and human disturbances within a unified geographic spatial framework, while neglecting changes in ecological connectivity between coral reef patches under risk stress, leading to delayed and one-sided development of protection strategies. Therefore, there is an urgent need for a comprehensive analytical method that can integrate multi-temporal remote sensing, environmental parameters, and baseline data, and based on GIS spatial analysis technology, to achieve dynamic superposition of multiple risk factors, construction of ecological resistance surfaces, and assessment of corridor connectivity, thereby providing accurate and forward-looking spatial decision support for the protection and restoration of coral reefs. Summary of the Invention

[0003] This invention overcomes the shortcomings of existing technologies and provides a GIS-based method and system for analyzing multiple risks in coral ecosystems.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The first aspect of this invention discloses a GIS-based method for analyzing multiple risks overlaying in coral ecosystems, comprising the following steps: The baseline data on coral reef distribution was obtained, and remote sensing data on marine use changes and marine environmental parameters were collected simultaneously over multiple time periods to form a set of multi-source risk factors for coral ecology. Based on the aforementioned set of multi-source risk factors for coral ecology, a standardized risk intensity grid for each risk factor is generated, and a marine ecological resistance surface reflecting the degree of human activity interference is constructed for the marine use change data. The standardized risk intensity grids and the marine ecological resistance surface are spatially overlaid on the GIS platform, and the ecological sensitivity index is introduced to correct the overlay weights, generating a spatial distribution map of coral ecological risk that comprehensively reflects the space of coral reef stress. Based on the aforementioned spatial distribution map of coral ecological risks, potential ecological corridors connecting the remaining suitable habitats in the risk zones are identified, and the risk of corridor disruption due to changes in marine use is assessed based on the cumulative resistance values ​​traversed by the corridors. Based on the spatial distribution map of coral ecological risks and the assessment results of corridor fracture risks, core areas for coral protection supervision and ecological corridor restoration areas with different priorities are delineated, and targeted supervision recommendations are generated.

[0005] Furthermore, baseline data on coral reef distribution was obtained, and remote sensing data on marine use changes and marine environmental parameters were collected simultaneously over multiple time periods. These data together constitute a multi-source risk factor set for coral ecosystems, specifically: By importing historical survey data and remote sensing interpretation results into the GIS platform, baseline data on the distribution range and health of coral reefs are obtained. After spatial registration and vectorization, a coral reef ecological baseline vector map layer is generated. By simultaneously retrieving satellite remote sensing images from multiple time series, we can identify marine use activity patches, including land reclamation, channel dredging, and aquaculture area expansion, and construct a multi-time series remote sensing dataset of marine use changes. Based on the spatial range of the coral reef ecological background vector layer, marine environmental parameter data covering the target area are extracted, including sea surface temperature, chlorophyll concentration and suspended sediment concentration. Then, spatiotemporal resolution normalization and missing value imputation are performed to obtain a standardized environmental parameter raster set. The multi-time series remote sensing dataset of marine use changes is used to track change trajectories and quantify intensity. Based on the known ecological impact mechanisms of various marine use activities on coral reefs, an initial impact coefficient is assigned to each type of activity to generate a list of ecological disturbances caused by marine use changes. The coral reef ecological background vector layer, the standardized environmental parameter raster set, and the list of ecological disturbances caused by changes in marine use are spatiotemporally correlated and fused to form a multi-source risk factor set for coral ecology.

[0006] Furthermore, based on the aforementioned set of multi-source risk factors for coral ecology, a standardized risk intensity raster is generated for each risk factor, and a marine ecological resistance surface reflecting the degree of human disturbance is constructed for marine use change data, specifically as follows: Based on the known ecological relationship between various environmental parameters and coral reef stress, risk thresholds are set for each parameter in the standardized environmental parameter grid set, and the corresponding parameter values ​​are converted into standardized risk intensity values ​​through a nonlinear normalization function to generate risk intensity grids corresponding to each environmental parameter. For the aforementioned list of ecological disturbances caused by changes in marine use, the spatial location and initial impact coefficient of each marine use activity patch are extracted, and the spatial proximity between each patch and the coral reef is calculated based on the coral reef ecological background vector layer. The initial impact coefficient and spatial proximity are coupled and calculated to generate a marine use activity disturbance intensity index that reflects the spatial attenuation effect. The disturbance intensity index of marine activities is assigned to the corresponding marine activity patch, and a time decay factor is introduced to weight and fuse multi-time series marine use change data to characterize the persistent impact of recent human activity interference, thereby generating a marine use change risk intensity raster with spatiotemporal dynamic characteristics. The marine use change risk intensity grid and the environmental parameter risk intensity grid are unified to the same spatial coordinate system and pixel scale. Based on the intensity of human activity disturbance represented by the marine use change risk intensity grid, a continuously distributed marine ecological resistance surface with resistance value increasing with the intensity of disturbance is constructed by spatial interpolation method.

[0007] Furthermore, the standardized risk intensity grids are spatially overlaid with the aforementioned marine ecological resistance surface on a GIS platform, and an ecological sensitivity index is introduced to correct the overlay weights, generating a spatial distribution map of coral ecological risk that comprehensively reflects the space under stress to coral reefs, specifically: Based on the spatial distribution of coral reefs defined by the coral reef ecological background vector layer, the minimum cumulative resistance value between each grid cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined. Based on the principles of coral reef ecology, a resistance threshold is set, and areas where the minimum cumulative resistance value is lower than the resistance threshold are identified as the potential affected areas of the coral reef ecosystem. Within the potential affected area, the risk intensity grid of marine use change is weighted and summed with the risk intensity grid of each environmental parameter. The initial weights are determined based on the initial influence coefficients of each risk factor to obtain the initial comprehensive risk intensity value. Based on the minimum cumulative resistance value of each grid cell within the potential affected area, an ecological sensitivity index negatively correlated with the resistance value is constructed to characterize the susceptibility of coral reefs to the impact of distant human activities. The initial comprehensive risk intensity value is nonlinearly corrected using the ecological sensitivity index to generate a spatial distribution map of coral ecological risk.

