Wetland elevation inversion method

Abstract

The invention relates to a wetland elevation inversion method. The method comprises the following steps: receiving water level data of a wetland area on that day; the day water level data comprises a water level point and a corresponding water level value; inputting the water level data of the day into the wetland elevation distribution model to obtain elevation grid data; the elevation raster data is used for representing the elevation distribution of the wetland area. By adopting the method, the elevation information of the wetland ecosystem can be updated in real time based on the water level data, expensive equipment and labor cost required by a traditional elevation measurement method are reduced, and the method is suitable for rapid monitoring of a wide-range wetland area.

Description

Technical Field

[0001] The invention belongs to the field of wetland ecological monitoring, and in particular relates to an inversion method for wetland elevation. Background Art

[0002] With the development of geographic information and remote sensing imaging technology, there has emerged information technology for obtaining large-scale surface information through satellite remote sensing. This technology has the advantages of high resolution, multi-spectral data, rapid acquisition and update capabilities, large-area synchronous observation, and multi-field application. It has been widely used in many fields such as resource monitoring, environmental assessment, and urban planning.

[0003] Traditionally, wetland elevation measurement relies primarily on ground-based surveying techniques such as total stations and RTK, or aerial laser scanning. Ground-based surveying involves setting up measurement stations in the field and using instruments to directly measure the elevation of target points. Aerial laser scanning, on the other hand, uses aircraft equipped with laser radar to scan wetlands from the air, acquiring high-precision elevation data.

[0004] However, the use of ground-based surveying technologies such as total stations and RTK, as well as aerial laser scanning, is costly, requiring significant investment in equipment, personnel training, and field surveying. These methods are also inefficient, particularly in extensive wetland and low-lying areas. Faced with the need for large-scale wetland monitoring, they struggle to complete survey tasks quickly and fail to meet the high-frequency monitoring requirements of large wetlands. Furthermore, relying solely on remote sensing data makes it difficult to establish accurate elevation models, making it impossible to comprehensively and accurately obtain wetland elevation information, presenting significant limitations in wetland elevation measurement. Summary of the Invention

[0005] Based on this, it is necessary to provide a wetland elevation inversion method that can dynamically monitor the elevation changes of wetland ecosystems in response to the above technical problems.

[0006] In a first aspect, the present application provides a wetland elevation inversion method, comprising:

[0007] Receive the daily water level data of the wetland area; the daily water level data includes the water point and the corresponding water level value;

[0008] The water level data of the day is input into the wetland elevation distribution model to obtain elevation raster data; the elevation raster data is used to characterize the elevation distribution of the wetland area.

[0009] In one embodiment, the wetland elevation distribution model is based on the following method to obtain elevation raster data:

[0010] Based on the water body boundary point set, the water level values corresponding to the water points are integrated into the historical water body boundary point dataset to obtain a new water body boundary point dataset; the historical water body boundary point dataset includes the spatial position of each water body boundary point and the corresponding water level value;

[0011] Perform vegetation height error correction on the new water body boundary point dataset to obtain a corrected water body boundary point dataset;

[0012] The spatial interpolation method is used to calculate the wetland elevation distribution under the corrected water body boundary point dataset to obtain elevation raster data.

[0013] In one embodiment, the wetland elevation distribution model is constructed by the following method:

[0014] Obtain a remote sensing image set and water level data for the corresponding period; the remote sensing image set includes remote sensing images for multiple periods;

[0015] The improved normalized water index was calculated for each remote sensing image, and the OTSU algorithm was used to extract the water area to obtain the wetland water area;

[0016] Generate a water body boundary point set based on the wetland water body area;

[0017] Combine the water body boundary point set with the corresponding water level data to obtain the water body boundary point data;

[0018] Merge multiple water body boundary point data corresponding to multiple periods into a historical water body boundary point dataset;

[0019] The water level values of the historical water body boundary point dataset are corrected based on the vegetation height to obtain a corrected historical water body boundary point dataset;

[0020] Ordinary Kriging interpolation method was used to spatially interpolate the revised historical water body boundary point dataset to obtain wetland elevation raster data;

[0021] The wetland elevation distribution model was obtained by removing the year-round water-covered areas and year-round water-free areas from the wetland elevation raster data.

