Method for analyzing relationship between lake water level and water surface based on DEM and remote sensing image

Abstract

The application relates to the technical field of water level-water surface relationship analysis, in particular to a lake water level-water surface relationship analysis method based on DEM and remote sensing images, which comprises the following steps: based on DEM and remote sensing image data, analyzing the spatial relationship between measured data and DEM, correcting abnormal elevations and adjusting original data, screening boundary points with small elevation differences, counting water surface areas under different water levels, and obtaining a water level-area change sequence. In the application, through multi-source spatial data collaborative adjustment and automatic fusion, elevation correction, spatial anomaly screening and time sequence change tracking, the spatial data consistency is improved, the elevation abnormal information is dynamically corrected, the discrimination ability of the water body spatial distribution boundary is strengthened, dense water surface change information extraction is realized by relying on spatial overlap data, the spatial nodes and area change trends under the water level grading condition can be automatically summarized, and the dynamic response characteristics of the lake spatial structure are systematically reflected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water level and water surface relationship analysis, and particularly relates to a lake water level and water surface relationship analysis method based on DEM and remote sensing images. BACKGROUND

[0002] The water level and water surface relationship analysis field belongs to the category of geographic information science and environmental monitoring, including water surface range change, terrain feature extraction and water body coverage dynamic monitoring under different water level conditions of lakes or reservoirs and the like, and is widely applied in water resource scheduling, ecological protection and flood control management and the like. Among them, the traditional water level and water surface relationship analysis refers to collecting water level change records by arranging hydrological monitoring sites based on artificial field observation and topographic map data collection, and collecting basic geographic information such as contour lines, depth lines, water bank lines and the like by using large-scale topographic maps, and combining field surveying and mapping data to calculate the corresponding water surface range and area under different water levels by using a manual method or a simple area calculation formula, so as to clarify the change rule and area distribution of the lake water surface under different water level conditions, and to provide basic data support and scientific basis for lake water resource management, scheduling scheme formulation, ecological protection, water storage capacity estimation, flood control safety, environmental assessment and the like.

[0003] The prior art is difficult to comprehensively obtain lake area micro-geomorphology change information due to the influence of point arrangement and terrain conditions, the spatial distribution of the collected region is sparse, the attribute information is prone to distortion, data integration is difficult to track water surface form dynamic change, water level and area change analysis is often lack of spatial fine expression, area estimation and real distribution are prone to deviation, information updating is slow, regional discrimination is fuzzy, spatial continuous monitoring capacity is insufficient, and water body distribution recognition is prone to delay, which is difficult to provide efficient data support for water resource scheduling and risk response of environmental sensitive areas. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art, and a lake water level and water surface relationship analysis method based on DEM and remote sensing images is provided.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: a lake water level and water surface relationship analysis method based on DEM and remote sensing images, comprising the following steps:

[0006] S1: based on DEM and remote sensing image data, analyzing the spatial correspondence relationship of GNSS and RTK measured data and DEM elevation data, screening elevation abnormal points, optimizing and adjusting the elevation of abnormal measurement points, and then obtaining partition elevation adjustment parameters by neighborhood mean correction;

[0007] S2: based on the partition elevation adjustment parameter, the spatial distance of the remote sensing image derived submergence boundary is calculated, the corresponding relationship of two types of spatial points is judged, the boundary points with small elevation difference and spatial closeness are screened, the connected region is updated, and the connected boundary coordinate sequence is obtained;

[0008] S3: based on the connected boundary coordinate sequence, the spatial distribution of the multi-temporal remote sensing water body boundary is compared, the spatial point change across time is analyzed, the position abnormal point is screened, the elevation is corrected by using the mean value of the adjacent non-offset points, and the shoreline time sequence correction result is obtained.

[0009] S4: calling the shoreline time sequence correction result, judging the spatial overlap relationship between the remote sensing image water body boundary and the DEM derived boundary, screening the overlap region with small elevation change amplitude, optimizing the spatial boundary, calculating the actual water surface coverage range, and obtaining the spatial coverage area index.

[0010] The application improves that the partition elevation adjustment parameter includes correction elevation band, spatial block identifier, reference mean value index, the connected boundary coordinate sequence includes spatial node group, connected path index, region boundary classification identifier, the shoreline time sequence correction result includes time sequence shoreline point set, trajectory correlation index, position correction mark, and the spatial coverage area index includes coverage range code, area quantization data and distribution contour set.