[0008] Specifically, based on the spatial distribution of coral reefs defined by the coral reef ecological background vector map layer, the minimum cumulative resistance value between each grid cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined as follows: The coral reef ecological background vector layer is rasterized, and the pixel values ​​of the coral reef distribution area are identified as unique source codes, while the pixel values ​​of the non-coral reef distribution area are identified as invalid values, thus generating a coral reef source raster. Using the coral reef source grid as the starting point and the marine ecological resistance surface as the cost surface, the cost distance method is used for iterative calculation. A cumulative resistance cost grid with the same range as the resistance surface is initialized, wherein the initial resistance value of the cell corresponding to the coral reef source grid is zero, and the other cells are the maximum values ​​to be calculated. Traverse each cell in the cumulative resistance cost grid, search its neighboring cells based on the eight-neighbor direction, and accumulate the cumulative resistance value of the current cell with the marine ecological resistance surface resistance value of the neighboring cell to generate a series of potential cumulative resistance values. The minimum value among the potential cumulative resistance values ​​is compared and selected, and then updated in the cumulative resistance cost grid of the target cell. This process is repeated multiple times until the cumulative resistance values ​​of all cells tend to stabilize, and finally, the minimum cumulative resistance surface formed by the minimum cumulative resistance value is generated.

[0009] Furthermore, based on the aforementioned spatial distribution map of coral ecological risks, potential ecological corridors connecting remaining suitable habitats within risk zones are identified. The risk of corridor disruption due to changes in marine use on coral reef ecological connectivity is assessed based on the cumulative resistance values ​​traversed by these corridors. Specifically: The spatial distribution map of coral ecological risk is reclassified, and areas with risk values ​​below a preset risk threshold are identified as remaining suitable habitats. Spatially isolated suitable patches are extracted from these areas as sources and targets for ecological corridor connections. Using the minimum cumulative resistance surface as the cost basis, the minimum resistance path between every two suitable patches is calculated in the GIS platform. The trajectory of the minimum resistance path is determined by minimizing the sum of the cumulative resistance values ​​of all pixels on the path. By aggregating all calculated minimum resistance paths, a potential ecological corridor network connecting suitable patches is constructed. For each corridor in the potential ecological corridor network, the resistance value in the minimum cumulative resistance surface corresponding to the corridor crossing path is extracted, and the average or maximum value is calculated as the corridor crossing resistance value of the corresponding corridor. The corridor crossing resistance value is compared with the corridor resistance threshold determined based on historical ecological data. Different corridor breakage risk levels are classified according to the degree to which the corridor resistance threshold is exceeded, thereby completing the assessment of the impact of changes in marine use on ecological connectivity.

[0010] Furthermore, based on the aforementioned spatial distribution map of coral ecological risks and the assessment results of corridor fracture risks, core coral protection and regulatory zones and ecological corridor restoration zones of different priorities are delineated, and targeted regulatory recommendations are generated, specifically: The areas with the lowest risk level in the spatial distribution map of coral ecological risk are identified as core habitat patches, and the patches connected by the corridors with the highest risk level of corridor fracture are marked as key connectivity nodes. The core habitat patches and the key connecting nodes are spatially superimposed and merged to form a preliminary protection core area. Based on the risk value of the coral ecological risk spatial distribution map of the area where the preliminary protection core area is located and the risk level of the corridor fracture, a weighted calculation is performed to generate a comprehensive protection priority index. Based on the numerical distribution of the comprehensive protection priority index, the initial protection core area is divided into coral protection and supervision core areas of different priorities. For corridors in the potential ecological corridor network that are at risk of fracture, the restoration urgency index of each corridor is determined based on the magnitude of the corresponding corridor crossing resistance value and the risk gradient of the coral ecological risk spatial distribution map it crosses. Based on the restoration urgency index, ecological corridor restoration areas are delineated, and combined with the priority of the coral protection and supervision core area, targeted supervision recommendation maps containing spatial scope, protection level, and restoration sequence are generated.

[0011] The second aspect of the present invention discloses a GIS-based coral ecology multi-risk overlay analysis system, the coral ecology multi-risk overlay analysis system including a memory and a processor, the memory storing a coral ecology multi-risk overlay analysis method program, and when the coral ecology multi-risk overlay analysis method program is executed by the processor, the steps of the coral ecology multi-risk overlay analysis method described in any one of the claims are implemented.

[0012] This invention addresses the technical deficiencies in the prior art and has the following beneficial effects: By integrating multi-source risk data and constructing a marine ecological resistance surface, this invention achieves a precise assessment of the multiple stresses on coral reef ecosystems, identifies high-risk areas and key ecological connectivity corridors, and generates a spatial distribution map of coral ecological risks and corridor fracture risk levels. This provides a direct and scientific basis for delineating differentiated protection zones and formulating targeted restoration strategies, thereby improving the precision and foresight of coral reef protection and management. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a flowchart illustrating the workflow of our method for analyzing multiple risks in coral ecology. Figure 2 This is a system structure diagram of the coral ecology multi-risk superposition analysis system. Detailed Implementation

[0015] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0016] 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 therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0017] like Figure 1 As shown, the first aspect of this invention discloses a GIS-based method for analyzing multiple risks overlaying in coral ecosystems, comprising the following steps: S1. Obtain baseline data on coral reef distribution, and simultaneously collect remote sensing data on marine use changes and marine environmental parameters over multiple time periods, which together constitute a set of multi-source risk factors for coral ecology. S2. Based on the aforementioned set of multi-source risk factors for coral ecology, a standardized risk intensity grid for each risk factor is generated, and a marine ecological resistance surface reflecting the degree of human activity interference is constructed for the marine use change data. S3. Spatial overlay calculations are performed on each standardized risk intensity grid and the marine ecological resistance surface in the GIS platform, and the ecological sensitivity index is introduced to correct the overlay weights to generate a spatial distribution map of coral ecological risk that comprehensively reflects the space of coral reef stress. S4. Based on the aforementioned spatial distribution map of coral ecological risks, identify potential ecological corridors connecting the remaining suitable habitats in the risk zones, and assess the risk of corridor disruption to coral reef ecological connectivity due to changes in marine use based on the cumulative resistance values ​​traversed by the corridors. S5. Based on the spatial distribution map of coral ecological risks and the assessment results of corridor fracture risks, delineate coral protection and supervision core areas and ecological corridor restoration areas with different priorities, and generate targeted supervision recommendations.

[0018] Furthermore, baseline data on coral reef distribution was obtained, and remote sensing data on marine use changes and marine environmental parameters were collected simultaneously over multiple time periods. These data together constitute a multi-source risk factor set for coral ecosystems, specifically: By importing historical survey data and remote sensing interpretation results into the GIS platform, baseline data on the distribution range and health of coral reefs are obtained. After spatial registration and vectorization, a coral reef ecological baseline vector map layer is generated. By simultaneously retrieving satellite remote sensing images from multiple time series, we can identify marine use activity patches, including land reclamation, channel dredging, and aquaculture area expansion, and construct a multi-time series remote sensing dataset of marine use changes. Based on the spatial range of the coral reef ecological background vector layer, marine environmental parameter data covering the target area are extracted, including sea surface temperature, chlorophyll concentration and suspended sediment concentration. Then, spatiotemporal resolution normalization and missing value imputation are performed to obtain a standardized environmental parameter raster set. The multi-time series remote sensing dataset of marine use changes is used to track change trajectories and quantify intensity. Based on the known ecological impact mechanisms of various marine use activities on coral reefs, an initial impact coefficient is assigned to each type of activity to generate a list of ecological disturbances caused by marine use changes. The coral reef ecological background vector layer, the standardized environmental parameter raster set, and the list of ecological disturbances caused by changes in marine use are spatiotemporally correlated and fused to form a multi-source risk factor set for coral ecology.