[0022] In one embodiment, an improved normalized water index is calculated for each remote sensing image, and the OTSU algorithm is used to extract the water area to obtain the wetland water area, including:

[0023] Calculate the improved normalized water index for each remote sensing image and obtain the improved normalized water index histogram;

[0024] Calculating an improved normalized water index for each remote sensing image, and using the improved normalized water index to update the remote sensing image to obtain an updated remote sensing image;

[0025] Counting the grayscale of the updated remote sensing image to obtain a grayscale distribution histogram;

[0026] Generate segmentation threshold using grayscale distribution histogram;

[0027] Generate water body mask based on segmentation threshold;

[0028] The water body area of the updated remote sensing image is extracted using the water body mask to obtain the wetland water body area.

[0029] In one embodiment, calculating an improved normalized water index for each remote sensing image includes:

[0030] The improved normalized water index is obtained through the following formula:

[0031]

[0032] Among them, MNDWI is the modified normalized difference water index; SR_B2 is the green band; SR_B5 is the shortwave infrared band.

[0033] In one embodiment, generating a water body mask according to a segmentation threshold comprises:

[0034] Generate an initial binary water mask using the segmentation threshold;

[0035] The initial binary water mask is subjected to removal of small non-water patches and small water patches to obtain a water mask.

[0036] In one embodiment, generating a water body boundary point set based on a wetland water body area includes:

[0037] Segment the boundaries of the water area corresponding to the wetland water area to obtain a water area boundary line data set;

[0038] The water body area boundary line data set is discretely sampled to obtain a water body boundary point set.

[0039] In a second aspect, the present application also provides a wetland elevation inversion device, comprising:

[0040] A data receiving module is used to receive the daily water level data of the wetland area;

[0041] The model algorithm module is used to input the water level data of the day into the wetland elevation distribution model to obtain elevation grid data.

[0042] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned wetland elevation inversion methods when executing the computer program.

[0043] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned wetland elevation inversion methods.

[0044] The above-mentioned wetland elevation inversion method can achieve wetland elevation distribution by reversely inferring water levels. That is, using the constructed wetland elevation distribution model, only the daily water level data is needed to quickly obtain elevation raster data. This enables effective monitoring of large-scale wetland areas, reduces measurement costs, and is suitable for rapid monitoring of large wetland areas. As wetland water levels continuously change, the dynamic changes in wetland elevation can be monitored based on real-time changes in water levels, prompting users to promptly adjust protection strategies and early warning measures. This is suitable for wetland areas with large water level fluctuations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 Schematic diagram of the wetland elevation inversion method of the present invention;

[0047] Figure 2 Schematic diagram of the step-by-step process of step S102;

[0048] Figure 3 A schematic diagram of the wetland elevation distribution model construction process of the present invention;

[0049] Figure 4 This is a structural diagram of the wetland elevation inversion device of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0051] In one embodiment, Figure 1 As shown, a wetland elevation inversion method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0052] S101. Receive the daily water level data of the wetland area; the daily water level data includes the water point and the corresponding water level value.

[0053] Illustratively, daily water level data is the instantaneous water level of a wetland body, obtained through real-time monitoring at hydrological stations, such as ground-based observation stations. For example, it represents the absolute water level of the wetland body surface relative to sea level, measured by a fixed or buoyed water level gauge. Within the spatial scope of the wetland, the physical location corresponding to the observation point of the hydrological station is the water point, and the water level value monitored at that water point is the water level value corresponding to that water point. Optionally, the physical location is represented by latitude and longitude coordinates.

[0054] S102. Input the water level data of the day into the wetland elevation distribution model to obtain elevation grid data; the elevation grid data is used to characterize the elevation distribution of the wetland area.

