Method and system for multi-index quantitative evaluation of ecological effect of coastal wetland restoration

Abstract

The application provides a coastal wetland rearing recovery ecological effect multi-index quantitative evaluation method and system, belongs to the technical field of ecological restoration evaluation, and comprises the following steps: acquiring remote sensing, measurement, account book and GIS data of a sample plot to construct sample plot index standard data set; constructing an ecological restoration effect index system based on a pressure-state-response framework; calculating the weight of each index in the ecological restoration effect index system by using an analytic hierarchy process, an entropy weight method and a variation coefficient method, and taking the arithmetic mean of the obtained weights as the final combined weight of each index; constructing an ecological restoration effect index EREI by linear weighting according to the combined weight; and taking the EREI value of each sample plot as input, and dividing the sample plot into a restoration effect grade by using a fuzzy clustering analysis method, so that the problems of un-systematic index system, large subjective weighting deviation, poor grading method stability and limited sample plot data in the current ecological restoration effect evaluation of wetland rearing areas are solved.

Description

Technical Field

[0001] This application relates to the field of ecological restoration assessment technology, specifically to a multi-indicator quantitative assessment method and system for the ecological effectiveness of coastal wetland restoration through aquaculture reduction. Background Technology

[0002] Coastal wetlands play crucial ecological roles such as maintaining biodiversity, conserving water resources, and storing carbon. However, under the combined disturbances of urban expansion and land use conversion, local ecological degradation and landscape fragmentation are prominent problems. As special areas where wetland reclamation zones have shifted from human-used methods such as aquaculture to ecological restoration, the scientific assessment of their ecological restoration effectiveness is of great significance for guiding restoration practices and optimizing management strategies.

[0003] Currently, the assessment of wetland ecological restoration effectiveness mainly employs methods such as field observation data, bioindicator methods, and indicator system methods. Field observation methods reflect ecological status by monitoring changes in the community structure of sensitive biological populations, providing a direct view of ecological responses; however, they have significant limitations in terms of spatial generalization and temporal continuity. Indicator system methods construct comprehensive health indices using multidimensional indicators, broadening the assessment perspective, but still heavily rely on expert experience for weighting and are susceptible to variations in data availability. In recent years, researchers have combined remote sensing observations with ground-based statistical data and employed combined weighting models to optimize indicator weights, thereby improving the scientific rigor and spatiotemporal comparability of ecosystem health assessments.

[0004] However, existing assessment approaches often rely on single indicators or empirical judgments, exhibiting significant subjective bias in weighting, insufficient adaptation to regional ecological heterogeneity, and limited comparability across scales and time series. Specifically, the indicator system lacks a systematic framework for guidance, making it difficult to comprehensively reflect the entire "disturbance-state-intervention" process; the weight determination methods are simplistic, exhibiting strong subjectivity or insufficient objectivity; effectiveness grading relies on empirical thresholds, resulting in poor robustness; and under conditions of limited sample plots and insufficient monitoring data, accurate quantitative assessment is difficult to achieve.

[0005] Therefore, how to integrate multi-source data to construct a systematic indicator system, reduce subjective weighting bias, achieve accurate quantitative grading of restoration effectiveness, and overcome the limitations of limited time for sample plot evaluation has become a challenge in the field of ecological restoration effectiveness assessment in wetland aquaculture depletion areas. Summary of the Invention

[0006] In view of this, the embodiments of the present invention are committed to providing a multi-indicator quantitative evaluation method and system for the ecological effectiveness of coastal wetland restoration by abandonment of aquaculture, so as to solve the problems of unsystematic indicator system, large subjective weighting bias, poor robustness of grading method and limited sample plot data in the current evaluation of the ecological restoration effectiveness of wetland abandonment areas.

[0007] In a first aspect, the present invention provides a multi-indicator quantitative evaluation method for the ecological effectiveness of coastal wetland restoration through aquaculture reduction, including:

[0008] Acquire remote sensing, field measurement, ledger, and GIS data of the sample plots, complete image correction and data normalization, and construct a sample plot indicator standard dataset;

[0009] Construct an ecological restoration effectiveness indicator system based on the stress-state-response framework;

[0010] The weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the analytic hierarchy process (AHP), the entropy weight method, and the coefficient of variation method, respectively. The arithmetic mean of the weights obtained by the AHP, the entropy weight method, and the coefficient of variation method is taken as the final combined weight of each indicator.

