Lake water level and water surface relation analysis method based on DEM and remote sensing image

Abstract

The invention relates to the technical field of water level and water surface relation analysis, in particular to a lake water level and water surface relation analysis method based on a DEM and a remote sensing image, which comprises the following steps of: based on DEM and remote sensing image data, analyzing a space relation between measured data and the DEM, correcting abnormal elevation and adjusting original data, screening boundary points with small elevation difference, and calculating the water level and water surface relation of a lake; and performing statistics on water surface areas under different water levels to obtain a water level area change sequence. According to the method, through cooperative adjustment and automatic fusion based on multi-source spatial data, through elevation correction, spatial anomaly screening and time sequence change tracking, the consistency of spatial data is improved, elevation anomaly information is dynamically corrected, the discrimination capability of a water body spatial distribution boundary is enhanced, and dense water surface change information extraction is realized based on spatial overlapping data; the space node and area change trend under the water level grading condition can be automatically concluded, and the dynamic response characteristics of the lake space structure can be systematically reflected.

Description

Technical Field

[0001] This invention relates to the field of water level and water surface relationship analysis technology, and in particular to a method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery. Background Technology

[0002] The field of water level-water surface relationship analysis falls under the scope of geographic information science and environmental monitoring. It encompasses changes in the water surface area of ​​lakes or reservoirs under different water level conditions, extraction of topographic features, and dynamic monitoring of water body coverage. It is widely applied in water resource allocation, ecological protection, and flood control management. Traditional water level-water surface relationship analysis involves manual field observation and topographic map data collection. This is achieved by deploying hydrological monitoring stations to record water level changes and using large-scale topographic maps to collect basic geographic information such as contour lines, isobaths, and shorelines. Combined with field survey data, manual methods or simple area calculation formulas are used to estimate the corresponding water surface area and region at different water levels. The aim is to clarify the changing patterns and area distribution of lake water surfaces under different water level conditions, providing fundamental data support and scientific basis for lake water resource management, allocation plan formulation, ecological protection, water storage capacity estimation, flood control safety, and environmental assessment.

[0003] Existing technologies are limited by the location of monitoring points and terrain conditions, making it difficult to comprehensively acquire information on micro-geomorphological changes in lake areas. The spatial distribution of the data collection area is sparse, attribute information is prone to distortion, data integration is difficult to track dynamic changes in water surface morphology, water level and area change analysis often lacks spatial refinement, area estimation is prone to deviation from the actual distribution, information updates are slow, regional identification is ambiguous, and spatial continuous monitoring capabilities are insufficient, which can easily lead to delays in water body distribution identification and make it difficult to provide efficient data support for water resource allocation and risk response in environmentally sensitive areas. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing images.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery, comprising the following steps: S1: Based on DEM and remote sensing image data, analyze the spatial correspondence between GNSS and RTK measured data and DEM elevation data, screen elevation anomalies, optimize and adjust the elevation of the anomaly points, and then obtain the zonal elevation adjustment parameters through neighborhood mean correction. S2: Based on the partition elevation adjustment parameters, calculate the spatial distance between the submerged boundary and the remote sensing image, determine the correspondence between the two types of spatial points, filter the boundary points with small elevation differences and close spatial proximity, update the connected region, and obtain the connected boundary coordinate sequence. S3: Based on the connected boundary coordinate sequence, compare the spatial distribution of water body boundaries in multiple time phases, analyze the temporal changes of each spatial point, screen out location anomalies, and use the mean of nearby non-offset points to correct the elevation, thus obtaining the shoreline time series correction result. S4: Call the shoreline time-series correction results, determine the spatial overlap relationship between the water body boundary of the remote sensing image and the DEM derived boundary, filter the overlapping areas with small elevation changes, optimize the spatial boundary, calculate the actual water surface coverage, and obtain the spatial coverage area index.

[0006] The present invention is improved in that the partition elevation adjustment parameters include a corrected elevation zone, a spatial block identifier, and a reference mean index; the connected boundary coordinate sequence includes a spatial node group, a connected path index, and a boundary classification identifier; the shoreline time-series correction result includes a time-series shoreline point set, a trajectory association index, and a location correction marker; and the spatial coverage area index includes a coverage range code, area quantification data, and a distribution contour set.

[0007] The present invention is improved in that the step of obtaining the partition elevation adjustment parameters is specifically as follows: S111: Based on DEM and remote sensing image data, analyze the spatial location of GNSS and RTK measuring points, pair them with DEM data, compare the elevation information of each measuring point, filter measuring points with elevation differences, determine their spatial distribution characteristics, and obtain a group of measuring points with spatial differences. S112: Optimize the elevation data in the spatial difference anomaly measuring point group, calculate the average elevation of its adjacent measuring points, adjust the elevation information of the anomaly measuring points, and analyze the elevation relationship between the measuring points to obtain the elevation correction data group. S113: Based on the elevation correction data set, determine the elevation distribution of each spatial region, adjust the elevation reference standard of each region, and summarize the elevation adjustment results of the spatial regions to obtain the regional elevation adjustment parameters.

[0008] The present invention is improved in that the step of obtaining the connected boundary coordinate sequence is specifically as follows: S211: Based on the partition elevation adjustment parameters, analyze the spatial points of the submerged boundary generated by the remote sensing image, calculate the actual spatial distance between each spatial point, filter the closely spaced spatial points, and obtain a set of spatially adjacent point pairs. S212: Based on the set of spatially adjacent point pairs, compare the DEM elevation of each spatial point with the remote sensing boundary elevation, determine whether the elevation differences are close, identify spatial points with similar elevation information, and adjust boundary points that do not meet the conditions to invalid points, thereby obtaining a set of boundary points with consistent elevation. S213: Call the set of elevation-consistent boundary points, analyze the spatial interval and elevation fluctuation of each continuous boundary node, optimize the boundary information through the spatial and elevation features of the continuous nodes, calculate the spatial connectivity level of the boundary segment, and obtain the connected boundary coordinate sequence.

[0009] The present invention is improved in that the specific steps for obtaining the shoreline time series correction result are as follows: S311: Based on the connected boundary coordinate sequence, analyze the spatial distribution of the multi-temporal remote sensing water body boundary, and calculate the actual spatial variation range of the point by comparing the coordinate differences of each spatial point in different remote sensing phases, thereby obtaining multi-temporal spatial offset data; S312: Based on the multi-temporal spatial offset data, filter out points with prominent spatial position changes, determine the surrounding coordinate points that have not changed for each abnormal change point, optimize the elevation data of the points, and calculate the spatial mean to obtain the average elevation of the non-offset points. S313: Based on the average elevation of the non-offset points, compare the spatial difference between the average elevation and the elevation of the outlier points, update the elevation of the outlier points, and obtain the shoreline time-series correction result.

