Lagon face vegetation restoration background reference value intelligent determination method

Abstract

The invention relates to the technical field of ecological restoration, in particular to a lagoon face wetland vegetation restoration background reference value intelligent determination method which comprises the following steps: S1, collecting lagoon face quadrat community structure, elevation, salinity and sediment data, recognizing vegetation distribution in combination with a remote sensing image, dividing a tidal upper zone, a tidal middle zone and a tidal lower zone according to the elevation and the tidal level, and constructing a multi-layer vegetation partition map; s2, inputting quadrat data in a tidal zone into a disturbance response model, simulating a structure recovery path under hydrological disturbance, calculating a coverage-density fluctuation index, a height recovery coefficient and a diversity change rate, and screening out a stable structure quadrat; and S3, extracting frequency, height interval, cover quartile, salinity and elevation range of the dominant species, and generating a vegetation restoration target template in combination with restoration area matching degree weighted recommendation. According to the method, intelligent extraction and location-based configuration of the lagoon face wetland vegetation restoration reference value are achieved, and scientificity, stability and practicability of a restoration scheme are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of ecological restoration technology, and in particular to a method for intelligently determining the baseline value of vegetation restoration in lagoon wetlands. Background Technology

[0002] Lagoon wetlands, as typical coastal ecosystems, play an important role in ecological barriers and biodiversity maintenance. Their vegetation restoration is crucial in regional ecological restoration projects. Due to the influence of multiple factors such as topography, hydrodynamics, and salinity gradient in the marine-terrestrial transition zone, lagoon wetlands exhibit a distinct tidal zone differentiation pattern. The vegetation type and community structure vary with the supratidal, midtidal, and subtidal zones. Therefore, determining the baseline values ​​of lagoon wetland vegetation is a prerequisite for ensuring the scientific validity and suitability of restoration plans.

[0003] However, existing technologies mostly use qualitative experience or statistical methods based on average values ​​to set vegetation restoration benchmarks, ignoring regional ecological heterogeneity and tidal zone distribution characteristics. They lack a systematic characterization of the dynamic response of community structure under hydrological disturbances, making it difficult to identify quadrats with strong ecological restoration capabilities and high structural stability. At the same time, they lack a mechanism to quantitatively match quadrat structural parameters with measured environmental conditions in the restoration area, resulting in insufficient adaptability of restoration benchmarks and difficulty in effectively guiding vegetation configuration and zonation layout. Summary of the Invention

[0004] This invention provides an intelligent method for determining baseline values ​​for vegetation restoration in lagoon wetlands. By constructing a progressive strategy of tidal zone zoning, disturbance response simulation, baseline parameter extraction, and adaptation recommendation, and integrating field quadrat survey data, multi-temporal remote sensing images, and elevation-tide models, it achieves automatic identification and parameter summarization of stable quadrats. Furthermore, it combines environmental factors of the restoration area for weighted recommendation, effectively improving the spatial adaptability, community stability, and scientific validity of restoration schemes.

[0005] A method for intelligently determining baseline values ​​for vegetation restoration in lagoon wetlands includes the following steps: S1. Collect field quadrat data in the lagoon area, including quadrat community structure information, elevation, salinity, and sediment indicators. Combine remote sensing images to identify vegetation distribution boundaries. Based on elevation and historical tidal data, divide the tidal zones into supratidal, midtidal, and subtidal zones. Construct a multi-layer vegetation zoning map to reflect the differences in vegetation community structure in each tidal zone. S2. Input the quadrat data in each tidal zone into the disturbance response window to generate a model, simulate its structural recovery path under hydrological disturbance conditions, and combine the cover-density fluctuation response index, growth height recovery coefficient and species diversity change rate to screen out quadrats with low structural fluctuation and strong recovery capacity, and construct a set of stable structural quadrats in the tidal zone. S3, based on the stable structural quadrat set within each tidal zone, extracts the frequency distribution of dominant plant species, the minimum-maximum interval of average plant height, the upper and lower quartile intervals of canopy coverage, and the suitable distribution range of soil salinity and elevation, generating a corresponding baseline parameter set. Combined with the elevation and salinity matching degree of the target restoration area, the baseline parameter set is weighted and recommended to output the vegetation restoration target baseline template.

