A method, system, and electronic equipment for grading and evaluating the blue carbon sequestration potential of coastal wetlands.

Abstract

This invention provides a method, system, and electronic equipment for grading and evaluating the blue carbon sequestration potential of coastal wetlands. The invention acquires multi-source spatiotemporal data of the target coastal zone and its associated watersheds, preprocesses this data to construct a unified spatial scale dataset of evaluation units, and constructs a dynamic upstream pressure index for each evaluation unit, extracting the spatial explicit sensitivity of each unit to changes in upstream pressure and the ecological suitability results of each unit. Under a preset pressure mitigation scenario, the blue carbon sequestration potential value of each evaluation unit is calculated. The blue carbon sequestration potential values ​​of each evaluation unit are then screened, standardized, and graded to generate a grading and evaluation result for the blue carbon sequestration potential of coastal wetlands. This invention can identify highly sensitive areas with a real response basis to pressure mitigation; by introducing the proportion of wetland type structure and upstream pressure, it quantifies and identifies the differentiated response intensity of mangroves, salt marshes, and tidal flats to changes in upstream pressure, and can identify priority areas for blue carbon sequestration in coastal wetlands.

Description

Technical Field

[0001] This invention relates to the fields of ecological environment assessment, remote sensing information processing, geographic information analysis and coastal zone resource management, and in particular to a method, system and electronic equipment for graded evaluation of blue carbon sequestration potential in coastal wetlands. Background Technology

[0002] Coastal wetlands are crucial transitional zones where land, rivers, and oceans interact, possessing multiple ecological functions including carbon sequestration, pollution interception, shoreline protection, biological habitat, and hydrological regulation. They are typical blue carbon ecosystems. Ecological units such as mangroves, salt marshes, and tidal flats form important carbon sinks through vegetation production, sedimentary burial, and organic carbon accumulation, playing a vital role in addressing climate change, maintaining regional ecological security, and supporting coastal zone restoration and management. Existing methods for assessing or prioritizing coastal wetland restoration typically focus on current ecological quality, habitat suitability, or static carbon storage calculations, which have the following shortcomings: First, existing technologies are insufficient in characterizing the land-sea linkage process. Most methods only focus on the natural environmental characteristics within the evaluation area or local shorelines, lacking a comprehensive expression of the transmission, accumulation, and delayed effects of human activities in the upstream basin to downstream coastal wetlands via water catchment pathways. This makes it difficult to truly represent the exogenous stresses borne by estuary-nearshore wetlands.

[0003] Second, existing technologies are insufficient in identifying the heterogeneous responses of different wetland types. Mangroves, salt marshes, and tidal flats differ significantly in terms of ecological structure, stability, resilience, and response mechanisms to external stresses. Traditional holistic regression or single empirical evaluation models often assume that the stress responses of different spatial units are consistent, making it difficult to extract explicit spatial vulnerabilities and key restoration areas.

[0004] Third, in existing technologies, suitability assessment and restoration potential assessment are disconnected. High suitability does not necessarily mean that significant carbon sequestration benefits can be generated after governance intervention. On the contrary, although some regions have the foundation for habitat restoration, if they are not sensitive to changes in upstream pressure, the marginal carbon sequestration benefits generated by restoration investment may be limited. Prioritizing based solely on suitability probability or current carbon storage is insufficient to support accurate management decisions.

[0005] Fourth, existing technologies lack detailed information on the graded evaluation process. Although some studies can obtain continuous model results, such as sensitivity values, suitability probabilities, and potential values, they have not yet provided a complete technical process from result screening, indicator standardization, grade classification to priority area extraction.

[0006] Therefore, there is an urgent need for a hierarchical evaluation method for the blue carbon sequestration potential of coastal wetlands that can couple upstream watershed pressure, wetland heterogeneous ecological response, spatial suitability constraints, and stress reduction scenario benefits. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method, system, and electronic equipment for grading and evaluating the blue carbon sequestration potential of coastal wetlands, thereby resolving the problems existing in the prior art.

[0008] The technical solution of this invention is: a method for grading and evaluating the blue carbon sequestration potential of coastal wetlands, comprising: Multi-source spatiotemporal data of the target coastal zone and its associated watersheds were acquired, and after preprocessing, a unified spatial scale evaluation unit dataset was constructed. Based on the topological connectivity between the target watershed and its upstream sub-watersheds, upstream land use pressure is identified, and a dynamic upstream pressure index for the evaluation unit is constructed. Based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, a heterogeneous time-delay response model is constructed to extract the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure. Dynamic covariates are read from preprocessed multi-source spatiotemporal data, and a spatial suitability evaluation model is constructed based on the dynamic covariates. Based on the spatial suitability evaluation model, the ecological suitability results and uncertainty characteristics of each evaluation unit are obtained. Under the pre-set upstream pressure mitigation scenario, the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation unit is coupled, and combined with the effective physical area, the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit are quantitatively assessed. Based on the probability distribution characteristics of the blue carbon sequestration potential, a two-dimensional feature space of expected return and downside risk is constructed for strategy partitioning. Combined with spatial topology, vector overall summarization is performed to generate a graded evaluation map of the blue carbon sequestration potential of coastal wetlands.

[0009] Preferably, the multi-source spatiotemporal data includes at least land use data, watershed topology data, coastal wetland type distribution data, ecological function characterization data, and climate and hydrological data.

[0010] As a preferred option, a unified spatial scale evaluation unit dataset is constructed, including: The target area is divided using a regular square grid as the standard evaluation unit; Based on the preprocessed coastal wetland type distribution data, spatial overlay and area statistics were performed on each standard evaluation unit, and the area proportions of mangroves, salt marshes and tidal flats in each standard evaluation unit were calculated. Based on the preprocessed ecological function characterization data, using the standard evaluation unit as the statistical unit, the spatial mean method was used to extract the ecological function characterization indicators for each year. The evaluation unit's geographic code, spatial coordinates, area proportion of various wetlands, annual ecological function characterization indicators, as well as topographic, climate, hydrological, and watershed topology information are integrated into an evaluation unit dataset.