[0010] The present invention is improved in that the steps for obtaining the spatial coverage area index are as follows: S411: Call the shoreline time series correction results, analyze the spatial correspondence between shoreline points and remotely sensed water body boundaries, compare the coordinate consistency and elevation changes of each overlapping node, calculate the elevation change amplitude of spatial nodes during the period of difference, identify key spatial nodes of change, and obtain the elevation change intensity sequence. S412: Based on the elevation change intensity sequence, filter adjacent spatial nodes with similar change amplitudes, determine whether the nodes constitute a spatially connected region, optimize the spatial relationship of each group of connected nodes, and obtain a set of overlapping boundary connected nodes. S413: Based on the set of connected nodes along the overlapping boundary, analyze the differences in coordinates and elevations of each node under the remote sensing boundary and DEM data, identify the area coverage and distribution, and obtain the spatial coverage area index.

[0011] The present invention is improved in that the steps further include: S5: Based on the spatial coverage area index, analyze the spatial changes of DEM under different water levels, screen the inundated areas under each water level condition, determine the spatial correspondence between the remote sensing water body boundary and DEM, summarize the water surface area under different water levels, optimize the spatial distribution, and obtain the water level area change sequence. The water level area change sequence includes water level interval groups, area change list, and distribution node index.

[0012] The present invention is improved in that the specific steps for obtaining the water level area change sequence are as follows: S511: Based on the spatial coverage area index, analyze the elevation distribution of DEM data under different water level conditions, calculate the topographic relief within the area corresponding to each water level, filter areas that are lower than or equal to the current water level elevation, and determine whether the areas are connected to obtain the submerged area pixel group. S512: Based on the pixel group of the flooded area, compare the positional relationship with the boundary of the remotely sensed water body, identify pixels that overlap in space, analyze the distribution characteristics of overlapping pixels in the difference area, mark pixels with spatial matching relationship, and obtain spatial overlapping pixel identifiers. S513: Based on the spatial overlapping pixel identifier, calculate the spatial coverage of the overlapping area under each water level condition, analyze the boundary structure of each area, determine the trend of area change, arrange the difference coverage area according to the water level condition, and obtain the water level area change sequence.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, relying on the collaborative adjustment and automatic fusion of multi-source spatial data, the consistency of spatial data is improved through elevation correction, spatial anomaly screening, and time-series change tracking. Elevation anomaly information is dynamically corrected, the ability to distinguish the spatial distribution boundary of water bodies is strengthened, and the information on changes in dense water surfaces is extracted based on spatially overlapping data. The spatial node and area change trends under water level classification conditions can be automatically summarized, systematically reflecting the dynamic response characteristics of the lake's spatial structure. This meets the needs for fine analysis of spatial information in areas with complex terrain and environmental changes, and promotes the upgrading of spatial monitoring results towards timeliness, accuracy, and automation, so as to support the continuous output and monitoring of data in areas with varied landforms and fluctuating hydrology. Attached Figure Description

[0014] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the zonal elevation adjustment parameters in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the coordinate sequence of the connected boundary in this invention. Figure 4 This is a flowchart illustrating the process of obtaining shoreline timing correction results in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the spatial coverage area index in this invention. Figure 6 This is a flowchart illustrating the process of obtaining the water level area change sequence in this invention. Figure 7 This is a diagram of the three-dimensional feature lines and three-dimensional feature points of the present invention; Figure 8 This is a digital elevation model diagram of the present invention; Figure 9 This is a flowchart illustrating the calculation of water surface area corresponding to water level in this invention. Figure 10 This is a diagram illustrating the water surface area calculation method of the present invention; Figure 11 This is a diagram showing the interpretation results of the remote sensing image of the South Four Lakes in April 2021, according to the present invention. Figure 12 This is a water level and surface relationship curve of the upper lake of the South Four Lakes according to the present invention; Figure 13 This is a curve showing the relationship between water level and surface area of ​​the lower lakes of the South Four Lakes according to the present invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Please see Figure 1 This invention provides a technical solution: a method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery, comprising the following steps: S1: Based on DEM and remote sensing image data, analyze the spatial correspondence between GNSS and RTK measured data and DEM elevation data, determine the elevation difference between each measuring point, screen measuring points with abnormal differences, optimize the elevation of abnormal measuring points, and adjust and replace the original elevation data by calculating the average measured elevation data of adjacent areas to obtain the zonal elevation adjustment parameters. S2: Based on the partitioned elevation adjustment parameters, calculate the spatial distance between them and the inundation boundary derived from the remote sensing image, determine the correspondence between the two spatial points, filter the boundary points with close spatial positions and limited elevation differences, adjust the boundary points that do not meet the conditions to an invalid state, optimize the boundary information of the connected region, and obtain the connected boundary coordinate sequence. S3: Based on the connected boundary coordinate sequence, compare it with the spatial distribution of the water body boundary in multi-temporal remote sensing, analyze the positional change of each spatial point across time phases, screen points with abnormal positional changes, calculate the average elevation of surrounding non-offset points, and adjust the elevation information of abnormal points to ensure the continuity of shoreline space and obtain shoreline temporal correction results. S4: Call the shoreline time series correction results, determine the spatial overlap relationship of the water body boundary derived from the remote sensing image, compare the elevation changes of corresponding points in space, select the overlapping area with the best elevation change amplitude, optimize the spatial boundary data of this range, and calculate the actual water surface coverage of the area to obtain the spatial coverage area index. S5: Based on the spatial coverage area index, analyze the spatial changes of DEM under different water level conditions, screen the inundated areas under each water level condition, determine the spatial correspondence between the remote sensing water body boundary and DEM data, summarize the water surface coverage area of ​​each water level, optimize the spatial data distribution, and obtain the water level area change sequence.

[0018] The parameters for zonal elevation adjustment include corrected elevation zones, spatial block identifiers, and reference mean indexes. The coordinate sequence of connected boundaries includes spatial node groups, connected path indexes, and boundary classification identifiers. The shoreline temporal correction results include temporal shoreline point sets, trajectory association indexes, and location correction markers. The spatial coverage area indicators include coverage area codes, area quantification data, and distribution contour sets. The water level area change sequence includes water level interval groups, area change lists, and distribution node indexes.

[0019] In S1, spatial correspondence refers to the matching between the geographic spatial location of GNSS and RTK measured points and the spatial coordinates of the three-dimensional feature points or feature lines of the Digital Elevation Model (DEM). It achieves the fusion of different data sources through spatial coordinate consistency and is often used in scenarios of comparing and correcting measured data with DEM elevation data. Elevation difference refers to the difference between the elevation data of the same spatial location in GNSS and RTK measured points and the DEM model. It is used to find DEM errors and correct the accuracy of micro-topography areas.

[0020] In S2, the inundation boundary refers to the outer boundary of the area below a given water level within a lake area extracted from DEM data. This boundary represents the theoretically water-covered area and is used for area calculation and spatial matching analysis. The correspondence relationship refers to the one-to-one correspondence or matching of points or line segments from two different data sources in spatial data on the coordinate plane. It is used for comparison and overlay analysis and is often used in the process of matching spatial points between the DEM inundation boundary and the water body boundary extracted by remote sensing. Boundary points refer to the spatial coordinate points used to define spatial polygons or polylines. These points together constitute the inundation area or water body boundary and are used for spatial overlay, distance calculation, and area analysis. The condition not met refers to the fact that some boundary points do not meet the spatial or attribute matching requirements under standards such as spatial distance and elevation difference. These points are automatically excluded in spatial analysis or connectivity identification.

