Small and micro wetland value evaluation method and system fusing multi-source data

Abstract

The invention provides a multi-source data fused small and micro wetland value evaluation method and system, and relates to the technical field of ecological environment monitoring and remote sensing, and the method comprises the steps: collecting multi-source remote sensing data of a research area, and extracting small and micro wetland pattern spots from the multi-source remote sensing data; performing index evaluation on the small and micro wetland pattern spots based on a kernel density analysis method and landscape indexes to obtain landscape structure and connectivity value indexes; inverting ecological function parameters corresponding to the small and micro wetland pattern spots according to the time sequence remote sensing data to obtain ecological function and specific value indexes; calculating social service and proximity value indexes based on the multi-source remote sensing data and the interest points and media data of the small and micro wetland pattern spots in a preset service radius; and constructing a small and micro wetland value evaluation comprehensive index system based on the plurality of value indexes, calculating a comprehensive value index, and generating a small and micro wetland value grade map according to the comprehensive value index. According to the method, the refinement degree of the value evaluation result of the small and micro wetlands can be improved.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and remote sensing technology, and in particular to a method and system for evaluating the value of small wetlands by integrating multi-source data. Background Technology

[0002] Small wetlands refer to wetland ecological units that are small in area (usually less than 8 hectares), scattered in distribution, and diverse in form. Despite their limited individual size, small wetlands play an irreplaceable ecological role as an important component of the ecosystem, maintaining regional biodiversity, purifying water quality, and regulating local climate. However, due to their "small" characteristics (such as fragmented spatial distribution, highly dynamic hydrological conditions, and networked ecological functions), traditional evaluation systems that focus on large wetlands are significantly insufficient in terms of scale adaptability and indicator specificity, making it difficult to accurately quantify their unique ecological and service value. Existing evaluation techniques mostly focus on the macro-ecological processes of large wetlands, lacking a specific indicator system for the spatial heterogeneity and functional specificity of small wetlands, leading to a systematic underestimation or neglect of their ecological value.

[0003] Chinese patent CN110852579A discloses a method for evaluating hydrological connectivity based on the landscape connectivity index, including the following steps: extracting water body information of the study area; generating node files and connection files; determining distance thresholds; calculating the hydrological connectivity of the area using the landscape connectivity index; and screening important water body patches. However, the above scheme only constructs an evaluation index system based on a single ecological or landscape feature, and only qualitatively or semi-quantitatively evaluates the value of wetlands based on a few structural features such as area and shape. It lacks a system design for multi-dimensional collaborative measurement, making it difficult for the evaluation results to reflect the comprehensive value of small wetlands. Therefore, it is necessary to provide a method and system for evaluating the value of small wetlands that integrates multi-source data to improve the refinement of the evaluation results. Summary of the Invention

[0004] In view of this, the present invention proposes a method and system for evaluating the value of small wetlands by integrating multi-source data.

[0005] This invention provides a method for evaluating the value of small wetlands by integrating multi-source data, the method comprising: Collect multi-source remote sensing data of the study area and extract small wet map patches from the multi-source remote sensing data; Based on kernel density analysis and landscape indices, the small and micro wetland patches are evaluated to obtain the landscape structure and connectivity value indices corresponding to the small and micro wetland patches. Ecological function parameters corresponding to the small and micro wetland patches are retrieved based on time-series remote sensing data to obtain the ecological function and specific value indicators corresponding to the small and micro wetland patches. Based on the multi-source remote sensing data and the points of interest and media data of the small wetland patches within the preset service radius, calculate the social service and proximity value index corresponding to the small wetland patches; Based on the landscape structure and connectivity value indicators, the ecological function and specificity value indicators, and the social service and proximity value indicators, a comprehensive index system for evaluating the value of micro-wetlands is constructed and a comprehensive value index is calculated. A value level map of micro-wetlands is generated based on the comprehensive value index.

[0006] Based on the above technical solutions, preferably, the multi-source remote sensing data of the collected study area specifically includes: Acquire optical remote sensing data or radar remote sensing data, and perform radiometric calibration, atmospheric correction, geometric fine correction and cropping on the optical remote sensing data or radar remote sensing data to obtain registered remote sensing data; Based on image classification methods, wetland land features are identified by fusing spectral, texture, and shape features from the registered remote sensing data, and small wetland patches with areas within a preset region are extracted from the registered remote sensing data.

[0007] Based on the above technical solutions, preferably, the step of obtaining the landscape structure and connectivity value indicators corresponding to the small wetland patches specifically includes: The spatial distribution of the small wetland patches is calculated based on the kernel density analysis method to obtain the spatial clustering intensity of the small wetlands; Based on landscape ecology methods, various landscape indices corresponding to the small wetland patches are calculated. The landscape indices include average patch area, landscape shape index, aggregation index, nearest neighbor distance, spread index, scattering and juxtaposition index, and Shannon diversity index. According to the preset index system, the spatial agglomeration intensity index and the various landscape indices corresponding to the small and micro wetland patches are divided into landscape structure and connectivity value indicators, and each small and micro wetland patch is assigned a corresponding landscape structure and connectivity value indicator.

[0008] More preferably, the landscape structure and connectivity value index includes a landscape structure value index and a connectivity value index, wherein, The landscape structure value index includes one or more of the average patch area, the landscape shape index, the Shannon diversity index, and the spread index. The landscape structure value index is used to characterize the patch size, shape complexity, and overall pattern formed by the small and micro wetland patches and other landscape types. The connectivity value index includes one or more of the nearest neighbor distance, the aggregation index, the dispersion and juxtaposition index, and the spatial clustering intensity. The connectivity value index is used to characterize the spatial connectivity, mutual adjacency, and potential ecological corridors between small wetland patches.

[0009] More preferably, the step of retrieving the ecological function parameters corresponding to the small wetland patches based on time-series remote sensing data to obtain the ecological function and specific value indicators of the small wetland patches specifically includes: Based on the time-series remote sensing data, the ecological function parameters of the small wetland corresponding to the small wetland patches, including hydrological dynamics, vegetation growth, and water quality, are retrieved. Based on the ecological function parameters, ecological function and specific value indicators are constructed, and each small wetland patch is assigned a corresponding ecological function and specific value indicator. The ecological function and specific value indicators include hydrological dynamic index, habitat quality index, and water purification potential.