[0011] Preferably, based on the topological connectivity between the target watershed and its upstream sub-watersheds, upstream land use pressure is identified, and a dynamic upstream pressure index for the evaluation unit is constructed; including: Unify land use data to the spatial scale corresponding to the evaluation unit; Based on land use data at a uniform scale, we identify stress land types that have a negative ecological impact on downstream coastal wetlands and assign different ecological stress weights to different stress land types. Based on the watershed topology, all upstream sub-watersheds of the target watershed are traced. The weighted pressure area of ​​each upstream sub-watershed and the total land area are statistically analyzed to calculate the dynamic upstream pressure index corresponding to the evaluation unit, i.e.: ; In the formula, represent Target watershed at any time The dynamic upstream pressure index; Represents the target watershed The entire set of upstream sub-basins; Representative set The first in One upstream sub-basin; The total number of land use or land cover types within the study area; Representing the A specific type of land use / cover; Representing the upstream sub-basin exist Time of the first The actual physical area of ​​cultivated land; Representing the The human activity pressure weighting coefficient corresponding to the land-farming category is a spatial static parameter that does not change with time. Representing the upstream sub-basin exist The total land area at any given time.

[0012] Preferably, a heterogeneous time-delay response model is constructed based on the dynamic upstream pressure index, wetland structure composition information, and control variables to extract the spatial explicit sensitivity of each evaluation unit to changes in upstream pressure; including: The ecological function characterization index of the evaluation unit is used as the dependent variable; the dynamic upstream pressure index is used as the explanatory variable; and spatiotemporal panel data are constructed by combining wetland structure composition information and climate and hydrological control variables; wherein the climate and hydrological control variables include at least one or more of the following: air temperature, precipitation, runoff, shortwave radiation and soil moisture. A heterogeneous time-delay response model is established based on the spatiotemporal panel data to identify the differentiated marginal responses of different wetland types to changes in upstream pressure. The spatial explicit sensitivity of each evaluation unit is calculated based on the parameter results of the heterogeneous time-delay response model, and evaluation units with an effective response basis to changes in upstream pressure are selected.

[0013] Preferably, the spatial suitability evaluation model is the maximum entropy model, and its expression is: ; As a preferred approach, under a pre-defined upstream pressure mitigation scenario, the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation units is coupled, and combined with the effective physical area, to quantitatively assess the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit, including: Scenarios for easing upstream pressure are set, including a 10% decrease in upstream pressure, a 20% decrease in upstream pressure, and a 30% decrease in upstream pressure; The expected increase in productivity per unit area is calculated based on the spatial explicit sensitivity of each evaluation unit, i.e.: Set up evaluation unit The spatial explicit sensitivity follows a normal distribution In the first In the Monte Carlo iteration, the sensitivity parameters are extracted. And calculate the incremental increase in productivity per unit area under this iteration. ,Right now: ; In the formula, The mean of the sensitivity expectation, The standard error of sensitivity is obtained using the Delta method; when sampling sensitivity When the negative response condition is not met, it is considered to have no ecological gain. .

[0014] Based on the expected productivity increase per unit area of ​​the evaluation unit, combined with the probability of ecological suitability and the effective area of ​​the evaluation unit, the blue carbon sequestration potential of each evaluation unit is calculated; that is: Assume that the probability of ecological suitability follows a truncated normal distribution, and on the t-th... In the next iteration, the suitability probability is drawn. And strictly bound by Within the interval. Combined with the effective area of ​​the evaluation unit. Calculate the blue carbon sequestration potential value under this iteration. : ; Pre-set Sub-independent iterations (such as) After that, the evaluation unit is obtained. The set of probability distributions of blue carbon sequestration potential Based on this, the expected mean value is extracted as the total potential for foreign exchange increase, and the frequency of potential values ​​being less than or equal to 0 is defined as the probability of downside risk.

[0015] Preferably, a two-dimensional feature space of expected return-downside risk is constructed based on the probability distribution characteristics of the blue carbon sequestration potential for strategy partitioning. This space is then combined with spatial topology for vector-based aggregation to generate a graded evaluation map of the blue carbon sequestration potential of coastal wetlands, including: The average expected exchange rate increase potential of each evaluation unit is used as the expected return indicator, and the probability of downside risk is used as the risk constraint indicator. Select a predetermined percentage of expected return quantiles as the return threshold, and set a tolerable downside risk probability limit as the risk threshold. Candidate evaluation units are classified into four levels based on threshold values ​​to obtain a four-level potential area for capital accumulation. Patch aggregation is performed on spatially adjacent evaluation units to obtain priority remediation areas and classification results.

[0016] Secondly, the present invention provides a graded evaluation system for the blue carbon sequestration potential of coastal wetlands, comprising: The data acquisition unit is used to acquire multi-source spatiotemporal data of the target coastal zone and its associated watersheds, and after preprocessing, constructs an evaluation unit dataset with a unified spatial scale. The pressure index construction module is used to identify upstream land use pressure based on the topological connectivity between the target watershed and its upstream sub-watersheds, and to construct a dynamic upstream pressure index for the evaluation unit. The sensitivity extraction module is used to construct a heterogeneous time-delay response model based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, and to extract the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure. The suitability assessment module is used to read dynamic covariates from preprocessed multi-source spatiotemporal data, construct a spatial suitability assessment model based on the dynamic covariates, and obtain the ecological suitability results and uncertainty characteristics of each assessment unit based on the spatial suitability assessment model. The potential estimation module is used to couple the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation unit under a preset upstream pressure mitigation scenario, and combine the effective physical area to quantitatively assess the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit. The grading module is used to construct a two-dimensional feature space of expected return and downside risk based on the probability distribution characteristics of the blue carbon sequestration potential, perform strategy partitioning, and combine spatial topology for vector overall summarization to generate a grading evaluation map of the blue carbon sequestration potential of coastal wetlands.

[0017] Thirdly, the present invention provides an electronic device, comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor. When the computer program is executed by at least one processor, it implements a graded evaluation method for the blue carbon sequestration potential of coastal wetlands.