[0019] In practice, the data management module of a GIS platform (such as ArcGIS or QGIS) is used to import historical coral reef survey reports, scientific research data, and interpretation results of high-resolution remote sensing images (such as the WorldView series) from authoritative departments to obtain baseline information on the spatial distribution range and health status (such as coverage and bleaching level) of coral reefs. Then, the spatial registration tools of GIS are used to unify the data from different sources to the same coordinate system (such as the WGS84 geographic coordinate system or a suitable projected coordinate system), and through vectorization processing, a coral reef ecological baseline vector map layer (such as Shapefile or GeoJSON format) with attribute information (such as reef area number and health level) is generated.

[0020] Simultaneously, multi-temporal satellite remote sensing image data sources (such as Landsat series and Sentinel-2 series images) covering the target area are retrieved. Using object-oriented classification methods or deep learning models, marine use activity patches at different times (such as annually or every five years) are identified and extracted. These activities specifically include land reclamation (manifested as coastline advancement and changes in land cover), channel dredging (manifested as water deepening and channel alignment characteristics), and aquaculture area expansion (manifested as regularly arranged net cages or floating rafts). By performing change detection analysis on multiple images (such as image difference or post-classification comparison methods), a multi-temporal remote sensing dataset reflecting the spatiotemporal evolution of marine use activities is constructed.

[0021] Based on the spatial extent defined by the aforementioned coral reef ecological baseline vector layer (which may include buffer zones), marine environmental parameter data for the corresponding region and time period are extracted from publicly available marine environmental databases (such as NASA's MODIS and ESA's GlobColour project). Key parameters include sea surface temperature (SST), chlorophyll a concentration (Chl-a), and suspended sediment concentration (TSM). Since these data may originate from different sensors and have different spatiotemporal resolutions (e.g., daily, 8-day, monthly averages, with spatial resolutions ranging from 1 kilometer to several hundred meters), standardization preprocessing is required: resampling techniques (such as bilinear interpolation or nearest neighbor method) are used to unify the data to the same spatial grid (cell size), and time series interpolation methods (such as linear interpolation or kriging interpolation) are used to fill in data gaps caused by cloud cover and other factors, ultimately resulting in a standardized environmental parameter raster set (such as GeoTIFF format) with consistent spatiotemporal dimensions.

[0022] For the aforementioned multi-time-series remote sensing dataset of marine use changes, it is necessary to perform change trajectory tracking and intensity quantification. Specifically, "known ecological impact mechanisms of various marine use activities on coral reefs" refers to the pathways and degrees by which different marine use activities affect the survival and health of coral reefs, which are clearly recognized by those skilled in the art through existing ecological research. For example: (1) The impact mechanism of land reclamation is mainly manifested in the direct physical destruction of coral reef habitats and may change local hydrodynamic conditions, leading to increased sediment cover. Its initial impact coefficient is usually assigned a high value.

[0023] (2) The main impact mechanism of channel dredging is to increase water turbidity (suspended sediment), reduce light availability, and may cause dredged material to settle and bury corals. Its initial impact coefficient needs to take into account the scale of dredging and the distance from the reef.

[0024] (3) The main impact mechanism of aquaculture expansion is that aquaculture waste (uneaten feed, feces) leads to eutrophication of the water body, which may cause excessive algal growth and competition with corals, as well as the risk of pathogen transmission. Its initial impact coefficient needs to be set in combination with aquaculture density and type.

[0025] Based on the above mechanism, each identified type of marine use activity is assigned a quantified initial impact coefficient (e.g., on a scale of 0-1 or 1-10), which reflects the severity of the activity type's relative ecological impact. Combining information such as the area and duration of activity patches, a list of ecological disturbances caused by changes in marine use is generated, including spatial location, activity type, time of occurrence, intensity, and initial impact coefficient.

[0026] Finally, within the GIS platform, spatial connectivity and attribute association functions are used to link and integrate the coral reef ecological baseline vector layer (serving as the benchmark for risk assessment), the standardized environmental parameter raster set (representing natural environmental stress factors), and the marine use change ecological disturbance inventory (representing human activity stress factors) in the spatiotemporal dimensions. For example, environmental parameter values ​​are extracted to the coral reef distribution area and surrounding areas, and marine use activity patches are overlaid with coral reef locations for analysis, ultimately forming a complete and structured set of multi-source risk factors for coral ecology. This dataset integrates multi-dimensional information such as static baseline, dynamic environment, and human activities.

[0027] Furthermore, based on the aforementioned set of multi-source risk factors for coral ecology, a standardized risk intensity raster is generated for each risk factor, and a marine ecological resistance surface reflecting the degree of human disturbance is constructed for marine use change data, specifically as follows: Based on the known ecological relationship between various environmental parameters and coral reef stress, risk thresholds are set for each parameter in the standardized environmental parameter grid set, and the corresponding parameter values ​​are converted into standardized risk intensity values ​​through a nonlinear normalization function to generate risk intensity grids corresponding to each environmental parameter. Specifically, for marine environmental parameter data, based on the known ecological relationships between each environmental parameter and coral reef stress (e.g., sea surface temperature has a known threshold window for coral bleaching, with a sharp increase in risk above 30°C; abnormally high chlorophyll concentration may indicate eutrophication; suspended sediment concentration directly affects light transmittance), a risk threshold is set for each parameter in the standardized environmental parameter raster set. Subsequently, a nonlinear normalization function (e.g., a sigmoid function or a piecewise linear function) is used to convert the actual observed values ​​of each parameter into standardized risk intensity values ​​within the range of 0-1. Taking sea surface temperature as an example, values ​​below 28°C are mapped to 0, values ​​between 28°C and 30°C transition smoothly according to an sigmoid curve, and values ​​above 30°C are mapped to 1. This process converts each environmental parameter raster into a comparable risk intensity raster, allowing environmental pressures of different dimensions to be measured under the same standard.