[0010] More preferably, the step of calculating the social service and proximity value index of the small wetland based on the multi-source remote sensing data and the points of interest and media data of the small wetland patches within a preset service radius specifically includes: Based on the boundary and surrounding land cover information of the small and micro wetland patches, the size of the beneficiary population, the spatial distance from residential areas and main transportation routes, and the spatial adjacency relationship with public service facilities and ecologically sensitive targets within the preset service radius of each small and micro wetland patch are calculated. Based on the size of the beneficiary population, the spatial distance, and the spatial adjacency relationship, a social service and proximity value index is constructed, and each small wetland patch is assigned a corresponding social service and proximity value index, wherein the social service and proximity value index includes a social service accessibility index, a beneficiary population index, and a proximity index.

[0011] More preferably, the points of interest and media data include basic geographic and transportation data, public service and sensitive target data, climate regulation service data, and aesthetic and cultural perception data.

[0012] A second aspect of this application provides a value assessment system for small wetlands that integrates multi-source data. The system includes a data acquisition module, an indicator evaluation module, and a value assessment module. The data acquisition module is used to collect multi-source remote sensing data of the study area and extract small wet map patches from the multi-source remote sensing data. The indicator evaluation module is used to evaluate the small wetland patches based on kernel density analysis and landscape indices, obtain the landscape structure and connectivity value indicators corresponding to the small wetland patches, invert the ecological function parameters corresponding to the small wetland patches based on time-series remote sensing data, obtain the ecological function and specific value indicators corresponding to the small wetland patches, and calculate the social service and proximity value indicators corresponding to the small wetland patches based on the multi-source remote sensing data and the points of interest and media data of the small wetland patches within a preset service radius. The value evaluation module is used to construct a comprehensive index system for evaluating the value of micro-wetlands based on the landscape structure and connectivity value indicators, the ecological function and specificity value indicators, and the social service and proximity value indicators, and to calculate the comprehensive value index. Based on the comprehensive value index, a value level map of micro-wetlands is generated.

[0013] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0014] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the steps of a method for evaluating the value of small wetlands by integrating multi-source data.

[0015] The method and system for evaluating the value of small wetlands by integrating multi-source data provided by this invention have the following advantages over existing technologies: (1) By fusing multi-source remote sensing data, small wetland patches can be extracted in detail, which can more accurately identify small wetlands that are small in area, irregular in shape, and scattered in distribution. Combined with kernel density analysis and landscape index, the characteristics of small wetlands can be characterized from multiple dimensions such as spatial distribution, morphological structure and connectivity. Furthermore, by using time-series remote sensing data to invert ecological function parameters, the changing trend of ecological functions of small wetlands can be reflected from the time dimension. The ecological functions and specific value of small wetlands can be quantitatively characterized. Based on the points of interest and media data of small wetlands within the preset service radius, social service and proximity value indicators can be established. Overcoming the shortcomings of traditional wetland assessments that only emphasize ecological attributes and neglect human use and service accessibility, this approach makes assessment results more aligned with actual management and utilization needs. Furthermore, it integrates three major categories of indicators into a comprehensive indicator system, using a comprehensive value index to quantify and rank the value of small wetlands. This facilitates horizontal comparisons and prioritization in multi-objective, multi-scale management scenarios, reducing omissions and misjudgments, and improving the precision of small wetland value assessment results. The comprehensive value index generates a small wetland value level map, visually representing complex, multi-source, multi-dimensional indicator results as a spatial distribution layer.

[0016] (2) The spatial distribution of small wetland patches is calculated by kernel density analysis to quantify the spatial aggregation intensity. Compared with traditional visual interpretation or simple distance statistics, it can more objectively and continuously reflect the aggregation and dispersion pattern of small wetlands in the region. At the same time, by introducing a variety of landscape indices such as average patch area, landscape shape index, aggregation index, nearest neighbor distance, spread index, scattering and juxtaposition index, and Shannon diversity index through landscape ecology methods, the landscape structure of small wetland patches can be systematically quantified from multiple dimensions such as area scale, shape complexity, aggregation degree, spatial proximity, landscape connectivity and expansion, patch combination relationship and landscape diversity. Compared with the method of using only a single or a few structural indicators, it can more comprehensively reflect the complexity and heterogeneity of the landscape pattern of small wetlands. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for evaluating the value of small wetlands that integrates multi-source data, provided by this invention; Figure 2 This is a schematic diagram of the structure of the micro-wetland value assessment system provided by the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0019] Explanation of reference numerals in the attached diagram: 1. Small wetland value assessment system; 11. Data acquisition module; 12. Indicator evaluation module; 13. Value assessment module; 2. Electronic equipment; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. Detailed Implementation

[0020] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses a method for evaluating the value of small wetlands by integrating multi-source data, with reference to Figure 1The method includes steps S1 to S5.

[0022] Step S1: Collect multi-source remote sensing data of the study area and extract small wet map patches from the multi-source remote sensing data.

[0023] In this step, the study area can be an administrative unit boundary (such as a county, city, or watershed unit) or a custom area delineated according to ecological protection needs, represented by a vector boundary file. Based on the determined study area, the spatial resolution and temporal requirements of the remote sensing data are determined according to the typical spatial scale and identification accuracy requirements of small wetlands. Preferably, the area of ​​small wetlands is generally in the range of tens of square meters to several hectares; therefore, the spatial resolution of the remote sensing data is preferably no greater than 30 m, and more preferably at the 10 m level. Simultaneously, considering local hydrological rhythms and vegetation growth characteristics, several representative temporal phases reflecting the wet season, dry season, and typical growing season are selected as the temporal conditions for subsequent remote sensing data acquisition.

[0024] Based on clearly defined spatial and temporal requirements, multi-source remote sensing data covering the study area were acquired from publicly available or authorized remote sensing data platforms. Multi-source remote sensing data included multispectral optical remote sensing imagery and / or synthetic aperture radar (SAR) imagery. Multispectral optical remote sensing imagery provided high-resolution spectral and textural information to facilitate the identification of water bodies, wetland vegetation, and surrounding land use types; synthetic aperture radar (SAR) imagery provided backscattering information insensitive to cloud and rain conditions to facilitate the identification of water bodies and chronically saturated surfaces under cloudy and rainy conditions.

[0025] In practice, one or more of the following high-resolution optical satellite data—Sentinel2, Landsat8 / 9, and the GF series—can be selected as the optical data source; Sentinel1 or similar C-band SAR data can be selected as the radar data source. The required image data can be retrieved and downloaded through the ESA Copernicus open data platform or other legitimate channels, based on conditions such as the research area boundary, preset time range, and cloud cover threshold.