[0018] The beneficial effects of this invention are as follows: 1. This invention integrates upstream basin pressure, heterogeneous ecological response of coastal wetlands, spatial suitability constraints, and scenario-based stress reduction benefits to achieve integrated analysis of land, watershed, and sea areas. 2. This invention can identify highly sensitive areas with a real response basis to pressure relief, avoiding bias caused by ranking based solely on suitability or static carbon reserves; 3. This invention introduces an interaction term between the proportion of wetland type structure and upstream pressure to quantify and identify the differentiated response intensity of different wetland units such as mangroves, salt marshes, and tidal flats to changes in upstream pressure, extracts spatial explicit sensitivity, and solves the problems of traditional models assuming homogeneous spatial response and being unable to locate highly sensitive restoration units. 4. This invention unifies the calculation of marginal carbon sequestration benefits after pressure relief, habitat restoration feasibility, and spatial scale constraints, avoiding decision-making bias caused by prioritizing based solely on suitability or current carbon storage. 5. This invention transforms the results of a continuous model into four levels of potential zones and priority remediation patches through sensitivity screening, suitability screening, area screening, standardization, and grading. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the method of Embodiment 1 of the present invention; Figure 2 This is a framework diagram of the system in Embodiment 2 of the present invention; In the diagram, 100 is the data acquisition unit; 200 is the stress index construction module; 300 is the sensitivity extraction module; 400 is the suitability evaluation module; 500 is the potential estimation module; and 600 is the grading module. Detailed Implementation

[0020] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings: like Figure 1 As shown in the figure, this embodiment provides a graded evaluation method for the blue carbon sequestration potential of coastal wetlands, including: S1: Acquire multi-source spatiotemporal data of the target coastal zone and its associated watershed, and construct a unified spatial scale evaluation unit dataset after preprocessing; S2: Based on the topological connectivity between the target watershed and its upstream sub-watersheds, upstream land use pressure is identified, and a dynamic upstream pressure index for the evaluation unit is constructed. S3: Based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, construct a heterogeneous time-delay response model and extract the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure. S4: Read dynamic covariates from preprocessed multi-source spatiotemporal data, construct a spatial suitability evaluation model based on the dynamic covariates, and obtain the ecological suitability results and uncertainty characteristics of each evaluation unit based on the spatial suitability evaluation model; S5: Under the pre-set upstream pressure relief scenario, the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation unit is coupled, and combined with the effective physical area, the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit are quantitatively assessed. S6: Based on the probability distribution characteristics of the blue carbon sequestration potential, construct a two-dimensional feature space of expected return and downside risk for strategy partitioning, and combine spatial topology for vector overall summarization to generate a graded evaluation map of the blue carbon sequestration potential of coastal wetlands.

[0021] In this embodiment, the multi-source spatiotemporal data in step S1 includes at least land use data, watershed topology data, coastal wetland type distribution data, ecological function characterization data, and climate and hydrological data.

[0022] In this embodiment, step S1, constructing an evaluation unit dataset with a unified spatial scale, includes: The target area is divided using a regular square grid as the standard evaluation unit; Based on the preprocessed coastal wetland type distribution data, spatial overlay and area statistics were performed on each standard evaluation unit, and the area proportions of mangroves, salt marshes and tidal flats in each standard evaluation unit were calculated. Based on the preprocessed ecological function characterization data, using standard evaluation units as statistical units, the spatial mean method was used to extract ecological function characterization indicators for each year, including one or more combinations of net primary productivity, vegetation index, aboveground biomass, and carbon sink proxy indicators.

[0023] The evaluation unit's geographic code, spatial coordinates, area proportion of various wetlands, annual ecological function characterization indicators, as well as topographic, climate, hydrological, and watershed topology information are integrated into an evaluation unit dataset.

[0024] In this embodiment, step S2 involves identifying upstream land use pressure based on the topological connectivity between the target watershed and its upstream sub-watersheds, and constructing a dynamic upstream pressure index for the evaluation unit; including: S21. Unify land use data to the spatial scale corresponding to the evaluation unit; S22. Based on land use data at a uniform scale, identify stress land types that have a negative ecological impact on downstream coastal wetlands, and assign different ecological stress weights to different stress land types. In this embodiment, the land types under pressure include construction land, irrigated farmland or paddy fields, dry land, herbaceous mulch farmland, and shrubland, orchards, or woody economic forests; wherein: The ecological pressure weight for impermeable surfaces or construction land is 1.0; The ecological pressure weight for irrigated farmland or paddy fields is 0.4; The ecological pressure weight for dryland and herbaceous-covered cultivated land is 0.3; The ecological pressure weight for shrublands, orchards, or woody economic forests is 0.2.

[0025] S23. Based on the watershed topology, trace all upstream sub-watersheds of the target watershed, calculate the weighted pressure area of ​​each upstream sub-watershed and the total land area, and calculate the dynamic upstream pressure index corresponding to the evaluation unit, i.e.: ; In the formula, represent Target watershed at any time The dynamic upstream pressure index; Represents the target watershed The entire set of upstream sub-basins; Representative set The first in One upstream sub-basin; The total number of land use or land cover types within the study area; Representing the A specific type of land use or cover (e.g., construction land, farmland, forest land, water bodies, etc.); Representing the upstream sub-basin exist Time of the first The actual physical area of ​​cultivated land; Representing the The human activity pressure weight coefficient corresponding to the land type (usually assigned a value based on the intensity of the disturbance of the land type to the downstream water quality, water quantity or ecosystem, such as the highest weight for construction land and the lowest weight for nature reserves) is a spatial static parameter that does not change with time. Representing the upstream sub-basin exist The total land area at a given time (its value is usually equal to the sum of the areas of all land types within that sub-basin, i.e.) ).

[0026] In this embodiment, step S3 involves constructing a heterogeneous time-delay response model based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, and extracting the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to upstream pressure changes; including: S31. The ecological function characterization index of the evaluation unit is used as the dependent variable; the dynamic upstream pressure index is used as the explanatory variable; and spatiotemporal panel data is constructed by combining wetland structure composition information and climate and hydrological control variables; wherein the climate and hydrological control variables include at least one or more of the following: air temperature, precipitation, runoff, shortwave radiation and soil moisture.

[0027] S32. Based on the spatiotemporal panel data, establish a heterogeneous time-delay response model to identify the differentiated marginal responses of different wetland types to changes in upstream pressure.

[0028] S33. Calculate the spatial explicit sensitivity of each evaluation unit based on the parameter results of the heterogeneous time-delay response model, and screen the evaluation units that have an effective response basis to changes in upstream pressure.