[0055] Schematically, a corresponding wetland elevation distribution model was constructed for the wetland area under study. The model extracted and stored the water boundary points of the studied wetland area through remote sensing imagery. The daily water level data was assigned to the corresponding water boundary points and then spatiotemporally fused with multiple periods of data to obtain images of the area affected by water level fluctuations. The water level value represents the elevation of the water boundary point. Furthermore, based on the spatial autocorrelation of the water boundary points, spatial interpolation was performed to generate a continuous elevation grid surface. This means that the elevation values of unknown points are estimated using the elevation values of known points to obtain elevation grid data. The spatial interpolation scale is consistent with the spatial resolution.

[0056] Furthermore, each time daily water level data is input, the elevation grid data is updated to reflect real-time changes in wetland elevation. This elevation grid data can be used to analyze the topography, slope, and aspect of the wetland area, characterizing changes in wetland elevation based on changes in the elevation grid data. Elevation grid data can also be used to construct wetland geographic models and for flood warnings.

[0057] The aforementioned wetland elevation inversion method transforms discrete water level data into a continuous elevation surface through spatial mapping between instantaneous water levels and water body boundaries and spatial interpolation techniques. Using water level fluctuations as a proxy variable, the elevation distribution characteristics of wetland ecosystems are inverted, providing a consistent, efficient, and dynamic solution for wetland ecological monitoring. This method is suitable for elevation measurement and monitoring in low-lying areas such as wetlands and lakes.

[0058] In one embodiment, Figure 2 As shown in the figure, the wetland elevation distribution model is based on the following method to obtain elevation raster data:

[0059] S201. Based on the water body boundary point set, the water level value corresponding to the water point is integrated into the historical water body boundary point dataset to obtain a new water body boundary point dataset; the historical water body boundary point dataset includes the spatial position of each water body boundary point and the corresponding water level value.

[0060] Schematically, the historical water body boundary point dataset is constructed based on the wetland elevation distribution model based on multi-period remote sensing images and combined with hydrological station water level data. It contains the spatial location of wetland water body boundary points and the corresponding water level values at different time points, and records the historical changes in wetland water levels.

[0061] The spatial position of the water point in the water level data of the day is consistent with or correlated with the position of the water body boundary point in the historical water body boundary point dataset. The sample coverage is expanded through spatiotemporal superposition, and the new water level value is integrated into the historical dataset based on the water body boundary point set, that is, the data of the water body boundary point set is dynamically updated. Based on this, the new water body boundary point dataset not only retains the time series characteristics of the historical data, but also incorporates the latest water level information, so that the data can better reflect the current actual water level conditions of the wetland. For example, the water point of the water level data of the day is matched with the spatial position of the water body boundary point in the historical water body boundary point dataset. If the latitude and longitude coordinates of the two are the same, the water level value in the water level data of the day is updated to the water level value corresponding to the historical water body boundary point dataset. The new water body boundary point dataset obtained after fusion can reflect the long-term fluctuation pattern of the water level and the actual conditions of the water level on the day.

[0062] S202: Correct the vegetation height error of the new water body boundary point dataset to obtain a corrected water body boundary point dataset.

[0063] In wetland environments, vegetation can affect water level measurements. For example, a water boundary may contain areas covered by emergent vegetation such as reeds and mosses. The height of the vegetation stems can lead to an inflated water level. This means the measured water level actually represents the elevation when the vegetation is completely submerged. Therefore, the error caused by the vegetation height must be removed to accurately reflect the water boundary elevation.

[0064] Different vegetation types have different average heights and standard deviations. When correcting the vegetation height error of a new water body boundary point dataset, the vegetation type of the area where each water body boundary point is located should be determined based on its spatial position. Schematically, supervised classification is used to divide wetland vegetation types and generate a vegetation type raster map. Based on the average height of the vegetation type, the corresponding height is subtracted from the water level value of the corresponding point. For example, if a water body boundary point is located in an area covered by a moss community, the average height of the moss community, 0.499m, is subtracted. Furthermore, when there are multiple types of vegetation at the spatial position of the water body boundary point, the average height is calculated weighted by the area ratio. Optionally, taking into account the natural fluctuations in vegetation height, the correction value can be fine-tuned according to actual conditions and accuracy requirements, so as to obtain a corrected water body boundary point dataset that more accurately reflects the actual water level boundary elevation.