[0026] This step also includes steps S11 to S12.

[0027] Step S11: Acquire optical remote sensing data or radar remote sensing data, and perform radiometric calibration, atmospheric correction, geometric fine correction and cropping on the optical remote sensing data or radar remote sensing data to obtain registered remote sensing data.

[0028] In this step, radiometric calibration and atmospheric correction are performed on the optical remote sensing images in the optical remote sensing data to convert the original digital values ​​into surface reflectance in order to eliminate sensor characteristics and atmospheric effects; radiometric calibration and terrain correction are performed on the SAR images in the radar remote sensing data to convert them into backscattering coefficients.

[0029] Geometric and orthorectification corrections were performed on each source image to eliminate geometric distortions caused by terrain and attitude, unifying optical and radar imagery to the same map projection and coordinate reference system. Using a specific data source or high-precision vector as a reference, precise spatial registration was performed on images from different data sources to ensure a one-to-one correspondence of pixel positions; for data sources with different resolutions, they were resampled to a uniform spatial resolution. Using the quality control bands or cloud detection algorithms provided by each satellite, clouds and cloud shadows in the optical images were identified and masked, with images containing large amounts of cloud cover being removed or excluded from subsequent calculations. After completing the above preprocessing, all preprocessed images were cropped according to the study area boundaries to obtain a multi-source remote sensing dataset covering the study area.

[0030] Step S12: Based on the image classification method, wetland land features are identified in the registered remote sensing data by fusing spectral features, texture features and shape features, and small wetland patches with areas within a preset range are extracted from the registered remote sensing data.

[0031] In this step, the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) are calculated based on optical images. The NDVI and MNDWI can be expressed as follows: in, NIR Represents the surface reflectance in the near-infrared range. RED The surface reflectance of red light GREEN The surface reflectivity of green light SWIR This represents the surface reflectance in the near-shortwave infrared band.

[0032] In optical imagery, decision rules were constructed using the improved Normalized Difference Water Index (MNDWI) and the Normalized Difference Vegetation Index (NDVI) to determine the criteria for... MNDWI > T 1 and NDVI < T Pixels with a value of 2 are labeled as candidate pixels for water bodies or wetlands, where, T 1 indicates the lower threshold of the improved water quality index MNDWI. T 2 represents the upper threshold of the Normalized Difference Vegetation Index (NDVI). T 1 and T 2. Set up through sample statistics or experience.

[0033] In one example, the lower limit threshold of the Modified Water Index (MNDWI) T1 is typically between 0 and 0.2. For example, in many studies utilizing Landsat or Sentinel-2 imagery, this value is often used. T 1=0 or T A threshold of 1 = 0.1 is used to separate water bodies from non-water bodies. The upper limit threshold for the Normalized Difference Vegetation Index (NDVI) is also set. T 2 is typically between 0.1 and 0.3. For example, it is commonly used... T A value of 2=0.2 is used to effectively exclude most vegetation cover pixels, thereby distinguishing low NDVI water bodies / wetlands from high NDVI vegetation.

[0034] threshold T 1 and T The value of 2 can be determined based on one or a combination of the following sample statistics method, histogram valley point method, and empirical reference range: Sample Statistical Method: Within the study area, select known pure water bodies (such as reservoir centers or deep water areas) and typical non-water bodies (such as dense vegetation or bare soil) as training samples, and calculate their MNDWI and NDVI statistical values ​​(such as mean and standard deviation). By analyzing the distribution histograms or scatter plots of the index values ​​of the two classes of samples, select the sample that minimizes the classification error. T 1 and T 2. For example, a common practice is to take the mean MNDWI of a water sample minus 1-2 times the standard deviation as... T For reference, the mean NDVI of the vegetation sample is taken plus 1-2 times the standard deviation. T Reference 2.

[0035] Histogram valley point method: Calculate the MNDWI and NDVI histograms of the entire study area or typical sub-regions. T 1. You can try setting it near the valley point between the water body peak and the non-water body peak in the MNDWI histogram; T 2. You can try setting the value near the valley point between the low peak of water / bare soil and the high peak of vegetation in the NDVI histogram.

[0036] Scope of experience reference: Based on publicly available literature and common practices of those skilled in the art. T The typical value range for 1 is from 0 to 0.2. T The typical value range for 2 is 0.1 to 0.3. In a specific embodiment, when using Sentinel-2 images, the value can be set... T 1=0, T We used 2=0.2 as the initial threshold for the experiment, and then fine-tuned it based on the extraction results.

[0037] After obtaining the pixel-scale distribution results of wetlands / water bodies, they are converted into vector patches and filtered according to features such as the area and shape of "micro-wetlands". Connectivity analysis and vectorization are performed on the wetland / water body category raster, and adjacent wetland / water body pixels are merged into polygonal patches to form the initial wetland patch layer.

[0038] Set upper and lower limits for the area of ​​small wetlands A min and A max For areas smaller than A min Patches larger than [a certain size] are considered noise and removed. A max Patches deemed non-"small-scale" wetlands, such as large lakes and reservoirs, were removed, retaining only wetland patches within the preset area. Combining existing river and reservoir vector data with land use status data, wide perennial river channels and large and medium-sized reservoirs that do not meet the definition of "small-scale wetlands" were further removed. The retained patches underwent topological checks and corrections to eliminate overlaps, redundant small fragments, and hanging polygons, and each patch was assigned a unique number, forming a small-scale wetland patch vector layer for the study area.

[0039] In one example, small wetlands are defined as wetland ecological units that are small in area (usually less than 8 hm²), scattered in distribution, diverse in form, and distributed in a point-like or small patchy pattern within a region. Based on this, the upper limit for the area of ​​small wetlands is preliminarily determined to be less than 8 hm², i.e. A max ≤8 hm².

[0040] In the extraction of small wetlands, medium-to-high resolution remote sensing imagery with a spatial resolution of no more than 30m is used, with remote sensing data (such as Sentinel2, GF2, etc.) having a spatial resolution of approximately 10m being preferred. Under this resolution condition, the area of ​​a single pixel is approximately 100 m². 2 =0.001 hm 2 .