[0021] In S3, multi-temporal remote sensing water body boundaries refer to the spatial distribution data of lake water body boundaries at different times obtained by interpreting multiple remote sensing images, which is used to analyze the dynamic changes and spatiotemporal trajectories of lake water bodies; cross-temporal positional changes refer to the changes in the boundary position of the same spatial point in multiple remote sensing image periods, which is used to analyze the trend of water body boundary changes or detect data anomalies; points with abnormal changes refer to spatial points whose positional changes exceed the normal range in multi-temporal spatial trajectories, which need to be corrected by spatial correction or neighboring point mean correction to ensure the continuity and accuracy of analysis results.

[0022] In S4, spatial overlap refers to the overlap and intersection between spatial regions obtained from different data sources. It is often used to analyze the degree of spatial overlap between water body boundaries in remote sensing images and water body boundaries derived from DEMs, and is used for result verification, area output, and spatial consistency testing.

[0023] In S5, the submerged area refers to all spatial regions in the digital elevation model whose elevation is less than or equal to the water level under given water level conditions. It is used for water surface coverage area statistics and spatial distribution analysis and is the core spatial unit for lake water level-water surface relationship analysis. The spatial correspondence refers to the one-to-one correspondence between the water body boundary of the remote sensing image and the submerged area of ​​the DEM in spatial coordinates. It is used for spatial consistency comparison and area statistics, and provides a spatial basis for the construction of lake water level-water surface relationship curves.

[0024] For digital elevation model construction A digital elevation model (DEM) of the Nansi Lake area was constructed using a 1:10,000 topographic map of the Nansi Lake region as the base map. The resolution of the DEM was 5m × 5m. Three-dimensional feature lines and points representing surface and underwater topographic information were collected from the 1:10,000 topographic map containing underwater topographic information. These mainly included contour lines, isobaths, landform lines, shorelines of isobaths, surface elevation points, and underwater elevation points. Non-surface elevation annotations such as those for bridges and culverts were deleted and not included in the model construction. Landform symbols such as steep cliffs, slopes, and double-line gullies were converted to contour lines. Elevation feature points or lines were interpolated for hilltops, depressions, and passes lacking elevation points. Special treatment was applied to the fishponds widely distributed in the South Four Lakes area. The top lines of the slopes on both sides of the fishpond embankments were collected, and elevation values ​​were assigned to them based on nearby land elevation markers. Similarly, the bottom slope lines of the fishponds were collected, and elevation values ​​were assigned to them based on underwater elevation markers, thus fully representing the topographic features of the fishponds. Figure 7As shown. After the 3D feature lines and 3D feature points are acquired, topology and rationality checks are performed. The 3D feature lines and 3D feature points are then input into ArcGIS software to construct an irregular triangular network (TIN) model. This TIN model is then checked, and unreasonable triangles are modified. A digital elevation model is then generated through interpolation. The results are shown below. Figure 8 As shown. 110 points were selected from a uniformly distributed area of ​​the lake, and GNSS RTK field measurements were performed and compared. Statistical analysis showed that the elevation error was 0.3 meters.

[0025] For water surface area calculation The water surface area of ​​the South Four Lakes at different water levels was calculated using the constructed digital elevation model. The calculation method is as follows: Figure 9 As shown, the calculation water level is first determined. Extracting data from digital elevation models using ArcGIS software's attribute extraction tool. The raster area is considered a rough inundation zone, indicating a potential for inundation. The raster-to-polygon tool is used to convert this rough inundation zone into vector polygons. Using a digital elevation model, it is determined whether each polygon in the rough inundation zone can be connected to the lake area via waterways, shallow channels, or ditches. Polygons that can be connected to the lake area are considered actual inundation zones, and polygons that are too far from the lake area and cannot be connected are deleted. After merging adjacent fragmented polygons, the area of ​​a single polygon must be greater than 30 km². 2 The water surface is divided into contiguous water surfaces, and the remaining non-adjacent polygons are divided into scattered water surfaces. The contiguous water surfaces and the scattered water surfaces together constitute the water surface. water surface area For water surface The area.

[0026] Combination Figure 10 Polygonal elements representing the water surface The apex is organized in a clockwise or counterclockwise direction. , Given the number of vertices, the area of ​​an irregular polygon can be calculated using the following formula: ; In the formula, As vertices coordinate.

[0027] For remote sensing image interpretation The collected remote sensing images of the South Four Lakes were used to interpret and analyze land cover, with a focus on extracting water surface cover. The main process is as follows: (1) Through preliminary indoor interpretation and analysis of relevant data, the main land types of the South Four Lakes that need to be interpreted in this study include contiguous water surfaces, shallow channels, fish ponds, paddy fields and land. The areas surrounding Zaolin Village and Wanzhuang Village were selected as two typical interpretation sample areas.

[0028] (2) Conduct field investigations in the South Four Lakes area to investigate the characteristics and distribution of major land types. For each land type, select at least 10 typical examples distributed in different locations in the lake area, take photos and record the land type name, latitude and longitude, shooting time, color characteristics, and geomorphic environment. Establish a correspondence with remote sensing images and establish an interpretation mark library. Establish interpretation rules based on direct features such as shape and color characteristics as the basis for interpretation classification, combined with the surrounding geomorphic environment correlation analysis, and using protective forests, dikes, rural roads, etc. as references for land type boundaries.

[0029] (3) The interpretation and classification are carried out by combining computer-automated classification with manual visual interpretation. The computer-automated classification adopts the supervised classification method, which automatically extracts typical land features by establishing templates. After classification, the classification results are judged by manual visual interpretation. Classification errors or boundary errors are manually corrected, and the land feature distribution boundaries are corrected based on the actual field verification.

[0030] (4) Store the interpretation results as an SHP surface data file, perform a topological check on the vector data, and summarize and statistically analyze the area of ​​each land type after confirming that there are no errors. The results are as follows: Figure 11 As shown.

[0031] Results Verification Analysis Using daily water level observation data of the Nansi Lake from the Nanyang and Weishan hydrological stations, the water level elevations of the upper and lower lakes corresponding to the dates of remote sensing image capture were obtained. The interpreted water surface area and vector graphics from the remote sensing images were compared with the water surface area and vector graphics calculated using a digital elevation model for the same water level to analyze their consistency.

[0032] (5) Analysis of water surface changes after raising the water level The water surface area was calculated for every 0.5m elevation step between the ecological water level and the flood control water level of the South Four Lakes. The normal water storage level and three water level raising schemes of 0.3m, 0.5m and 1.0m were added. The calculation results were compiled and summarized, and the water level-water surface relationship curve was plotted.

[0033] Extract vector graphics of the water surface corresponding to the normal water level of the South Four Lakes (34.5m for the upper lake and 32.5m for the lower lake) and the three schemes of water level increase of 0.3m, 0.5m and 1.0m. Calculate the area of ​​contiguous water surface and scattered water surface respectively, and analyze the increase in water surface area relative to the normal water level.