[0011] The application improves that the acquisition step of the partition elevation adjustment parameter is specifically:

[0012] S111: based on DEM and remote sensing image data, the spatial position of GNSS and RTK measuring point is analyzed, the DEM data is matched, the elevation information of each measuring point is compared, the measuring point with elevation difference is screened, the spatial distribution characteristics are judged, and the spatial difference abnormal measuring point group is obtained.

[0013] S112: the elevation data in the spatial difference abnormal measuring point group is optimized, the elevation mean value of the adjacent measuring point is calculated, the elevation information of the abnormal measuring point is adjusted, and the elevation relationship between the measuring points is analyzed, and the elevation correction data group is obtained.

[0014] S113: according to the elevation correction data group, the elevation distribution of each region in space is judged, the elevation reference standard of each region is adjusted, the elevation adjustment result of the spatial region is summarized, and the partition elevation adjustment parameter is obtained.

[0015] The application improves that the acquisition step of the connected boundary coordinate sequence is specifically:

[0016] S211: based on the partition elevation adjustment parameter, the spatial points of the submergence boundary generated by the remote sensing image are analyzed, the actual spatial distance between each spatial point is calculated, the spatial points with close distance are screened, and the spatial adjacent point pair set is obtained.

[0017] 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 difference is close, identify spatial points with similar elevation information, and adjust the boundary points that do not meet the conditions to invalid points to obtain a set of elevation consistent boundary points;

[0018] S213: Call the set of elevation consistent boundary points, analyze the spatial interval and elevation float of each continuous boundary node, optimize the boundary information through the spatial and elevation characteristics of the continuous nodes, calculate the spatial connectivity level of the boundary segment, and obtain a connected boundary coordinate sequence.

[0019] The present application improves that the step of obtaining the shoreline time sequence correction result is specifically:

[0020] S311: Based on the connected boundary coordinate sequence, analyze the spatial distribution of multi-temporal remote sensing water body boundary, calculate the actual spatial change range of the point by comparing the coordinate difference of each spatial point in the different remote sensing phases, and obtain multi-temporal spatial offset data;

[0021] S312: Based on the multi-temporal spatial offset data, filter the points with prominent spatial position changes, determine the coordinate points around each abnormal change point that have not changed, optimize the elevation data of the points, and calculate the spatial mean value to obtain the elevation mean value of the non-offset points;

[0022] S313: According to the elevation mean value of the non-offset points, compare the spatial difference between the elevation mean value and the elevation of the abnormal points, update the elevation of the abnormal change points, and obtain the shoreline time sequence correction result.

[0023] The present application improves that the step of obtaining the spatial coverage area index is specifically:

[0024] S411: Call the shoreline time sequence correction result, analyze the spatial correspondence relationship between the shoreline point and the remote sensing water body boundary, compare the coordinate consistency and elevation change of each coincident node, calculate the elevation change amplitude of the spatial node in the different periods, identify the spatial nodes with significant changes, and obtain an elevation change intensity sequence;

[0025] S412: According to the elevation change intensity sequence, filter the adjacent spatial nodes with close change amplitudes, determine whether the nodes constitute a spatially connected region, optimize the spatial relationship of each group of connected nodes, and obtain an overlapping boundary connected node set;

[0026] S413: Based on the overlapping boundary connected node set, analyze the coordinate and elevation difference of each node under the remote sensing boundary and DEM data, identify the regional coverage range and area distribution, and obtain a spatial coverage area index.

[0027] The present application improves that the step further comprises:

[0028] S5: According to the spatial coverage area index, analyze the DEM spatial change under the different water levels, screen the submerged areas under each water level condition, judge the correspondence relationship between the remote sensing water body boundary and the DEM space, summarize the water surface area under different water levels, optimize the spatial distribution, and obtain the water level area change sequence;

[0029] The water level area change sequence includes a water level interval group, an area change list and a distribution node index.