[0011] The indicators in the aforementioned ecological restoration effectiveness indicator system are processed uniformly for both positive and negative values, and the ecological restoration effectiveness index EREI is constructed by linearly weighting the indicators according to their combined weights.

[0012]

[0013] in, , , The combined weights are for the three primary indicators of stress, state, and response; P, S, and R are the sub-indices of the three dimensions of stress, state, and response, respectively, calculated by weighting the secondary indicators they contain.

[0014] Using the EREI value of each sample plot as input, fuzzy clustering analysis is employed to assess the remediation effectiveness level of the sample plot.

[0015] Furthermore, the method also includes using GIS technology to spatially visualize and map the EREI values ​​and grade classification results, intuitively displaying the spatial differentiation pattern of the restoration effectiveness.

[0016] Furthermore, the weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the analytic hierarchy process (AHP), including: constructing a hierarchical diagram based on the selected indicators, constructing judgment matrices for the first-level indicators and each group of second-level indicators, calculating the weight of each indicator and performing a consistency check to obtain the weights.

[0017] Furthermore, the entropy weight method is used to calculate the weight of each indicator in the ecological restoration effectiveness indicator system, including: normalizing the original data of each indicator, distinguishing between positive and negative indicators, calculating the information entropy of each evaluation indicator, and then obtaining the information utility value, and obtaining the entropy weight through normalization.

[0018] Furthermore, the weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the coefficient of variation method, including: normalizing the original data, calculating the mean and standard deviation of each indicator, and then calculating the coefficient of variation (CV) value, and obtaining the weights through normalization.

[0019] Furthermore, the ecological restoration effectiveness indicator system includes three dimensions: stress, status, and response. The stress dimension includes three indicators: land use change intensity, years of land reclamation, and distance from human activities, used to reflect the degree of external disturbance. The status dimension includes four indicators: NDVI, soil surface exposure index BI, salinity or moisture indicator index NDWI or salinity index, and landscape diversity, used to reflect the current state of the ecosystem. The response dimension includes three indicators: restoration measure intensity zoning, restoration area ratio, and vegetation recovery speed, used to reflect the effect of restoration intervention.

[0020] Furthermore, the repair effectiveness levels include:

[0021] Level I Significant Result: EREI < 40 indicates that the ecosystem is in a healthy state and the restoration measures have achieved the expected goals.

[0022] Level II general effectiveness: 40 ≤ EREI ≤ 80, indicating that the ecosystem is in a sub-healthy state, and the restoration measures have had a positive impact but have not yet reached a steady state.

[0023] Level III: Insignificant results: EREI > 80 indicates that the ecosystem is still damaged or under high stress, and restoration measures have not yet been able to effectively overcome environmental barriers.

[0024] Furthermore, the acquisition of remote sensing, field measurement, ledger, and GIS data of the sample plots, and the completion of image correction and data normalization to construct a sample plot indicator standard dataset, includes:

[0025] Basic management information for each sample plot was obtained through field surveys and local records. This basic management information includes the number of years since the land was decommissioned and the intensity level of restoration measures.

[0026] Record the key environmental parameters of the sample plots to form a field basic dataset for ecological restoration assessment. The key environmental parameters include: vegetation status, soil salinity, and moisture.

[0027] Acquire multiple medium-to-high resolution remote sensing images covering the study area. The time span of these images should cover the period before ecological restoration, the middle period of ecological restoration, and the current state, in order to reflect the temporal changes in ecological restoration.

[0028] Radiometric, geometric, and atmospheric corrections are performed on the images, and cloud and shadow masking is completed. Based on the processed images, land use change intensity and restoration area ratio information are extracted using supervised classification and change detection methods.

[0029] From the processed multi-phase remote sensing images, spectral index features of each plot location are extracted, including Normalized Difference Vegetation Index (NDVI), Soil Topsoil Exposure Index (BI or BSI), Normalized Difference Moisture Index (NDWI), and Soil Salinity Index.