[0034] A comparison of the water surface distribution graphics obtained by digital elevation modeling and remote sensing interpretation methods shows that they are basically consistent. A comparative analysis of the water surface areas obtained by the two methods is shown in Table 1.

[0035] Table 1. Comparison of water surface area interpreted from remote sensing images and water surface area calculated by digital elevation models:

[0036] As shown in Table 1, among the six remote sensing images compared with the results calculated by the digital elevation model, the largest area difference was observed in the image of Shangji Lake on October 26, 2020, with an area difference of 23.182 km². 2 The difference percentage was 4.08%. Except for the imagery from this period, the difference percentages for all other images were less than 2%. Therefore, it can be considered that the water surface area interpreted from this remote sensing imagery is basically consistent with the water surface area calculated by the digital elevation model. The digital elevation model constructed in this paper is accurate and reliable, and the water surface area calculation method is correct. Based on this, the water level-water surface relationship curve was calculated, and the water surface changes after raising the water level were analyzed. The water level-water surface relationship curve is shown in [reference needed]. Figure 12 (Superior) and Figure 13 (Subordinate).

[0037] Depend on Figure 12 and Figure 13 It can be concluded that when the water level of the upper lake is below 35.5m, the water surface area expands rapidly as the water level rises, but the increase in water surface area is not significant after the water level is above 35.5m; when the water level of the lower lake is below 34.0m, the water surface area expands rapidly as the water level rises, but the increase in water surface area is not significant after the water level is above 34.0m.

[0038] The analysis of the increase in area of ​​continuous and scattered water surfaces under the normal water level by raising the water level of the South Four Lakes by 0.3m, 0.5m and 1.0m is shown in Table 2.

[0039] Table 2. Changes in water surface area of ​​the South Four Lakes after the water level was raised:

[0040] Table 2 shows that the total surface area of ​​the South Four Lakes at the normal water level is 1157.053 km². 2 Of which, 400.049 km² is contiguous water surface. 2 Scattered water surface 757.005km 2 A 0.3m rise in water level can increase the continuous water surface area by 9.913km. 2 The total water surface area increased by 21.405 km². 2 A 0.5m rise in water level can increase the contiguous water surface area by 45.302km. 2 The total water surface area increased by 41.642 km². 2A 1.0m rise in water level can increase the contiguous water surface area by 219.509km. 2 The total water surface area increased by 75.954 km². 2 Analysis shows that as the water level gradually rises, while the total water surface area increases, some scattered water surfaces gradually connect with each other, transforming into contiguous water surfaces. Raising the normal water level of the South Four Lakes can effectively increase the area of ​​contiguous water surfaces.

[0041] Please see Figure 2 The specific steps for obtaining the zonal elevation adjustment parameters are as follows: S111: Based on DEM and remote sensing image data, analyze the spatial location of GNSS and RTK measuring points, pair them with DEM data, compare the elevation information of each measuring point, filter measuring points with elevation differences, determine their spatial distribution characteristics, and obtain a group of measuring points with spatial differences. First, GNSS and RTK measuring points were deployed within the lake area, and the geospatial coordinates and elevation values ​​of each point were recorded. After collection, all coordinates were unified to the same spatial reference system, such as converting WGS84 coordinates to a local projection coordinate system, to achieve accurate matching with the DEM elevation data. Then, elevation values ​​matching the x and y coordinates of each measuring point were extracted from the digital elevation model. The measured elevations were compared one by one with the elevations in the DEM, and a one-to-one elevation difference list was established for all measuring points. The statistical distribution of the overall difference data was obtained, and the average difference plus or minus two standard deviations was used as the anomaly screening range. Based on this, anomaly measuring points significantly higher or lower than this range were identified. For all anomalous measurement points, a proximity analysis is performed in space. Based on the buffer zone, it is searched to see if there are multiple other anomalous points within a 5-meter radius. If multiple measurement points with similar elevation deviations exist, they are marked as a group of clustered anomalous points. If there are no other anomalous points around them or the difference in elevation is huge, they are marked as independent anomalous measurement points. For example, if a measured point in a certain area has an elevation of 36.2 meters, while the DEM value at the same coordinate position is 34.4 meters, the deviation is 1.8 meters, while the average deviation in the entire lake area is only 0.2 meters, then this point should be designated as a difference anomalous point. If there are also more than 3 measurement points around it that show a deviation of more than 1 meter, then a group of anomalous distributions is formed, resulting in a group of spatial difference anomalous measurement points.

[0042] S112: Optimize the elevation data in the spatial difference anomaly measuring point group, calculate the average elevation of its adjacent measuring points, adjust the elevation information of the anomaly measuring points, and analyze the elevation relationship between measuring points to obtain the elevation correction data group. When optimizing the elevation data of spatially anomalous measurement points, a set of neighboring measurement points needs to be constructed centered on each anomalous point. Normal measurement points within 10 meters of the anomalous point are retrieved, their corresponding elevation data are extracted, and their average value is calculated as the new correction value for that anomalous point. The original elevation of this point is then replaced with the new correction value, and its elevation relationship with surrounding measurement points is reassessed. If the elevation difference between this point and its neighboring measurement points remains significant after correction, the point is considered uncorrectable; otherwise, the correction is considered successful. For example, if the original elevation of a point deviates by 1.5 meters, and the average of the five neighboring points is 36.3 meters, the corrected elevation is set to 36.3 meters, keeping the elevation difference within a reasonable tolerance. After replacing the elevations of all anomalous points, the corrected data is merged with the original data to form the basis for subsequent interpolation and reconstruction. For each point, it is recorded whether it has been corrected, its values ​​before and after correction, and the number of surrounding points for summary analysis, forming an elevation correction data set.

[0043] S113: Based on the elevation correction data set, determine the elevation distribution of each spatial region, adjust the elevation reference standard of each region, and summarize the elevation adjustment results of the spatial regions to obtain the regional elevation adjustment parameters; The elevation correction data set is divided into spatial blocks, establishing multiple partition units. All measurement points within each block, including correction points and original points, are extracted. The elevation mean and variability of each block are statistically analyzed. By comparing the elevation mean offset of each block with the overall region, it is determined whether there is a systematic deviation within that block. If the offset of a block exceeds 0.5 meters, it is identified as an abnormal area, and its own mean is used as a new elevation reference value for subsequent unified elevation correction. Correspondingly, the DEM raster elevation values ​​within the block are adjusted upwards or downwards to match this reference value. For example, the statistical elevation mean of a western block is 34.7 meters, while the average for the entire region is 34.2 meters, a deviation of more than 0.5 meters. Therefore, all DEM values ​​within that block need to be increased by 0.5 meters to achieve uniform standardization. All processing results form a set of parameters within each block, including the elevation reference value, adjustment range, and data fluctuation range, which serve as the partition elevation adjustment parameters used in subsequent analysis.