[0006] Optionally, S1 includes: S11, within the lagoon wetland restoration area, multiple field quadrat stations were set up to collect information including vegetation community structure, elevation values ​​of the corresponding quadrat, soil salinity, and sedimentary indicators, forming a basic dataset of quadrat ecological elements; S12 combines vegetation spatial distribution maps extracted from multi-temporal remote sensing images with historical tide level sequences and elevation distribution data for matching analysis, constructs an elevation-tide joint threshold model, divides the region into supratidal, midtidal, and subtidal zones based on the frequency of wet-dry alternation and the degree of hydrological disturbance, classifies field quadrats according to their respective tidal zones, and generates multi-layer vegetation zoning maps that reflect vegetation distribution patterns and community differences within each tidal zone based on their community structure characteristics.

[0007] Optionally, S11 includes: S111, within the target area for lagoon wetland restoration, representative sample points were initially selected based on remote sensing vegetation distribution maps and habitat types (Suaeda salsa zone, Reed zone, Tamarix zone). Quadrats were then laid out according to the principles of representativeness and uniform coverage, with each quadrat being a uniform square shape and assigned a unique number. Where i is the tidal zone number (1=subtidal zone, 2=midtidal zone, 3=supratidal zone), and j is the quadrat number within that tidal zone; S112, in each quadrat Inside, record vegetation community structure information, including dominant species names and plant cover. Plant density ,average height ; S113, collect environmental factors for each quadrat center, including the elevation of the quadrat center. Soil salinity Sediment indicators, including hydrogen hydrolysate. Quick-acting phosphorus Quick-acting potassium ; S114 will collect vegetation community structure information and environmental factors at the center of each quadrat, along with latitude and longitude coordinates. This forms the basic dataset of ecological elements in the sample plots.

[0008] Optionally, S12 includes: S121. By acquiring a multi-temporal remote sensing image dataset of the lagoon area, the Normalized Difference Vegetation Index (NDVI) for each temporal phase is calculated, and the images are fused temporally to form a composite map of the maximum values. It is used to express potential long-term vegetation distribution areas in space, combined with elevation data obtained by UAVs or DEM (Digital Elevation Model). ; S122, retrieve the historical tide level series of the past 5 years, and identify the key quantiles (average annual high tide level). Normal water level Extreme low tide ), and the elevation value corresponding to the pixel By comparison, an index of the frequency of dry and wet alternation was constructed. ; S123, according to A threshold value is set for tidal zone classification, tidal zones are divided, and multi-layer vegetation zoning maps are generated. When... At that time, it indicates the subtidal zone (perennially flooded). When, it is represented as the mid-tidal zone (periodic alternation), when At that time, it is indicated as the supratidal zone (mainly the area exposed during the dry season).

[0009] Optionally, S2 includes: S21, based on the divided tidal zones, inputs the sample plot data in each tidal zone into the disturbance response window to generate a model. By simulating the dynamic evolution of the sample plot community structure under hydrological disturbance scenarios, a structural recovery path is formed within a continuous time window. The entire process of the sample plot from degradation to stability after disturbance is characterized by the time-series NDVI rebound curve, cover and density fluctuation trends, which is used to reflect its ecological structure recovery capacity and disturbance sensitivity. S22. Based on the structural parameter change trajectory recorded in the recovery path, three types of disturbance response evaluation indicators are calculated for each quadrat, including the cover-density fluctuation response index (measuring structural stability), growth height recovery coefficient (reflecting individual regeneration capacity), and species diversity change rate (assessing the extent of community reconstruction). The three indicators are evaluated jointly according to the set threshold, and quadrats that show small fluctuation amplitude and fast recovery speed under disturbance conditions are selected. Finally, a set of stable structural quadrats in each tidal zone is constructed.

[0010] Optionally, S21 includes: S211, within each tidal zone, select each quadrat. (where u represents the sample plot number), specifying a time window before and after the disturbance. The time series of its structural indicators, including the NDVI value series, were extracted. Vegetation cover sequence Plant density sequence ; S212, Set the time point of the disturbance event as... Using it as the center for quadrats Establish disturbance response window Simulate the structural changes before and after the disturbance, and calculate the normalized offset of each index in the structural recovery path, including the coverage offset. Density offset Through analysis Identify structural evolution patterns during the degradation, fluctuation, and recovery phases by analyzing the changing trends of the sequence; S213, combined with remote sensing NDVI data, extract sample plots. The normalized NDVI rebound curve within the perturbation window is used to calculate the NDVI recovery at different time points after the perturbation.