[0030] Calculate the landscape diversity index;

[0031] By combining vector data of roads, residential areas, and industrial and mining areas, the shortest distance from the sample plot to the source of human activity disturbance is calculated.

[0032] Secondly, this invention provides a multi-indicator quantitative evaluation system for the ecological effectiveness of coastal wetland restoration through aquaculture reduction, including:

[0033] Acquisition module: Used to acquire remote sensing, field measurement, ledger, and GIS data of sample plots, and to complete image correction and data normalization in order to construct a sample plot indicator standard dataset;

[0034] The first building module is used to construct an ecological restoration effectiveness indicator system based on the stress-state-response framework.

[0035] The first calculation module is used to calculate the weight of each indicator in the ecological restoration effectiveness indicator system using the analytic hierarchy process (AHP), the entropy weight method, and the coefficient of variation method, and to take the arithmetic mean of the weights obtained by the AHP, the entropy weight method, and the coefficient of variation method as the final combined weight of each indicator.

[0036] The second construction module is used to perform unified positive and negative processing on each indicator in the ecological restoration effectiveness indicator system, and to construct the ecological restoration effectiveness index EREI by linear weighting according to the combined weights.

[0037]

[0038] Among them, , , The combined weights are for the three primary indicators: stress, state, and response; P, S, and R are the sub-indices of the three dimensions of stress, state, and response, respectively, calculated by weighting the secondary indicators they contain.

[0039] The second calculation module is used to classify the remediation effectiveness levels of each sample plot by taking the EREI value of each sample plot as input and using fuzzy clustering analysis.

[0040] This invention achieves at least the following beneficial effects: It provides a quantitative evaluation method and system for the ecological restoration effectiveness of wetland aquaculture depletion areas based on a pressure-state-response framework, multi-source data fusion, and combined weighting. The weighting adopts a combined weighting approach of analytic hierarchy process (AHP), entropy weighting, and coefficient of variation method. The three methods are used in parallel to calculate the weights and then the arithmetic mean is taken to reduce the bias of a single method. After unifying the positive and negative values ​​and standardizing the processing, an ecological restoration effectiveness index is constructed by linear weighting according to the combined weights. Fuzzy clustering is used to accurately quantify and classify the restoration effectiveness of sample plots. This solves the problems of unsystematic index system, large subjective weighting bias, poor robustness of classification methods, and limited sample plot data in the current evaluation of the ecological restoration effectiveness of wetland aquaculture depletion areas. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method flow provided in the embodiments of this specification;

[0042] Figure 2 This is a schematic diagram of the method route provided in the embodiments of this specification;

[0043] Figure 3 This is a schematic diagram of the pressure-state-response framework evaluation system provided in the embodiments of this specification;

[0044] Figure 4 This is a combined weighting diagram of the various indicators provided in the embodiments of this specification. Detailed Implementation

[0045] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0046] The following describes the embodiments in conjunction with the appendix to the instruction manual. Figures 1-4 Specific details:

[0047] like Figures 1-4 As shown in Example 1 of this specification, a multi-indicator quantitative evaluation method for the ecological effectiveness of coastal wetland restoration through aquaculture reduction is provided, including:

[0048] S1: Acquire remote sensing, field measurement, ledger, and GIS data of the sample plots, complete image correction and data normalization, and construct a sample plot indicator standard dataset;

[0049] Acquire remote sensing, field measurement, ledger, and GIS data of the sample plots, complete image correction and data normalization, and construct a sample plot indicator standard dataset, including:

[0050] Through field surveys and local records, basic management information for each sample plot was obtained, including the number of years since the land was decommissioned and the intensity level of restoration measures.

[0051] Record key environmental parameters such as vegetation status, soil salinity and moisture in the sample plots to form a basic dataset for ecological restoration assessment.

[0052] Acquire multiple medium-to-high resolution remote sensing images covering the study area, such as Landsat and Sentinel-2 satellite images. The time span of the images should cover the period before ecological restoration, the middle period of ecological restoration, and the current state, so as to reflect the temporal changes in ecological restoration.

[0053] Radiometric, geometric, and atmospheric corrections were performed on the images, and cloud and shadow masking was completed. Based on the processed images, land use change intensity and restoration area ratio information were extracted using supervised classification and change detection techniques.