[0030] The water level area change sequence is obtained by the following steps:

[0031] S511: According to the spatial coverage area index, analyze the elevation distribution of DEM data under the condition of different water levels, calculate the terrain undulation in the area corresponding to each water level, screen the area lower than or equal to the current water level elevation, and judge whether the area is connected, to obtain a submerged area pixel group;

[0032] S512: Based on the submerged area pixel group, compare the positional relationship with the remote sensing water body boundary, identify the pixels that exist in space overlap, analyze the distribution characteristics of the overlapping pixels in the difference area, mark the pixels with spatial matching relationship, and obtain the spatial overlapping pixel identifier;

[0033] S513: According to the spatial overlapping pixel identifier, calculate the spatial coverage range of the overlapping area under each water level condition, analyze the boundary structure of each area, judge the area change trend, arrange the difference coverage area according to the water level condition, and obtain the water level area change sequence.

[0034] Compared with the prior art, the advantages and positive effects of the present application are as follows:

[0035] In the present application, relying on multi-source spatial data collaborative adjustment and automatic fusion, through elevation correction, spatial anomaly screening and time series change tracking, the spatial data consistency is improved, the elevation anomaly information is dynamically corrected, the discrimination ability of the water body spatial distribution boundary is strengthened, the dense water surface change information extraction is realized relying on the spatial overlapping data, the spatial node and area change trend under the water level grading condition can be automatically summarized, the dynamic response characteristics of the lake spatial structure are systematically reflected, the demand for fine analysis of spatial information in the region with complex terrain and environmental change is met, the spatial monitoring results are upgraded in the direction of timeliness, accuracy and automation, so as to support the continuous output and monitoring of data in the region with variable topography and hydrological fluctuation. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 The main step flow chart of the present application is shown in the figure;

[0037] Figure 2 The flow chart of obtaining the partition elevation adjustment parameter in the present application is shown in the figure;

[0038] Figure 3 Flow chart for obtaining the sequence of connected boundary coordinates in the application;

[0039] Figure 4 Flow chart for obtaining the shoreline timing correction result in the application;

[0040] Figure 5 Flow chart for obtaining the spatial coverage area index in the application;

[0041] Figure 6 Flow chart for obtaining the water level area change sequence in the application;

[0042] Figure 7 Three-dimensional feature line and three-dimensional feature point map of the application;

[0043] Figure 8 Digital elevation model map of the application;

[0044] Figure 9 Water level corresponding water surface area calculation flow chart of the application;

[0045] Figure 10 Water surface area calculation method of the application;

[0046] Figure 11 Nansi Lake remote sensing image interpretation result map of the application in April 2021;

[0047] Figure 12 Nansi Lake superior lake water level and water surface relationship curve map of the application;

[0048] Figure 13 Nansi Lake lower lake water level and water surface relationship curve map of the application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the application more clear and explicit, the application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application and not to limit the application.

[0050] In the description of the application, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore it cannot be understood as a limitation of the application. In addition, in the description of the application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0051] Referring to Figure 1 The application provides a technical solution: a lake water level and water surface relationship analysis method based on DEM and remote sensing images, including the following steps:

[0052] S1: Based on DEM and remote sensing image data, analyze the spatial correspondence of GNSS and RTK measured data and DEM elevation data, judge the elevation difference between each measuring point, filter out 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 the adjacent region, to obtain the partition elevation adjustment parameter;

[0053] S2: Based on the partition elevation adjustment parameter, calculate the spatial distance between it and the submerged boundary derived from the remote sensing image, judge the correspondence of the spatial points, filter out boundary points with close spatial position and limited elevation difference, adjust the boundary points that do not meet the conditions to invalid state, optimize the boundary information of the connected region, and obtain the connected boundary coordinate sequence;

[0054] S3: Based on the connected boundary coordinate sequence, compare with the spatial distribution of multi-temporal remote sensing water body boundary, analyze the position change of each spatial point across time, filter out points with abnormal position change, calculate the average elevation of the surrounding non-offset points, and adjust the elevation information of the abnormal points to ensure the continuity of the shoreline space, and obtain the shoreline time sequence correction result;

[0055] S4: Call the shoreline time sequence correction result, judge the spatial overlap relationship of the water body boundary derived from the remote sensing image, compare the elevation change of the spatial corresponding points, filter out the overlap area with the optimal elevation change amplitude, optimize the spatial boundary data of the range, and calculate the actual water surface coverage range of the area, to obtain the spatial coverage area index;

[0056] S5: According to the spatial coverage area index, analyze the spatial change of DEM under the condition of different water levels, filter out the submerged area under each water level, judge the spatial correspondence between the remote sensing water body boundary and the 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.