[0011] Optionally, S22 includes: S221, for sample plots Within the disturbance response time window, the relative change rate sequence of cover and density is extracted, and the cover-density fluctuation response index is calculated. ; S222, the growth height recovery coefficient is obtained by calculating the ratio of the average vegetation height at the end of the disturbance recovery period to that before the disturbance in the quadrat. ,when A value >1 indicates enhanced individual recovery. A value less than 1 indicates insufficient recovery; S223, let the species composition of the quadrat before and after the perturbation be sets respectively. and Then its species diversity change rate is ; S224, the coverage-density fluctuation response index Growth height recovery coefficient and the rate of change in species diversity Joint normalization processing was performed, and a comprehensive evaluation score for quadrat stability was constructed. And based on the set scoring threshold Filter out those that meet the requirements The sample plots were used as a set of stable structural sample plots within the tidal zone.

[0012] Optionally, S3 includes: S31. Based on the selected stable structure quadrat sets of each tidal zone, the ecological structure parameters in the stable structure quadrat sets are statistically analyzed and extracted, including the frequency distribution of dominant plant species, the minimum-maximum interval of average plant height, the upper and lower quartile intervals of cover, and the suitable distribution range of soil salinity and elevation, to generate a baseline parameter set covering the ecological state of each tidal zone. S32, the measured elevation and salinity information of the target restoration area are matched and compared with the background parameter set of the tidal zone. Based on the matching degree function, a weight factor is constructed, and the background parameter sets under different tidal zones are weighted, sorted and recommended for suitability. Finally, a vegetation restoration target benchmark template is formed, including recommended plant community structure, height and cover control range, suitable vegetation types and corresponding planting area division suggestions.

[0013] Optionally, S31 includes: S311, for each tidal zone (Where j=1,2,3, corresponding to the subtidal zone, midtidal zone, and supratidal zone respectively), extract the stable structure quadrat set. The dominant plant species recorded were analyzed, and the frequency of occurrence of each plant species was counted. Calculate its relative frequency ; S312, extract each sample plot separately. The structural parameters, including average height Coverage ,salinity Elevation The range of the intervals is calculated by grouping the tidal zones, including the intervals of plant height, canopy coverage, salinity and elevation. S313, based on relative frequency The set of baseline parameters for tidal zone is generated by considering plant height range, canopy range, and salinity and elevation range. .

[0014] Optionally, S32 includes: S321, divide the area to be repaired into several geographical unit blocks, denoted as... For each unit block, the elevation of its center point is collected. With soil salinity As an input environmental constraint factor, the environmental state of each unit block is denoted as... ; S322, for each repair unit Calculate its relationship with the three tidal zone baseline reference parameter sets. Match score ; S323, based on each repair unit Matching score for each tidal zone Calculate the tidal zone recommendation weight vector And the tidal zone recommendation weight vector The ecological structure recommendations are used to weight and combine tidal zone parameter sets, and the restoration target template for each unit block is output. This includes recommended community types, recommended plant height ranges, recommended canopy coverage ranges, suggested planting types, and spatial divisions.

[0015] The beneficial effects of this invention are: This invention constructs a multi-layered tidal zone delineation method that combines remote sensing imagery and field quadrat data. This method can accurately depict the vegetation distribution patterns of the supratidal, midtidal, and subtidal zones in lagoon wetlands. Furthermore, by utilizing the frequency of wet-dry alternation and the elevation-tidal level joint analysis mechanism, it significantly improves the environmental adaptability and ecological rationality of tidal zone identification. This effectively avoids the problem of insufficient hydrological dynamic response in traditional delineation methods and provides a scientific spatial zoning basis for restoration schemes.

[0016] This invention introduces a disturbance response window generation model and combines it with multi-dimensional evaluation indicators such as the cover-density fluctuation response index, growth height recovery coefficient, and species diversity change rate. This allows for the quantitative assessment of the structural stability and ecological restoration capacity of each quadrat under disturbance conditions, and the selection of representative stable structural quadrats. This overcomes the limitations of traditional schemes, which suffer from strong subjectivity in static sample selection and lack of dynamic verification support, and enhances the scientific rigor and robustness of baseline parameter extraction.

