Multi-index fusion-based wetland ecological quality comprehensive evaluation method and system

Abstract

This invention discloses a comprehensive assessment method and system for wetland ecological quality based on multi-indicator fusion, belonging to the field of wetland ecological protection technology. The method includes acquiring water environment index data, biodiversity index data, carbon storage index data, and remote sensing images of wetland edges; establishing a multi-indicator fusion model based on the water environment index data, biodiversity index data, and carbon storage index data to generate a basic wetland ecological quality index; generating a predicted value of the basic wetland ecological quality index based on the basic wetland ecological quality index; and generating a comprehensive wetland ecological quality index based on the remote sensing images of wetland edges and the predicted value of the basic wetland ecological quality index, and assessing the wetland ecological quality. This invention can integrate multi-dimensional ecological indicators such as water environment, biodiversity, and carbon sink function, achieving dynamic prediction of wetland ecological quality and quantitative assessment of the impact of external pressures, thus improving the comprehensiveness and foresight of the assessment results.

Description

Technical Field

[0001] This invention belongs to the field of wetland ecological protection technology, and in particular relates to a comprehensive assessment method and system for wetland ecological quality based on multi-indicator fusion. Background Technology

[0002] Wetlands, as an important ecosystem type, play an irreplaceable role in maintaining biodiversity, regulating regional climate, purifying water quality, and serving as carbon sinks. However, with accelerated urbanization and intensified human activities, wetland ecosystems face increasingly serious threats of degradation. Accurately assessing wetland ecological quality is of great significance for wetland protection, ecological restoration, and sustainable development.

[0003] Traditional wetland assessment methods often focus on single or a few ecological indicators, making it difficult to comprehensively reflect the overall health of wetland ecosystems. Currently, wetland ecological quality assessment mainly employs evaluation methods based on single indicator systems. Some assessment methods focus on water quality, evaluating it by monitoring water quality indicators such as pH, chemical oxygen demand, and nitrogen and phosphorus content; others focus on biodiversity, assessing ecological status by investigating the population size and distribution of flora and fauna; still others attempt to reflect wetland ecological function from a carbon sink perspective, by measuring the organic carbon content of soil and sediment. In terms of data processing, existing technologies mostly use simple weighted averages or exponential calculations, lacking in-depth fusion analysis of multi-source data.

[0004] However, existing technologies have significant shortcomings: First, traditional assessment methods often analyze various ecological indicators in isolation, lacking a synergistic consideration of water environment, biodiversity, and carbon sequestration functions, making it difficult to comprehensively reflect the overall quality of wetland ecosystems. Second, existing assessment systems are mostly static, unable to predict future trends in wetland ecological quality, and lack forward-looking guidance value. Finally, most methods do not fully consider the impact of external environmental pressures on wetland ecosystems, especially the potential threats posed by surrounding land-use changes, leading to discrepancies between assessment results and actual ecological risks. These problems severely restrict the scientific rigor and effectiveness of wetland ecological protection efforts. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a comprehensive assessment method and system for wetland ecological quality based on multi-indicator fusion, thus solving the aforementioned problems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive assessment method for wetland ecological quality based on multi-indicator fusion, specifically including the following steps:

[0007] Acquire data on wetland water environment indicators, biodiversity indicators, carbon storage indicators, and remote sensing images of wetland edges;

[0008] A multi-indicator fusion model is established based on wetland water environment index data, biodiversity index data, and carbon storage index data to generate a basic index of wetland ecological quality.

[0009] Based on the basic wetland ecological quality index, a predicted value for the basic wetland ecological quality index is generated.

[0010] A comprehensive ecological quality assessment model is established based on remote sensing images of wetland edges and predicted values ​​of basic wetland ecological quality indices, and a comprehensive wetland ecological quality index is generated.

[0011] The ecological quality of wetlands is assessed based on the comprehensive wetland ecological quality index.

[0012] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0013] Further technical solutions: The specific methods for generating the basic wetland ecological quality index include:

[0014] Based on the water environment index data, biodiversity index data, and carbon storage index data of the wetland, water environment index scores, biodiversity index scores, and carbon storage index scores are generated respectively.

[0015] A multi-indicator fusion model is established based on water environment index scores, biodiversity index scores, and carbon storage index scores to generate a basic index of wetland ecological quality.

[0016] Further technical solutions: The specific methods for generating the water environment index score, biodiversity index score, and carbon storage index score include:

[0017] Through the formula:

[0018] ;

[0019] Generate indicator score Q for indicator data type i i ;

[0020] In the formula, the indicator score Q i This includes scores for water environment indicators, biodiversity indicators, and carbon storage indicators. j This represents the standardized value of the j-th data in index data type i. Index data type i includes water environment index data, biodiversity index data, and carbon storage index data. j This represents the weight coefficient of the j-th data in index data type i, and m represents the number of data in index data type i.

[0021] Specifically, this also includes substituting wetland water environment index data, biodiversity index data, and carbon storage index data into the formula to obtain water environment index scores, biodiversity index scores, and carbon storage index scores, respectively.

[0022] Further technical solution: The expression of the multi-index fusion model is specifically as follows:

[0023] ;

[0024] In the expression, EQI base This represents the basic index of wetland ecological quality, Q. i This represents the indicator score for indicator data type i, where Q is the indicator score. i This includes water environment index scores, biodiversity index scores, and carbon storage index scores, k i This represents the weight coefficient of indicator data type i, and n represents the number of indicator data types i.