[0065] S203. Calculate the wetland elevation distribution under the corrected water body boundary point dataset using a spatial interpolation method to obtain elevation raster data.

[0066] Although the revised water body boundary point dataset contains the water level and location information of many water body boundary points, the water body boundary points are discretely distributed and cannot directly present the continuous wetland elevation distribution.

[0067] Spatial interpolation analyzes the spatial relationship between discrete points based on spatial autocorrelation, using information from surrounding known points to estimate the elevation of unknown locations. Schematically, during the calculation process, a reasonable weight is assigned to each estimated location based on factors such as the distance, direction, and spatial autocorrelation between points, thereby generating a smooth and continuous elevation distribution surface. The continuous elevation values are then stored and expressed in raster form, resulting in elevation raster data. Elevation raster data can intuitively and accurately display the elevation distribution of the entire wetland, providing important data support for the research, management, and protection of wetland ecosystems.

[0068] In one embodiment, Figure 3 As shown in Figure 2, the wetland elevation distribution model is constructed using the following methods:

[0069] S301. Acquire a remote sensing image set and water level data of a corresponding period; the remote sensing image set includes remote sensing images of multiple periods.

[0070] For example, multi-temporal imagery covering the wetland study area was obtained. This multi-temporal imagery could be obtained using satellite imagery such as Landsat and Sentinel-2. The time span needed to meet monitoring requirements, covering at least 40 periods, provided surface information about the wetland over time. Furthermore, the remote sensing imagery should have low cloud cover to fully capture the wetland's extent. Water level data were obtained from hydrological station observations synchronized with each period of remote sensing imagery. This data included absolute water level values and the geographic coordinates of the hydrological station observation points, reflecting the height of the wetland water at that specific moment.

[0071] S302: Calculate the improved normalized water index for each remote sensing image, and use the OTSU algorithm to extract the water area to obtain the wetland water area.

[0072] The Modified Normalized Difference Water Index (MNDWI) is calculated for each remote sensing image. Compared to the classic Normalized Difference Water Index (NDWI), the Modified Normalized Difference Water Index effectively suppresses interference from vegetation and soil by introducing shortwave infrared bands. It is suitable for extracting water-land boundaries in complex environments, and its measured inversion results are better than those of NDWI. Water bodies have high reflectivity in the green band and very low reflectivity in the shortwave infrared band. Calculating the MNDWI can amplify the contrast between water and non-water areas. MNDWI values in water areas are close to 1, while those in non-water areas are lower or negative. Remote sensing images are analyzed using the MNDWI values.

[0073] The OTSU algorithm is a method for automatically determining the image binarization threshold. It segments the image by maximizing the inter-class variance between water bodies and non-water bodies. That is, it finds the threshold to divide the remote sensing image into two categories: water body area and non-water body area, and maximizes the difference between the two categories, thereby extracting the water body area and obtaining the wetland water body area.

[0074] S303: Generate a water body boundary point set based on the wetland water body area.

[0075] The extracted wetland water area is represented by a polygon vector, and the polygonal outer contour of the polygon vector is extracted to form a closed linear vector. Furthermore, points are generated at fixed intervals along the water body boundary line, and the boundary of the water body area is converted into a point set, and the spatial distribution is ensured to be uniform. Each point represents a position on the water body boundary to characterize the shape and position information of the water body boundary.

[0076] S304: Combine the water body boundary point set with the corresponding water level data to obtain water body boundary point data.

[0077] Schematically, the water body boundary point set is combined with the corresponding water level data. That is, the water level data is assigned to the corresponding points in the water body boundary point set. This gives each water body boundary point the dual attributes of spatial location and water level value, forming water body boundary point data and linking spatial information with hydrological information. For example, if the boundary point has corresponding water level data, the water level value measured at the station is directly used. If the boundary point does not have corresponding water level data, the regional average water level value is used.