[0029] In this embodiment, the heterogeneous time-delay response model is a two-way fixed-effects panel model, and its expression is: ; In the formula, As an evaluation unit At any moment Ecological function characterization values; Represents the intercept term of the model; This represents the baseline marginal effect of upstream pressure on NPP (i.e., the basic impact without taking into account differences in wetland composition). Represented as evaluation unit The watershed has a lag order. The dynamic upstream pressure index under the following lag order The value range is from 0 to 3 years; This represents the moderating coefficient of the k-th wetland composition on the upstream pressure effect; Representing the evaluation unit The Middle The multi-year average structural proportion of wetland-like types; Regression coefficients representing the dynamic wetland coverage ratio; Representative evaluation unit In the The annual dynamic wetland cover ratio is used to control the scale effect caused by interannual fluctuations in the physical area of ​​wetlands. Represents the coefficient vector corresponding to the control variables; superscript Indicates the transpose operation; For control variables; For spatial fixed effects; This is a time-fixed effect; For disturbance terms; In this embodiment, the calculation formula for the spatial explicit sensitivity of each evaluation unit to upstream pressure changes, extracted from the heterogeneous time-delay response model, is expressed as follows: ; In the formula, Representing the evaluation unit Spatially explicit sensitivity to changes in upstream pressure; Represents the coefficients of the interaction term composed of all wetland types ( A column vector consisting of ) Representing the evaluation unit The specific wetlands constitute a proportional vector; Furthermore, using the Delta method, based on the parameter covariance matrix estimated by the heterogeneous time-delay response model, the spatial explicit sensitivity of each evaluation unit is calculated. The standard error and confidence interval were determined using the Delta method. Based on the parameter covariance matrix estimated by the heterogeneous time-delay response model, the spatial explicit sensitivity of each evaluation unit was calculated. Standard error and confidence interval: ; In the formula, Representing the evaluation unit Estimated variance of spatial explicit sensitivity; This represents the variance of the baseline marginal effect estimate; This represents the adjustment coefficient of various wetlands ( The covariance matrix formed by ) This represents the covariance vector between the baseline marginal effect and the adjustment coefficients of each wetland. Representing the evaluation unit A specific wetland constitutes a proportional column vector; superscript This indicates the transpose operation.

[0030] Based on the above sensitivity estimation variance, the judgment condition expression for each evaluation unit showing significant negative sensitivity to upstream pressure is extracted as follows: ; In the formula, Representing the evaluation unit Standard error of spatial explicit sensitivity; Indicates a given significance level Below Distribution critical value; if the upper limit of the above confidence interval is strictly less than Then determine the evaluation unit It exhibits a significant negative sensitivity to changes in upstream pressure and is therefore retained.

[0031] In this embodiment, step S4 involves reading dynamic covariates from preprocessed multi-source spatiotemporal data, constructing a spatial suitability evaluation model based on the dynamic covariates, and obtaining the ecological suitability results and uncertainty characteristics of each evaluation unit based on the spatial suitability evaluation model; including: S41. Read dynamic covariates from the preprocessed multi-source spatiotemporal data, including the mean, standard deviation, and time trend term of the dynamic upstream pressure index; the multi-year mean, extreme values, and volatility characteristics of climate and hydrological factors; and topographic factors and environmental constraint factors related to the distribution of the target wetland ecosystem.

[0032] S42. Perform correlation screening on dynamic covariates and remove redundant dynamic covariates with an absolute value of Pearson correlation coefficient greater than 0.8; S43. Construct a spatial suitability evaluation model based on the selected dynamic covariates, and output the ecological suitability probability of each evaluation unit. ; The spatial suitability evaluation model is a maximum entropy model, and its expression is: ; Among them, the basic probability distribution density The expression is: ; In the formula, Representing the Each evaluation unit Ecological suitability probability after Cloglog transformation (values ​​range from 0 to 1, with values ​​closer to 1 indicating higher suitability for blue carbon sequestration). Representing the A specific spatial evaluation unit (or spatial location vector); The global entropy value (Entropy) represents the output distribution of the spatial suitability evaluation model and is used to characterize the degree of dispersion of the probability distribution of the spatial suitability evaluation model. The evaluation unit represents the spatial suitability evaluation model before conversion. The underlying probability distribution density (i.e., the original output value); The total number of dynamic covariates (characteristic functions) included in the spatial suitability assessment model; Representing the One dynamic covariate in the evaluation unit The characteristic function values ​​on; Representing the Each feature function is assigned weight coefficients through iterative optimization of the model; This represents the normalization constant, used to ensure that the sum of the basic probability densities of all units within the evaluation region equals 1.

[0033] Set After each Bootstrap iteration, the expected mean and standard deviation of the ecological suitability probability of each evaluation unit are extracted to generate the ecological suitability probability distribution function of each evaluation unit, providing spatial constraint parameters with uncertainty quantification for subsequent calculation of carbon sequestration potential.

[0034] In this embodiment, in step S5, under a preset upstream pressure mitigation scenario, the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation unit is coupled, and combined with the effective physical area, the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit are quantitatively assessed, including: S51. Set up upstream pressure relief scenarios, including upstream pressure reduction of 10%, upstream pressure reduction of 20%, and upstream pressure reduction of 30%; S52. Calculate the expected increase in productivity per unit area based on the spatial explicit sensitivity of each evaluation unit, i.e.: Set up evaluation unit The spatial explicit sensitivity follows a normal distribution In the first In the Monte Carlo iteration, the sensitivity parameters are extracted. And calculate the incremental increase in productivity per unit area under this iteration. ,Right now: ; In the formula, The mean of the sensitivity expectation, The standard error of sensitivity is obtained using the Delta method; when sampling sensitivity When the negative response condition is not met, it is considered to have no ecological gain. .

[0035] S53. Based on the expected increase in productivity per unit area of ​​the evaluation unit, and combined with the probability of ecological suitability and the effective area of ​​the evaluation unit, calculate the blue carbon sequestration potential of each evaluation unit; that is: Assume that the probability of ecological suitability follows a truncated normal distribution, and on the t-th... In the next iteration, the suitability probability is drawn. And strictly bound by Within the interval. Combined with the effective area of ​​the evaluation unit. Calculate the blue carbon sequestration potential value under this iteration. : ; Pre-set Sub-independent iterations (such as) After that, the evaluation unit is obtained. The set of probability distributions of blue carbon sequestration potential Based on this, the expected mean value is extracted as the total potential for foreign exchange increase, and the frequency of potential values ​​being less than or equal to 0 is defined as the probability of downside risk.

[0036] In this embodiment, in step S6, a two-dimensional feature space of expected return-downside risk is constructed based on the probability distribution characteristics of the blue carbon sequestration potential for strategic partitioning. This is then combined with spatial topology for vector-based aggregation, generating a graded evaluation map of the blue carbon sequestration potential of coastal wetlands, including: S61. Based on the blue carbon sequestration potential value of each evaluation unit, the candidate evaluation units are screened to obtain the screened candidate evaluation units, specifically: Based on the probability distribution characteristics of the foreign exchange increase potential output in step S5, the average expected foreign exchange increase potential of each candidate evaluation unit is extracted as the expected return indicator, and the downside risk probability is extracted as the risk constraint indicator; at the same time, the effective area is eliminated. Less than the minimum area threshold Or spatially isolated noise units, retaining valid candidate units.