[0025] Further technical solutions: The specific methods for generating the predicted values ​​of the basic wetland ecological quality index include:

[0026] Through the formula:

[0027] ;

[0028] Generate predicted values ​​of basic wetland ecological quality index ;

[0029] In the formula, SARIMA represents the prediction function of the SARIMA model, and EQI... base (t) represents the basic index of wetland ecological quality at the current time t. This represents the historical wetland ecological quality baseline index sequence, where M represents the time length of the historical wetland ecological quality baseline index.

[0030] Further technical solutions: The specific methods for generating the comprehensive wetland ecological quality index include:

[0031] An external pressure index is generated based on remote sensing images of the wetland edge.

[0032] A comprehensive ecological quality assessment model is established based on the predicted values ​​of the basic wetland ecological quality index and the external pressure index, and a comprehensive wetland ecological quality index is generated.

[0033] Further technical solutions: The method for generating the external pressure index specifically includes:

[0034] Based on the remote sensing image of the wetland edge, obtain the terrain type and the edge length connecting the wetland edge to the connecting terrain in the remote sensing image of the wetland edge.

[0035] Through the formula:

[0036] ;

[0037] Generate the external pressure index EPI(t+1);

[0038] In the formula, L r (t+1) represents the edge length of the r-th type of connected terrain after time t+1, L total C represents the total length of the wetland edge. r (t+1) represents the closure coefficient corresponding to the r-th connected terrain after time t+1; a r represents the influence weight coefficient of the r-th type of connected terrain, and p represents the number of connected terrains.

[0039] Further technical solution: The specific expression of the comprehensive ecological quality assessment model is as follows:

[0040] ;

[0041] In the expression, EQI f This represents the comprehensive index of wetland ecological quality. This represents the predicted value of the basic wetland ecological quality index, while EPI(t+1) represents the external pressure index. This represents the pressure attenuation coefficient.

[0042] A comprehensive wetland ecological quality assessment system based on multi-indicator fusion is used to execute the aforementioned comprehensive wetland ecological quality assessment method based on multi-indicator fusion, specifically including:

[0043] The data acquisition unit is used to acquire water environment index data, biodiversity index data, carbon storage index data, and remote sensing images of wetland edges.

[0044] The ecological quality basic analysis unit is used to establish a multi-indicator fusion model based on wetland water environment index data, biodiversity index data, and carbon storage index data to generate a wetland ecological quality basic index.

[0045] The prediction unit is used to generate predicted values ​​of the basic wetland ecological quality index based on the basic wetland ecological quality index.

[0046] The ecological quality comprehensive analysis unit is used to establish an ecological quality comprehensive assessment model based on remote sensing images of wetland edges and predicted values ​​of basic wetland ecological quality indices, and to generate a comprehensive wetland ecological quality index.

[0047] The assessment unit is used to assess the ecological quality of wetlands based on the comprehensive wetland ecological quality index.

[0048] Further technical solution: The ecological quality basic analysis unit specifically includes:

[0049] The scoring generation module is used to generate water environment index scores, biodiversity index scores, and carbon storage index scores based on wetland water environment index data, biodiversity index data, and carbon storage index data, respectively.

[0050] The multi-indicator fusion module is used to establish a multi-indicator fusion model based on water environment index scores, biodiversity index scores, and carbon storage index scores to generate a basic index of wetland ecological quality.

[0051] The comprehensive ecological quality analysis unit specifically includes:

[0052] The external pressure analysis module is used to generate an external pressure index based on remote sensing images of the wetland edge.

[0053] The comprehensive analysis module is used to establish a comprehensive ecological quality assessment model based on the predicted values ​​of the basic wetland ecological quality index and the external pressure index, and to generate a comprehensive wetland ecological quality index.

[0054] This invention provides a comprehensive assessment method and system for wetland ecological quality based on multi-indicator fusion, which has the following advantages compared with existing technologies:

[0055] This invention establishes a fusion model by acquiring multi-dimensional ecological indicator data to generate basic indices, and constructs a comprehensive assessment model by combining time series forecasting and quantitative analysis of external pressures. It can integrate multi-dimensional ecological indicators such as water environment, biodiversity, and carbon sink function to achieve dynamic prediction of wetland ecological quality and quantitative assessment of the impact of external pressures, thereby improving the comprehensiveness and foresight of the assessment results. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the comprehensive assessment method for wetland ecological quality based on multi-indicator fusion provided by the present invention.

[0057] Figure 2 This is a flowchart illustrating step S20 of the present invention.

[0058] Figure 3 This is a flowchart illustrating step S40 of the present invention.

[0059] Figure 4 This is a flowchart illustrating step S50 of the present invention.

[0060] Figure 5 This is a schematic diagram of the structure of the wetland ecological quality comprehensive assessment system based on multi-index fusion provided by the present invention.

[0061] Figure 6 This is a schematic diagram of the structure of the ecological quality basic analysis unit provided by the present invention.