[0078] S305: Merge the multiple water body boundary point data corresponding to multiple periods into a historical water body boundary point dataset.

[0079] Schematically, multiple water boundary point data corresponding to different time periods are merged into a historical water boundary point dataset. This integrates wetland water boundary and water level information at different times, constructing a comprehensive dataset containing time series to reflect the dynamic changes of the wetland. Furthermore, if the same location is extracted multiple times in different time periods, all timestamp data is retained and the coordinate system of all water boundary point data is unified. The historical water boundary point dataset is then stored.

[0080] S306 , correcting the water level values of the historical water body boundary point dataset based on the vegetation height to obtain a corrected historical water body boundary point dataset.

[0081] The measured water level data includes the height of vegetation. Taking into account the impact of vegetation on water level measurement, the water level values of the historical water body boundary point dataset are corrected based on the vegetation height. For example, based on Sentinel-2 multispectral data, a random forest model is trained to distinguish between types such as moss, reeds, and bare land. The NDVI threshold is used to improve the classification accuracy, and the average height of vegetation is obtained through field measurements or LiDAR data to obtain a vegetation height raster map. Furthermore, the water body boundary points are superimposed on the vegetation height raster map, and vegetation type attributes are assigned to each water body boundary point. According to the average height and standard deviation of different vegetation types, the water level value measured at each water body boundary point is subtracted from the vegetation height to obtain a corrected historical water body boundary point dataset, thereby more accurately reflecting the elevation data of the actual water level boundary and improving data accuracy.

[0082] S307. Use the ordinary Kriging interpolation method to perform spatial interpolation on the corrected historical water body boundary point dataset to obtain wetland elevation raster data.

[0083] The ordinary kriging interpolation method is used to perform spatial interpolation on the revised historical water body boundary point dataset. Ordinary kriging interpolation is based on spatial autocorrelation and uses the data of known points to estimate the values of unknown points. By analyzing the spatial relationship between the points, appropriate weights are assigned to each point to be interpolated, thereby generating continuous and smooth wetland elevation raster data, and converting discrete point data into a raster form that can intuitively display the wetland elevation distribution. Schematically, the semi-variance of all water body boundary point pairs in the historical water body boundary point dataset is calculated to generate a semi-variance-distance scatter plot, that is, the empirical variation function. Further, it is fitted with the theoretical model, and the spherical model is Where c0 is the nugget value, c is the sill value, and a is the range. Parameters were optimized using the least squares method to bring the theoretical model curve closer to the empirical value. Furthermore, an interpolation operation was performed, setting the maximum search radius a and the minimum number of points. The weights of each point were solved using the kriging equations to generate a continuous elevation surface, which was then output as a raster to obtain wetland elevation raster data.

[0084] S308. Remove the year-round water-covered areas and the year-round water-free areas from the wetland elevation raster data to obtain a wetland elevation distribution model.

[0085] In schematic form, through multi-period remote sensing image analysis, the frequency of pixels covered by water bodies and the frequency of pixels without water coverage are counted, and a binary mask is created, with 1 representing the valid area and 0 representing the invalid area. The wetland elevation raster data is multiplied by the binary mask to remove the year-round water-covered areas and the year-round water-free areas in the wetland elevation raster data. The elevation change patterns of the year-round water-covered areas and the year-round water-free areas are different from those of the areas affected by water level fluctuations. Removing them can avoid interference with the research results, so that the obtained wetland elevation distribution model can focus more on the wetland elevation changes under the influence of water level fluctuations, more accurately reflect the actual elevation characteristics of the wetland, and provide a reliable model basis for wetland ecosystem research, management and protection.

[0086] The wetland elevation distribution model of this application has dynamic monitoring capabilities and can update the elevation information of the wetland ecosystem in real time as the water level changes, which is more in line with the needs of wetland and other areas with large water level fluctuations. Using the OTSU algorithm to extract the water body boundaries of images from the past five years, and then combining the Kriging interpolation method to perform spatial interpolation of water level data, can provide high-precision elevation data, which is suitable for the refined management of wetland ecosystems. By combining remote sensing images, water level data and interpolation technology, it can replace traditional elevation measurements in real time and accurately, providing a new technical approach for wetland ecological protection, resource management and environmental monitoring.