[0057] The partition elevation adjustment parameter includes the corrected elevation band, the spatial block identifier, and the reference average index. The connected boundary coordinate sequence includes the spatial node group, the connected path index, and the zone boundary classification identifier. The shoreline time sequence correction result includes the time sequence shoreline point set, the trajectory association index, and the position correction mark. The spatial coverage area index includes the coverage range code, the area quantization data, and the distribution contour set. The water level area change sequence includes the water level interval group, the area change list, and the distribution node index.

[0058] In S1, the spatial correspondence refers to the matching between the geographical spatial position 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. The fusion of different data sources is achieved through spatial coordinate consistency, which is commonly used in the comparison and correction of measured data and DEM elevation data.

[0059] In S2, the submerged boundary refers to the external boundary of the area below the given water level within the lake area extracted using DEM data. This boundary represents the theoretically water-covered range and is used for area calculation and spatial matching analysis. The correspondence refers to the one-to-one correspondence or matching of points or line segments in spatial data from two different data sources in coordinates, which is used for comparison and overlay analysis, and is commonly used in spatial point matching between DEM submerged boundaries and remotely sensed water body boundaries. The boundary point refers to the spatial coordinate point used to define a spatial polygon or polyline. The points collectively constitute the submerged area or water body boundary and are used for spatial overlay, distance calculation, and area analysis. The points that do not meet the conditions refer to certain boundary points that do not meet the spatial or attribute matching requirements under the standards of spatial distance and elevation difference. These points are automatically excluded in spatial analysis or connectivity identification.

[0060] In S3, the multi-temporal remote sensing water body boundary refers to the spatial distribution data of lake water body boundaries at different time periods obtained by interpreting multi-period remote sensing images, which is used to analyze the dynamic changes and spatio-temporal trajectories of lake water bodies. The position change across time refers to the change in boundary position of the same spatial point in multiple remote sensing image periods, which is used to analyze the change trend of water body boundaries or detect data anomalies. The points with abnormal changes refer to spatial points with position changes beyond the normal range in multi-temporal spatial trajectories, which need to be corrected through spatial correction or neighboring point mean correction to ensure the continuity and accuracy of the analysis results.

[0061] In S4, the spatial overlap relationship refers to the intersection and intersection of spatial regions obtained from different data sources, which is commonly used for analyzing the spatial overlap of remotely sensed water body boundaries and DEM-derived water body boundaries, and is used for result verification, area output, and spatial consistency verification.

[0062] In S5, the submerged area refers to the spatial area in the digital elevation model where all elevations are less than or equal to the water level under the given water level condition, which 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 remotely sensed water body boundary and the DEM submerged area in spatial coordinates, which is used for spatial consistency comparison and area statistics, and provides a spatial basis for the construction of the lake water level-water surface relationship curve.

[0063] For the construction of digital elevation model

[0064] The 1:10000 topographic map of Nansi Lake was used as the basic map to build the digital elevation model of Nansi Lake. The resolution of the digital elevation model was 5m x 5m. The 1:10000 topographic map containing underwater topographic information was used to collect 3D feature lines and 3D feature points representing the surface and underwater topographic information, mainly including contour lines, depth lines, landform lines, water area shorelines, surface elevation points, and underwater elevation points. For non-surface elevation annotation points such as bridge decks and culverts, they should be deleted and not participate in the model building. Cliff, slope, and double-line gully symbols were converted to contour lines. For mountain tops, concave land, and passageways lacking elevation points, interpolation elevation feature points or elevation feature lines were added. For the widely distributed fish ponds in Nansi Lake, special processing was performed. The fish pond dike double-side slope top lines were collected, and the nearby land elevation annotation points were used to assign elevation values. The fish pond bottom slope lines were collected, and the fish pond underwater elevation annotation points were used to assign elevation values, fully representing the topographic features of the fish ponds, as shown in Figure 7 After the 3D feature lines and 3D feature points were collected, topological and rationality checks were performed. The 3D feature lines and 3D feature points were input into ArcGis software to build a Triangulated Irregular Network (TIN) model, which was checked and modified to generate a digital elevation model, as shown in Figure 8 A total of 110 evenly distributed points in the lake area were extracted and measured using GNSS RTK in the field. The statistical results showed that the elevation mean error was 0.3 meters.