[0028] For the aforementioned list of ecological disturbances caused by changes in marine use, the spatial location and initial impact coefficient of each marine use activity patch are extracted, and the spatial proximity between each patch and the coral reef is calculated based on the coral reef ecological background vector layer. The initial impact coefficient and spatial proximity are coupled and calculated to generate a marine use activity disturbance intensity index that reflects the spatial attenuation effect. Specifically, for the aforementioned inventory of ecological disturbances caused by changes in marine use, the spatial location of each marine use activity patch and its corresponding initial influence coefficient are extracted (this coefficient is set based on the activity type, such as highest for land reclamation and second highest for channel dredging). Based on the coral reef ecological background vector layer, the spatial proximity between each patch and the coral reef is calculated. Specifically, Euclidean distance can be used to calculate the distance *s* from each marine use activity patch to the nearest coral reef distribution area. Then, the initial influence coefficient and spatial proximity are coupled and calculated, for example, using an exponential decay model or a linear decay model, to generate a marine use activity disturbance intensity index that reflects the spatial decay effect. The formula can be expressed as: , where k is the attenuation coefficient; s is the distance from each marine activity patch to the nearest coral reef distribution area.

[0029] The disturbance intensity index of marine activities is assigned to the corresponding marine activity patch, and a time decay factor is introduced to weight and fuse multi-time series marine use change data to characterize the persistent impact of recent human activity interference, thereby generating a marine use change risk intensity raster with spatiotemporal dynamic characteristics. It should be noted that, in order to characterize the persistence of human activity impacts, a time decay factor is introduced to weight and fuse multi-time series marine use change data. For example, different weights are assigned to marine use activity patches from different years, with higher weights for recent activities (e.g., within the last 3 years) and lower weights for historical activities over time. The disturbance intensity index, obtained through spatiotemporal weighted fusion, is then assigned to the corresponding marine use activity patches, thereby generating a raster of marine use change risk intensity with spatiotemporal dynamic characteristics.

[0030] The marine use change risk intensity grid and the environmental parameter risk intensity grid are unified to the same spatial coordinate system and pixel scale. Based on the intensity of human activity disturbance represented by the marine use change risk intensity grid, a continuously distributed marine ecological resistance surface with resistance value increasing with the intensity of disturbance is constructed by spatial interpolation method.

[0031] It should be noted that, in order to spatially integrate all risk factors and lay the foundation for subsequent ecological connectivity analysis, the aforementioned marine use change risk intensity raster and the environmental parameter risk intensity raster need to be unified to the same spatial coordinate system and pixel scale. The key is to construct a continuously distributed marine ecological resistance surface based on the intensity of human activity disturbance represented by the marine use change risk intensity raster, using spatial interpolation methods (such as Inverse Distance Weighting (IDW) and Kriging). The core characteristic of this resistance surface is that the resistance value increases with the intensity of disturbance; that is, the stronger the human activity disturbance in a region, the greater the "resistance" or "cost" it poses to coral reef ecological processes (such as larval dispersal and species migration).

[0032] Furthermore, the standardized risk intensity grids are spatially overlaid with the aforementioned marine ecological resistance surface on a GIS platform, and an ecological sensitivity index is introduced to correct the overlay weights, generating a spatial distribution map of coral ecological risk that comprehensively reflects the space under stress to coral reefs, specifically: Based on the spatial distribution of coral reefs defined by the coral reef ecological background vector layer, the minimum cumulative resistance value between each grid cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined. Based on the principles of coral reef ecology, a resistance threshold is set, and areas where the minimum cumulative resistance value is lower than the resistance threshold are identified as the potential affected areas of the coral reef ecosystem. Within the potential affected area, the risk intensity grid of marine use change is weighted and summed with the risk intensity grid of each environmental parameter. The initial weights are determined based on the initial influence coefficients of each risk factor to obtain the initial comprehensive risk intensity value. Based on the minimum cumulative resistance value of each grid cell within the potential affected area, an ecological sensitivity index negatively correlated with the resistance value is constructed to characterize the susceptibility of coral reefs to the impact of distant human activities. The initial comprehensive risk intensity value is nonlinearly corrected using the ecological sensitivity index to generate a spatial distribution map of coral ecological risk.

[0033] Specifically, based on the spatial distribution of coral reefs defined by the coral reef ecological background vector layer, the minimum cumulative resistance value between each grid cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined. This involves: rasterizing the coral reef ecological background vector layer, identifying the pixel values ​​of the coral reef distribution area as unique source codes, and identifying the pixel values ​​of non-coral reef distribution areas as invalid values, thus generating a coral reef source raster; using the coral reef source raster as the calculation starting point and the marine ecological resistance surface as the cost surface, iterative calculations are performed using the cost distance method to initialize a cumulative resistance cost raster with the same range as the resistance surface. The initial resistance value of the cell corresponding to the coral reef source grid is zero, and the other cells are the maximum values ​​to be calculated. Each cell in the cumulative resistance cost grid is traversed, and its neighboring cells are searched based on the eight-neighbor direction. The cumulative resistance value of the current cell is added to the resistance value of the marine ecological resistance surface at the location of the neighboring cells to generate a series of potential cumulative resistance values. The minimum value among the potential cumulative resistance values ​​is compared and selected, and updated to the cumulative resistance cost grid of the target cell. Through multiple iterations until the cumulative resistance values ​​of all cells tend to stabilize, the minimum cumulative resistance surface composed of the minimum cumulative resistance value is finally generated.

[0034] It is important to explain that, after standardizing the various risk factors and constructing the marine ecological resistance surface, the core of this invention lies in how to scientifically integrate this spatial data to generate a spatial distribution map that accurately and comprehensively reflects the multidimensional stresses experienced by coral reefs. Existing simple grid overlay and summation methods have significant drawbacks: firstly, they assume that the impact on coral reefs is homogeneous across all regions, ignoring the attenuation effect of spatial distance and connectivity on the transmission of stress effects; secondly, they fail to reflect the varying sensitivities of the coral reef ecosystem itself to human disturbances from different distances and pathways. This leads to risk assessment results that may exaggerate the impact of distant or isolated areas while underestimating the cumulative effects within highly connected channels.