[0054] From the processed multi-phase remote sensing images, spectral index features were extracted for each sample plot location, including Normalized Difference Vegetation Index (NDVI), Soil Exposure Index (BI or BSI), Normalized Difference Moisture Index (NDWI), and Soil Salinity Index; landscape diversity index was also extracted. Landscape diversity, as a key indicator reflecting the structural integrity of the ecosystem, plays an important role in wetland ecological health assessment. The Shannon Diversity Index (SHDI), derived from information theory, comprehensively reflects land cover richness and evenness of distribution, and is an internationally recognized method for measuring landscape heterogeneity, widely used in ecosystem health assessment systems. The landscape diversity index was calculated based on land use classification results. The Shannon Diversity Index (SHDI) was used to quantify land cover richness and evenness of distribution.

[0055]

[0056] Where Pi represents the area proportion of land type i within the sample plot, and n is the total number of land types. This index can effectively reflect the spatial heterogeneity of habitat types in wetland conservation areas.

[0057] By combining vector data of roads, residential areas, and industrial and mining sites, the shortest distance from the sample plot to the source of human activity disturbance is calculated. Specifically, prior to this, sample plots need to be established. Several typical sample plots are set up as evaluation units within the target wetland conservation area. The number of sample plots is determined based on the area and heterogeneity of the study area, generally no less than 30. The selection of sample plots should comprehensively consider different conservation periods, wetland types, and spatial distribution characteristics to represent the main environmental gradient and ecological status of the conservation area. Basic management information such as the conservation period and restoration measure intensity level for each sample plot is obtained through field surveys and local records. The conservation period is recorded according to the actual conservation time, and the restoration measure intensity is divided into three levels: strong, medium, and weak, based on policy documents and project implementation. Simultaneously, key environmental parameters such as vegetation status, soil salinity, and moisture are recorded to form a basic field dataset for ecological restoration assessment.

[0058] S2: An ecological restoration effectiveness indicator system is constructed based on a stress-state-response framework. This system comprises three dimensions: stress, state, and response. The stress dimension includes three indicators: land use change intensity, years of wetland aquaculture withdrawal, and distance from human activities, reflecting the degree of external disturbance. The state dimension includes four indicators: NDVI, soil surface exposure index (BI), NDWI or salinity index, and landscape diversity, reflecting the current state of the ecosystem. The response dimension includes three indicators: intensity of restoration measures (zoning), proportion of restored area, and vegetation recovery rate, reflecting the effectiveness of restoration interventions. These indicators cover the entire disturbance-state-intervention chain, systematically reflecting the ecological restoration process and effectiveness of wetland aquaculture withdrawal areas.

[0059] S3: The weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the analytic hierarchy process (AHP), entropy weight method, and coefficient of variation method, respectively. The arithmetic mean of the weights obtained by the AHP, entropy weight method, and coefficient of variation method is taken as the final combined weight of each indicator. This combined weight method effectively balances expert experience and data objectivity, reducing the subjective bias of a single method. The AHP method is used to calculate the weights of each indicator in the ecological restoration effectiveness indicator system, including: constructing a hierarchical diagram based on the selected indicators; constructing judgment matrices for the first-level indicators and each group of second-level indicators; calculating the weight of each indicator and performing a consistency check to obtain the weights.

[0060] The entropy weight method is used to calculate the weights of each indicator in the ecological restoration effectiveness indicator system. This includes: normalizing the original data of each indicator, distinguishing between positive and negative indicators, calculating the information entropy of each evaluation indicator, and then obtaining the information utility value. The entropy weight is obtained through normalization.

[0061] The coefficient of variation (CV) method was used to calculate the weights of each indicator in the ecological restoration effectiveness indicator system. This included normalizing the original data, calculating the mean and standard deviation of each indicator, calculating the coefficient of variation (CV), and obtaining the weights through normalization.

[0062] S4: The indicators in the ecological restoration effectiveness indicator system are processed uniformly for both positive and negative aspects. Positive indicators are processed using a benefit-based standardized formula, while negative indicators are processed using a cost-based standardized formula, eliminating the influence of dimensions. An ecological restoration effectiveness index (EREI) is constructed by linearly weighting the indicators according to their combined weights.