[0041] In object-oriented classification and patch recognition, patches consisting of only a very small number of pixels (e.g., 1-2 pixels) are easily affected by image noise, classification errors, and geometric correction errors, resulting in low reliability. To improve the stability of small wetland identification, small wetland patches must consist of at least a number of consecutive pixels. Setting the minimum number of patch pixels to 20, the corresponding minimum area is:

[0042] A min =20×0.001 hm 2 =0.02 hm² Therefore, this invention considers water patches with an area of ​​less than 0.02 hm² as noise or unstable targets and removes them in subsequent processing.

[0043] Building upon this foundation, this invention further integrates existing river and reservoir vector data with current land use data to conduct a secondary discrimination of candidate patches that have passed area screening. Patches that do not conform to the definition of small wetlands, such as those belonging to wide, perennial river main channels or the main water surfaces of large and medium-sized reservoirs, are deleted, thereby obtaining a set of small wetland patches that meet the definition of small wetlands. By setting and applying the aforementioned area thresholds, remote sensing classification noise and excessively large water bodies can be effectively eliminated, ensuring that the extraction results objectively reflect the spatial distribution characteristics of small wetlands within the study area.

[0044] Step S2: Based on kernel density analysis and landscape indices, evaluate the indicators of small and micro wetland patches to obtain the landscape structure and connectivity value indicators corresponding to the small and micro wetland patches.

[0045] This step also includes steps S21 to S23.

[0046] Step S21: Calculate the spatial distribution of small wetland patches based on kernel density analysis to obtain the spatial clustering intensity of small wetlands.

[0047] In this step, the nuclear density analysis method can be expressed as: in, Indicates spatial location s The kernel density estimate at that location, n This represents the total number of small wetland patches within the calculation range. Indicates the first i The spatial location of the geometric center of a small wetland patch k ( x ) represents the Gaussian kernel function. h Indicates the search radius. Indicates position s With position The standardized distance between them quantifies the weighted spatial clustering intensity of small wetland patches per unit area. The calculation results can intuitively reveal the spatial distribution hotspots and high-value connectivity corridors of small wetlands.

[0048] Step S22: Calculate various landscape indices corresponding to small wetland patches based on landscape ecology methods. The landscape indices include average patch area, landscape shape index, aggregation index, nearest neighbor distance, spread index, scattering and juxtaposition index, and Shannon diversity index.

[0049] In this step, the average patch area It can be represented as: in, N This is expressed as the number of plaques. a i Indicates the first i Area of ​​each patch, average patch area Used to measure the degree of patch fragmentation.

[0050] The Landscape Shape Index (LSI) can be expressed as: Where E represents the total perimeter of the landscape, A represents the total area of ​​the landscape, and the landscape shape index (LSI) characterizes the degree to which patches are affected by microtopography and human boundaries.

[0051] The clustering index AI can be represented as: in, g ij Indicates landscape type i With landscape type j The number of internal adjacent pixel pairs, p i Indicates landscape type area ratio, m The index represents the number of landscape types, and the clustering index AI measures the aggregation trend of similar patches.

[0052] Nearest neighbor distance ENN MN It can be represented as: in, h i Characterizing the first i The Euclidean distance from each patch to its nearest neighbor patch, and the nearest neighbor distance. ENN MN Used to characterize the resistance to species migration in patches.

[0053] The spread index CONTAG can be expressed as: in, Indicates landscape type With landscape type The length of the adjacent boundary, P Indicates the total boundary length. m The number of landscape types is indicated by the CONTAG index, which measures the continuity and aggregation of similar patches in a landscape.

[0054] The dispersion and juxtaposition index IJI can be expressed as: in, e ij Indicates landscape type i With landscape type j The adjacency length, E represents the total dissimilar adjacency length. The Scattering and Conjunction Index (IJI) is used to reflect the uniformity of the interweaving of different landscape types. By calculating the adjacency probability between different patch types, the Scattering and Conjunction Index (IJI) effectively reveals the spatial interaction relationship between micro-wetlands and other landscape elements.

[0055] The Shannon Diversity Index (SHDI) can be expressed as: in, p i Indicates landscape type The Shannon Diversity Index (SHDI) is used to describe the diversity of landscape type composition. The SHDI quantifies the richness and evenness of landscape types using the information entropy theory.

[0056] Step S23: Based on the preset index system, the spatial agglomeration intensity index and the various landscape indices corresponding to small and micro wetland patches are divided into landscape structure and connectivity value indices, and each small and micro wetland patch is assigned a corresponding landscape structure and connectivity value index.

[0057] In one example, the landscape structure and connectivity value index includes a landscape structure value index and a connectivity value index, wherein, Landscape structural value indicators include one or more of the following: average patch area, landscape shape index, Shannon diversity index, and spread index. These indicators are used to characterize the patch size, shape complexity, and overall pattern of small and micro wetland patches in combination with other landscape types. Connectivity value indicators include one or more of the following: nearest neighbor distance, aggregation index, dispersion and juxtaposition index, and spatial clustering intensity. Connectivity value indicators are used to characterize the spatial connectivity, adjacency relationship, and potential ecological corridors between small wetland patches.

[0058] In this embodiment, the spatial distribution of small wetland patches is calculated using kernel density analysis to quantify the spatial clustering intensity. Compared with traditional visual interpretation or simple distance statistics, this method can more objectively and continuously reflect the clustering and dispersion patterns of small wetlands within the region. At the same time, by introducing various landscape indices such as average patch area, landscape shape index, aggregation index, nearest neighbor distance, spread index, scattering and juxtaposition index, and Shannon diversity index through landscape ecology methods, the landscape structure of small wetland patches can be systematically quantified from multiple dimensions such as area size, shape complexity, aggregation degree, spatial proximity, landscape connectivity and expansion, patch combination relationship, and landscape diversity. Compared with the method of using only a single or a few structural indicators, this method can more comprehensively reflect the complexity and heterogeneity of the landscape pattern of small wetlands.

[0059] Step S3: Based on time-series remote sensing data, the ecological function parameters corresponding to small and micro wetland patches are inverted to obtain the ecological function and specific value indicators corresponding to the small and micro wetland patches.

[0060] This step also includes steps S31 to S32.

[0061] Step S31: Based on time-series remote sensing data, the ecological function parameters of the small wetland corresponding to the small wetland patches, including hydrological dynamics, vegetation growth, and water quality, are retrieved.

[0062] In this step, based on multi-source remote sensing data, a time-series remote sensing dataset for the study period is constructed according to preset time intervals. The time-series remote sensing data includes at least one type of optical multispectral data. The time-series remote sensing images are spatially overlaid with vector layers of small wetland patches. For each time-phase remote sensing image, the effective pixel set falling within each small wetland patch is extracted through area raster overlay operations, and the statistical values ​​of each band or index within the patch are calculated, realizing the spatial mapping of pixel-level remote sensing information to patch-level ecological function parameters.