[0035] Therefore, this embodiment provides an innovative spatial overlay and correction method. The specific implementation process is as follows: First, in order to quantify the spatial isolation effect, based on the spatial distribution of coral reefs defined by the coral reef ecological background vector layer, the minimum cumulative resistance value between each raster cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined. The specific implementation method of this step is as follows: In a GIS platform (such as the "Cost Distance" tool in ArcGIS or the relevant module of the open-source software GRASS GIS), the coral reef ecological background vector layer is rasterized, and a unique source code (such as a value of 1) is assigned to the cells of the coral reef distribution area, and the non-distribution area is set to NoData, generating a coral reef source raster. Subsequently, starting from this source raster, and using the previously constructed marine ecological resistance surface (where the resistance value represents the ease of ecological passage) as the cost surface, a cumulative resistance cost raster with the same range as the resistance surface is initialized using the cost distance method. The initial value of the cell corresponding to the source raster is set to 0, and the remaining cells are set to the maximum value (such as 999999). Next, each cell is traversed along eight neighborhood directions, and the cumulative resistance value of its neighboring cells is added to the resistance value of the marine ecological resistance surface corresponding to that location to obtain a series of potential cumulative resistance values. The minimum value is always updated in the target cell. Through iterative calculation until the values ​​of all cells are stable, a minimum cumulative resistance surface is finally generated. Each value on this surface represents the total ecological resistance that needs to be overcome from that location to the nearest coral reef habitat.

[0036] Then, a resistance threshold is set based on coral reef ecology principles (e.g., this threshold can be determined from literature or empirical data based on the effective dispersal distance of coral larvae or the migratory capacity of typical marine organisms). Areas with minimum cumulative resistance values ​​below this threshold are identified as the potential affected areas of the coral reef ecosystem. This step limits the assessment scope to ecologically relevant spaces, excluding areas that are essentially isolated from the coral reef due to significant ecological resistance, making the assessment more scientific and targeted. Within the defined potential affected areas, preliminary risk overlay is performed. The risk intensity grid of marine change is weighted and summed with the risk intensity grids of each environmental parameter. The initial weights can be determined based on the initial impact coefficients of each risk factor (e.g., weights are determined using the analytic hierarchy process), thus obtaining the initial comprehensive risk intensity value for each grid cell.

[0037] However, the initial overlay results still did not reflect differences in spatial susceptibility. Therefore, this invention introduces a key correction factor: an ecological sensitivity index negatively correlated with the minimum cumulative resistance value of each grid cell within the potentially affected area. For example, this index can be designed as: Ecological Sensitivity Index = 1 / (1 + Minimum Cumulative Resistance Value). This index characterizes that the closer an area is to the coral reef and the better its ecological connectivity (i.e., the lower the minimum cumulative resistance value), the more susceptible the coral reef is to the impact of human activities in that area.

[0038] Finally, the initial composite risk intensity value is nonlinearly corrected using the ecological sensitivity index. One feasible approach is to multiply the two values: final risk value = initial composite risk intensity value × ecological sensitivity index. This correction highlights the risk level of areas that, even with low initial risk intensity, possess potentially significant impacts due to their excellent connectivity with coral reef habitats (high sensitivity); conversely, the final risk value of areas with high initial risk intensity but far from coral reefs and poor connectivity will be appropriately reduced.

[0039] Furthermore, based on the aforementioned spatial distribution map of coral ecological risks, potential ecological corridors connecting remaining suitable habitats within risk zones are identified. The risk of corridor disruption due to changes in marine use on coral reef ecological connectivity is assessed based on the cumulative resistance values ​​traversed by these corridors. Specifically: The spatial distribution map of coral ecological risk is reclassified, and areas with risk values ​​below a preset risk threshold are identified as remaining suitable habitats. Spatially isolated suitable patches are extracted from these areas as sources and targets for ecological corridor connections. It should be noted that the aforementioned coral ecological risk spatial distribution map is reclassified, and a preset risk threshold is set (this threshold can be determined through statistical methods such as the natural breakpoint method, or based on known critical risk values ​​leading to coral decline in historical data). Areas with risk values ​​below the threshold are identified as remaining suitable habitats. Low-risk areas are key patches for coral survival and recovery. Using GIS spatial analysis tools (such as "raster to polygon" and "fragmentation removal" functions), spatially isolated suitable patches that meet a certain minimum area requirement are extracted from suitable areas and defined as source patches and target patches that need to be connected by ecological corridors.

[0040] Using the minimum cumulative resistance surface as the cost basis, the minimum resistance path between every two suitable patches is calculated in the GIS platform. The trajectory of the minimum resistance path is determined by minimizing the sum of the cumulative resistance values ​​of all pixels on the path. It's important to note that the minimum cumulative resistance surface is used as the cost basis for finding the optimal path connecting isolated patches. In a GIS platform, a minimum path algorithm (such as the "Cost Path" tool in ArcGIS) is used to calculate the minimum resistance path between every two suitable patches. The core principle is that the path's trajectory is determined by minimizing the sum of the cumulative resistance values ​​of all raster cells traversed by the path. This simulates how organisms (such as coral larvae) instinctively choose the path with the lowest energy consumption and highest survival probability when selecting a diffusion path.

[0041] By aggregating all calculated minimum resistance paths, a potential ecological corridor network connecting suitable patches is constructed. For each corridor in the potential ecological corridor network, the resistance value in the minimum cumulative resistance surface corresponding to the corridor crossing path is extracted, and the average or maximum value is calculated as the corridor crossing resistance value of the corresponding corridor. It should be noted that, in order to quantify the accessibility pressure faced by each corridor, the resistance value of the minimum cumulative resistance surface corresponding to the traversal path is extracted for each corridor in the potential ecological corridor network. In practice, the corridor path can be converted into a linear element, and the average (reflecting the overall accessibility level) or maximum (reflecting the maximum bottleneck point on the path) resistance value of the area traversed by the path can be calculated using regional statistical methods as the corridor traversal resistance value.

[0042] The corridor crossing resistance value is compared with the corridor resistance threshold determined based on historical ecological data. Different corridor breakage risk levels are classified according to the degree to which the corridor resistance threshold is exceeded, thereby completing the assessment of the impact of changes in marine use on ecological connectivity.

[0043] It should be noted that, to achieve a quantitative risk assessment, the calculated corridor crossing resistance value is compared with a corridor resistance threshold determined based on historical ecological data. This resistance threshold is calibrated by studying the historical resistance status of known corridors that have experienced ecological functional disruption. Different corridor disruption risk levels (e.g., "low risk," "medium risk," "high risk") are assigned based on the degree to which the corridor resistance threshold is exceeded (e.g., categorized as "slightly exceeded," "moderately exceeded," "severely exceeded," etc.). This process transforms the abstract concept of connectivity into a concrete and measurable disruption risk level, thereby completing an objective and spatially explicit assessment of the impacts of human activities such as marine use changes on ecological connectivity.