[0062] Figure 7 This is a schematic diagram of the structure of the ecological quality comprehensive analysis unit provided by the present invention. Detailed Implementation

[0063] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0064] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0065] Please see Figure 1 and Figure 4 The wetland ecological quality comprehensive assessment method based on multi-index fusion provided in this embodiment of the invention includes the following steps:

[0066] Step S10: Acquire water environment index data, biodiversity index data, carbon storage index data, and remote sensing images of wetland edges;

[0067] Step S20: Establish a multi-indicator fusion model based on wetland water environment index data, biodiversity index data, and carbon storage index data to generate a basic index of wetland ecological quality;

[0068] Step S30: Generate predicted values ​​of the basic wetland ecological quality index based on the basic wetland ecological quality index;

[0069] Step S40: Establish a comprehensive ecological quality assessment model based on remote sensing images of wetland edges and predicted values ​​of basic wetland ecological quality indices, and generate a comprehensive wetland ecological quality index;

[0070] Step S50: Assess the wetland ecological quality based on the comprehensive wetland ecological quality index. The assessment method may be to compare the comprehensive wetland ecological quality index with a preset threshold to output the assessment result of the wetland ecological quality. For example, if the comprehensive wetland ecological quality index is greater than or equal to the preset threshold, the wetland ecological quality is in a good state; if the comprehensive wetland ecological quality index is less than the preset threshold, the wetland ecological quality is in a dangerous state.

[0071] Among them, water environment index data refers to a set of parameters that reflect the physicochemical characteristics of wetland water bodies, including but not limited to parameters such as pH, COD, and total phosphorus; these can be obtained through laboratory testing or continuous online sensor monitoring, and are used to characterize the degree of water pollution.

[0072] Biodiversity index data refers to observational data that reflects the characteristics of plant and animal communities, including but not limited to parameters such as the survival rate of rare wild animals and the growth rate of rare wild plants; these can be obtained through field surveys or infrared camera monitoring and are used to assess the species richness of ecosystems.

[0073] Carbon storage index data refers to measurement data that characterizes the carbon sequestration capacity of wetlands, including but not limited to parameters such as soil organic carbon content, sediment organic carbon content, and DOC. Specifically, it can be obtained by soil sampling analysis or spectral inversion technology and is used to quantify carbon fixation function.

[0074] Remote sensing images of wetland edges refer to image data containing spatial information about wetland boundaries. These images can be obtained using satellite remote sensing or drone aerial photography techniques and are used to identify surrounding land use types and their impacts.

[0075] Specifically, a database covering the multidimensional characteristics of the ecosystem is established by simultaneously collecting data on three types of indicators: water environment, biodiversity, and carbon storage. A linear weighted model is used to convert heterogeneous indicators into standardized scores, and weight allocation reflects the differences in importance of different ecological elements. Time series analysis is employed to predict the trends of basic indices and capture the dynamic changes in ecological quality. Remote sensing image analysis technology is used to extract the type and length data of wetland boundary connecting terrain, constructing a pressure index reflecting the intensity of external disturbances. The predicted basic indices and the pressure index are coupled for calculation, and an exponential decay model is used to quantify the impact of external pressure on ecological quality, ultimately outputting a comprehensive assessment result.

[0076] Compared to existing technologies, traditional methods employ only a single type of indicator for static assessment, such as analyzing water quality parameters or biological population sizes in isolation. This method integrates data on water environment, biodiversity, and carbon sequestration function through a multi-indicator fusion model, enabling synergistic analysis of multidimensional ecosystem characteristics. Existing technologies lack dynamic prediction capabilities, while this method can predict trends in ecological quality evolution. Furthermore, existing schemes do not consider external environmental pressures; this method quantifies the impact of surrounding land use change through remote sensing image analysis, incorporating the effects of external disturbances into the assessment results.

[0077] Through the above technical solutions, this application achieves synergistic analysis of multi-dimensional indicators of wetland ecosystems, solving the problem of single data dimensions in traditional methods; it establishes a dynamic prediction mechanism for ecological quality, making up for the lack of foresight in static assessments; and it quantifies external environmental disturbances through a stress index, overcoming the limitation of existing technologies that ignore external influencing factors. The final comprehensive assessment results can simultaneously reflect the current ecological status of wetlands, future trends, and external pressure risks, providing data support for formulating precise protection strategies.

[0078] For preferred options, please refer to [link / reference]. Figure 2The present invention further proposes a method for generating the basic index of wetland ecological quality, specifically including:

[0079] Step S21: Based on the wetland's water environment index data, biodiversity index data, and carbon storage index data, generate water environment index scores, biodiversity index scores, and carbon storage index scores, respectively.

[0080] Step S22: Establish a multi-indicator fusion model based on water environment index scores, biodiversity index scores, and carbon storage index scores to generate a basic wetland ecological quality index;

[0081] Among them, the water environment index score refers to the comprehensive score obtained by standardizing and weighting water quality parameters such as pH value, total phosphorus, and total nitrogen. Specifically, the weighting coefficient can be determined by combining the linear weighting method with the expert scoring method, which is used to quantitatively reflect the water environment quality of wetlands.

[0082] Biodiversity index scoring refers to a comprehensive evaluation after normalizing biological data such as the retention of rare wild animals and the growth of rare wild plants. Specifically, it can be achieved by allocating weight coefficients for different species using the analytic hierarchy process (AHP) to characterize the biodiversity level of wetland ecosystems.

[0083] Carbon storage index scoring refers to the comprehensive calculation result that integrates carbon sink indicators such as soil organic carbon content, sediment organic carbon content, dissolved organic carbon and particulate organic carbon. Specifically, it can be achieved by extracting key influencing factors using principal component analysis, and is used to evaluate the carbon storage function of wetlands.

[0084] The multi-indicator fusion model is a mathematical model that comprehensively calculates the scores of ecological indicators from different dimensions. Specifically, it can be implemented by using a dynamic weighted fusion method combined with the entropy weight method to determine the fusion weight coefficients, and is used to solve the problem of collaborative analysis of heterogeneous data.