[0017] This invention intelligently matches the elevation and salinity information of the target restoration area with the baseline parameter set of the tidal zone, and constructs a tidal zone recommendation weight vector to achieve parameter weighted recommendation. Based on the ecological suitability of the restoration unit, it can output a personalized restoration target template, including community structure, plant height and cover range, planting combination suggestions and spatial configuration. It has high adaptability and practicality, and provides an efficient, objective and scalable vegetation configuration decision support tool for lagoon wetland ecological restoration projects. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the determination method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the layout of lagoon wetland quadrats and the division of tidal zones according to an embodiment of the present invention. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0021] like Figures 1-2 As shown, the intelligent method for determining the baseline value of vegetation restoration in lagoon wetlands includes the following steps: S1. Collect field quadrat data in the lagoon area, including quadrat community structure information, elevation, salinity, and sediment indicators. Combine remote sensing images to identify vegetation distribution boundaries. Based on elevation and historical tidal data, divide the tidal zones into supratidal, midtidal, and subtidal zones. Construct a multi-layer vegetation zoning map to reflect the differences in vegetation community structure in each tidal zone. S2. Input the quadrat data in each tidal zone into the disturbance response window to generate a model, simulate its structural recovery path under hydrological disturbance conditions, and combine the cover-density fluctuation response index, growth height recovery coefficient and species diversity change rate to screen out quadrats with low structural fluctuation and strong recovery capacity, and construct a set of stable structural quadrats in the tidal zone. S3, based on the stable structural quadrat set within each tidal zone, extracts the frequency distribution of dominant plant species, the minimum-maximum interval of average plant height, the upper and lower quartile intervals of canopy coverage, and the suitable distribution range of soil salinity and elevation, generating a corresponding baseline parameter set. Combined with the elevation and salinity matching degree of the target restoration area, the baseline parameter set is weighted and recommended to output the vegetation restoration target baseline template.

[0022] S1 includes: S11, within the lagoon wetland restoration area, multiple field quadrat stations were set up to collect information including vegetation community structure, elevation values ​​of the corresponding quadrat, soil salinity, and sedimentary indicators, forming a basic dataset of quadrat ecological elements; S12 combines vegetation spatial distribution maps extracted from multi-temporal remote sensing images with historical tide level sequences and elevation distribution data for matching analysis, constructs an elevation-tide joint threshold model, divides the region into supratidal, midtidal, and subtidal zones based on the frequency of wet-dry alternation and the degree of hydrological disturbance, classifies field quadrats according to their respective tidal zones, and generates multi-layer vegetation zoning maps that reflect vegetation distribution patterns and community differences within each tidal zone based on their community structure characteristics.

[0023] S11 includes: S111, within the target area for lagoon wetland restoration, representative sample points were initially selected based on remote sensing vegetation distribution maps and habitat types (Suaeda salsa zone, Reed zone, Tamarix zone). Quadrats were then laid out according to the principles of representativeness and uniform coverage. The quadrats were uniformly square in shape, with 1m×1m (for large vegetation) or 0.5m×0.5m (for low-lying communities). Each quadrat was assigned a unique number. Where i is the tidal zone number (1=subtidal zone, 2=midtidal zone, 3=supratidal zone), and j is the quadrat number within that tidal zone; S112, in each quadrat Inside, record vegetation community structure information, including dominant species names and plant cover. Plant density ,average height ; S113, collect environmental factors for each quadrat center, including the elevation of the quadrat center. Soil salinity Sediment indicators, including hydrogen hydrolysate. Quick-acting phosphorus Quick-acting potassium ; S114 will collect vegetation community structure information and environmental factors at the center of each quadrat, along with latitude and longitude coordinates. This forms the basic dataset of ecological elements in the sample plots.

[0024] S12 includes: S121. By acquiring a multi-temporal remote sensing image dataset of the lagoon area, the Normalized Difference Vegetation Index (NDVI) for each temporal phase is calculated, and the images are fused temporally to form a composite map of the maximum values. It is used to express potential long-term vegetation distribution areas in space, combined with elevation data obtained by UAVs or DEM (Digital Elevation Model). , represented as: ; in, For the t-th time point, the position NDVI value, For the t-th time phase, the pixel Near-infrared reflectivity, For the t-th time phase, the pixel Reflectivity in the red light band; ; in, For pixels The maximum value of NDVI across all time phases represents the vegetation growth potential at that location, where T is the total number of time phases of the remote sensing image used for synthesis. S122, retrieve the historical tide level series of the past 5 years, and identify the key quantiles (average annual high tide level). Normal water level Extreme low tide ), and the elevation value corresponding to the pixel By comparison, an index of the frequency of dry and wet alternation was constructed. , represented as: ; in, This is an index of the frequency of wet and dry alternation, representing the pixel. The annual frequency of tidal flooding, where N is the total number of observation days. This is an indicator function that takes the value 1 if the condition is true and 0 otherwise. The tide level on day t; S123, according to A threshold value is set for tidal zone classification, tidal zones are divided, and multi-layer vegetation zoning maps are generated. When... At that time, it indicates the subtidal zone (perennially flooded). When, it is represented as the mid-tidal zone (periodic alternation), when At that time, it is indicated as the supratidal zone (mainly the area exposed during the dry season).