[0044] Furthermore, based on the aforementioned spatial distribution map of coral ecological risks and the assessment results of corridor fracture risks, core coral protection and monitoring areas and ecological corridor restoration areas of different priorities are delineated, and targeted monitoring plans are generated, specifically: The areas with the lowest risk level in the spatial distribution map of coral ecological risk are identified as core habitat patches, and the patches connected by the corridors with the highest risk level of corridor fracture are marked as key connectivity nodes. The core habitat patches and the key connecting nodes are spatially superimposed and merged to form a preliminary protection core area. Based on the risk value of the coral ecological risk spatial distribution map of the area where the preliminary protection core area is located and the risk level of the corridor fracture, a weighted calculation is performed to generate a comprehensive protection priority index. Based on the numerical distribution of the comprehensive protection priority index, the initial protection core area is divided into coral protection and supervision core areas of different priorities. For corridors in the potential ecological corridor network that are at risk of fracture, the restoration urgency index of each corridor is determined based on the magnitude of the corresponding corridor crossing resistance value and the risk gradient of the coral ecological risk spatial distribution map it crosses. Based on the restoration urgency index, ecological corridor restoration areas are delineated, and combined with the priority of the coral protection and supervision core area, targeted supervision recommendation maps containing spatial scope, protection level, and restoration sequence are generated.

[0045] It should be noted that the areas with the lowest risk level in the coral ecological risk spatial distribution map are identified as core habitat patches (i.e., areas with the best current health status and requiring strict protection). Simultaneously, patches connected by corridors with the highest corridor breakage risk level are marked as key connectivity nodes (these patches are crucial for maintaining the connectivity of the entire ecological network; their loss could lead to network fragmentation). In the GIS platform, spatial overlay tools (such as "join" or "merge") are used to spatially overlay and merge the core habitat patches and key connectivity nodes, forming a preliminary protection core area. To scientifically prioritize the preliminary protection core areas, a weighted calculation is performed based on the risk value of the coral ecological risk spatial distribution map of the area and the corridor breakage risk level of the associated corridors. For example, a comprehensive protection priority index can be constructed, calculated as: Priority Index = (α * reciprocal of risk value) + (β * corridor breakage risk level), where α and β are predefined weighting coefficients used to balance the relative importance of habitat protection and connectivity maintenance. Based on the numerical distribution of the index (such as using the natural breakpoint method for classification), the initial protection core area is divided into coral protection and supervision core areas of different priorities (such as primary core area and secondary core area), providing a basis for differentiated supervision.

[0046] For corridors in the potential ecological corridor network that are at risk of fracture, a restoration urgency index is determined for each corridor. This index is determined based on the magnitude of the corridor's crossing resistance value (higher resistance values ​​indicate a more urgent need for restoration) and the risk gradient of the coral ecological risk spatial distribution map it crosses (e.g., the importance level of the core habitat patches connected at both ends of the corridor, or the length of the corridor crossing high-risk areas). The resistance value and risk gradient can be standardized and weighted to generate the restoration urgency index. Then, based on the restoration urgency index, ecological corridor restoration zones (e.g., urgent restoration zones, general restoration zones) are delineated. Combined with the priority of the coral protection and supervision core areas, a targeted supervision recommendation map is generated in the GIS platform. The map not only includes a spatial extent layer but should also clearly specify the protection level and restoration sequence recommendations for each area in the attribute table (e.g., "Level 1 core area, all development activities are prohibited"; "Urgent restoration zone, it is recommended to start dredging and artificial reef placement projects within one year").

[0047] In practical applications, this method also includes: Based on the minimum cumulative resistance surface, the resistance values ​​of all grid cells on the potential ecological corridor path are extracted, and the grid cells with resistance values ​​higher than the corridor resistance threshold are identified as corridor resistance bottleneck cells. Using these corridor resistance bottleneck units as the starting point for tracing, the reverse path tracing algorithm is used to reverse the deduction along the direction of decreasing resistance value on the minimum cumulative resistance surface, and trace to the coastal land grid where the resistance value jumps, thereby locating the land-based resistance source area that has a dominant contribution to the formation of high resistance in the corridor. By integrating digital elevation model data and using the located land-based resistance source area as the sink point, hydrological analysis tools are used to simulate its upstream catchment area to generate key land-based catchment areas. Within key land-based water catchment areas, high-resolution land use data are overlaid to identify potential pollution source patches corresponding to land use types with high-intensity human activities (such as industrial and urban construction land and contiguous farmland areas). By coupling analysis of potential pollution source patches with their spatial location, slope, and proximity to waterway networks within key land-based catchment areas, a source control recommendation layer is generated that identifies priority control areas and specific types of control measures.

[0048] It should be noted that in the GIS platform, based on the minimum cumulative resistance surface, the resistance values ​​of all raster cells covered by the potential ecological corridor path are extracted using regional statistical functions. Cells with resistance values ​​higher than the determined corridor resistance threshold (e.g., 500) are identified as "corridor resistance bottleneck cells," which are the direct spatial locations leading to the disruption of corridor connectivity. Starting from these corridor resistance bottleneck cells, a reverse path tracing algorithm (e.g., using the "cost path" tool in GIS, but setting the starting point as the bottleneck cell, the target as the land area, and using the minimum cumulative resistance surface as the reverse cost surface) is used to reverse the tracing along the direction of decreasing resistance values ​​until a coastal land raster with a significant step increase in resistance value is found. This locates the "land-based resistance source area" that plays a dominant role in the formation of high corridor resistance. This area marks the land-sea boundary where human activity disturbance begins to accumulate significantly. Then, integrating digital elevation model data, and using the hydrological analysis module of GIS (such as ArcGIS's Hydrology toolset), with the located land-based resistance source area as the catchment point, the upstream catchment area is simulated by filling depressions, calculating water flow direction and accumulation, and generating a "critical land-based catchment area." This catchment area defines the complete terrestrial space where pollutants flow into the source area via surface runoff. Within the critical land-based catchment area, high-resolution land use data (such as national land survey data) is overlaid, and through attribute queries and spatial filtering, "potential pollution source patches" corresponding to high-intensity human activity land types such as industrial and urban construction land and contiguous farmland are identified. By coupling analysis of potential pollution source patches with their spatial attributes (such as location and slope) in key land-based catchment areas and their proximity to waterway networks (for example, patches near main rivers or located in steep slope areas are given higher priority), a source control recommendation layer is generated that identifies priority control areas and specific types of control measures (such as setting up buffer zones and upgrading drainage facilities), thereby achieving a closed loop from discovering marine ecological problems to guiding precise terrestrial governance.

[0049] In practical applications, this method further includes: based on the sea surface temperature risk intensity grid in the coral ecological risk spatial distribution map, combined with regional sea surface temperature prediction data, spatial overlay calculations are performed to generate coral bleaching risk probability distribution maps under different warming scenarios; in the coral bleaching risk probability distribution maps, areas whose risk probability is consistently below the critical level under a preset warming threshold are identified, and spatial intersection calculations are performed between these areas and the core area for coral protection and supervision to extract candidate core patches with heat stress resistance potential within and around the core protected area; with the candidate core patches as the center, hydrodynamic models are used to simulate the water retention time distribution characteristics under typical tides and currents, and surrounding waters with longer retention times are identified as potential artificial intervention areas; based on the water depth and flow field characteristics of the potential artificial intervention areas, corresponding emergency cooling measures are configured, including the deployment of water disturbance devices or upwelling guidance facilities at specific locations to enhance local heat exchange.