[0085] Specifically, the data for the three categories of indicators—water environment, biodiversity, and carbon storage—are first standardized to eliminate dimensional differences. For example, pH values ​​in the water environment indicators can be converted to the 0-1 range using deviation standardization. Next, a weighted summation formula is used to calculate scores for each type of indicator. The weighting coefficients can be dynamically adjusted according to wetland type differences; for example, the weighting coefficient for carbon storage can be increased for coastal wetlands. Subsequently, the scores of the three types of indicators are input into a multi-indicator fusion model, and an adaptive weighting algorithm is used for fusion calculation. The weighting coefficients can be determined by combining objective weighting using entropy weighting with subjective weighting based on expert experience. Finally, a basic wetland ecological quality index is output. This index integrates information from three dimensions—water environment health, biodiversity level, and carbon sink function intensity—to form a comprehensive quantitative evaluation of wetland ecological quality.

[0086] Compared to existing technologies, traditional methods typically only perform simple superposition calculations of single-type indicators, such as calculating water quality indices or biodiversity indices separately, lacking in-depth fusion analysis of cross-dimensional data. Existing linear weighting methods using fixed weight coefficients cannot adapt to the characteristic differences of different wetland types; for example, they do not consider the difference in carbon sequestration function between coastal and inland wetlands. This solution, by processing heterogeneous data in stages and dynamically adjusting weight coefficients, achieves effective fusion of multi-dimensional ecological indicators, solving the problems of single assessment dimensions and rigid weight settings in traditional methods.

[0087] Through the aforementioned technical solution, this application effectively integrates three types of heterogeneous ecological data: water environment, biodiversity, and carbon storage. It transforms indicators with different dimensions and attributes into comparable standardized scores, and adapts to the assessment needs of different wetland types through a dynamic weight adjustment mechanism, ultimately generating a basic index that comprehensively reflects the health status of wetland ecosystems. This technical solution overcomes the limitations of traditional assessment methods that rely on isolated analysis of single-type indicators, providing a multi-dimensional quantitative analysis method for wetland ecological quality assessment.

[0088] Preferably, the present invention further proposes a method for generating the water environment index score, biodiversity index score, and carbon storage index score, specifically including:

[0089] Through the formula:

[0090] ;

[0091] Generate indicator score Q for indicator data type i i ;

[0092] In the formula, the indicator score Q i This includes scores for water environment indicators, biodiversity indicators, and carbon storage indicators. j This represents the standardized value of the j-th data in index data type i. Index data type i includes water environment index data, biodiversity index data, and carbon storage index data. j This represents the weight coefficient of the j-th data in index data type i, and m represents the number of data in index data type i.

[0093] Specifically, this also includes: substituting the water environment index data, biodiversity index data, and carbon storage index data of wetlands into the formulas respectively to obtain the water environment index score, biodiversity index score, and carbon storage index score.

[0094] Standardized values ​​refer to normalized values ​​that eliminate the influence of different units of measurement through data preprocessing. Specifically, range standardization or Z-score standardization methods can be used to achieve this, making the data of each indicator comparable.

[0095] Weighting coefficients are parameters that reflect the relative importance of each sub-indicator within its category. They can be calculated using the analytic hierarchy process or the entropy weighting method, and the rationality of weight allocation can be ensured through objective weighting.

[0096] Specifically, for the three categories of indicators—water environment, biodiversity, and carbon storage—standardization is performed to eliminate dimensional differences, and then a linear weighted summation formula is used to calculate the score for each category. For example, parameters such as pH and total phosphorus in the water environment indicator data are standardized and converted to values ​​within the range of 0-1, then assigned different weights according to their degree of ecological impact, and the weighted summation yields the water environment indicator score. In the biodiversity indicator, the weight coefficient for rare species can be set higher than that for common species, and in the carbon storage indicator, the weight for soil organic carbon content can be higher than that for dissolved organic carbon content, thereby ensuring the dominant role of key indicators in the scoring results.

[0097] Compared with existing technologies, traditional methods, which use a single indicator for scoring, do not consider the differences in the dimensions of the indicators, and the weight allocation relies on subjective experience. This solution achieves the comparability fusion of multi-source data through standardization processing, and establishes a scientific weighting system by combining objective weighting methods, thus solving the technical obstacles of multi-dimensional data integration.

[0098] Through the above technical solutions, this application realizes the standardized conversion and scientific weighted fusion of multiple types of ecological indicator data, effectively improving the objectivity and accuracy of the scoring results and providing a reliable data foundation for subsequent ecological quality index calculation.

[0099] Preferably, the present invention further proposes the following expression for the multi-index fusion model:

[0100] ;

[0101] In the expression, EQI base This represents the basic index of wetland ecological quality, Q. i This represents the indicator score for indicator data type i, where Q is the indicator score. i This includes water environment index scores, biodiversity index scores, and carbon storage index scores, k i This represents the weight coefficient of indicator data type i, and n represents the number of indicator data types i.

[0102] In this embodiment, the value of n is 3; it should be noted that in some embodiments, the value of n can be determined based on the number of index data types.

[0103] Among them, the indicator score Q of indicator data type i iIt refers to the quantitative scoring of three indicators—water environment, biodiversity, and carbon storage—obtained through linear weighted calculation. Specifically, it can be achieved by summing the products of the standardized values ​​of each indicator and their corresponding weight coefficients, which is used to eliminate the differences in the dimensions of different indicators and reflect their relative contributions.