[0063] Based on optical images of various time phases, the improved normalized water index MNDWI or other water indices are calculated. The average water index value of each small wetland patch is obtained to form a water index time series arranged in chronological order. When multi-temporal SAR images exist, water-sensitive parameters such as backscattering coefficient can be further extracted and corresponding time series can be constructed.

[0064] By setting a water index discrimination threshold, small wetland patches in each time phase are divided into "water surface / saturated state" and "non-water surface / unsaturated state". The number of times or time proportion of each patch is in the water surface or wet state during the study period is counted to obtain the hydrological state frequency parameter reflecting the persistence and seasonal changes of hydrology.

[0065] Furthermore, the hydrological dynamic index can be used to calculate the inundation frequency based on time-series SAR data. The expression for the inundation frequency is: Among them, Inundation Frequency N represents the submersion frequency. wet N represents the number of times water bodies were identified. total This indicates the total number of images.

[0066] The Normalized Difference Vegetation Index (NDVI) was calculated based on optical images of various time phases, and the mean value of each small wetland patch was obtained to form an NDVI time series, which reflects the temporal variation characteristics of vegetation cover and biomass.

[0067] Based on the NDVI time series, statistical characteristic parameters for characterizing vegetation growth status are extracted, including: the annual average or growing season average of NDVI, used to characterize the overall productivity level; the annual maximum or peak value of NDVI and its occurrence time, used to characterize the maximum growth period intensity and its temporal position; and the integral value of the NDVI time series, used to reflect the cumulative productivity throughout the year or growing season.

[0068] To reflect the complexity of vegetation spatial structure and habitat heterogeneity, a gray-level co-occurrence matrix (GLCM) is constructed within the neighborhood of small wetland patches based on NDVI or raw multispectral images from typical growing season phases. Texture parameters such as texture entropy are calculated and statistically analyzed at the patch scale to characterize the complexity of habitat structure. The habitat quality index can be calculated by combining NDVI and texture entropy, and is expressed as:

[0069] Habitat Quality =NDVI×(1+Texture Entropy ) Wherein, NDVI represents the Normalized Difference Vegetation Index, Texture Entropy The texture entropy is represented by the gray-level co-occurrence matrix (GLCM), and the habitat quality index is used to comprehensively assess habitat complexity and productivity per unit area.

[0070] Water purification potential is calculated based on remote sensing inversion and pollution source proximity, i.e., Purification_Potentiali = Proximity_to_Pollutioni, where Purification_Potential represents water purification potential and Proximity_to_Pollution represents pollution source proximity. This assesses the potential of the water as a distributed "water purifier," with higher potential observed when the water is close to a pollution source and exhibits significant turbidity variations. Within the temporal and spatial range determined to be water surface or wet conditions, spectral indices sensitive to water turbidity, algae content, or organic matter concentration are calculated based on green, red, near-infrared, and short-wave infrared bands. For each small wetland patch, the average value of its water spectral indices at different times is statistically analyzed, forming a corresponding time series.

[0071] Statistical analysis was performed on the time series of the above-mentioned water body spectral indices, extracting features such as mean, variance, maximum, minimum, and coefficient of variation to reflect the water quality status and temporal fluctuations of small wetlands. Patches with index values ​​consistently in the high pollution indicator range or exhibiting drastic fluctuations can be identified as wetlands with severely disturbed water quality; patches with generally good index values ​​and gentle fluctuations can be considered as wetland units with relatively good water quality and high water purification potential.

[0072] Step S32: Construct ecological function and specific value indicators based on ecological function parameters, and assign corresponding ecological function and specific value indicators to each small wetland patch. Among them, ecological function and specific value indicators include hydrological dynamic index, habitat quality index and water purification potential.

[0073] Step S4: Based on multi-source remote sensing data and points of interest and media data of small wetland patches within a preset service radius, calculate the social service and proximity value indicators corresponding to the small wetland patches.

[0074] Points of interest and media data include basic geographic and transportation data, public service and sensitive target data, climate regulation service data, and aesthetic and cultural perception data.

[0075] This step also includes steps S41 to S42.

[0076] Step S41: Based on the boundary and surrounding land cover information of the small wetland patches, calculate the size of the beneficiary population, the spatial distance to residential areas and transportation arteries, and the spatial adjacency relationship with public service facilities and ecologically sensitive targets within the preset service radius for each small wetland patch.

[0077] In this step, the settlement and population data includes vector data of settlement boundaries and spatial distribution data of population. Population data can be rasterized population density data or population statistics aggregated by settlement or administrative unit, spatialized through area weighting or population allocation methods. Transportation artery data includes vector data of highways, national and provincial trunk roads, main roads, urban expressways, and important rural roads, used to characterize resident travel and wetland accessibility conditions. Public service facility data includes point or area data of public service facilities closely related to the ecosystem services of small wetlands, such as parks and green spaces, scenic area entrances, ecological education bases, water sources, and water plant intakes. Ecologically sensitive target data includes vector boundaries or point data of ecologically sensitive targets such as drinking water source protection areas and nature reserves. All of the above data are projected onto the same coordinate reference system as the small wetland patches.

[0078] Based on the spatial impact range of small wetlands in providing social services such as recreation, landscape appreciation, and water conservation, a service radius Rs for the small wetland is pre-defined. The service radius can be determined based on experience such as urban land density and residents' daily walking distance, for example, 500 m, 1 km, or 2 km.

[0079] Based on this, a service buffer zone centered on each small wet patch is constructed, using the boundary of each small wet patch as a basis. i For each small wet patch, a buffer polygon with radius Rs is generated based on its polygonal boundary. B i All buffers are clipped to the study area to obtain a buffer layer representing the potential service impact range of each micro-wetland.

[0080] When population data is in raster format, combine the population raster with the buffer. B i The cells are overlaid, all population raster cells falling within the buffer area are extracted, and the population values ​​of each cell are summed to obtain the first... i The size of the population benefiting from small wetland patches Pop i When population data is vector data statistically analyzed by settlement or administrative unit, the boundaries of the settlement or unit and the buffer zone should be considered. B i The population is then superimposed within the buffer zone according to the area superposition ratio or population allocation model, and the results are summed to obtain the desired population size. Pop i Through the above steps, the population size attribute of each small wetland patch within the preset service radius is obtained. Pop i .