[0025] S2 includes: S21, based on the divided tidal zones, inputs the sample plot data in each tidal zone into the disturbance response window to generate a model. By simulating the dynamic evolution of the sample plot community structure under hydrological disturbance scenarios, a structural recovery path is formed within a continuous time window. The entire process of the sample plot from degradation to stability after disturbance is characterized by the time-series NDVI rebound curve, cover and density fluctuation trends, which is used to reflect its ecological structure recovery capacity and disturbance sensitivity. S22. Based on the structural parameter change trajectory recorded in the recovery path, three types of disturbance response evaluation indicators are calculated for each quadrat, including the cover-density fluctuation response index (measuring structural stability), growth height recovery coefficient (reflecting individual regeneration capacity), and species diversity change rate (assessing the extent of community reconstruction). The three indicators are evaluated jointly according to the set threshold, and quadrats that show small fluctuation amplitude and fast recovery speed under disturbance conditions are selected. Finally, a set of stable structural quadrats in each tidal zone is constructed.

[0026] S21 includes: S211, within each tidal zone, select each quadrat. (where u represents the sample plot number), specifying a time window before and after the disturbance. The time series of its structural indicators, including the NDVI value series, were extracted. Vegetation cover sequence Plant density sequence ; S212, Set the time point of the disturbance event as... Using it as the center for quadrats Establish disturbance response window Simulate the structural changes before and after the disturbance, and calculate the normalized offset of each index in the structural recovery path, including the coverage offset. Density offset Through analysis The changing trends of the sequence identify the structural evolution patterns during the degradation, fluctuation, and recovery phases, represented as follows: ; ; in, This is the reference time before the disturbance. For sample plots Coverage offset at time t For sample plots Density shift at time t; The phase division of the structural evolution model is represented as follows: Degeneration period: when or hour, ; Fluctuation period: when and hour, ; Recovery period: when or hour, ; in, The relative rate of change of coverage. The relative rate of change of density, The degradation threshold is set to 0.05, which represents a 5% decrease. To restore the judgment threshold (set to 0.05 to represent a 5% increase), The stage to which time t belongs; S213, combined with remote sensing NDVI data, extract sample plots. The normalized NDVI rebound curve within the perturbation window is used to calculate the NDVI recovery at different time points after the perturbation, expressed as follows: ; in, For sample plots NDVI recovery at time t The time when the disturbance occurs. To recover to the point where a stable plateau period has been reached, The lowest NDVI value at the instant of the disturbance. The mean NDVI value during the post-recovery plateau period.

[0027] S22 includes: S221, for sample plots Within the disturbance response time window, the relative change rate sequence of cover and density is extracted, and the cover-density fluctuation response index is calculated. , represented as: ; in, For sample plots The coverage-density fluctuation response index , Let be the coverage and density at time q of the perturbation window, respectively. , , respectively, represent the average coverage and density within the entire disturbance window, and L is the total number of time steps within the disturbance window; S222, the growth height recovery coefficient is obtained by calculating the ratio of the average vegetation height at the end of the disturbance recovery period to that before the disturbance in the quadrat. ,when A value >1 indicates enhanced individual recovery. When the value is less than 1, it indicates insufficient recovery, represented as: ; in, To restore the average height of vegetation during the plateau period, The average height at the reference time before the disturbance; S223, let the species composition of the quadrat before and after the perturbation be sets respectively. and Then its species diversity change rate is , represented as: ; in, =0 indicates no change, meaning the species composition remains stable; the larger the value, the greater the extent of community reconstruction. S224, the coverage-density fluctuation response index Growth height recovery coefficient and the rate of change in species diversity Joint normalization processing was performed, and a comprehensive evaluation score for quadrat stability was constructed. And based on the set scoring threshold Filter out those that meet the requirements The sample plots, representing a set of stable structures within the tidal zone, are denoted as: ; in, , , They are respectively , , The normalized value, , , These are the corresponding weight coefficients; Scoring threshold The specific settings include: (1) Grouping quadrats according to their tidal zones: All quadrats are divided into three scoring sets according to their tidal zones (subtidal, mid-tidal, supratidal), as shown below: For subtidal zone sample plot scoring set; For the scoring set of quadrats in the mid-tidal zone; For the scoring set of the uptidal zone sample plots; (2) Calculate the quantile threshold within each tidal zone score set: For each score set Let the threshold be its qth quantile (q=0.75), expressed as: ; in, The stability score threshold in tidal zone i. For the rating set The value of the qth quantile.