[0104] Weight coefficient k of indicator data type i i It refers to the importance coefficient of the three types of indicators in the comprehensive evaluation. Specifically, it can be determined by the analytic hierarchy process or the expert scoring method, and is used to adjust the degree of influence of different ecological functions on the overall quality.

[0105] Specifically, the scores for water environment indicators, biodiversity indicators, and carbon storage indicators are standardized and weighted, then linearly superimposed according to preset weighting coefficients. For example, when biodiversity has a significant impact on wetland quality, its weighting coefficient k... i The weighting coefficient can be set higher than the other two categories of indicators. A basic index characterizing the overall ecological quality of wetlands is generated by multiplying the scores of the three core indicators by their weighting coefficients and then summing the results. The dynamic adjustment mechanism of the weighting coefficients in this model can adapt to the needs of different wetland types or protection objectives; for example, the weight of the carbon storage indicator can be increased in wetlands where carbon sink functions are prioritized.

[0106] Compared to existing technologies, traditional methods often employ single-indicator assessments or simple weighted averages, such as isolated analyses using only water quality indicators or biomass data, which fail to reflect the multidimensional characteristics of ecosystems. This proposed solution, however, establishes a fusion model of multiple core indicators to achieve collaborative analysis of water environment, biodiversity, and carbon storage at the mathematical level. Furthermore, it introduces adjustable weighting coefficients to match different assessment scenarios, thus addressing the problems of one-sided indicator selection and insufficient data fusion.

[0107] Through the above technical solution, this application achieves a comprehensive quantitative assessment of three core indicators: water environment quality, biodiversity, and carbon sequestration function. By dynamically configuring weighting coefficients, key ecological functions are highlighted, generating a basic index that characterizes the overall quality of wetlands. This model avoids the assessment biases caused by redundant indicators or fixed weights in traditional methods, providing a reliable data foundation for subsequent ecological quality prediction and external pressure analysis.

[0108] Preferably, the present invention further proposes a method for generating the predicted value of the basic wetland ecological quality index, specifically including:

[0109] Through the formula:

[0110] ;

[0111] Generate predicted values ​​of basic wetland ecological quality index ;

[0112] In the formula, SARIMA represents the prediction function of the SARIMA model, and EQI... base (t) represents the basic index of wetland ecological quality at the current time t. This represents the historical wetland ecological quality basic index sequence, where M represents the time length of the historical wetland ecological quality basic index.

[0113] Among them, the SARIMA model refers to the seasonal difference autoregressive moving average model, which can be implemented by a combination of parameters including the seasonal difference order, autoregressive term and moving average term, and is used to capture the periodic fluctuations and trend changes in the ecological quality index.

[0114] The time length M refers to the time window span of historical data. It can be dynamically adjusted using a sliding window mechanism, for example, set to 12 months to cover the entire annual cycle, ensuring that the model input contains sufficient seasonal features.

[0115] The historical wetland ecological quality basic index series refers to a set of continuous observations arranged in chronological order. Specifically, it can be obtained through regular monitoring, such as collecting data once a month and storing it as a time series database to provide basic data support for prediction.

[0116] Specifically, by inputting the basic wetland ecological quality indices from the current moment and multiple historical time points into the SARIMA model, the model utilizes the linear dependence of its autoregressive term on historical data, combines a moving average term to eliminate random interference, and handles periodic fluctuations through seasonal differencing. For example, when M is 12, the model analyzes the basic index sequence over the past 12 months, identifies monthly variation patterns, and then predicts the basic index value for the next month. The model automatically adjusts the differencing order and lag order through a parameter optimization process to adapt to different wetland ecological quality change patterns, ultimately outputting prediction results with temporal continuity.

[0117] Compared to existing technologies, traditional wetland assessment methods typically rely on static data from a single point in time, failing to reflect the dynamic evolution of ecological quality. This proposed solution, by introducing a time-series prediction model, effectively utilizes the temporal correlations of historical data to identify periodic patterns and potential trends in ecological quality changes. The SARIMA model, through differencing and seasonal parameter settings, significantly improves prediction accuracy and adaptability.

[0118] Through the above technical solution, this application solves the problem that existing assessment methods cannot predict future trends in ecological quality, and realizes dynamic monitoring and forward-looking early warning of wetland ecological status. For example, when the model predicts that the baseline index will decline continuously over the next three months, ecological restoration measures can be triggered in advance to prevent further deterioration of wetland ecological quality. This technical approach provides wetland management decision-making with temporal extension capabilities, enabling protection strategies to shift from passive response to proactive intervention.

[0119] For preferred options, please refer to [link / reference]. Figure 3 The present invention further proposes a method for generating the comprehensive wetland ecological quality index, specifically including:

[0120] Step S41: Generate an external pressure index based on remote sensing images of the wetland edge;

[0121] Step S42: Establish a comprehensive ecological quality assessment model based on the predicted values ​​of the basic wetland ecological quality index and the external pressure index, and generate a comprehensive wetland ecological quality index;

[0122] Among them, the external pressure index is a quantitative index calculated by analyzing the terrain type and edge length in remote sensing images of wetland edges, combined with the closure coefficient and the influence weight coefficient. Specifically, remote sensing image processing technology can be used to extract the connection features between the wetland edge and the surrounding terrain, and the quantitative superposition of the influence of different terrains can be achieved through a weighted summation formula.