[0065] For water surface area calculation

[0066] The digital elevation model was used to calculate the water surface area corresponding to different water levels in Nansi Lake. The calculation method is shown in Figure 9 . First, the calculation water level was determined. The raster area of in the digital elevation model was extracted as a rough submerged area, which was considered to have the possibility of being submerged. The rough submerged area was converted to a vector polygon using the raster-to-surface feature tool. The digital elevation model was used to determine whether each rough submerged polygon was connected to the lake area through channels, shallow trenches, ditches, and other means. The polygons that could not be connected to the lake area were deleted. After merging adjacent broken polygons, the single polygon area greater than 30km 2 was divided into contiguous water surface, and the remaining non-adjacent polygons were divided into scattered water surface. The contiguous water surface and scattered water surface together formed the water surface , and the water surface area was the area of the water surface .

[0067] Combining Figure 10 , the vertices of the polygonal elements of the water surface are organized in a clockwise or counterclockwise manner , , The area of the irregular polygon can be calculated as follows:

[0068] ;

[0069] In the formula, are the vertex coordinates.

[0070] For remote sensing image interpretation

[0071] The collected remote sensing images of Nansi Lake were used to interpret and analyze the land cover, focusing on extracting the water surface cover. The main process is as follows:

[0072] (1) Through indoor preliminary interpretation and combined with relevant data analysis, the main land types of Nansi Lake that need to be interpreted in this study include continuous water surface, channel shallow trench, fish farming pond, paddy field, and land. Jiaolin Village and Wanfang Village are selected as two typical interpretation sample areas.

[0073] (2) Go to Nansi Lake for field investigation to investigate the characteristics and distribution of each land type. At least 10 typical examples of each land type are selected from different locations in the lake area, and photos are taken to record the land type name, latitude and longitude, shooting time, color characteristics, and landform environment. Corresponding relationship with remote sensing images is established to establish an interpretation symbol library. Direct features such as shape and color characteristics are used as the basis for interpretation classification, combined with surrounding landform environment correlation analysis, and interpretation rules are established based on dike protection forest, dike, and rural road as land class boundary reference.

[0074] (3) The interpretation classification is carried out by combining computer automatic classification with manual visual interpretation. The computer automatic classification uses the method of supervised classification, and the automatic classification extraction is carried out by establishing typical ground object templates. After classification, manual visual interpretation is used to judge the classification results, and manual modification is carried out for classification errors or boundary errors. According to the actual field verification, the land class distribution boundary is corrected.

[0075] (4) The interpretation results are stored as SHP surface data files, and the vector data is topologically checked. After checking, the area of each land class is summarized and counted, and the results are shown in Figure 11 .

[0076] Result verification analysis

[0077] The water level of the upper and lower lakes corresponding to the date of remote sensing image shooting was obtained by using the daily water level observation data of Nanyang and Weishan hydrological stations. The water surface area and vector graphics interpreted from remote sensing images were compared with those calculated by digital elevation model to analyze whether they were consistent.

[0078] (5) Analysis of water surface change after raising water storage level

[0079] The water surface area was calculated for every 0.5 m elevation step in the range of ecological water level to flood control water level of Nansi Lake, and the normal storage level and three water level raising schemes of 0.3 m, 0.5 m and 1.0 m were added. The calculation results were sorted out and summarized, and the water level-water surface relationship curve was drawn.

[0080] The vector graphics of the water surface corresponding to the normal storage level (34.5 m for the upper lake and 32.5 m for the lower lake) and the three schemes of water level raising by 0.3 m, 0.5 m and 1.0 m were extracted, and the areas of continuous and scattered water surfaces were counted respectively, and the increased area of water surface relative to the normal storage level was analyzed.

[0081] The water surface distribution graphics obtained by the two analysis methods of digital elevation model and remote sensing interpretation were compared, and they were basically consistent. The water surface areas obtained by the two analysis methods were compared and analyzed, and the results are shown in Table 1.

[0082] Table 1 Comparison of water surface area interpreted from remote sensing image and calculated by digital elevation model

[0083]

[0084] As shown in Table 1, among the six remote sensing images for comparative analysis of digital elevation model calculation results, the largest area difference was the upper lake image on October 26, 2020, with an area difference of 23.182 km 2 , a difference percentage of 4.08%. Except for this image, the difference percentages of the remaining images were less than 2%, which could be considered that the water surface area interpreted from remote sensing image was basically consistent with that calculated by digital elevation model, the digital elevation model constructed in this paper was accurate and reliable, and the water surface area calculation method was correct. Based on this, the water level-water surface relationship curve and the analysis of water surface change after raising water storage level were calculated, and the water level-water surface relationship curves are shown in Figure 12 (upper lake) and Figure 13 (lower lake).