[0028] S3 includes: S31. Based on the selected stable structure quadrat sets of each tidal zone, the ecological structure parameters in the stable structure quadrat sets are statistically analyzed and extracted, including the frequency distribution of dominant plant species, the minimum-maximum interval of average plant height, the upper and lower quartile intervals of cover, and the suitable distribution range of soil salinity and elevation, to generate a baseline parameter set covering the ecological state of each tidal zone. S32, the measured elevation and salinity information of the target restoration area are matched and compared with the background parameter set of the tidal zone. Based on the matching degree function, a weight factor is constructed, and the background parameter sets under different tidal zones are weighted, sorted and recommended for suitability. Finally, a vegetation restoration target benchmark template is formed, including recommended plant community structure, height and cover control range, suitable vegetation types and corresponding planting area division suggestions.

[0029] S31 includes: S311, for each tidal zone (Where j=1,2,3, corresponding to the subtidal zone, midtidal zone, and supratidal zone respectively), extract the stable structure quadrat set. The dominant plant species recorded were analyzed, and the frequency of occurrence of each plant species was counted. Calculate its relative frequency , represented as: ; in, Let be the relative frequency of plant species s in the tidal zone j. This is a collection of plant species that have appeared in the tidal zone j; S312, extract each sample plot separately. The structural parameters, including average height Coverage ,salinity Elevation The range of the zone is calculated by grouping by tidal zone, including the range of plant height, canopy coverage, salinity, and elevation. Specifically, it includes: Plant height range: ; ; in, , These are the minimum and maximum values ​​of the average height of the quadrat in tidal zone j, respectively. Coverage range: ; ; in, , These are the first and third quartiles of the cover set in tidal zone j, respectively. Let J be the set of coverage of stable quadrats in tidal zone j; Salinity and elevation range: ; ; ; ; in, , Let be the minimum and maximum values ​​of the salinity set in tidal zone j, respectively. , These are the minimum and maximum values ​​of the elevation set in tidal zone j, respectively. S313, based on relative frequency The set of baseline parameters for tidal zone is generated by considering plant height range, canopy range, and salinity and elevation range. , represented as: ; in, The dominant species frequency threshold.

[0030] S32 includes: S321, divide the area to be repaired into several geographical unit blocks, denoted as... For each unit block, the elevation of its center point is collected. With soil salinity As an input environmental constraint factor, the environmental state of each unit block is denoted as... ; S322, for each repair unit Calculate its relationship with the three tidal zone baseline reference parameter sets. Match score , represented as: ; in, The score represents the matching degree between unit k and tidal zone l; the closer to 1, the better the fit. The median elevation of the tidal zone l. This represents the median salinity of the tidal zone. , These are the matching tolerance coefficients for elevation and salinity, respectively. S323, based on each repair unit Matching score for each tidal zone Calculate the tidal zone recommendation weight vector And the tidal zone recommendation weight vector The ecological structure recommendations are used to weight and combine tidal zone parameter sets, and the restoration target template for each unit block is output. This includes recommended community type, recommended plant height range, recommended canopy coverage range, suggested planting type, and spatial division, expressed as: ; ; in, For tidal zone index variables, For repair unit Total matching value for all tidal zones; Recommended community types are sorted by weighted dominant species, and are represented as follows: ; in, For the kth repair unit The overall recommended score for plant species s in the middle; The recommended plant height range is expressed as follows: ; in, , These represent the recommended lower and upper limits for plant height in repair unit k, respectively. , These represent the minimum and maximum plant heights in the stable quadrat of tidal zone l, respectively. The recommended coverage range is expressed as follows: ; in, , These are the recommended lower and upper bounds of the coverage for repair unit k, respectively. , These are the first and third quartiles of the stable quadrat cover in tidal zone l, respectively; The recommended planting type should be based on the top-ranked plant species and the environmental indicators (elevation) of the target site. ,salinity Suitability assessment (e.g., whether it falls within its suitable growth parameter range) is conducted to determine the appropriate plant combination type (e.g., monoculture, mixed community, shrub-grass combination, etc.). Spatial partitioning divides each unit block The recommended community type is bound to the parameters to form a recommended community type-spatial location mapping table, and spatial vegetation configuration maps are generated according to geographical order or ecological zoning.