[0123] The comprehensive ecological quality assessment model is a mathematical model that uses the external pressure index as a correction factor to dynamically adjust the predicted value of the basic index of wetland ecological quality. Specifically, it can use an exponential decay function to reflect the potential impact of external pressure on ecological quality.

[0124] Specifically, remote sensing images are used to identify the connection types and corresponding edge lengths between wetland edges and surrounding terrain. Combined with the enclosure coefficients and influence weighting coefficients for different terrain types, an external pressure index is calculated to quantify the potential threats from human activities or natural disturbances. Subsequently, the predicted basic ecological quality index and the external pressure index are input into the assessment model. The basic index is dynamically adjusted using mathematical relationships, ensuring that the comprehensive index not only reflects the evolutionary trend of the wetland's own ecological quality but also reflects the cumulative impact of external environmental pressures on the ecosystem. For example, when the wetland edge connects to industrial land, a higher enclosure coefficient and influence weighting coefficient lead to a significant increase in the external pressure index, thereby reducing the comprehensive index value and accurately reflecting the threat of industrial pollution to the wetland ecosystem.

[0125] Compared to existing technologies, traditional methods typically assess wetland ecological quality based solely on static indicators, neglecting the dynamic pressures from surrounding land use changes. This proposed solution, however, dynamically monitors wetland edge changes using remote sensing images, quantifies the impact differences of various terrain types using a pressure index, and introduces a predictive model to achieve a synergistic analysis of future ecological quality and external pressures. This addresses the problem of traditional assessments ignoring the dynamic effects of external disturbance sources.

[0126] Through the above technical solutions, this application can more accurately assess the actual risks of wetland ecosystems, quantify the decay effect of external pressure on ecological quality, improve the sensitivity of assessment results to external factors such as pollutant migration and human activity disturbance, and provide dynamic and spatial data support for wetland protection decisions.

[0127] Preferably, the present invention further proposes a method for generating the external pressure index, specifically including:

[0128] Based on the remote sensing image of the wetland edge, obtain the terrain type and the edge length connecting the wetland edge to the connecting terrain in the remote sensing image of the wetland edge.

[0129] Through the formula:

[0130] ;

[0131] Generate the external pressure index EPI(t+1);

[0132] In the formula, L r (t+1) represents the edge length of the r-th type of connected terrain after time t+1, L total C represents the total length of the wetland edge. r (t+1) represents the closure coefficient corresponding to the r-th connected terrain after time t+1; a r This represents the influence weight coefficient of the r-th type of connected terrain, and p represents the number of connected terrains;

[0133] Among them, the connecting terrain type refers to the different land cover types that come into contact with the edge of the wetland and the outside world. This can be achieved by using remote sensing image classification technology to distinguish the differences in the impact of different types of human activities, such as industrial land and urban construction land, on the wetland.

[0134] Edge length refers to the boundary length where wetland contacts a specific terrain feature. It can be extracted using image processing algorithms and reflects the contact area of ​​potential interference sources.

[0135] The enclosure coefficient refers to the quantitative value of the flow efficiency of pollutants or disturbances at a specific topographic interface. It can be calibrated by combining field monitoring data. For example, the enclosure coefficient of the connected terrain with direct water body connection is higher than that of the connected terrain with natural earth slope.

[0136] The influence weighting coefficient of the connecting terrain refers to the relative intensity of the impact of different terrain types on wetland ecology. Specifically, it can be set by combining pollutant migration models with historical data analysis. For example, the influence weighting coefficient of industrial land is higher than that of urban construction land.

[0137] Specifically, by analyzing remote sensing images of wetland edges to identify connected terrain types and measuring the edge length of each type, the total edge length is normalized and multiplied by the corresponding terrain's enclosure coefficient and influence weight, then summed to obtain a comprehensive external pressure index. For example, when the contact length between the wetland edge and industrial land is relatively large, the external pressure index increases significantly due to the high influence weight of industrial land and the high enclosure of direct water body connectivity, reflecting the high infiltration risk of industrial pollution. This calculation method simultaneously considers spatial distribution characteristics and interface barrier effects, quantifying the dynamic impact of different pressure sources.

[0138] Compared to existing technologies, traditional methods typically only calculate the area proportion of land use types surrounding wetlands, ignoring differences in boundary contact length and interfacial permeability characteristics. For example, existing technologies may directly use the proportion of industrial land area as a stress indicator, but fail to consider the actual contact boundary length and the barrier effect of interfacial structures such as dikes and protective forests on pollution migration, leading to discrepancies between the assessment results and the actual ecological risks.

[0139] Through the above technical solution, this application can accurately quantify the spatial intensity and infiltration efficiency of different external pressure sources, solving the problem of neglecting boundary morphology and interface characteristics in traditional assessments. For example, after constructing an ecological buffer zone at the boundary between wetlands and farmland, although the farmland area is not reduced, the external pressure index can accurately reflect the actual effect of ecological restoration measures by reducing the closure coefficient and the proportion of contact length, providing a reliable basis for management decisions.

[0140] Preferably, the present invention further proposes the following expression for the comprehensive ecological quality assessment model:

[0141] ;

[0142] In the expression, EQI f This represents the comprehensive index of wetland ecological quality. This represents the predicted value of the basic wetland ecological quality index, while EPI(t+1) represents the external pressure index. This represents the pressure attenuation coefficient, which can be adjusted according to the sensitivity of the wetland.

[0143] Among them, the wetland ecological quality comprehensive index refers to the comprehensive assessment value after reflecting the impact of external pressure. Specifically, it can be realized by using an index decay model to couple the basic index with the external pressure index. Its role is to quantify the dynamic impact of external disturbances on ecological quality.