[0044] Please see Figure 3 The specific steps for obtaining the connected boundary coordinate sequence are as follows: S211: Based on the zoning elevation adjustment parameters, analyze the spatial points of the inundation boundary generated by the remote sensing image, calculate the actual spatial distance between each spatial point, filter the closely spaced spatial points, and obtain a set of spatially adjacent point pairs. First, the adjusted elevation reference values ​​for each zoned area are obtained and spatially cross-referenced with the water inundation boundary lines extracted from the remote sensing imagery. The coordinates of each boundary point are read, and its corresponding geographic coordinates in the raster space are extracted. Then, the elevation point closest to these coordinates is retrieved from the zoned, adjusted DEM, and the horizontal and vertical coordinate distances between them are determined. The Euclidean distance from the boundary point to the nearest DEM point is calculated. This process is repeated for all remote sensing boundary points. Boundary point pairs with a distance of less than 2.5 meters are selected from all distance data. This threshold is used as the definition of a close spatial point relationship. Based on a remote sensing image spatial resolution of 2 meters, and considering image distortion errors and DEM pixel side length tolerances, the resolution is increased to 2.5 meters. If the coordinates of a remote sensing boundary point are (125432.6, 3847212.3), and the corresponding DEM point coordinates are (125431.4, 3847210.8), the calculated distance between them is 1.73 meters, which is less than the 2.5-meter threshold. Therefore, this point pair is included in the set of adjacent point pairs. If the distance is 3.2 meters, it is removed. This process needs to be performed in batches in a spatial data processing tool, and the coordinate value accuracy of the results needs to be verified. After confirming that there are no errors, the information of all boundary point pairs that meet the conditions is output, thus obtaining the set of spatially adjacent point pairs.

[0045] S212: Based on the set of spatially adjacent point pairs, compare the DEM elevation of each spatial point with the remote sensing boundary elevation, determine whether the elevation differences are close, identify spatial points with similar elevation information, and adjust boundary points that do not meet the conditions to invalid points, thus obtaining a set of boundary points with consistent elevation. For each pair of points in the set, extract the elevation value from its DEM and the corresponding elevation value from the remotely sensed water body boundary point. If the remotely sensed boundary point itself has no additional elevation data, then its elevation value should be based on the water level of the hydrological station corresponding to the time the remotely sensed image was captured. Based on this, compare the elevation differences between the pairs of points, statistically analyze the elevation differences of all point pairs, and calculate the mean and standard deviation of the elevation differences for the entire set. The standard for judging consistent elevation differences is set as within ±0.4 meters of the average value; that is, elevation differences within 0.4 meters are considered close. For example, if the remotely sensed point represents a water level of 32.1 meters and the DEM point has an elevation of 31.85 meters... If the difference between the two is 0.25 meters, it falls within the close range and is marked as a point with consistent elevation. If another point pair has a remote sensing water level of 32.1 meters and a DEM point of 31.4 meters, the difference is 0.7 meters, exceeding the threshold. In this case, it is determined to be an elevation inconsistency, and the boundary point is marked as invalid and will no longer be used for subsequent boundary connectivity analysis. The threshold setting is based on the remote sensing water level resolution and DEM elevation error tolerance. The combined value of the remote sensing elevation error of ±0.2 meters and the DEM tolerance of ±0.2 meters is selected as the upper limit for judgment. All point pairs with consistent elevation retain their original boundary markings and are output uniformly to form a set of boundary points with consistent elevation.

[0046] S213: Utilize the consistent elevation boundary point set to analyze the spatial interval and elevation fluctuation of each continuous boundary node segment. Optimize the boundary information using the spatial and elevation characteristics of the continuous nodes, employing the following formula: ; Calculate the spatial connectivity level of the boundary segment This yields the coordinate sequence of the connected boundary, where, Represents the set of elevation-consistent boundary points. The spatial interval between each node and its neighboring nodes Indicates the first The elevation difference between a node and its neighboring nodes Indicates the first The elevation fluctuation of each node within the boundary segment Indicates the first The degree of spatial continuity of each node within its boundary segment. It represents the total number of nodes in a continuous boundary segment.

[0047] Spatial connectivity level refers to the degree of tightness and continuity of the connection between nodes on a boundary line in space. In the analysis of the relationship between lake water level and water surface, spatial connectivity level reflects the integrity and lack of discontinuity of the set of boundary points with consistent elevation in space, as well as the degree of actual physical connection between the nodes on the boundary. In layman's terms, the higher the spatial connectivity level, the more continuous and complete the boundary segment is in space, with smaller spatial jumps and elevation fluctuations, and the more consistent the spatial and elevation characteristics of each node.

[0048] First, connect the nodes in the boundary point set according to spatial order to determine the spatial spacing between adjacent nodes within each boundary segment. Extract the elevation difference of the corresponding nodes. and elevation fluctuation range At the same time, the degree of continuity of the arrangement is quantified based on the change in the included angle between adjacent nodes. Calculate the spatial connectivity level of the boundary segment. If there are four sets of consecutive nodes within a certain boundary segment, the original parameters are as follows: Group 1: Spacing 2.6 meters, after normalization The elevation difference is 1.1 meters, after normalization The elevation fluctuates by 0.8 meters, after normalization Continuity level 1.2, after normalization ; Group 2: Spacing 3.0 meters, after normalization The elevation difference is 0.9 meters, after normalization The elevation fluctuates by 0.7 meters, after normalization. Continuity level 1.4, after normalization ; Group 3: Spacing 2.4 meters, after normalization The elevation difference is 1.3 meters, after normalization Elevation fluctuation of 1.0 meter, after normalization Continuity level 1.6, after normalization ; Group 4: Spacing 2.2 meters, after normalization The elevation difference is 1.0 meter, after normalization The elevation fluctuates by 0.6 meters, after normalization. Continuity level 1.1, after normalization .

[0049] Substitute the parameters into the formula and calculate each of the four terms one by one: Item 1: ; Item 2: ; Item 3: ; Item 4: ; Summing all terms yields the overall connectivity index: ; The results indicate that the calculated spatial connectivity index It reflects the overall stability level of the current boundary segment in terms of spatial structure. The higher the value, the more coordinated the nodes of the segment are in terms of spatial spacing, elevation consistency and arrangement continuity, and the stronger the overall coherence of the boundary. The numerical result is directly used to determine which continuous boundary segments have a usable spatial order structure, and accordingly, the node coordinates in the segment are extracted according to the original spatial arrangement order to form a two-dimensional coordinate sequence.