[0087] In one embodiment, an improved normalized water index is calculated for each remote sensing image, and the OTSU algorithm is used to extract the water area to obtain the wetland water area, including:

[0088] S41. Calculate an improved normalized water index for each remote sensing image, and use the improved normalized water index to update the remote sensing image to obtain an updated remote sensing image.

[0089] Add the MNDWI calculation results as a new band to the remote sensing image.

[0090] S42. Count the grayscale of the updated remote sensing image to obtain a grayscale distribution histogram.

[0091] The MNDWI value is linearly stretched to 0-255 gray levels. The OTSU algorithm counts the number of pixels at each gray level of the remote sensing image and normalizes the pixels with non-gray level distribution to obtain the gray distribution histogram.

[0092] S43. Generate a segmentation threshold using the grayscale distribution histogram.

[0093] For each candidate threshold, the image is divided into two categories, for example, pixels with grayscale values of non-water bodies ≤ the candidate threshold and pixels with grayscale values of water bodies > the candidate threshold. Calculate the inter-class variance σ 2 (t)=ω1(t)ω2(t)[μ1(t)-μ2(t)] 2 , where ω1 and ω2 are the weights of non-water pixels and water pixels; μ1 and μ2 are the average grayscale values of non-water and water pixels; t is the candidate threshold. Traverse all grayscale levels, calculate the inter-class variance of each candidate threshold t, and select the variance σ 2 The threshold t corresponding to the maximum value of (t) is determined as the optimal segmentation threshold.

[0094] S44. Generate a water body mask according to the segmentation threshold.

[0095] The OTSU algorithm is used to segment the MNDWI grayscale image into water bodies and non-water bodies according to the segmentation threshold. Specifically, corresponding to the grayscale distribution, a binary mask is generated according to the segmentation threshold, and high MNDWI values are divided into water bodies and low MNDWI values are divided into non-water bodies. The corresponding binary mask is 1 for water bodies and 0 for non-water bodies.

[0096] S45. Using the water body mask, extract the water body area of the updated remote sensing image to obtain the wetland water body area.

[0097] The binary mask is superimposed on the remote sensing image to retain the information of the water body area and converted into vector surface data to obtain the wetland water body area.

[0098] In one embodiment, calculating an improved normalized water index for each remote sensing image includes:

[0099] The improved normalized water index is obtained through the following formula:

[0100]

[0101] Among them, MNDWI is the modified normalized difference water index; SR_B2 is the green band; SR_B5 is the shortwave infrared band.

[0102] Schematically, the green band of Landsat satellite images is 0.52-0.60 μm, and the shortwave infrared band is 1.55-1.75 μm, while the green band of Sentinel-2 is 0.55 μm, and the shortwave infrared band is 1.61 μm.

[0103] In one embodiment, generating a water body mask according to a segmentation threshold comprises:

[0104] S51. Generate an initial binary water body mask using the segmentation threshold.

[0105] Schematic, initial binary water mask MNDWI(x,y) is the remote sensing image processed by MNDWI, t * is the segmentation threshold.

[0106] S52. Remove small non-water patches and small water patches from the initial binary water mask to obtain a water mask.

[0107] Small non-water patches are tiny areas that are misclassified as water bodies, such as cloud shadows, bare soil, etc.; small water patches are tiny water areas that are misclassified as non-water bodies. Specifically, small non-water patches are corrected to water bodies. Schematically, non-water areas with a mask value of 0 in the binary mask are extracted, and the connected pixel values of each non-water patch are calculated. Non-water patches with an area less than a threshold are set as water bodies. For example, the threshold is set to 6 pixels. Small water patches are corrected to non-water bodies. Schematically, water areas with a mask value of 1 in the binary mask are extracted, and the connected pixel values of each water patch are calculated. Water patches with an area less than the threshold are set as non-water bodies. Optionally, a closing operation is used to fill the holes at the water body boundary, and then an opening operation is used to smooth the irregular edges of the water body boundary.