[0144] The pressure attenuation coefficient is a parameter that adjusts the intensity of the influence of external pressure. It can be set to different values ​​based on the wetland type and ecological sensitivity through expert experience or historical data analysis. Its role is to enable the model to adapt to different wetland ecosystems.

[0145] Specifically, this technical solution uses an exponential function to nonlinearly model the impact of external pressures on ecological quality. In the calculation process, a baseline index for the predicted future period is first used as a benchmark value. Then, an external pressure index reflecting environmental disturbances is combined, and an exponential decay mechanism is used to simulate the cumulative effect of external pressures on ecological quality. For example, when the external pressure index increases, the absolute value of the power term of the exponential function increases accordingly, leading to an accelerated decline in the comprehensive index. This aligns with the diminishing marginal returns of pressure factors on ecological quality in actual ecosystems. The pressure decay coefficient can be dynamically adjusted based on the sensitivity of the wetland ecosystem. For example, for ecologically fragile coastal wetlands, a larger coefficient value can be used to amplify the weight of the impact of external pressures. This modeling approach preserves the baseline ecological state reflected by the basic indicators while integrating the potential risks brought by external environmental pressures, enabling the assessment results to more accurately reflect the actual condition of the wetland ecosystem.

[0146] Compared to existing technologies, traditional methods often employ linear weighting to handle external stress factors, failing to reflect the nonlinear impact of cumulative stress on ecological quality. For example, existing technologies may directly subtract or proportionally calculate the stress index from the baseline index, resulting in insufficient sensitivity of the assessment results to high-stress scenarios. This proposed solution, through an exponential decay model, can more accurately simulate the ecological quality decline process under sustained external stress. In particular, when the stress index exceeds a critical value, the rapid decline in the comprehensive index can effectively warn of the risk of ecosystem collapse. Furthermore, the introduction of a stress decay coefficient enables the model to adapt to different wetland types, overcoming the assessment bias caused by fixed parameters in traditional methods.

[0147] Through the aforementioned technical solution, this application addresses the technical shortcomings of existing assessment methods that fail to adequately consider the dynamic impact of external pressures, achieving a synergistic analysis of external environmental disturbances and the ecological baseline state. Specifically, this solution can more accurately quantify the potential threats of surrounding land use changes to wetland ecosystems. For example, when industrial land connects to the edge of a wetland, a higher enclosure coefficient and impact weight will significantly reduce the comprehensive index, thus accurately reflecting the actual ecological risk. Simultaneously, by dynamically adjusting the pressure attenuation coefficient, it can be adapted to wetland types with different sensitivities. For instance, a higher coefficient can be used for urban wetlands to strengthen the assessment weight of human activity disturbances, enhancing the scientific rigor and practicality of the assessment results.

[0148] Please see Figure 5 This invention also proposes a comprehensive wetland ecological quality assessment system based on multi-indicator fusion. This system is used to execute the aforementioned comprehensive wetland ecological quality assessment method based on multi-indicator fusion, specifically including:

[0149] Data acquisition unit 10 is used to acquire water environment index data, biodiversity index data, carbon storage index data and remote sensing images of wetland edges;

[0150] The ecological quality basic analysis unit 20 is used to establish a multi-indicator fusion model based on wetland water environment index data, biodiversity index data and carbon storage index data, and generate a wetland ecological quality basic index.

[0151] Prediction unit 30 is used to generate predicted values ​​of the wetland ecological quality basic index based on the wetland ecological quality basic index.

[0152] The ecological quality comprehensive analysis unit 40 is used to establish an ecological quality comprehensive assessment model based on remote sensing images of wetland edges and predicted values ​​of basic wetland ecological quality indices, and to generate a comprehensive wetland ecological quality index.

[0153] Assessment unit 50 is used to assess the ecological quality of wetlands based on the comprehensive wetland ecological quality index.

[0154] For preferred options, please refer to [link / reference]. Figure 6 The present invention further proposes that the ecological quality basic analysis unit 20 specifically includes:

[0155] The scoring generation module 21 is used to generate water environment index scores, biodiversity index scores and carbon storage index scores respectively based on wetland water environment index data, biodiversity index data and carbon storage index data.

[0156] The multi-indicator fusion module 22 is used to establish a multi-indicator fusion model based on water environment index scores, biodiversity index scores, and carbon storage index scores to generate a basic index of wetland ecological quality.

[0157] For preferred options, please refer to [link / reference]. Figure 7 The present invention further proposes that the ecological quality comprehensive analysis unit 40 specifically includes:

[0158] External pressure analysis module 41 is used to generate an external pressure index based on remote sensing images of the wetland edge;

[0159] The comprehensive analysis module 42 is used to establish a comprehensive ecological quality assessment model based on the predicted values ​​of the basic wetland ecological quality index and the external pressure index, and to generate a comprehensive wetland ecological quality index.