[0031] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0032] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent determination of background reference values for lagoon wetland vegetation restoration, characterized in that, Comprise the following steps: S1, collect the field plot data of the lagoon area, including plot community structure information, elevation, salinity, sediment index, and identify the vegetation distribution boundary combining remote sensing image, divide the tidal zone according to the elevation and historical tidal data, including supratidal zone, intertidal zone and subtidal zone, and construct a multi-layer vegetation zoning map reflecting the differences in vegetation community structure in each tidal zone; S2, input the plot data in each tidal zone into the disturbance response window generation model to simulate its structure recovery path under hydrological disturbance conditions, combine the coverage-density fluctuation response index, growth height recovery coefficient and species diversity change rate to screen out the plots with low structure fluctuation and strong recovery capacity, and construct a stable structure plot set in the tidal zone; S3, based on the stable structure plot set in each tidal zone, extract the frequency distribution of dominant plant species, the minimum-maximum interval of average plant height, the upper and lower quartile interval of coverage, the suitable distribution range of soil salinity and elevation, generate the corresponding background benchmark parameter set, and combine the elevation and salinity matching degree of the target restoration area to recommend the background benchmark parameter set with weight, and output the vegetation restoration target benchmark template.

2. The method according to claim 1, wherein, The S1 comprises: S11, in the lagoon wetland restoration area, arrange multiple field plot stations, collect the vegetation community structure information, the elevation value of the corresponding plot, the soil salinity and the sediment index to form the plot ecological element basic data set; S12, combine the vegetation spatial distribution map extracted from the multi-temporal remote sensing image with the historical tidal level sequence and elevation distribution data for matching analysis, construct an elevation-tidal level joint threshold model, divide the area into supratidal zone, intertidal zone and subtidal zone according to the dry-wet alternating frequency and hydrological disturbance degree, classify the field plots according to the tidal zone they belong to, and generate a multi-layer vegetation zoning map reflecting the vegetation distribution pattern and community difference in each tidal zone according to their community structure characteristics.

3. The method according to claim 2, wherein, The S11 comprises: S111, in the target area of the lagoon wetland restoration, combining the remote sensing vegetation distribution map and the habitat type to carry out the initial selection of the representative sample points, and arranging the quadrats according to the principle of representation and uniform coverage, the shape of the quadrat is unified as a square, each quadrat is assigned a unique number where i is the tidal zone number, and j is the sample number in the tidal zone. S112, in each quadrat Inside, record vegetation community structure information, including dominant species names and plant cover. Plant density ,average height ; S113, collect environmental factors at the center of each sample plot, including the elevation of the sample plot center , soil salinity , sediment indicators, including hydrolytic hydrogen , available phosphorus , available potassium ; S114, the vegetation community structure information of each sample plot, the environmental factors at the center of the sample plot, and the matching latitude and longitude coordinates are collected to form a sample plot ecological element basic data set.

4. The method according to claim 3, wherein, The S12 comprises: S121, by acquiring the multi-temporal remote sensing image data set of the lagoon area, calculating the normalized vegetation index NDVI of each time phase, and fusing to form a maximum composite graph in time sequence , for expressing the potential long-term distribution area of vegetation in space, combined with the elevation data obtained by the unmanned aerial vehicle or DEM ; S122, call the historical tidal level sequence of the past 5 years, identify the key quantile, and the elevation value corresponding to the pixel Comparison, build dry-wet alternating frequency index ; S123, in accordance with the value setting the tidal zone classification threshold value, dividing the tidal zone and generating a multi-layer vegetation zoning map, wherein, when , it is represented as a subtidal zone, when , it is represented as a mid-tidal zone, and when , it is represented as a supratidal zone.