[0108] In one embodiment, generating a water body boundary point set based on a wetland water body area includes:

[0109] S61. Segment the boundaries of the water body area corresponding to the wetland water body area to obtain a water body area boundary line data set.

[0110] The vector surface data of the wetland water body area is converted into boundary line data, that is, polygons are converted into line features, and the outer contour lines of all water bodies are extracted.

[0111] S62. Discretely sample the water body area boundary line data set to obtain a water body boundary point set.

[0112] Generate points at regular intervals or regular patterns along the boundaries of a water body, converting continuous line data into discrete points for subsequent water level assignment and spatial interpolation. For example, generate a point every d meters along the boundary. Preserve the original vertices of line features to avoid missing important inflection points. Increase point density in areas of large curvature (such as river bends) and reduce it in flat areas.

[0113] Taking Dongting Lake as an example, the wetland elevation inversion method of this application first obtains the remote sensing image data of Dongting Lake in the past five years, covering wetland data of different periods (more than 40 periods). First, the MNDWI value of each remote sensing image is calculated, and then the OTSU algorithm is used to perform threshold segmentation on the remote sensing image processed by MNDWI to distinguish between water bodies and non-water bodies and extract water body areas. The extracted water body area boundary is first converted into line data to accurately represent the boundary outline of the water body. Subsequently, through further spatial processing, the water body boundary line data is converted into a point set, each point representing a specific location on the water body boundary. The water level data of the day is extracted from the hydrological station data and assigned to each point to obtain point data. The water level value of each point corresponds to its water body position in the remote sensing image, thereby providing accurate water level data for each point. The water body boundary point data obtained at different times in the past five years will be merged into a complete point data set. Each point data includes its spatial position and corresponding water level value, which reflects the water level conditions of the wetland ecosystem at each time point. The water level represents the elevation at which vegetation is completely submerged by water. To accurately reflect the water boundary elevation, the Dongting Lake region must be divided into two parts: the moss and reed communities. Ordinary kriging was used to spatially interpolate the merged water boundary point data. The resulting water level raster data can directly reflect wetland elevation changes. Within the study area, East Dongting Lake contains small areas of extremely low elevation, such as river channels that are flooded year-round, and extremely high elevation areas, such as the Junshan Wetland Park, which is free of water year-round. Because these areas are unaffected by water level fluctuations, they were removed to maintain accuracy. The resulting water level raster data replaces traditional elevation measurement methods to develop an elevation distribution model for the wetland region. This model can reflect water level changes in real time and dynamically update the elevation distribution of the wetland ecosystem. It provides real-time, accurate elevation data for wetland ecosystem management and hydrological monitoring, and is particularly suitable for areas with significant water level fluctuations and gently sloping terrain, such as the Dongting Lake wetland.

[0114] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0115] Based on the same inventive concept, embodiments of the present application also provide a wetland elevation inversion device for implementing the aforementioned wetland elevation inversion method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more wetland elevation inversion device embodiments provided below can be found in the limitations of the wetland elevation inversion method described above and will not be repeated here.

[0116] In an exemplary embodiment, Figure 4 As shown, a wetland elevation inversion device is provided, comprising:

[0117] A data receiving module is used to receive the daily water level data of the wetland area;

[0118] The model algorithm module is used to input the water level data of the day into the wetland elevation distribution model to obtain elevation grid data.

[0119] In one embodiment, the invention further includes a data processing module, an error correction module and a spatial interpolation module, wherein the data processing module is used to integrate the water level value corresponding to the water position into the historical water body boundary point data set based on the water body boundary point set to obtain a new water body boundary point data set;

[0120] The error correction module is used to perform vegetation height error correction on the new water body boundary point dataset to obtain a corrected water body boundary point dataset;

[0121] The spatial interpolation module is used to calculate the wetland elevation distribution under the corrected water body boundary point dataset using the spatial interpolation method to obtain elevation raster data.