[0160] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A comprehensive assessment method for wetland ecological quality based on multi-indicator fusion, characterized in that, Specifically, the following steps are included: Acquire data on wetland water environment indicators, biodiversity indicators, carbon storage indicators, and remote sensing images of wetland edges; A multi-indicator fusion model is established based on wetland water environment index data, biodiversity index data, and carbon storage index data to generate a basic index of wetland ecological quality. Based on the basic wetland ecological quality index, a predicted value for the basic wetland ecological quality index is generated. A comprehensive ecological quality assessment model is established based on remote sensing images of wetland edges and predicted values ​​of basic wetland ecological quality indices, and a comprehensive wetland ecological quality index is generated. The ecological quality of wetlands is assessed based on the comprehensive wetland ecological quality index. The specific methods for generating the comprehensive wetland ecological quality index include: An external pressure index is generated based on remote sensing images of the wetland edge. A comprehensive ecological quality assessment model is established based on the predicted values ​​of the basic wetland ecological quality index and the external pressure index, and a comprehensive wetland ecological quality index is generated. The specific methods for generating the external pressure index include: Based on the remote sensing image of the wetland edge, obtain the terrain type and the edge length connecting the wetland edge to the connecting terrain in the remote sensing image of the wetland edge. Through the formula: ； Generate the external pressure index EPI(t+1); In the formula, L r (t+1) represents the edge length of the r-th type of connected terrain after time t+1, L total C represents the total length of the wetland edge. r (t+1) represents the closure coefficient corresponding to the r-th connected terrain after time t+1; a r This represents the influence weight coefficient of the r-th type of connected terrain, and p represents the number of connected terrains; The specific expression of the comprehensive ecological quality assessment model is as follows: ； In the expression, EQI f This represents the comprehensive index of wetland ecological quality. This represents the predicted value of the basic wetland ecological quality index, while EPI(t+1) represents the external pressure index. This represents the pressure attenuation coefficient.

2. The comprehensive assessment method for wetland ecological quality based on multi-indicator fusion according to claim 1, characterized in that, The specific methods for generating the basic wetland ecological quality index include: Based on the water environment index data, biodiversity index data, and carbon storage index data of the wetland, water environment index scores, biodiversity index scores, and carbon storage index scores are generated respectively. A multi-indicator fusion model is established based on water environment index scores, biodiversity index scores, and carbon storage index scores to generate a basic index of wetland ecological quality.

3. The comprehensive assessment method for wetland ecological quality based on multi-indicator fusion according to claim 2, characterized in that, The specific methods for generating the water environment index score, biodiversity index score, and carbon storage index score include: Through the formula: ； Generate indicator score Q for indicator data type i i ; In the formula, the indicator score Q i This includes scores for water environment indicators, biodiversity indicators, and carbon storage indicators. j This represents the standardized value of the j-th data in index data type i. Index data type i includes water environment index data, biodiversity index data, and carbon storage index data. j This represents the weight coefficient of the j-th data in index data type i, and m represents the number of data in index data type i. Specifically, this also includes substituting wetland water environment index data, biodiversity index data, and carbon storage index data into the formula to obtain water environment index scores, biodiversity index scores, and carbon storage index scores, respectively.

4. The comprehensive assessment method for wetland ecological quality based on multi-indicator fusion according to claim 3, characterized in that, The specific expression of the multi-index fusion model is as follows: ； In the expression, EQI base This represents the basic index of wetland ecological quality, Q. i This represents the indicator score for indicator data type i, where Q is the indicator score. i This includes water environment index scores, biodiversity index scores, and carbon storage index scores, k i This represents the weight coefficient of indicator data type i, and n represents the number of indicator data types i.

5. The comprehensive assessment method for wetland ecological quality based on multi-indicator fusion according to claim 1, characterized in that, The specific methods for generating the predicted values ​​of the basic wetland ecological quality index include: Through the formula: ； Generate predicted values ​​of basic wetland ecological quality index ; In the formula, SARIMA represents the prediction function of the SARIMA model, and EQI... base (t) represents the basic index of wetland ecological quality at the current time t. This represents the historical wetland ecological quality baseline index sequence, where M represents the time length of the historical wetland ecological quality baseline index.

6. A comprehensive wetland ecological quality assessment system based on multi-indicator fusion, characterized in that, This system is used to execute the comprehensive wetland ecological quality assessment method based on multi-indicator fusion as described in any one of claims 1-5, specifically including: The data acquisition unit is used to acquire water environment index data, biodiversity index data, carbon storage index data, and remote sensing images of wetland edges. The ecological quality basic analysis unit is used to establish a multi-indicator fusion model based on wetland water environment index data, biodiversity index data, and carbon storage index data to generate a wetland ecological quality basic index. The prediction unit is used to generate predicted values ​​of the basic wetland ecological quality index based on the basic wetland ecological quality index. The ecological quality comprehensive analysis unit is used to establish an ecological quality comprehensive assessment model based on remote sensing images of wetland edges and predicted values ​​of basic wetland ecological quality indices, and to generate a comprehensive wetland ecological quality index. The assessment unit is used to assess the ecological quality of wetlands based on the comprehensive wetland ecological quality index.

7. The comprehensive wetland ecological quality assessment system based on multi-index fusion according to claim 6, characterized in that, The ecological quality basic analysis unit specifically includes: The scoring generation module is used to generate water environment index scores, biodiversity index scores, and carbon storage index scores based on wetland water environment index data, biodiversity index data, and carbon storage index data, respectively. The multi-indicator fusion module is used to establish a multi-indicator fusion model based on water environment index scores, biodiversity index scores, and carbon storage index scores to generate a basic index of wetland ecological quality. The comprehensive ecological quality analysis unit specifically includes: The external pressure analysis module is used to generate an external pressure index based on remote sensing images of the wetland edge. The comprehensive analysis module is used to establish a comprehensive ecological quality assessment model based on the predicted values ​​of the basic wetland ecological quality index and the external pressure index, and to generate a comprehensive wetland ecological quality index.

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