[0081] Based on the vector data of residential areas, the Euclidean distance between the geometric center (or the nearest point on its boundary) of each small wetland patch and the nearest residential boundary is calculated to obtain the first... i The nearest residential distance for each patch is calculated. The nearest distance analysis is performed between the traffic artery vector data and the boundaries of the small wet map patches. The minimum distance from each patch to the nearest traffic artery (such as a main road or higher-level road) is calculated. The minimum distance to highways, main roads, or rail transit stations can be calculated separately based on road level, allowing for subsequent stratified analysis based on different travel modes. Through the above calculations, the spatial distance parameters between each small wet map patch and its residential areas and traffic arteries are obtained.

[0082] Furthermore, the vector data of small wetland patches are overlaid with the vector data of public service facilities and ecologically sensitive targets to determine whether there are intersecting, inclusive, or boundary contact relationships. For patches with the above relationships, they are marked as "adjacent to the corresponding facilities or sensitive targets" and the corresponding adjacency flag is recorded as 1. If there is no contact or overlay, it is marked as 0.

[0083] When adjacency alone is insufficient to distinguish importance, the degree of adjacency can be further calculated. This includes the minimum distance between small wetland patches and public service facilities or ecologically sensitive targets, and the proportion of overlapping area or contact boundary length between small wetland patches and facilities or sensitive targets. For example, the proportion of overlapping area to wetland patch area, or the proportion of shared boundary length to patch perimeter. These indicators quantify the spatial connection strength between small wetlands and objects such as drinking water source protection areas, nature reserves, and ecological education sites.

[0084] When considering multiple types of public service facilities and ecologically sensitive targets, adjacency indicators with each type of object can be calculated separately, and then weighted and aggregated according to preset weights to form the first... i The comprehensive adjacency relationship parameters of small wet map patches.

[0085] For each small wetland patch, the following is calculated: the size of the beneficiary population within the preset service radius. Pop i The spatial distances to the nearest residential area and the nearest major transportation route; and the spatial adjacency parameters and related sub-indicators between the area and public service facilities and ecologically sensitive targets are recorded. These parameters are then written as attribute fields into the micro-wetland map patch vector dataset to form a database of social service and proximity spatial parameters.

[0086] Step S42: Based on the size of the beneficiary population, spatial distance, and spatial adjacency, construct social service and proximity value indicators, and assign corresponding social service and proximity value indicators to each small wetland patch. Among them, social service and proximity value indicators include social service accessibility index, beneficiary population index, and proximity index.

[0087] In this step, the service radius reachability index is... Population It can be represented as: Service Population =∑ P i , for d i ≤R in, P i This represents the population of residential area i. d i R represents the distance to the micro-wetland, and R is the service radius.

[0088] Furthermore, the social services and proximity value indicators also include climate regulation services (Cooling). Intensity Aesthetics and Cultural Perception Perception Climate regulation services Cooling Intensity It can be represented as: Cooling Intensity =LST buffer -LST wetland The intensity of the cold island effect is calculated based on the inversion of land surface temperature (LST). buffer LST represents the average surface temperature of the surrounding built-up area. wetland This represents the average surface temperature of the wetland.

[0089] Aesthetics and Cultural Perception Perception Cultural data can be calculated by combining social media data (such as geotagged photos) with kernel density values. Perception =Photo Count Photo Count This indicates the number of photos or videos with geographic coordinates posted on social media platforms such as Weibo, Xiaohongshu, and Douyin within the 500m buffer zone of the small wetland.

[0090] Step S5: Based on landscape structure and connectivity value indicators, ecological function and specificity value indicators, and social service and proximity value indicators, construct a comprehensive index system for evaluating the value of micro-wetlands and calculate the comprehensive value index. Generate a value level map of micro-wetlands based on the comprehensive value index.

[0091] In this step, based on the dominant functions and service targets of small wetlands, the evaluation indicators are divided into three primary indicator layers: First, the "Landscape Structure and Connectivity Value" indicator layer, mainly including landscape structure value indicators and landscape connectivity value indicators for each small wetland patch; second, the "Ecological Function and Specific Value" indicator layer, mainly including indicators reflecting ecological processes and ecosystem services such as hydrological dynamics index, habitat quality index, and water purification potential; and third, the "Social Service and Proximity Value" indicator layer, mainly including indicators reflecting social benefits and spatial proximity relationships such as social service accessibility index, beneficiary population index, and proximity index. These three primary indicator layers and their subordinate specific indicators together constitute a comprehensive indicator system for the value evaluation of small wetlands.

[0092] The raw values ​​of all indicators are transformed and scaled proportionally to a dimensionless range between zero and one. For positive indicators where larger values ​​indicate higher value, the scaled results are used directly. For negative indicators where larger values ​​indicate adverse effects, such as distance to residential areas or major transportation routes, conversion methods such as inversion or reversal are used to ensure that larger converted values ​​also indicate higher value or greater contribution. Through this process, a set of standardized indicator values ​​with consistent direction and uniform scale is formed.

[0093] Within each primary indicator layer, a comprehensive index is calculated. For the "Landscape Structure and Connectivity Value" indicator layer, the weights of each indicator within this layer are determined through methods such as expert consultation, analytic hierarchy process (AHP), or entropy weighting. Based on the standardized indicator values ​​of each small wetland patch, a weighted sum is performed according to the predetermined weights to obtain the "Landscape Structure and Connectivity Comprehensive Index" for that patch. For the "Ecological Function and Specific Value" and "Social Service and Proximity Value" indicator layers, the weights of each indicator within each layer are determined using the same method, and the standardized indicator values ​​are then weighted and summed according to the weights to obtain the "Ecological Function and Specific Value Comprehensive Index" and "Social Service and Proximity Comprehensive Index" for each patch, respectively. Thus, each small wetland patch corresponds to three primary comprehensive indices.

[0094] After obtaining the three primary comprehensive indices, the comprehensive value index of the small wetland is further calculated. Based on the planning and management objectives and expert judgment, the relative importance of the three primary indicator layers—"landscape structure and connectivity value," "ecological function and uniqueness value," and "social service and proximity value"—in the overall evaluation is determined. The three primary comprehensive indices of each small wetland patch are then weighted and superimposed according to the above weights to obtain the comprehensive value index of that patch. The higher the comprehensive value index value, the higher the overall contribution of the small wetland in terms of landscape pattern maintenance, ecological function performance, and social service provision.