[0050] Please see Figure 4 The specific steps for obtaining the shoreline time-series correction results are as follows: S311: Based on the connected boundary coordinate sequence, analyze the spatial distribution of water body boundaries in multi-temporal remote sensing. By comparing the coordinate differences of each spatial point in different remote sensing phases, calculate the actual spatial variation range of the point and obtain multi-temporal spatial offset data. Water body boundary lines corresponding to each time phase of remote sensing image are extracted and converted into a set of spatial coordinate points to establish a multi-time phase image water body boundary point database. Time phase identifiers are sequentially numbered according to the image acquisition time, and a coordinate identifier code is assigned to each spatial point under its corresponding time phase. Spatial trajectory pairing is performed on spatial points at the same location in all time phases. Specifically, each boundary point is searched for coordinate matching in other time phases using a fixed spatial window. If the same point exists in multiple remote sensing images, the difference between its horizontal and vertical coordinates in each time phase is recorded. Then, based on the spatial distance calculation method, the change in its position over different time intervals is determined. For example, in the April 2020 image, the coordinates of a boundary point are (324523.6, 3704211.2), and in the April 2021 image... The coordinates of the same numbered point in the image are (324526.1, 3704213.8). The horizontal and vertical differences between them are 2.5 meters and 2.6 meters, respectively. Therefore, the spatial offset of this point in this time period is approximately 3.6 meters. Through this type of processing, a cross-temporal coordinate offset table is established for all points to record the displacement changes of each point in each temporal phase. The spatial offset discrimination benchmark value is set to 2.0 meters, the spatial resolution of the reference remote sensing image is 2 meters, the allowable spatial error value is ±0.5 meters, and the maximum offset recognition threshold is set to 2.0 meters. If the spatial distance between any two temporal phases of a point exceeds this threshold, the point is marked as an "abnormal offset point"; otherwise, it is a "stable point". The set of spatial displacements of all points in different temporal phases is output to form multi-temporal spatial offset data.

[0051] S312: Based on multi-temporal spatial migration data, filter points with prominent spatial location changes, determine the coordinates of the surrounding unchanged points of each abnormal change point, optimize the elevation data of the points, and calculate the spatial mean to obtain the average elevation of the unmigrated points. The spatial offset values ​​of all points are sorted and screened. Points with offsets greater than a set threshold are extracted to form an "abnormal change point set." The spatial range of each abnormal point is recorded. Then, for each abnormal change point, a search is performed using a fixed buffer radius centered on it to extract a set of spatial points within a 10-meter radius. It is determined whether the offset values ​​of each point within this range are less than a set threshold of 2.0 meters. If the offset values ​​of most points are less than this threshold, the environment of the area where the abnormal point is located is considered stable, and the abnormal point can be regarded as a single-point disturbance. The elevation values ​​of the abnormal point are averaged according to the surrounding stable points, and the operation is to extract the points within the buffer range of that point. The DEM elevation values ​​of all stable points are summed and averaged to obtain the elevation correction value for the outlier. For example, if an outlier has an elevation of 35.6 meters and there are 6 stable points around it with elevation values ​​of 35.3, 35.4, 35.2, 35.5, 35.3, and 35.4 meters respectively, with a corresponding average of 35.35 meters, then the elevation of the outlier is corrected to 35.35 meters. If there are not enough stable points around it or the elevation fluctuation of each point is greater than 0.8 meters, then the point is marked as "uncorrectable" and excluded from further analysis. The elevation set of all corrected outliers and their corresponding neighboring stable points is output and used as the average elevation of the unshifted points.

[0052] S313: Based on the average elevation of the non-offset points, compare the spatial difference between the average elevation and the elevation of outliers using the following formula: ; The elevations of points with abnormal changes are updated to obtain the shoreline time-series correction results, among which, Indicates the first The first phase of time Spatial elevation correction differences at anomalous change points Indicates the first The average elevation of neighboring points that did not change at the point of abnormal change in each time phase. Indicates the first At this time, the first The original elevations of the points of abnormal change. It is the number of neighboring unchanged points. and They represent the first The spatial x and y coordinates of an unchanged point and They represent the first The spatial x and y coordinates of the points of abnormal change.

[0053] Spatial elevation correction difference is calculated by comparing the average elevation of a point on the shoreline that has undergone abnormal spatial changes with the average elevation of its surrounding unchanged points and the spatial distance between them under a specific remote sensing time phase. This comprehensively reflects the overall difference in elevation and spatial distribution between the abnormal point and the surrounding stable area. The calculation results are used to guide the adjustment of the elevation data of the abnormally changed points, thereby optimizing and correcting the spatial continuity and elevation accuracy of the shoreline.

[0054] Taking anomaly point B013 as an example, its original elevation is The corresponding elevations of the nearest non-offset points in the same remote sensing time phase are respectively The calculated average elevation of the non-offset points is The elevation difference is Meanwhile, the spatial coordinates of anomaly point B013 and its neighboring points are as follows: ; Its own coordinates are The horizontal and vertical Euclidean distances are respectively After normalization, they are respectively The sum is The average normalized spatial offset is Substitute into the formula: ; Therefore, the elevation correction difference is calculated as follows: This difference value is used to update the original elevation data to obtain the corrected elevation: ; This result indicates that outlier B013 is in the [missing information]. The updated elevation under the given time phase is: The numerical results are directly related to the spatial elevation correction differences. The combination reflects its adaptability to the surrounding terrain, forming the shoreline time-series correction results. The formula introduces elevation differences and spatial distance averages together to construct a spatial elevation correction expression under a unified scale, which enhances the geometric adaptability and data stability of the correction values.

[0055] Please see Figure 5 The specific steps for obtaining the spatial coverage area index are as follows: S411: Call the shoreline time series correction results, analyze the spatial correspondence between shoreline points and remotely sensed water body boundaries, compare the coordinate consistency and elevation changes of each overlapping node, calculate the elevation change amplitude of spatial nodes during the period of difference, identify key spatial nodes of change, and obtain the elevation change intensity sequence. Each shoreline spatial point is numbered and classified according to the acquisition time of its associated remote sensing image. A correspondence between shoreline points and remote sensing water body boundary points is established. The matching operation is based on coordinate similarity, and the spatial distance between the shoreline point and the remote sensing water body boundary point is calculated sequentially. Point pairs with a distance within 2.0 meters are selected as overlapping nodes. Furthermore, the water level data of the remote sensing image in which each overlapping point pair is located is extracted, and the elevation value of the same location is extracted from the DEM. By comparing the elevation between two time phases, the elevation change of the point is recorded. If the water level corresponding to a point in the remote sensing image of the 2020 time phase is 33.1 meters, and the DEM elevation corresponding to the same spatial location in the remote sensing image of the 2021 time phase is 33.9 meters, then its elevation is determined. The change is +0.8 meters. After processing all matching point pairs, the absolute values ​​of the elevation change values ​​are processed and sorted. The change discrimination threshold is set to 0.5 meters. Nodes above this threshold are marked as "critical change nodes", and those below the threshold are marked as "stable nodes". This discrimination value is set based on the remote sensing water level extraction error being controlled within ±0.2 meters and the DEM interpolation error tolerance being ±0.3 meters, with a total joint tolerance of 0.5 meters. If a certain overlapping point has a water level of 34.2 meters in the 2021 image and 34.8 meters in the 2022 image, the difference is +0.6 meters, which exceeds the 0.5-meter threshold. Therefore, this point is determined to be a critical change node. The change information of each node is output according to the point number, spatial location, and elevation change amplitude to obtain the elevation change intensity sequence.