[0037] S62. Establish threshold indicators for the screened candidate evaluation units, namely: Based on the distribution of expected return metrics for all valid candidate units, a preset statistical quantile (e.g., the 75th percentile) is selected as the return threshold. And based on the management tolerance, a downside risk probability limit (e.g., 5% or 0.05) is set as the risk threshold. .

[0038] S63. Based on the profit threshold and risk threshold, the candidate evaluation units are classified into four levels of foreign exchange reserve potential zones, namely: Level I: Robust Priority: Expected Returns And the probability of downside risk This region is highly sensitive to stress relief, has high suitability, and possesses the strongest certainty of carbon sequestration growth; therefore, ecological restoration and stress management should be prioritized.

[0039] Level II: Conditional Opportunity Zone: Expected Returns And the probability of downside risk The region has a huge potential for foreign exchange reserves, but due to uncertainties and risks, investment should only proceed after on-site verification.

[0040] Level III: Stable but low return zone: Expected returns And the probability of downside risk The region has a high certainty of foreign exchange gains, but the total expected returns are relatively low, making it suitable as a zone for routine maintenance and bottom-line control.

[0041] Level IV: Caution / Low Priority Zone: Expected Returns And the probability of downside risk The current foreign exchange gains in this region are limited and at risk of becoming ineffective; therefore, it is only designated as a long-term monitoring area.

[0042] S64. Patch aggregation is performed on spatially adjacent evaluation units to obtain priority remediation areas and classification results, specifically as follows: Based on spatial topological intersection rules, the policy zoning attributes and expected carbon sequestration potential of the evaluation units are injected into vector polygons (shapefiles) representing administrative divisions or specific ecological protection units in the target study area. The distribution area and total blue carbon sequestration potential of each policy zoning zone are then statistically analyzed based on the polygons (e.g., converted to...). This outputs macro-spatial decision maps, such as the first-level priority repair area and the second-level key control area, which are accurate to specific management polygons.

[0043] like Figure 2 As shown, this embodiment provides a graded evaluation system for the blue carbon sequestration potential of coastal wetlands, including: Data acquisition unit 100 is used to acquire multi-source spatiotemporal data of the target coastal zone and its associated watershed, and after preprocessing, construct an evaluation unit dataset with a unified spatial scale. The pressure index construction module 200 is used to identify upstream land use pressure based on the topological connectivity between the target watershed and its upstream sub-watersheds, and to construct a dynamic upstream pressure index for the evaluation unit. The sensitivity extraction module 300 is used to construct a heterogeneous time-delay response model based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, and to extract the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure. The suitability assessment module 400 is used to read dynamic covariates from preprocessed multi-source spatiotemporal data, construct a spatial suitability assessment model based on the dynamic covariates, and obtain the ecological suitability results and uncertainty characteristics of each assessment unit based on the spatial suitability assessment model. The potential estimation module 500 is used to couple the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation unit under a preset upstream pressure mitigation scenario, and combine the effective physical area to quantitatively assess the dynamic blue carbon sequestration potential and probability distribution characteristics of each evaluation unit. The grading module 600 is used to construct a two-dimensional feature space of "expected return - downside risk" based on the probability distribution characteristics of the blue carbon sequestration potential, perform strategy partitioning, and combine spatial topology for vector overall summarization to generate a grading evaluation map of the blue carbon sequestration potential of coastal wetlands.

[0044] In this embodiment, the multi-source spatiotemporal data collected by the data acquisition unit 100 includes at least land use data, watershed topology data, coastal wetland type distribution data, ecological function characterization data, and climate and hydrological data.

[0045] In this embodiment, the pressure index construction module 200 identifies upstream land use pressure based on the topological connectivity between the target watershed and its upstream sub-watersheds, and constructs a dynamic upstream pressure index for the evaluation unit; including: Unify land use data to the spatial scale corresponding to the evaluation unit; Based on land use data at a uniform scale, we identify stress land types that have a negative ecological impact on downstream coastal wetlands and assign different ecological stress weights to different stress land types. In this embodiment, the land types under pressure include construction land, irrigated farmland or paddy fields, dry land, herbaceous mulch farmland, and shrubland, orchards, or woody economic forests; wherein: The ecological pressure weight for impermeable surfaces or construction land is 1.0; The ecological pressure weight for irrigated farmland or paddy fields is 0.4; The ecological pressure weight for dryland and herbaceous-covered cultivated land is 0.3; The ecological pressure weight for shrublands, orchards, or woody economic forests is 0.2.

[0046] Based on the watershed topology, all upstream sub-watersheds of the target watershed are traced. The weighted pressure area of ​​each upstream sub-watershed and the total land area are statistically analyzed to calculate the dynamic upstream pressure index corresponding to the evaluation unit, i.e.: ; In the formula, represent Target watershed at any time The dynamic upstream pressure index; Represents the target watershed The entire set of upstream sub-basins; Representative set The first in One upstream sub-basin; The total number of land use or cover (land category) types within the study area; Representing the A specific type of land use or cover (e.g., construction land, farmland, forest land, water bodies, etc.); Representing the upstream sub-basin exist Time of the first The actual physical area of ​​cultivated land; Representing the The human activity pressure weight coefficient corresponding to the land type (usually assigned a value based on the intensity of the disturbance of the land type to the downstream water quality, water quantity or ecosystem, such as the highest weight for construction land and the lowest weight for nature reserves) is a spatial static parameter that does not change with time. Representing the upstream sub-basin exist The total land area at a given time (its value is usually equal to the sum of the areas of all land types within that sub-basin, i.e.) ).

[0047] In this embodiment, the sensitivity extraction module 300 constructs a heterogeneous time-delay response model based on the dynamic upstream pressure index, wetland structure composition information, and control variables, and extracts the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure; including: The ecological function characterization index of the evaluation unit is used as the dependent variable; the dynamic upstream pressure index is used as the explanatory variable; and spatiotemporal panel data are constructed by combining wetland structure composition information and climate and hydrological control variables; wherein the climate and hydrological control variables include at least one or more of the following: air temperature, precipitation, runoff, shortwave radiation and soil moisture.

[0048] Based on the spatiotemporal panel data, a heterogeneous time-delay response model is established to identify the differentiated marginal responses of different wetland types to changes in upstream pressure.

[0049] The spatial explicit sensitivity of each evaluation unit is calculated based on the parameter results of the heterogeneous time-delay response model, and evaluation units with an effective response basis to changes in upstream pressure are selected.