5. The method according to claim 4, wherein, The S2 comprises: S21, based on the divided tidal zone, input the plot data in each tidal zone into the disturbance response window generation model, simulate the dynamic evolution process of plot community structure under hydrological disturbance scenario, form the structure recovery path in the continuous time window, and depict the whole process from degradation to stability of the plot after disturbance through the timing NDVI rebound curve, coverage and density fluctuation trend, for reflecting its ecological structure recovery capacity and disturbance sensitivity; S22, based on the structure parameter change trajectory recorded in the recovery path, calculate three types of disturbance response evaluation indexes of each plot, including coverage-density fluctuation response index, growth height recovery coefficient and species diversity change rate, evaluate the three indexes according to the set threshold, select the plots with small fluctuation amplitude and fast recovery speed under disturbance, and finally construct a stable structure plot set in each tidal zone.

6. The method of claim 5, wherein, The S21 comprises: S211, within each tidal zone, select each quadrat. Within a specified time window before and after the disturbance The time series of its structural indicators, including the NDVI value series, were extracted. Vegetation cover sequence Plant density sequence ; S212, Set the time point of the disturbance event as... Using it as the center for quadrats Establish disturbance response window Simulate the structural changes before and after the disturbance, and calculate the normalized offset of each index in the structural recovery path, including the coverage offset. Density offset Through analysis Identify structural evolution patterns during the degradation, fluctuation, and recovery phases by analyzing the changing trends of the sequence; S213, combined with remote sensing NDVI data, extract quadrat The normalized NDVI rebound curve in the disturbance window is calculated, and the NDVI recovery degree at different time points after the disturbance is calculated.

7. The method of claim 6, wherein, The S22 comprises: S221, for the sample The relative change rate sequence of its coverage and density is extracted within the disturbance response time window, and a coverage-density fluctuation response index is calculated ; S222, the recovery coefficient of growth height is obtained by calculating the ratio of the average height of vegetation in the end of disturbance recovery to that before disturbance when >1, it indicates that the individual recovery is enhanced, and when <1, it indicates that the recovery is insufficient; S223, the species composition of the sample plot before and after disturbance is set as a set and The rate of change of species diversity is ; S224, the cover degree-density fluctuation response index , the growth height recovery coefficient , and the species diversity change rate Joint normalization processing, and constructing the quadrat stability comprehensive evaluation score , and according to the set score threshold , the quadrats that meet are screened out as the stable structure quadrat set in the tidal zone.

8. The intelligent determination method of the lagoon wetland vegetation restoration baseline value according to claim 7, characterized in that, The S3 comprises: S31, based on the screened each tidal zone stable structure sample set, statistics and extract the ecological structure parameters in the stable structure sample set, including the frequency distribution of dominant plant species, the minimum-maximum interval of average plant height, the upper and lower quartile interval of coverage, the suitable distribution range of soil salinity and elevation, and generate the background benchmark parameter set covering the ecological state of each tidal zone; S32, match and compare the measured elevation and salinity information of the target repair area with the tidal zone background benchmark parameter set, construct a weight factor based on the matching degree function, weight and sort the background parameter set under different tidal zones, and adaptively recommend, finally form the vegetation repair target benchmark template, including the recommended plant community structure, height and coverage control range, suitable vegetation type and corresponding planting area division suggestion. 9.The method according to claim 8, wherein, The S31 comprises: S311, for each tidal zone , extract the dominant plant species recorded in the stable structure quadrat set , count the occurrence frequency of each plant species s , calculate the relative frequency ; S312, extract each sample plot separately. The structural parameters, including average height Coverage ,salinity Elevation The range of the intervals is calculated by grouping the tidal zones, including the intervals of plant height, canopy coverage, salinity and elevation. S313, based on the relative frequency , plant height interval, coverage interval, salinity and elevation interval to generate a tidal zone baseline parameter set . 10.The method according to claim 9, wherein, The S32 comprises: S321, divide the area to be repaired into several geographical unit blocks, denoted as , collect the elevation of the center point of each unit block and soil salinity , as an environmental constraint factor, record the environmental state of each unit block as ; S322, for each repair unit , compute its matching score with the three tidal band baseline parameter sets ;​ S323, calculating a tidal zone recommendation weight vector according to each repair unit a matching score of each tidal zone , calculating a tidal zone recommendation weight vector and weighting combining the ecological structure recommendation values in the tidal zone parameter set with the tidal zone recommendation weight vector to output a repair target template of each unit block including a recommended community type, a plant height recommendation interval, a coverage recommendation range, a recommended planting type, and a space division.

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