[0056] S412: Based on the elevation change intensity sequence, filter adjacent spatial nodes with similar change amplitudes, determine whether the nodes constitute a spatially connected region, optimize the spatial relationship of each group of connected nodes, and obtain the set of connected nodes with overlapping boundaries. The selection criteria should be based on the magnitude of elevation change. Spatially adjacent nodes with an elevation change difference within 0.2 meters should be selected. This magnitude determination standard refers to the reasonable fluctuation range of spatial evolution when the water body boundary is in a similar position. All adjacent point groups with similar elevation changes should be constructed. For each group of points, connectivity should be determined. The operation involves extracting the coordinates of any node in each group, constructing a circular search area with a buffer radius of 5 meters starting from that node, and sequentially checking whether other nodes in the same group fall into this area. If so, they are marked as connected. This process is iterated through the entire group of nodes to determine whether all nodes can be continuously connected. A closed path is formed. If the conditions are met, the group of nodes constitutes a spatially connected region. If a group consists of 6 nodes, located on the north shoreline, with the distance between any two points less than 4.2 meters, and the elevation variation of all points between +0.6 meters and +0.8 meters, then the group can be identified as a connected region. After identifying all connected groups, the spatial order of the nodes in each group is sorted, and the node indexes are arranged in a clockwise direction and uniformly numbered. At the same time, nodes that exist alone and do not constitute a connected group are removed. The set of connected nodes in each overlapping region is output to obtain the set of connected nodes at the overlapping boundary.

[0057] S413: Based on the set of connected nodes along overlapping boundaries, analyze the differences in coordinates and elevations of each node under remote sensing boundaries and DEM data to identify the area coverage and distribution, using the following formula: ; Obtain spatial coverage index ,in, Indicates the first The x-coordinates of the nodes connected by overlapping boundaries. Let x be the mean of the x-coordinates of all connected nodes on the overlapping boundary. Indicates the first The reference elevation of each node in the corrected elevation zone Indicates the first The original elevation of each node in the digital elevation model. This represents the total number of nodes in the set of connected nodes along the overlapping boundary.

[0058] The spatial coverage area index reflects the actual water surface coverage area corresponding to the spatially connected region after the water body boundary and elevation correction are extracted by remote sensing during the analysis of the relationship between lake water level and water surface. It is used to dynamically depict the spatial expansion and change trend of lake water surface under different time or water level conditions.

[0059] Extract the x-coordinate of each node The average of the x-coordinates of all nodes in the set By comparison, a lateral offset metric is constructed. Then, the original elevation of the corresponding node is extracted. Reference elevation in the corrected elevation zone Differences An elevation change index was constructed, and the product of two indices was used to construct the nodal spatial influence factor. And a spatial coverage area index is constructed by summing the influence factors of all nodes; The data of different dimensions involved in the calculation are normalized. The following is the calculation process for each node (normalization has been completed): Node A1: , , ; , , ,calculate: ; Node A2: , ; , , ,calculate: ; Node A3: , ; , , ,calculate: ; Node A4: , ; , , ,calculate: ; Node A5: , ; , , ,calculate: ; Adding the calculation results of all nodes together, we get: ; The total number of nodes is The average impact is: ; The results indicate that within the currently overlapping boundary connected region, the overall lateral spatial distribution of nodes deviates negatively from the average axis and its elevation change; that is, the elevation increase is mainly concentrated on the side where the x-coordinate is below the average value. This trend forms the basis for the regional elevation slope. As a quantitative expression of the comprehensive change trend of multiple nodes, it can be used to determine whether there are spatial asymmetry features or concentrated distribution of shoreline adjustment zones in the region.

[0060] Please see Figure 6 The specific steps for obtaining the water level area change sequence are as follows: S511: Based on the spatial coverage area index, analyze the elevation distribution of DEM data under different water level conditions, calculate the topographic relief within the area corresponding to each water level, filter areas that are lower than or equal to the current water level elevation, and determine whether the areas are connected to obtain the submerged area pixel group. The process involves reading DEM elevation raster data and determining the spatial location and corresponding elevation value of each cell. A target water level is then set as a benchmark, such as a current water level of 32.5 meters. The elevation values ​​of all cells in the DEM are compared with this benchmark. Cells with elevation values ​​less than or equal to this water level are marked as "potentially submerged cells." A Boolean array is generated from the marking results, indicating whether the cell meets the submersion criteria. Next, the spatial coordinates of all cells meeting the criteria are extracted, and a preliminary set of submerged areas is constructed. The connectivity of cells within this set is then analyzed using a 4-neighborhood connection principle. Each cell meeting the criteria is checked sequentially in its top, bottom, left, and right directions. If there are cells that also meet the conditions, and if there is at least one adjacent direction that is connected, then it is marked as connected. Continue to check the connectivity of all cells until a complete connected block is formed. If three consecutive points (102, 204), (102, 205), and (102, 206) in a certain area meet the elevation requirements and are laterally adjacent to each other, then they are determined to be the same connected region. If a cell that meets the conditions is surrounded by cells that do not meet the conditions, then the point constitutes an isolated region and can be marked as a non-main connected region. Select connected blocks with a connectivity scale of more than 20 cells as effective flooded regions. Output the set of all cells that meet the connectivity conditions to obtain the flooded region cell group.

[0061] S512: Based on the pixel group of the flooded area, compare the positional relationship with the boundary of the remotely sensed water body, identify pixels that overlap in space, analyze the distribution characteristics of overlapping pixels in the difference area, mark pixels with spatial matching relationship, and obtain spatial overlapping pixel identifiers. The spatial coordinates of each submerged pixel are compared with the water body vector layer formed by the remotely sensed water body boundary. Specifically, the center point coordinates of each submerged pixel are iterated and checked to see if it falls within the polygonal closed area formed by the remotely sensed water body boundary. If it does, it is marked as a "spatially overlapping pixel"; otherwise, it is marked as a "non-overlapping pixel." This process uses the principle of point-to-area determination. After the determination is completed, all overlapping pixels are summarized and a spatial index table is constructed. Further analysis is conducted on the differences in the distribution of overlapping pixels across water body boundaries in different remote sensing images. This is achieved by comparing water bodies from different remote sensing periods. The boundary vector structure identifies whether overlapping pixels repeatedly fall within the water body boundary in different periods. If a pixel coincides with the water body boundary in 4 out of 6 remote sensing images, it can be identified as a "stable water body overlapping pixel". Conversely, if it only coincides in 1 period, it is considered as an "occasional water body overlapping pixel". The benchmark for determining the stability of water body overlap is set as appearing within the water body boundary in at least 3 images to ensure that the identification results have multi-temporal consistency. Finally, all pixels that meet the requirements of spatial overlap and the number of temporal overlaps are numbered and spatially labeled to form spatial overlapping pixel identifiers.

[0062] S513: Based on the spatial overlapping pixel identifiers, calculate the spatial coverage of the overlapping area under each water level condition, analyze the boundary structure of each area, determine the area change trend, arrange the differential coverage area according to the water level condition, and obtain the water level area change sequence. For each water level condition, the overlapping pixel set is statistically analyzed. The spatial coverage area is obtained by multiplying the number of pixels by the unit area. Assuming a pixel resolution of 5 meters × 5 meters and an area of ​​25 square meters per pixel, if there are 22,000 overlapping pixels at a certain water level, the total coverage area is 550,000 square meters, or 0.55 square kilometers. After calculating the area for each water level condition, the boundary pixels of each coverage area are extracted. The boundary lines are reconstructed based on spatial coordinate connectivity. The closure, presence of broken structures, and expansion trends of the boundary lines are analyzed. The complexity of the boundary structure can be determined by... The boundary length to area ratio is used for indirect determination. If the area is 0.55 square kilometers and the total length of the boundary line is 3200 meters, its shape is relatively fragmented. Conversely, if the area is 1.3 square kilometers and the boundary line length is 2800 meters, the boundary shape is more concentrated. Then, the coverage area corresponding to each water level is arranged in ascending order of water level, and the trend of the coverage area changing with the rise of water level is observed. If the water level rises from 32.5 meters to 33.5 meters and the area increases from 0.55 square kilometers to 1.8 square kilometers, the trend is increasing. Finally, a one-to-one correspondence sequence is formed between the area change results and the water level area change sequence is output.