[0095] The distribution characteristics of the comprehensive value index of all small and micro wetland patches are statistically analyzed. One of the following methods is selected: natural discontinuity method, equidistant grading method, quantile grading method, or cluster analysis, to define several value level intervals. In one example, the comprehensive value can be divided into five levels: "extremely high value area," "high value area," "medium value area," "lower value area," and "low value area." The corresponding value level is recorded for each small and micro wetland patch in the attribute table. The small and micro wetland patches are then classified and colored according to their value levels in the geographic information system platform to generate a small and micro wetland value level map.

[0096] In this embodiment, by fusing multi-source remote sensing data, small wetland patches are extracted in detail, which can more accurately identify small wetlands that are small in area, irregular in shape, and scattered in distribution. Combined with kernel density analysis and landscape indices, the characteristics of small wetlands are characterized from multiple dimensions such as spatial distribution, morphological structure, and connectivity. Furthermore, by using time-series remote sensing data to invert ecological function parameters, the changing trends of the ecological functions of small wetlands can be reflected from a time dimension. This allows for the quantitative characterization of the ecological functions and specific value of small wetlands. Based on the points of interest and media data of small wetlands within a preset service radius, a social service and proximity value index is established. This new standard overcomes the shortcomings of traditional wetland assessments, which only focus on ecological attributes and neglect human use and service accessibility. It makes the assessment results more in line with actual management and utilization needs. At the same time, it integrates the three major categories of indicators into a comprehensive indicator system, and realizes the quantitative ranking of the value of small wetlands through a comprehensive value index. This is conducive to horizontal comparison and priority division in multi-objective and multi-scale management scenarios, reduces omissions and misjudgments, and improves the refinement of the value assessment results of small wetlands. The comprehensive value index generates a value level map of small wetlands, which intuitively expresses the complex multi-source and multi-dimensional indicator results as a spatial distribution layer.

[0097] Based on the above method, this application discloses a small wetland value assessment system that integrates multi-source data, with reference to... Figure 2 The micro-wetland value assessment system 1 includes a data acquisition module 11, an indicator evaluation module 12, and a value assessment module 13, among which... Data acquisition module 11 is used to collect multi-source remote sensing data of the study area and extract small wet map patches from the multi-source remote sensing data; The indicator evaluation module 12 is used to evaluate the indicators of small and micro wetland patches based on kernel density analysis and landscape index, obtain the landscape structure and connectivity value indicators corresponding to the small and micro wetland patches, invert the ecological function parameters corresponding to the small and micro wetland patches based on time-series remote sensing data, obtain the ecological function and specific value indicators corresponding to the small and micro wetland patches, and calculate the social service and proximity value indicators corresponding to the small and micro wetland patches based on multi-source remote sensing data and interest points and media data of small and micro wetland patches within a preset service radius. The value assessment module 13 is used to construct a comprehensive index system for evaluating the value of micro-wetlands based on landscape structure and connectivity value indicators, ecological function and specificity value indicators, and social service and proximity value indicators, and to calculate the comprehensive value index. Based on the comprehensive value index, a value level map of micro-wetlands is generated.

[0098] In one example, the data acquisition module 11 is used to acquire optical remote sensing data or radar remote sensing data, and to perform radiometric calibration, atmospheric correction, geometric fine correction and cropping on the optical remote sensing data or radar remote sensing data to obtain registered remote sensing data; based on the image classification method, the registered remote sensing data is used to identify wetland land features by fusing spectral features, texture features and shape features, and to extract small wetland patches with an area within a preset range from the registered remote sensing data.

[0099] In one example, the indicator evaluation module 12 is used to calculate the spatial distribution of small wetland patches based on kernel density analysis to obtain the spatial aggregation intensity of small wetlands; it calculates various landscape indices corresponding to small wetland patches based on landscape ecology methods, including average patch area, landscape shape index, aggregation index, nearest neighbor distance, spread index, scattering and juxtaposition index, and Shannon diversity index; according to the preset indicator system, the spatial aggregation intensity index and the various landscape indices corresponding to small wetland patches are divided into landscape structure and connectivity value indicators, and each small wetland patch is assigned a corresponding landscape structure and connectivity value indicator.

[0100] In one example, the landscape structure and connectivity value index includes a landscape structure value index and a connectivity value index, wherein, Landscape structural value indicators include one or more of the following: average patch area, landscape shape index, Shannon diversity index, and spread index. These indicators are used to characterize the patch size, shape complexity, and overall pattern of small and micro wetland patches in combination with other landscape types. Connectivity value indicators include one or more of the following: nearest neighbor distance, aggregation index, dispersion and juxtaposition index, and spatial clustering intensity. Connectivity value indicators are used to characterize the spatial connectivity, adjacency relationship, and potential ecological corridors between small wetland patches.

[0101] In one example, the indicator evaluation module 12 is used to invert the ecological function parameters of the small wetland hydrological dynamics, vegetation growth status and water quality status corresponding to the small wetland patches based on time-series remote sensing data; construct ecological function and specific value indicators based on the ecological function parameters, and assign corresponding ecological function and specific value indicators to each small wetland patch, wherein the ecological function and specific value indicators include hydrological dynamic index, habitat quality index and water purification potential.

[0102] In one example, the indicator evaluation module 12 is used to calculate the size of the beneficiary population, the spatial distance to residential areas and transportation arteries, and the spatial adjacency relationship with public service facilities and ecologically sensitive targets of each small wetland patch based on the boundary and surrounding land cover information of the small wetland patch. Based on the size of the beneficiary population, the spatial distance and the spatial adjacency relationship, a social service and proximity value index is constructed, and a corresponding social service and proximity value index is assigned to each small wetland patch. The social service and proximity value index includes the social service accessibility index, the beneficiary population index and the proximity index.

[0103] In one example, points of interest and media data include basic geographic and transportation data, public service and sensitive target data, climate regulation service data, and aesthetic and cultural perception data.

[0104] Please see Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 2 may include: at least one processor 21, at least one network interface 24, user interface 23, memory 25, and at least one communication bus 22.

[0105] The communication bus 22 is used to enable communication between these components.

[0106] The user interface 23 may include a display screen and a camera. Optionally, the user interface 23 may also include a standard wired interface and a wireless interface.

[0107] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0108] The processor 21 may include one or more processing cores. The processor 21 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 25, and by calling data stored in the memory 25. Optionally, the processor 21 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 21 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 21.