[0050] In this embodiment, the heterogeneous time-delay response model is a two-way fixed-effects panel model, and its expression is: ; In the formula, As an evaluation unit At any moment Ecological function characterization values; Represents the intercept term of the model; This represents the baseline marginal effect of upstream pressure on NPP (i.e., the basic impact without taking into account differences in wetland composition). Represented as evaluation unit The watershed has a lag order. The dynamic upstream pressure index under the following lag order The value range is from 0 to 3 years; This represents the moderating coefficient of the k-th wetland composition on the upstream pressure effect; Representing the evaluation unit The Middle The multi-year average structural proportion of wetland-like types; Regression coefficients representing the dynamic wetland coverage ratio; Representative evaluation unit In the The annual dynamic wetland cover ratio is used to control the scale effect caused by interannual fluctuations in the physical area of ​​wetlands. Represents the coefficient vector corresponding to the control variables; superscript Indicates the transpose operation; For control variables; For spatial fixed effects; This is a time-fixed effect; For disturbance terms; The formula for calculating the spatial explicit sensitivity of each evaluation unit to upstream pressure changes, extracted from the heterogeneous time-delay response model, is as follows: ; In the formula, Representing the evaluation unit Spatially explicit sensitivity to changes in upstream pressure; Represents the coefficients of the interaction term composed of all wetland types ( A column vector consisting of ) Representing the evaluation unit The specific wetlands constitute a proportional vector; Furthermore, using the Delta method, based on the parameter covariance matrix estimated by the heterogeneous time-delay response model, the spatial explicit sensitivity of each evaluation unit is calculated. The standard error and confidence interval were determined using the Delta method. Based on the parameter covariance matrix estimated by the heterogeneous time-delay response model, the spatial explicit sensitivity of each evaluation unit was calculated. Standard error and confidence interval: ; In the formula, Representing the evaluation unit Estimated variance of spatial explicit sensitivity; This represents the variance of the baseline marginal effect estimate; This represents the adjustment coefficient of various wetlands ( The covariance matrix formed by ) This represents the covariance vector between the baseline marginal effect and the adjustment coefficients of each wetland. Representing the evaluation unit A specific wetland constitutes a proportional column vector; superscript This indicates the transpose operation.

[0051] Based on the above sensitivity estimation variance, the judgment condition expression for each evaluation unit showing significant negative sensitivity to upstream pressure is extracted as follows: ; In the formula, Representing the evaluation unit Standard error of spatial explicit sensitivity; Indicates a given significance level Below Distribution critical value; if the upper limit of the above confidence interval is strictly less than Then determine the evaluation unit It exhibits a significant negative sensitivity to changes in upstream pressure and is therefore retained.

[0052] In this embodiment, the suitability assessment module 400 reads dynamic covariates from preprocessed multi-source spatiotemporal data, constructs a spatial suitability assessment model based on the dynamic covariates, and obtains the ecological suitability results and uncertainty characteristics of each assessment unit based on the spatial suitability assessment model; including: Dynamic covariates are read from preprocessed multi-source spatiotemporal data. These dynamic covariates include the mean, standard deviation, and time trend term of the dynamic upstream pressure index; the multi-year mean, extreme values, and volatility characteristics of climate and hydrological factors; as well as topographic factors and environmental constraint factors related to the distribution of the target wetland ecosystem.

[0053] Perform correlation screening on dynamic covariates to remove redundant dynamic covariates with an absolute Pearson correlation coefficient greater than 0.8; A spatial suitability assessment model is constructed based on the selected dynamic covariates, and the ecological suitability probability of each assessment unit is output. .

[0054] In this embodiment, the potential estimation module 500, under a preset upstream pressure mitigation scenario, couples the spatial explicit sensitivity and ecological suitability probability distribution of the evaluation unit, and combines the effective physical area to quantitatively assess the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit, including: Scenarios for easing upstream pressure are set, including a 10% decrease in upstream pressure, a 20% decrease in upstream pressure, and a 30% decrease in upstream pressure.

[0055] The expected increase in productivity per unit area is calculated based on the spatial explicit sensitivity of each evaluation unit, i.e.: Set up evaluation unit The spatial explicit sensitivity follows a normal distribution In the first In the Monte Carlo iteration, the sensitivity parameters are extracted. And calculate the incremental increase in productivity per unit area under this iteration. ,Right now: ; In the formula, The mean of the sensitivity expectation, The standard error of sensitivity is obtained using the Delta method; when sampling sensitivity When the negative response condition is not met, it is considered to have no ecological gain. .

[0056] Based on the expected productivity increase per unit area of ​​the evaluation unit, combined with the probability of ecological suitability and the effective area of ​​the evaluation unit, the blue carbon sequestration potential of each evaluation unit is calculated; that is: Assume that the probability of ecological suitability follows a truncated normal distribution, and on the t-th... In the next iteration, the suitability probability is drawn. And strictly bound by Within the interval. Combined with the effective area of ​​the evaluation unit. Calculate the blue carbon sequestration potential value under this iteration. : ; Pre-set Sub-independent iterations (such as) After that, the evaluation unit is obtained. The set of probability distributions of blue carbon sequestration potential Based on this, the expected mean value is extracted as the total potential for foreign exchange increase, and the frequency of potential values ​​being less than or equal to 0 is defined as the probability of downside risk.

[0057] In this embodiment, the grading module 600 constructs a two-dimensional feature space of expected return-downside risk based on the probability distribution characteristics of the blue carbon sequestration potential, performs strategy partitioning, and combines spatial topology for vector-based overall summarization to generate a grading evaluation map of the blue carbon sequestration potential of coastal wetlands, including: Based on the blue carbon sequestration potential value of each evaluation unit, the candidate evaluation units were screened to obtain the following: Based on the probability distribution characteristics of the foreign exchange increase potential output in step S5, the average expected foreign exchange increase potential of each candidate evaluation unit is extracted as the expected return indicator, and the downside risk probability is extracted as the "risk constraint indicator"; at the same time, the effective area is eliminated. Less than the minimum area threshold Or spatially isolated noise units, retaining valid candidate units.

[0058] Based on the distribution of expected return metrics for all valid candidate units, a preset statistical quantile (e.g., the 75th percentile) is selected as the return threshold. And based on the management tolerance, a downside risk probability limit (e.g., 5% or 0.05) is set as the risk threshold. .

[0059] S63. Based on the profit threshold and risk threshold, the candidate evaluation units are classified into four levels of foreign exchange reserve potential zones, namely: Level I: Robust Priority: Expected Returns And the probability of downside risk This region is highly sensitive to stress relief, has high suitability, and possesses the strongest certainty of carbon sequestration growth; therefore, ecological restoration and stress management should be prioritized.