[0063] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery, characterized in that, Includes the following steps: S1: Based on DEM and remote sensing image data, analyze the spatial correspondence between GNSS and RTK measured data and DEM elevation data, screen elevation anomalies, optimize and adjust the elevation of the anomaly points, and then obtain the zonal elevation adjustment parameters through neighborhood mean correction. S2: Based on the partition elevation adjustment parameters, calculate the spatial distance between the submerged boundary and the remote sensing image, determine the correspondence between the two types of spatial points, filter the boundary points with small elevation differences and close spatial proximity, update the connected region, and obtain the connected boundary coordinate sequence. S3: Based on the connected boundary coordinate sequence, compare the spatial distribution of water body boundaries in multiple time phases, analyze the temporal changes of each spatial point, screen out location anomalies, and use the mean of nearby non-offset points to correct the elevation, thus obtaining the shoreline time series correction result. S4: Call the shoreline time-series correction results, determine the spatial overlap relationship between the water body boundary of the remote sensing image and the DEM derived boundary, filter the overlapping areas with small elevation changes, optimize the spatial boundary, calculate the actual water surface coverage, and obtain the spatial coverage area index.

2. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 1, characterized in that, The partitioned elevation adjustment parameters include corrected elevation zones, spatial block identifiers, and reference mean indexes; the connected boundary coordinate sequence includes spatial node groups, connected path indexes, and boundary classification identifiers; the shoreline temporal correction results include temporal shoreline point sets, trajectory association indexes, and location correction markers; and the spatial coverage area index includes coverage area codes, area quantification data, and distribution contour sets.

3. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 1, characterized in that, The specific steps for obtaining the partition elevation adjustment parameters are as follows: S111: Based on DEM and remote sensing image data, analyze the spatial location of GNSS and RTK measurement points, pair them with DEM data, compare the elevation information of each measurement point, filter out measurement points with elevation differences, and determine their spatial distribution characteristics. A set of spatially different anomaly measurement points was obtained; S112: Optimize the elevation data in the spatial difference anomaly measuring point group, calculate the average elevation of its adjacent measuring points, adjust the elevation information of the anomaly measuring points, and analyze the elevation relationship between the measuring points to obtain the elevation correction data group. S113: Based on the elevation correction data set, determine the elevation distribution of each spatial region, adjust the elevation reference standard of each region, and summarize the elevation adjustment results of the spatial regions to obtain the regional elevation adjustment parameters.

4. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 1, characterized in that, The specific steps for obtaining the connected boundary coordinate sequence are as follows: S211: Based on the partition elevation adjustment parameters, analyze the spatial points of the submerged boundary generated by the remote sensing image, calculate the actual spatial distance between each spatial point, filter the closely spaced spatial points, and obtain a set of spatially adjacent point pairs. S212: Based on the set of spatially adjacent point pairs, compare the DEM elevation of each spatial point with the remote sensing boundary elevation, determine whether the elevation differences are close, identify spatial points with similar elevation information, and adjust boundary points that do not meet the conditions to invalid points, thereby obtaining a set of boundary points with consistent elevation. S213: Call the set of elevation-consistent boundary points, analyze the spatial interval and elevation fluctuation of each continuous boundary node, optimize the boundary information through the spatial and elevation features of the continuous nodes, calculate the spatial connectivity level of the boundary segment, and obtain the connected boundary coordinate sequence.

5. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 1, characterized in that, The specific steps for obtaining the shoreline time-series correction results are as follows: S311: Based on the connected boundary coordinate sequence, analyze the spatial distribution of the multi-temporal remote sensing water body boundary, and calculate the actual spatial variation range of the point by comparing the coordinate differences of each spatial point in different remote sensing phases, thereby obtaining multi-temporal spatial offset data; S312: Based on the multi-temporal spatial offset data, filter out points with prominent spatial position changes, determine the surrounding coordinate points that have not changed for each abnormal change point, optimize the elevation data of the points, and calculate the spatial mean to obtain the average elevation of the non-offset points. S313: Based on the average elevation of the non-offset points, compare the spatial difference between the average elevation and the elevation of the outlier points, update the elevation of the outlier points, and obtain the shoreline time-series correction result.

6. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 1, characterized in that, The specific steps for obtaining the spatial coverage area index are as follows: S411: Call the shoreline time series correction results, analyze the spatial correspondence between shoreline points and remotely sensed water body boundaries, compare the coordinate consistency and elevation changes of each overlapping node, calculate the elevation change amplitude of spatial nodes during the period of difference, identify key spatial nodes of change, and obtain the elevation change intensity sequence. S412: Based on the elevation change intensity sequence, filter adjacent spatial nodes with similar change amplitudes, determine whether the nodes constitute a spatially connected region, optimize the spatial relationship of each group of connected nodes, and obtain a set of overlapping boundary connected nodes. S413: Based on the set of connected nodes along the overlapping boundary, analyze the differences in coordinates and elevations of each node under the remote sensing boundary and DEM data, identify the area coverage and distribution, and obtain the spatial coverage area index.

7. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 1, characterized in that, The steps also include: S5: Based on the spatial coverage area index, analyze the spatial changes of DEM under different water levels, screen the inundated areas under each water level condition, determine the spatial correspondence between the remote sensing water body boundary and DEM, summarize the water surface area under different water levels, optimize the spatial distribution, and obtain the water level area change sequence. The water level area change sequence includes water level interval groups, area change list, and distribution node index.

8. The method for analyzing the relationship between lake water level and water surface based on DEM and remote sensing imagery according to claim 7, characterized in that, The specific steps for obtaining the water level area change sequence are as follows: S511: Based on the spatial coverage area index, analyze the elevation distribution of DEM data under different water level conditions, calculate the topographic relief within the area corresponding to each water level, filter areas that are lower than or equal to the current water level elevation, and determine whether the areas are connected to obtain the submerged area pixel group. S512: Based on the pixel group of the flooded area, compare the positional relationship with the boundary of the remotely sensed water body, identify pixels that overlap in space, analyze the distribution characteristics of overlapping pixels in the difference area, mark pixels with spatial matching relationship, and obtain spatial overlapping pixel identifiers. S513: Based on the spatial overlapping pixel identifier, calculate the spatial coverage of the overlapping area under each water level condition, analyze the boundary structure of each area, determine the trend of area change, arrange the difference coverage area according to the water level condition, and obtain the water level area change sequence.

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