[0063]

[0064] Among them, , , The combined weights are for the three primary indicators: stress, state, and response; P, S, and R are the sub-indices of the three dimensions of stress, state, and response, respectively, calculated by weighting the secondary indicators they contain.

[0065] S5: Using the EREI value of each sample plot as input, fuzzy clustering analysis is used to assess the restoration effectiveness level of the sample plots. The EREI value ranges from 0 to 100, with higher values ​​indicating more significant ecological restoration effectiveness.

[0066] The method also includes using GIS technology to spatially visualize and map the EREI values ​​and grading results, intuitively displaying the spatial differentiation pattern of the restoration results, and providing decision support for zoning governance and dynamic monitoring.

[0067] Repair effectiveness levels include:

[0068] Level I Significant Result: EREI < 40 indicates that the ecosystem is in a healthy state and the restoration measures have achieved the expected goals.

[0069] Level II general effectiveness: 40 ≤ EREI ≤ 80, indicating that the ecosystem is in a sub-healthy state, and the restoration measures have had a positive impact but have not yet reached a steady state.

[0070] Level III: Insignificant results: EREI > 80 indicates that the ecosystem is still damaged or under high stress, and restoration measures have not yet been able to effectively overcome environmental barriers.

[0071] Based on the same idea, this invention provides a multi-indicator quantitative evaluation system for the ecological effectiveness of coastal wetland restoration through aquaculture reduction, including:

[0072] Acquisition module: Used to acquire remote sensing, field measurement, ledger, and GIS data of sample plots, and to complete image correction and data normalization in order to construct a sample plot indicator standard dataset;

[0073] The first building module is used to construct an ecological restoration effectiveness indicator system based on the stress-state-response framework.

[0074] The first calculation module is used to calculate the weight of each indicator in the ecological restoration effectiveness indicator system using the analytic hierarchy process (AHP), entropy weight method, and coefficient of variation method respectively, and takes the arithmetic mean of the weights obtained by the AHP, entropy weight method, and coefficient of variation method as the final combined weight of each indicator.

[0075] The second construction module is used to uniformly process the positive and negative indicators in the ecological restoration effectiveness indicator system, and construct the ecological restoration effectiveness index EREI by linear weighting according to the combined weights.

[0076]

[0077] Among them, , , The combined weights of the three primary indicators—stress, state, and response—are respectively; P, S, and R are the sub-indices of the three dimensions—stress, state, and response—calculated by weighting them according to their respective secondary indicators.

[0078] The second calculation module is used to classify the remediation effectiveness levels of each sample plot by taking the EREI value of each sample plot as input and using fuzzy clustering analysis.

[0079] Example 2: The Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, undertook the restoration and reconstruction of degraded wetland vegetation in the southern buffer zone of the Yellow Sea wetlands in Yancheng, Jiangsu Province. Regarding the development of ecological restoration effectiveness assessment technologies, an experiment was conducted in the study area to assess the effectiveness of ecological restoration in the de-aquaculture zone, using the following methods:

[0080] I. Within the southern buffer zone of the Yancheng Yellow Sea Wetland, encompassing three administrative regions—Sheyang County, Dafeng District, and Dongtai City—55 typical sample plots were established as evaluation units. Basic information such as the duration of the wetland's withdrawal from cultivation, the intensity level of restoration measures, vegetation status, and soil salinity and moisture was obtained through field surveys and local records.

[0081] 2. Acquire multiple medium-to-high resolution remote sensing images of the study area, including Landsat and Sentinel-2, and perform radiometric, geometric, atmospheric correction, and cloud shadow processing. Extract spectral features such as NDVI, BI, NDWI, and soil salinity index, and combine them with GIS measurements to obtain indicators such as land use change intensity, restoration area ratio, landscape diversity, and distance from human activities.

[0082] III. Based on the PSR framework, an evaluation system is constructed, comprising 10 secondary indicators, including 3 stress indicators, 4 state indicators, and 3 response indicators.