[0109] The memory 25 may include random access memory (RAM) or read-only memory. Optionally, the memory 25 may include non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 25 may also be at least one storage device located remotely from the aforementioned processor 21. Figure 3 As shown, the memory 25, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for evaluating the value of small wetlands by integrating multi-source data.

[0110] exist Figure 3In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 21 can be used to call the application program stored in the memory 25, which is a method for evaluating the value of small wetlands by integrating multi-source data. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0111] A computer-readable storage medium storing instructions that, when executed by one or more processors, cause a computer to perform one or more methods as described in the above embodiments.

[0112] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0113] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0117] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the value of small and micro wetlands by fusing multi-source data, characterized in that, The method includes: Collect multi-source remote sensing data of the study area and extract small wet map patches from the multi-source remote sensing data; Based on kernel density analysis and landscape indices, the small and micro wetland patches are evaluated to obtain the landscape structure and connectivity value indices corresponding to the small and micro wetland patches. Ecological function parameters corresponding to the small and micro wetland patches are retrieved based on time-series remote sensing data to obtain the ecological function and specific value indicators corresponding to the small and micro wetland patches. Based on the multi-source remote sensing data and the points of interest and media data of the small wetland patches within the preset service radius, calculate the social service and proximity value index corresponding to the small wetland patches; Based on the landscape structure and connectivity value indicators, the ecological function and specificity value indicators, and the social service and proximity value indicators, a comprehensive index system for evaluating the value of micro-wetlands is constructed and a comprehensive value index is calculated. A value level map of micro-wetlands is generated based on the comprehensive value index.

2. The method according to claim 1, wherein, The multi-source remote sensing data of the study area specifically includes: Acquire optical remote sensing data or radar remote sensing data, and perform radiometric calibration, atmospheric correction, geometric fine correction and cropping on the optical remote sensing data or radar remote sensing data to obtain registered remote sensing data; Based on image classification methods, wetland land features are identified by fusing spectral, texture, and shape features from the registered remote sensing data, and small wetland patches with areas within a preset region are extracted from the registered remote sensing data.

3. The method according to claim 1, wherein, The acquisition of landscape structure and connectivity value indicators corresponding to the small wetland patches specifically includes: The spatial distribution of the small wetland patches is calculated based on the kernel density analysis method to obtain the spatial clustering intensity of the small wetlands; Based on landscape ecology methods, various landscape indices corresponding to the small wetland patches are calculated. The landscape indices include average patch area, landscape shape index, aggregation index, nearest neighbor distance, spread index, scattering and juxtaposition index, and Shannon diversity index. According to the preset index system, the spatial agglomeration intensity index and the various landscape indices corresponding to the small and micro wetland patches are divided into landscape structure and connectivity value indicators, and each small and micro wetland patch is assigned a corresponding landscape structure and connectivity value indicator.

4. The method according to claim 3, wherein, The landscape structure and connectivity value indicators include landscape structure value indicators and connectivity value indicators, wherein, The landscape structure value index includes one or more of the average patch area, the landscape shape index, the Shannon diversity index, and the spread index. The landscape structure value index is used to characterize the patch size, shape complexity, and overall pattern formed by the small and micro wetland patches and other landscape types. The connectivity value index includes one or more of the nearest neighbor distance, the aggregation index, the dispersion and juxtaposition index, and the spatial clustering intensity. The connectivity value index is used to characterize the spatial connectivity, mutual adjacency, and potential ecological corridors between small wetland patches.

5. The method according to claim 1, wherein, The step of retrieving the ecological function parameters corresponding to the small wetland patches based on time-series remote sensing data to obtain the ecological function and specific value indicators of the small wetland patches specifically includes: Based on the time-series remote sensing data, the ecological function parameters of the small wetland corresponding to the small wetland patches, including hydrological dynamics, vegetation growth, and water quality, are retrieved. Based on the ecological function parameters, ecological function and specific value indicators are constructed, and each small wetland patch is assigned a corresponding ecological function and specific value indicator. The ecological function and specific value indicators include hydrological dynamic index, habitat quality index, and water purification potential.

6. The method according to claim 1, wherein, The calculation of the social service and proximity value index of small wetlands based on the multi-source remote sensing data and the points of interest and media data of the small wetland patches within a preset service radius specifically includes: Based on the boundary and surrounding land cover information of the small and micro wetland patches, the size of the beneficiary population, the spatial distance from residential areas and main transportation routes, and the spatial adjacency relationship with public service facilities and ecologically sensitive targets within the preset service radius of each small and micro wetland patch are calculated. Based on the size of the beneficiary population, the spatial distance, and the spatial adjacency relationship, a social service and proximity value index is constructed, and each small wetland patch is assigned a corresponding social service and proximity value index, wherein the social service and proximity value index includes a social service accessibility index, a beneficiary population index, and a proximity index.

7. The method according to claim 6, wherein, The points of interest and media data include basic geographic and transportation data, public service and sensitive target data, climate regulation service data, and aesthetic and cultural perception data.

8. A system for evaluating the value of small and micro wetlands by fusing multi-source data, characterized by, The micro-wetland value assessment system (1) includes a data acquisition module (11), an indicator evaluation module (12), and a value assessment module (13), wherein, The data acquisition module (11) is used to acquire multi-source remote sensing data of the study area and extract small wet map patches from the multi-source remote sensing data; The indicator evaluation module (12) is used to evaluate the small wetland patches based on kernel density analysis and landscape index, obtain the landscape structure and connectivity value indicators corresponding to the small wetland patches, invert the ecological function parameters corresponding to the small wetland patches based on time-series remote sensing data, obtain the ecological function and specific value indicators corresponding to the small wetland patches, and calculate the social service and proximity value indicators corresponding to the small wetland patches based on the multi-source remote sensing data and the points of interest and media data of the small wetland patches within the preset service radius. The value evaluation module (13) is used to construct a comprehensive index system for evaluating the value of micro-wetlands based on the landscape structure and connectivity value index, the ecological function and specificity value index, and the social service and proximity value index, and to calculate the comprehensive value index and generate a value level map of micro-wetlands based on the comprehensive value index.

9. An electronic device, comprising: The device includes a processor (21), a memory (25), a user interface (23), and a network interface (24). The memory (25) is used to store instructions. The user interface (23) and the network interface (24) are used to communicate with other devices. The processor (21) is used to execute the instructions stored in the memory (25) to cause the electronic device (2) to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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