[0122] In one embodiment, it further includes a data acquisition module and an image cutting module;

[0123] The data acquisition module is used to obtain remote sensing image sets and water level data of the corresponding period;

[0124] The image cutting module is used to calculate the improved normalized water index for each remote sensing image, and use the OTSU algorithm to extract the water area to obtain the wetland water area.

[0125] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0126] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0127] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0128] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A wetland elevation inversion method, characterized in that: The method comprises: Receiving daily water level data of a wetland area; the daily water level data includes a water point and a corresponding water level value; The water level data of the day is input into the wetland elevation distribution model to obtain elevation grid data; the elevation grid data is used to characterize the elevation distribution of the wetland area.

2. The method according to claim 1, characterized in that The wetland elevation distribution model is based on the following method to obtain elevation grid data: Based on the water body boundary point set, the water level value corresponding to the water position point is integrated into the historical water body boundary point dataset to obtain a new water body boundary point dataset; the historical water body boundary point dataset includes the spatial position of each water body boundary point and the corresponding water level value; Performing vegetation height error correction on the new water body boundary point dataset to obtain a corrected water body boundary point dataset; The spatial interpolation method is used to calculate the wetland elevation distribution under the corrected water body boundary point dataset to obtain elevation raster data.

3. The method according to any one of claims 1 to 2, characterized in that The wetland elevation distribution model is constructed by the following method: Acquire a remote sensing image set and water level data of a corresponding period; the remote sensing image set includes remote sensing images of multiple periods; Calculating an improved normalized water index for each of the remote sensing images, and extracting water body areas using the OTSU algorithm to obtain wetland water body areas; generating the water body boundary point set based on the wetland water body area; Combining the water body boundary point set with the corresponding water level data to obtain water body boundary point data; Merging the plurality of water body boundary point data corresponding to a plurality of time periods into the historical water body boundary point dataset; Correcting the water level value of the historical water body boundary point dataset based on vegetation height to obtain a corrected historical water body boundary point dataset; Using the ordinary Kriging interpolation method to perform spatial interpolation on the revised historical water body boundary point dataset to obtain wetland elevation raster data; The wetland elevation distribution model is obtained by removing the year-round water-covered area and the year-round water-free covered area from the wetland elevation raster data.

4. The method according to claim 3, characterized in that The improved normalized water index is calculated for each remote sensing image, and the water area is extracted using the OTSU algorithm to obtain the wetland water area, including: Calculating an improved normalized water index for each of the remote sensing images, and using the improved normalized water index to update the remote sensing image to obtain an updated remote sensing image; Counting the grayscale of the updated remote sensing image to obtain a grayscale distribution histogram; Generating a segmentation threshold using the grayscale distribution histogram; generating a water body mask according to the segmentation threshold; The water body area of the updated remote sensing image is extracted using the water body mask to obtain the wetland water body area.

5. The method according to claim 4, characterized in that Calculating the improved normalized water index for each remote sensing image includes: The improved normalized water index is obtained through the following formula: Among them, MNDWI is the improved normalized water index; SR_B2 is the green band; SR_B5 is the shortwave infrared band.

6. The method according to claim 4, characterized in that Generating a water body mask according to the segmentation threshold comprises: generating an initial binary water body mask using the segmentation threshold; The initial binary water body mask is subjected to removal of small non-water body patches and small water body patches to obtain a water body mask.

7. The method according to claim 3, characterized in that The step of generating the water body boundary point set based on the wetland water body area includes: Segmenting the boundaries of the water body area corresponding to the wetland water body area to obtain a water body area boundary line data set; The water body area boundary line data set is discretely sampled to obtain a water body boundary point set.

8. A wetland elevation inversion device, characterized in that: The device comprises: A data receiving module is used to receive the daily water level data of the wetland area; The model algorithm module is used to input the water level data of the day into the wetland elevation distribution model to obtain elevation grid data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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