[0050] It should be noted that, based on the sea surface temperature risk intensity raster in the aforementioned coral ecological risk spatial distribution map (which already contains the spatial pattern of current temperature stress), and combined with regional sea surface temperature prediction data under different future scenarios (obtained from downscaling of global or regional climate models), spatial overlay calculations are performed in the GIS platform using a raster calculator. Based on the known ecological relationship model between coral bleaching and accumulated heat (such as the DHW temperature cycle model), a probability distribution map of coral bleaching risk under different warming scenarios is generated. This map uses probability values ​​to characterize the bleaching risk of each sea area in a specific future period.

[0051] In the coral bleaching risk probability distribution map, a preset warming threshold is set, and areas whose bleaching risk probability is consistently below a certain critical level (e.g., below 0.3) at this threshold are identified. Spatial intersection calculations are performed between these low-risk areas and the coral protection and monitoring core areas delineated in the preceding steps to extract areas within and around the core protected area that are currently protected and are likely to maintain low heat stress under future climate scenarios. These areas are defined as candidate core patches for climate refuge.

[0052] Centered on these core climate refuge candidate patches, a simulation range (e.g., a buffer zone with a radius of 5 km) is defined. Hydrodynamic models (such as MIKE, FVCOM, or Delft3D) are used to simulate the water residence time distribution characteristics of this region under typical tidal and ocean current conditions. The simulation results will generate a water residence time distribution grid. Surrounding waters with longer residence times (i.e., areas with slow water exchange and high heat accumulation) are identified as potential areas for artificial intervention; these areas are key locations for implementing emergency cooling measures.

[0053] Finally, based on the water depth and flow field characteristics of the potential artificial intervention area (such as those extracted from hydrological model outputs), a corresponding emergency cooling measure layout scheme is configured. For example, for areas with shallow water depth and weak flow field, the scheme may include deploying water disturbance devices (such as solar-powered mixing layer pumps) to promote vertical mixing; for areas near continental slopes or specific seafloor topography, the scheme may include deploying upwelling guidance facilities (such as artificial upwelling pipes) to guide the low-temperature bottom water to the surface, thereby enhancing local heat exchange and creating a thermal buffer zone for nearby climate refuge candidate core patches.

[0054] like Figure 2 As shown, the second aspect of the present invention discloses a GIS-based coral ecology multi-risk overlay analysis system. The coral ecology multi-risk overlay analysis system includes a memory and a processor. The memory stores a coral ecology multi-risk overlay analysis method program. When the coral ecology multi-risk overlay analysis method program is executed by the processor, the steps of the coral ecology multi-risk overlay analysis method described in any one of the claims are implemented.

[0055] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0056] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0057] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0058] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0059] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0060] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A GIS-based method for analyzing multiple risks in coral ecosystems, characterized in that... Includes the following steps: The baseline data on coral reef distribution was obtained, and remote sensing data on marine use changes and marine environmental parameters were collected simultaneously over multiple time periods to form a set of multi-source risk factors for coral ecology. Based on the aforementioned set of multi-source risk factors for coral ecology, a standardized risk intensity grid for each risk factor is generated, and a marine ecological resistance surface reflecting the degree of human activity interference is constructed for the marine use change data. The standardized risk intensity grids and the marine ecological resistance surface are spatially overlaid on the GIS platform, and the ecological sensitivity index is introduced to correct the overlay weights, generating a spatial distribution map of coral ecological risk that comprehensively reflects the space of coral reef stress. Based on the aforementioned spatial distribution map of coral ecological risks, potential ecological corridors connecting the remaining suitable habitats in the risk zones are identified, and the risk of corridor disruption due to changes in marine use is assessed based on the cumulative resistance values ​​traversed by the corridors. Based on the spatial distribution map of coral ecological risks and the assessment results of corridor fracture risks, core areas for coral protection supervision and ecological corridor restoration areas with different priorities are delineated, and targeted supervision recommendations are generated.

2. The GIS-based method for analyzing multiple risks in coral ecosystems according to claim 1, characterized in that, Baseline data on coral reef distribution was obtained, and remote sensing data on marine use changes and marine environmental parameters were collected simultaneously over multiple time periods. These data together constitute a multi-source risk factor set for coral ecosystems, specifically: By importing historical survey data and remote sensing interpretation results into the GIS platform, baseline data on the distribution range and health of coral reefs are obtained. After spatial registration and vectorization, a coral reef ecological baseline vector map layer is generated. By simultaneously retrieving satellite remote sensing images from multiple time series, we can identify marine use activity patches, including land reclamation, channel dredging, and aquaculture area expansion, and construct a multi-time series remote sensing dataset of marine use changes. Based on the spatial range of the coral reef ecological background vector layer, marine environmental parameter data covering the target area are extracted, including sea surface temperature, chlorophyll concentration and suspended sediment concentration. Then, spatiotemporal resolution normalization and missing value imputation are performed to obtain a standardized environmental parameter raster set. The multi-time series remote sensing dataset of marine use changes is used to track change trajectories and quantify intensity. Based on the known ecological impact mechanisms of various marine use activities on coral reefs, an initial impact coefficient is assigned to each type of activity to generate a list of ecological disturbances caused by marine use changes. The coral reef ecological background vector layer, the standardized environmental parameter raster set, and the list of ecological disturbances caused by changes in marine use are spatiotemporally correlated and fused to form a multi-source risk factor set for coral ecology.

3. The GIS-based method for analyzing multiple risks in coral ecosystems according to claim 1, characterized in that, Based on the aforementioned set of multi-source risk factors for coral ecology, a standardized risk intensity raster is generated for each risk factor. Furthermore, for marine use change data, a marine ecological resistance surface reflecting the degree of human disturbance is constructed, specifically as follows: Based on the known ecological relationship between various environmental parameters and coral reef stress, risk thresholds are set for each parameter in the standardized environmental parameter grid set, and the corresponding parameter values ​​are converted into standardized risk intensity values ​​through a nonlinear normalization function to generate risk intensity grids corresponding to each environmental parameter. For the aforementioned list of ecological disturbances caused by changes in marine use, the spatial location and initial impact coefficient of each marine use activity patch are extracted, and the spatial proximity between each patch and the coral reef is calculated based on the coral reef ecological background vector layer. The initial impact coefficient and spatial proximity are coupled and calculated to generate a marine use activity disturbance intensity index that reflects the spatial attenuation effect. The disturbance intensity index of marine activities is assigned to the corresponding marine activity patch, and a time decay factor is introduced to weight and fuse multi-time series marine use change data to characterize the persistent impact of recent human activity interference, thereby generating a marine use change risk intensity raster with spatiotemporal dynamic characteristics. The marine use change risk intensity grid and the environmental parameter risk intensity grid are unified to the same spatial coordinate system and pixel scale. Based on the intensity of human activity disturbance represented by the marine use change risk intensity grid, a continuously distributed marine ecological resistance surface with resistance value increasing with the intensity of disturbance is constructed by spatial interpolation method.