[0083] Fourth, the weights of the indicators are calculated using three methods: the analytic hierarchy process, the entropy weight method, and the coefficient of variation method. The arithmetic mean of these methods is then taken to obtain the combined weights of each indicator.

[0084] 5. After unifying and standardizing the positive and negative values ​​of each indicator, the EREI index is constructed by linearly weighting the combined weights.

[0085] VI. Fuzzy clustering analysis was used to classify the EREI values ​​of 55 sample plots into three levels of remediation effectiveness, and spatial distribution maps were generated in conjunction with GIS.

[0086] The experimental results show that the method of this invention can achieve an accuracy rate of over 85% in classifying the ecological restoration effectiveness of wetland reclamation areas. It can effectively identify the comprehensive relationship between restoration period, measure intensity and vegetation restoration, and can provide scientific support for regional restoration management and dynamic monitoring.

[0087] In summary, the application effects and subsequent recommendations of this patented technology are as follows: From 2017 to 2025, the ESH status of the Yancheng Yellow Sea wetland has significantly improved, with the area of ​​high-value zones increasing by 7% and low-value zones decreasing by 47.45%, resulting in an average improvement of 4.50 points. This improvement is mainly attributed to the removal of anthropogenic water pressure (contributing 81.3%). The restoration process exhibits a phased pattern of initial outbreak, mid-term bottleneck, and late-term steady-state. Dongtai has become a restoration hotspot, while a cold spot exists in central Dafeng. Water environment pressure remains the main limiting factor, and the risk of habitat homogenization needs to be monitored. In the future, the focus should shift from "area expansion" to "quality improvement," strengthening mid-term management and pressure control in cold spot areas, and scientifically regulating water and vegetation configuration.

[0088] The basic principles of the present invention have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in the present invention are merely examples and not limitations, and should not be considered as essential features of each embodiment of the present invention. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the present invention to the necessity of employing the aforementioned specific details.

[0089] The block diagrams of devices, apparatuses, devices, and systems involved in this invention are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0090] It should also be noted that in the apparatus, device, and method of the present invention, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of the present invention.

[0091] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of the invention. Therefore, the invention is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0092] It should be understood that the qualifying terms "first", "second", "third", "fourth", "fifth" and "sixth" used in the description of the embodiments of the present invention are only used to more clearly illustrate the technical solutions and are not intended to limit the scope of protection of the present invention.

[0093] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A multi-indicator quantitative evaluation method for the ecological effectiveness of coastal wetland restoration through aquaculture reduction, characterized in that, include: Acquire remote sensing, field measurement, ledger, and GIS data of the sample plots, complete image correction and data normalization, and construct a sample plot indicator standard dataset; Construct an ecological restoration effectiveness indicator system based on the stress-state-response framework; The weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the analytic hierarchy process (AHP), the entropy weight method, and the coefficient of variation method, respectively. The arithmetic mean of the weights obtained by the AHP, the entropy weight method, and the coefficient of variation method is taken as the final combined weight of each indicator. The indicators in the aforementioned ecological restoration effectiveness indicator system are processed uniformly for both positive and negative values, and the ecological restoration effectiveness index EREI is constructed by linearly weighting the indicators according to their combined weights. Among them, , , The combined weights are for the three primary indicators: stress, state, and response; P, S, and R are the sub-indices of the three dimensions of stress, state, and response, respectively, calculated by weighting the secondary indicators they contain. Using the EREI value of each sample plot as input, fuzzy clustering analysis is employed to assess the remediation effectiveness level of the sample plot.

2. The method according to claim 1, characterized in that, The method also includes using GIS technology to spatially visualize and map the EREI values ​​and grade assessment results, intuitively displaying the spatial differentiation pattern of the restoration effectiveness.

3. The method according to claim 1, characterized in that, The weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the analytic hierarchy process (AHP). This includes: constructing a hierarchical diagram based on the selected indicators; constructing judgment matrices for the first-level indicators and each group of second-level indicators; calculating the weight of each indicator and performing a consistency check to obtain the weights.

4. The method according to claim 1, characterized in that, The entropy weight method is used to calculate the weight of each indicator in the ecological restoration effectiveness indicator system, including: normalizing the original data of each indicator, distinguishing between positive and negative indicators, calculating the information entropy of each evaluation indicator, and then obtaining the information utility value, and obtaining the entropy weight through normalization.