[0060] Level II: Conditional Opportunity Zone: Expected Returns And the probability of downside risk The region has a huge potential for foreign exchange reserves, but due to uncertainties and risks, investment should only proceed after on-site verification.

[0061] Level III: Stable but low return zone: Expected returns And the probability of downside risk The region has a high certainty of foreign exchange gains, but the total expected returns are relatively low, making it suitable as a zone for routine maintenance and bottom-line control.

[0062] Level IV: Caution / Low Priority Zone: Expected Returns And the probability of downside risk The current foreign exchange gains in this region are limited and at risk of becoming ineffective; therefore, it is only designated as a long-term monitoring area.

[0063] In addition, this embodiment of the present application also provides an electronic device, including: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor. When the computer program is executed by at least one processor, it implements the graded evaluation method for the blue carbon sequestration potential of coastal wetlands described in Example 1.

[0064] In this embodiment, the memory 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 read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. A processor, coupled to the memory, is used to execute computer programs stored in the memory.

[0065] The embodiments and descriptions above are merely illustrative of the principles and preferred embodiments of the present invention. Various changes and modifications may be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A method for grading and evaluating the blue carbon sequestration potential of coastal wetlands, characterized in that, include: Multi-source spatiotemporal data of the target coastal zone and its associated watersheds were acquired, and after preprocessing, a unified spatial scale evaluation unit dataset was constructed. Based on the topological connectivity between the target watershed and its upstream sub-watersheds, upstream land use pressure is identified, and a dynamic upstream pressure index for the evaluation unit is constructed. Based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, a heterogeneous time-delay response model is constructed to extract the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure. Dynamic covariates are read from preprocessed multi-source spatiotemporal data, and a spatial suitability evaluation model is constructed based on the dynamic covariates. Based on the spatial suitability evaluation model, the ecological suitability results and uncertainty characteristics of each evaluation unit are obtained. Under the pre-set upstream pressure mitigation scenario, the probability distribution of the spatial explicit sensitivity and ecological suitability of the evaluation unit is coupled, and combined with the effective physical area, the dynamic blue carbon sequestration potential and its probability distribution characteristics of each evaluation unit are quantitatively assessed. Based on the probability distribution characteristics of the blue carbon sequestration potential, a two-dimensional feature space of expected return and downside risk is constructed for strategy partitioning. Combined with spatial topology, vector overall summarization is performed to generate a graded evaluation map of the blue carbon sequestration potential of coastal wetlands.

2. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 1, characterized in that: Construct a unified spatial scale evaluation unit dataset, including: The target area is divided using a regular square grid as the standard evaluation unit; Based on the preprocessed coastal wetland type distribution data, spatial overlay and area statistics were performed on each standard evaluation unit, and the area proportions of mangroves, salt marshes and tidal flats in each standard evaluation unit were calculated. Based on the preprocessed ecological function characterization data, using the standard evaluation unit as the statistical unit, the spatial mean method was used to extract the ecological function characterization indicators for each year. The evaluation unit's geographic code, spatial coordinates, area proportion of various wetlands, annual ecological function characterization indicators, as well as topographic, climate, hydrological, and watershed topology information are integrated into an evaluation unit dataset.

3. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 1, characterized in that: Based on the topological connectivity between the target watershed and its upstream sub-watersheds, upstream land use pressure is identified, and a dynamic upstream pressure index for each evaluation unit is constructed; including: Unify land use data to the spatial scale corresponding to the evaluation unit; Based on land use data at a uniform scale, we identify stress land types that have a negative ecological impact on downstream coastal wetlands and assign different ecological stress weights to different stress land types. Based on the watershed topology, all upstream sub-watersheds of the target watershed are traced. The weighted pressure area of ​​each upstream sub-watershed and the total land area are statistically analyzed to calculate the dynamic upstream pressure index corresponding to the evaluation unit, i.e.: ； In the formula, represent Target watershed at any time The dynamic upstream pressure index; Represents the target watershed The entire set of upstream sub-basins; Representative set The first in One upstream sub-basin; The total number of land use or land cover types within the study area; Representing the A specific type of land use or cover; Representing the upstream sub-basin exist Time of the first The actual physical area of ​​cultivated land; Representing the The weighting coefficient of human activity pressure corresponding to the category of farming; Representing the upstream sub-basin exist The total land area at any given time.

4. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 3, characterized in that: Based on the dynamic upstream pressure index, wetland structure composition information, and control variables, a heterogeneous time-delay response model is constructed to extract the spatial explicit sensitivity of each evaluation unit to changes in upstream pressure; including: The ecological function characterization index of the evaluation unit is used as the dependent variable; the dynamic upstream pressure index is used as the explanatory variable; and spatiotemporal panel data are constructed by combining wetland structure composition information and climate and hydrological control variables; wherein the climate and hydrological control variables include at least one or more of the following: air temperature, precipitation, runoff, shortwave radiation and soil moisture. A heterogeneous time-delay response model is established based on the spatiotemporal panel data to identify the differentiated marginal responses of different wetland types to changes in upstream pressure. The spatial explicit sensitivity of each evaluation unit is calculated based on the parameter results of the heterogeneous time-delay response model, and evaluation units with an effective response basis to changes in upstream pressure are selected.

5. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 4, characterized in that: The heterogeneous time-delay response model is a two-way fixed-effects panel model, and its expression is: ； In the formula, As an evaluation unit At any moment Ecological function characterization values; Represents the intercept term of the model; This represents the baseline marginal effect of upstream pressure on NPP; Represented as evaluation unit The watershed has a lag order. The dynamic upstream pressure index under the following lag order The value range is from 0 to 3 years; This represents the moderating coefficient of the k-th wetland composition on the upstream pressure effect; Representing the evaluation unit The Middle The multi-year average structural proportion of wetland-like types; Regression coefficients representing the dynamic wetland coverage ratio; Representative evaluation unit In the The dynamic wetland coverage ratio over the years; Represents the coefficient vector corresponding to the control variables; superscript Indicates the transpose operation; For control variables; For spatial fixed effects; This is a time-fixed effect; For disturbance terms; The formula for calculating the spatial explicit sensitivity of each evaluation unit to upstream pressure changes, extracted from the heterogeneous time-delay response model, is as follows: ； In the formula, Representing the evaluation unit Spatially explicit sensitivity to changes in upstream pressure; Represents the coefficients of the interaction term consisting of all wetland types. The column vector formed; Representing the evaluation unit The specific wetlands constitute a proportional vector; Using the Delta method, based on the parameter covariance matrix estimated by the heterogeneous time-delay response model, the spatial explicit sensitivity of each evaluation unit is calculated. Standard error and confidence interval: ； In the formula, Representing the evaluation unit Estimated variance of spatial explicit sensitivity; This represents the variance of the baseline marginal effect estimate; Represents the adjustment coefficients of various wetlands The covariance matrix formed by; This represents the covariance vector between the baseline marginal effect and the adjustment coefficients of each wetland. Representing the evaluation unit A specific wetland constitutes a proportional column vector; superscript Indicates the transpose operation; Based on the above sensitivity estimation variance, the judgment condition expression for each evaluation unit showing significant negative sensitivity to upstream pressure is extracted as follows: ； In the formula, Representing the evaluation unit Standard error of spatial explicit sensitivity; Indicates a given significance level Below Distribution critical value; if the upper limit of the above confidence interval is strictly less than Then determine the evaluation unit It exhibits a significant negative sensitivity to changes in upstream pressure and is therefore retained.

6. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 1, characterized in that: A spatial suitability assessment model is constructed based on the selected dynamic covariates, and the ecological suitability probability of each assessment unit is output. The probability of ecological suitability Represented as: ； Among them, the basic probability distribution density The expression is: ； In the formula, Representing the Each evaluation unit Ecological suitability probability after Cloglog transformation; Representing the A specific spatial evaluation unit; The global entropy value represents the output distribution of the spatial suitability evaluation model; The evaluation unit represents the spatial suitability evaluation model before conversion. The basic probability distribution density; This represents the total number of dynamic covariates included in the spatial suitability assessment model; Representing the One dynamic covariate in the evaluation unit The characteristic function values ​​on; Representing the Each feature function is assigned weight coefficients through iterative optimization of the model; Represents the normalization constant; Set After each Bootstrap iteration, the expected mean and standard deviation of the ecological suitability probability of each evaluation unit are extracted to generate the ecological suitability probability distribution function for each evaluation unit.

7. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 1, characterized in that: Under a pre-defined upstream pressure mitigation scenario, the uncertainty characteristics of the spatial suitability assessment model are coupled using Monte Carlo simulation to calculate the probability distribution of blue carbon sink potential for each assessment unit, specifically including: Scenarios for easing upstream pressure are set, including a 10% decrease in upstream pressure, a 20% decrease in upstream pressure, and a 30% decrease in upstream pressure; The expected increase in productivity per unit area is calculated based on the spatial explicit sensitivity of each evaluation unit, i.e.: Set up evaluation unit The spatial explicit sensitivity follows a normal distribution ; in the In the Monte Carlo iteration, the sensitivity parameters are extracted. And calculate the incremental increase in productivity per unit area under this iteration. ,Right now: ； In the formula, The mean of the sensitivity expectation, The standard error of sensitivity is obtained using the Delta method; when sampling sensitivity When this is considered to have no ecological benefit, then ; Based on the expected productivity increase per unit area of ​​the evaluation unit, combined with the probability of ecological suitability and the effective area of ​​the evaluation unit, the blue carbon sequestration potential of each evaluation unit is calculated; that is: Assume that the probability of ecological suitability follows a truncated normal distribution, and on the t-th... In the next iteration, the suitability probability is drawn. And strictly bound by Within the interval; combined with the effective area of ​​the evaluation unit. Calculate the blue carbon sequestration potential value under the current iteration. : ； Pre-set After several independent iterations, the evaluation unit is obtained. The set of probability distributions of blue carbon sequestration potential ; Based on this, the expected mean value is extracted as the total potential for foreign exchange increase, and the frequency of potential values ​​being less than or equal to 0 is defined as the probability of downside risk.

8. The method for graded evaluation of blue carbon sequestration potential in coastal wetlands according to claim 7, characterized in that: Based on the probability distribution characteristics of the blue carbon sequestration potential, a two-dimensional feature space of expected return-downside risk is constructed for strategy partitioning. Combined with spatial topology, vector aggregation is performed to generate a graded evaluation map of the blue carbon sequestration potential of coastal wetlands, including: The average expected exchange rate increase potential of each evaluation unit is used as the expected return indicator, and the probability of downside risk is used as the risk constraint indicator. Select a predetermined percentage of expected return quantiles as the return threshold, and set a tolerable downside risk probability limit as the risk threshold. Candidate evaluation units are classified into four levels based on threshold values ​​to obtain a four-level potential area for capital accumulation. Patch aggregation is performed on spatially adjacent evaluation units to obtain priority remediation areas and classification results.

9. A grading and evaluation system for the blue carbon sequestration potential of coastal wetlands, characterized in that, include: The data acquisition unit is used to acquire multi-source spatiotemporal data of the target coastal zone and its associated watersheds, and after preprocessing, constructs an evaluation unit dataset with a unified spatial scale. The pressure index construction module is used to identify upstream land use pressure based on the topological connectivity between the target watershed and its upstream sub-watersheds, and to construct a dynamic upstream pressure index for the evaluation unit. The sensitivity extraction module is used to construct a heterogeneous time-delay response model based on the dynamic upstream pressure index, wetland structure composition information, and spatiotemporal control variables, and to extract the spatial explicit sensitivity and uncertainty characteristics of each evaluation unit to changes in upstream pressure. The suitability assessment module is used to read dynamic covariates from preprocessed multi-source spatiotemporal data, construct a spatial suitability assessment model based on the dynamic covariates, and obtain the ecological suitability results and uncertainty characteristics of each assessment unit based on the spatial suitability assessment model. The potential estimation module, under a preset upstream pressure mitigation scenario, couples the spatial explicit sensitivity and ecological suitability probability distribution of the evaluation unit, and combines the effective physical area to quantitatively assess the dynamic blue carbon sequestration potential and probability distribution characteristics of each evaluation unit. The grading module constructs a two-dimensional feature space of expected return and downside risk based on the probability distribution characteristics of the blue carbon sequestration potential, performs strategy partitioning, and combines spatial topology for vector overall summarization to generate a grading evaluation map of the blue carbon sequestration potential of coastal wetlands.

10. An electronic device, comprising: At least one processor; and memory that is communicatively connected to at least one processor; The memory stores a computer program that can be executed by at least one processor, characterized in that when the computer program is executed by at least one processor, it implements the graded evaluation method for the blue carbon sequestration potential of coastal wetlands as described in any one of claims 1-8.

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