4. The GIS-based method for analyzing multiple risks in coral ecosystems according to claim 1, characterized in that, The standardized risk intensity grids are spatially overlaid with the marine ecological resistance surface on a GIS platform, and an ecological sensitivity index is introduced to correct the overlay weights, generating a spatial distribution map of coral ecological risk that comprehensively reflects the space under stress to coral reefs. Specifically: Based on the spatial distribution of coral reefs defined by the coral reef ecological background vector layer, the minimum cumulative resistance value between each grid cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined. Based on the principles of coral reef ecology, a resistance threshold is set, and areas where the minimum cumulative resistance value is lower than the resistance threshold are identified as the potential affected areas of the coral reef ecosystem. Within the potential affected area, the risk intensity grid of marine use change is weighted and summed with the risk intensity grid of each environmental parameter. The initial weights are determined based on the initial influence coefficients of each risk factor to obtain the initial comprehensive risk intensity value. Based on the minimum cumulative resistance value of each grid cell within the potential affected area, an ecological sensitivity index negatively correlated with the resistance value is constructed to characterize the susceptibility of coral reefs to the impact of distant human activities. The initial comprehensive risk intensity value is nonlinearly corrected using the ecological sensitivity index to generate a spatial distribution map of coral ecological risk.

5. The GIS-based method for analyzing multiple risks in coral ecosystems according to claim 4, characterized in that, Based on the spatial distribution of coral reefs defined by the coral reef ecological background vector layer, the minimum cumulative resistance value between each grid cell on the marine ecological resistance surface and the nearest coral reef distribution area is determined, specifically as follows: The coral reef ecological background vector layer is rasterized, and the pixel values ​​of the coral reef distribution area are identified as unique source codes, while the pixel values ​​of the non-coral reef distribution area are identified as invalid values, thus generating a coral reef source raster. Using the coral reef source grid as the starting point and the marine ecological resistance surface as the cost surface, the cost distance method is used for iterative calculation. A cumulative resistance cost grid with the same range as the resistance surface is initialized, wherein the initial resistance value of the cell corresponding to the coral reef source grid is zero, and the other cells are the maximum values ​​to be calculated. Traverse each cell in the cumulative resistance cost grid, search its neighboring cells based on the eight-neighbor direction, and accumulate the cumulative resistance value of the current cell with the marine ecological resistance surface resistance value of the neighboring cell to generate a series of potential cumulative resistance values. The minimum value among the potential cumulative resistance values ​​is compared and selected, and then updated in the cumulative resistance cost grid of the target cell. This process is repeated multiple times until the cumulative resistance values ​​of all cells tend to stabilize, and finally, the minimum cumulative resistance surface formed by the minimum cumulative resistance value is generated.

6. The GIS-based method for analyzing multiple risks in coral ecosystems according to claim 1, characterized in that, Based on the aforementioned spatial distribution map of coral ecological risks, potential ecological corridors connecting remaining suitable habitats within risk zones are identified. The risk of corridor disruption due to changes in marine use on coral reef ecological connectivity is assessed based on the cumulative resistance values ​​traversed by these corridors. Specifically: The spatial distribution map of coral ecological risk is reclassified, and areas with risk values ​​below a preset risk threshold are identified as remaining suitable habitats. Spatially isolated suitable patches are extracted from these areas as sources and targets for ecological corridor connections. Using the minimum cumulative resistance surface as the cost basis, the minimum resistance path between every two suitable patches is calculated in the GIS platform. The trajectory of the minimum resistance path is determined by minimizing the sum of the cumulative resistance values ​​of all pixels on the path. By aggregating all calculated minimum resistance paths, a potential ecological corridor network connecting suitable patches is constructed. For each corridor in the potential ecological corridor network, the resistance value in the minimum cumulative resistance surface corresponding to the corridor crossing path is extracted, and the average or maximum value is calculated as the corridor crossing resistance value of the corresponding corridor. The corridor crossing resistance value is compared with the corridor resistance threshold determined based on historical ecological data. Different corridor breakage risk levels are classified according to the degree to which the corridor resistance threshold is exceeded, thereby completing the assessment of the impact of changes in marine use on ecological connectivity.

7. The GIS-based method for analyzing multiple risks in coral ecosystems according to claim 1, characterized in that, Based on the spatial distribution map of coral ecological risks and the assessment results of corridor fracture risks, core coral protection and regulatory zones and ecological corridor restoration zones of different priorities were delineated, and targeted regulatory recommendations were generated, specifically: The areas with the lowest risk level in the spatial distribution map of coral ecological risk are identified as core habitat patches, and the patches connected by the corridors with the highest risk level of corridor fracture are marked as key connectivity nodes. The core habitat patches and the key connecting nodes are spatially superimposed and merged to form a preliminary protection core area. Based on the risk value of the coral ecological risk spatial distribution map of the area where the preliminary protection core area is located and the risk level of the corridor fracture, a weighted calculation is performed to generate a comprehensive protection priority index. Based on the numerical distribution of the comprehensive protection priority index, the initial protection core area is divided into coral protection and supervision core areas of different priorities. For corridors in the potential ecological corridor network that are at risk of fracture, the restoration urgency index of each corridor is determined based on the magnitude of the corresponding corridor crossing resistance value and the risk gradient of the coral ecological risk spatial distribution map it crosses. Based on the restoration urgency index, ecological corridor restoration areas are delineated, and combined with the priority of the coral protection and supervision core area, targeted supervision recommendation maps containing spatial scope, protection level, and restoration sequence are generated.

8. A GIS-based multi-risk overlay analysis system for coral ecosystems, characterized in that, The coral ecological multi-risk overlay analysis system includes a memory and a processor. The memory stores a coral ecological multi-risk overlay analysis method program. When the coral ecological multi-risk overlay analysis method program is executed by the processor, the coral ecological multi-risk overlay analysis method steps as described in any one of claims 1 to 7 are implemented.

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