5. The method according to claim 1, characterized in that, The weights of each indicator in the ecological restoration effectiveness indicator system are calculated using the coefficient of variation method, including: normalizing the original data, calculating the mean and standard deviation of each indicator, and then calculating the coefficient of variation (CV) value, and obtaining the weights through normalization.

6. The method according to claim 1, characterized in that, The ecological restoration effectiveness indicator system includes three dimensions: stress, status, and response. The stress dimension includes three indicators: land use change intensity, years of land reclamation, and distance from human activities, which reflect the degree of external disturbance. The status dimension includes four indicators: NDVI, soil surface exposure index BI, salinity or moisture indicator index NDWI or salinity index, and landscape diversity, which reflect the current state of the ecosystem. The response dimension includes three indicators: restoration measure intensity zoning, restoration area ratio, and vegetation recovery speed, which reflect the effect of restoration intervention.

7. The method according to claim 1, characterized in that, The levels of repair effectiveness include: Level I Significant Result: EREI < 40 indicates that the ecosystem is in a healthy state and the restoration measures have achieved the expected goals. Level II general effectiveness: 40 ≤ EREI ≤ 80, indicating that the ecosystem is in a sub-healthy state, and the restoration measures have had a positive impact but have not yet reached a steady state. Level III: Insignificant results: EREI > 80 indicates that the ecosystem is still damaged or under high stress, and restoration measures have not yet been able to effectively overcome environmental barriers.

8. The method according to claim 1, characterized in that, The process involves acquiring remote sensing, field measurement, ledger, and GIS data of the sample plots, completing image correction and data normalization, and constructing a sample plot indicator standard dataset, including: Basic management information for each sample plot was obtained through field surveys and local records. This basic management information includes the number of years since the land was decommissioned and the intensity level of restoration measures. Record the key environmental parameters of the sample plots to form a field basic dataset for ecological restoration assessment. The key environmental parameters include: vegetation status, soil salinity, and moisture. Acquire multiple medium-to-high resolution remote sensing images covering the study area. The time span of these images should cover the period before ecological restoration, the middle period of ecological restoration, and the current state, in order to reflect the temporal changes in ecological restoration. The images are subjected to radiometric, geometric, and atmospheric corrections, and cloud and shadow masking is performed. Based on the processed images, land use change intensity and restoration area ratio information are extracted using supervised classification and change detection methods. From the processed multi-phase remote sensing images, spectral index features of each plot location are extracted, including Normalized Difference Vegetation Index (NDVI), Soil Topsoil Exposure Index (BI or BSI), Normalized Difference Moisture Index (NDWI), and Soil Salinity Index. Calculate the landscape diversity index; By combining vector data of roads, residential areas, and industrial and mining areas, the shortest distance from the sample plot to the source of human activity disturbance is calculated.

9. A multi-indicator quantitative evaluation system for the ecological effectiveness of coastal wetland restoration through aquaculture reduction, characterized in that, include: Acquisition module: Used to acquire remote sensing, field measurement, ledger, and GIS data of sample plots, and to complete image correction and data normalization in order to construct a sample plot indicator standard dataset; The first building module is used to construct an ecological restoration effectiveness indicator system based on the stress-state-response framework. The first calculation module is used to calculate the weight of each indicator in the ecological restoration effectiveness indicator system using the analytic hierarchy process (AHP), the entropy weight method, and the coefficient of variation method, and to take the arithmetic mean of the weights obtained by the AHP, the entropy weight method, and the coefficient of variation method as the final combined weight of each indicator. The second construction module is used to perform unified positive and negative processing on each indicator in the ecological restoration effectiveness indicator system, and to construct the ecological restoration effectiveness index EREI by linear weighting according to the combined weights. in, , , The combined weights are for the three primary indicators of stress, state, and response; P, S, and R are the sub-indices of the three dimensions of stress, state, and response, respectively, calculated by weighting the secondary indicators they contain. The second calculation module is used to classify the remediation effectiveness levels of each sample plot by taking the EREI value of each sample plot as input and using fuzzy clustering analysis.

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