[0085] From Figure 12 and Figure 13It can be concluded that when the water level of the upper lake is below 35.5 m, the water surface rapidly expands with the increase of the water level, and the water surface area does not obviously increase when the water level is higher than 35.5 m; when the water level of the lower lake is below 34.0 m, the water surface rapidly expands with the increase of the water level, and the water surface area does not obviously increase when the water level is higher than 34.0 m.

[0086] The increase of the water surface area of the connected water surface and the scattered water surface of the Nansihu Lake under the three schemes of lifting the water level by 0.3 m, 0.5 m and 1.0 m compared with the normal water level was analyzed, and the results are shown in Table 2.

[0087] Table 2 Change of water surface area of Nansihu Lake after lifting the water level

[0088]

[0089] According to Table 2, the total water surface area of the Nansihu Lake under the normal water level is 1157.053 km 2 , the connected water surface is 400.049 km 2 , and the scattered water surface is 757.005 km 2 . The connected water surface can be increased by 9.913 km 2 , and the total water surface area can be increased by 21.405 km 2 when the water level is lifted by 0.3 m; the connected water surface can be increased by 45.302 km 2 , and the total water surface area can be increased by 41.642 km 2 when the water level is lifted by 0.5 m; the connected water surface can be increased by 219.509 km 2 , and the total water surface area can be increased by 75.954 km 2 when the water level is lifted by 1.0 m. According to the analysis, with the gradual lifting of the water level, part of the scattered water surface gradually connects with each other and changes into the connected water surface, and the normal water level of the Nansihu Lake is effectively increased.

[0090] Please refer to Figure 2 , the obtaining steps of the zoning elevation adjustment parameter are as follows:

[0091] S111: Based on the DEM and remote sensing image data, the spatial positions of the GNSS and RTK measuring points are analyzed, and the elevation information of each measuring point is compared with the DEM data to screen the measuring points with different elevations, judge the spatial distribution characteristics, and obtain a group of spatial difference abnormal measuring points;

[0092] First, GNSS and RTK measuring points are arranged in the lake area, and the geographic spatial coordinates and elevation values of each measuring point are recorded. After collection, all coordinates are unified to the same spatial reference system, such as converting WGS84 coordinates to local projection coordinate system, to realize accurate matching with DEM elevation data. Then, the elevation value matching the horizontal and vertical coordinates of each measuring point is extracted from the digital elevation model, and the elevation difference between the measured and DEM is compared one by one. A one-to-one corresponding elevation difference list is established for all measuring points, the statistical distribution of the overall difference data is obtained, and the average value plus or minus twice the standard deviation is used as the abnormal screening range. On this basis, abnormal measuring points significantly higher or lower than the range are identified. Then, a neighborhood analysis is performed on all abnormal measuring points in space, and whether there are multiple other abnormal points within a 5-meter range is searched based on the buffer. If there are multiple measuring points with similar elevation deviations, they are marked as a group of clustered abnormal points. If there are no other abnormal points around or the difference is too large, they are marked as independent abnormal measuring points. For example, the 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, with a deviation of 1.8 meters. The average deviation in the entire lake area is only 0.2 meters, so this point should be classified as a difference abnormal point. If there are also more than three measuring points around it that also show a deviation of more than 1 meter, they form a group of abnormal distribution, and a group of spatial difference abnormal measuring points is obtained.

[0093] S112: Optimize the elevation data of the spatial difference abnormal measuring point group, calculate the average elevation of the neighboring measuring points, adjust the elevation information of the abnormal measuring points, and analyze the elevation relationship between the measuring points to obtain the elevation correction data group.

[0094] When optimizing the elevation data of the spatial difference abnormal measuring point group, a set of neighboring measuring points is constructed around each abnormal point. The corresponding elevation data of the normal measuring points within 10 meters of the point is extracted, and the average value is calculated as the new corrected value of the abnormal point. Then, the original elevation of the point is replaced with the new corrected value, and the elevation relationship between the point and the surrounding measuring points is evaluated again. If the elevation difference between the corrected point and the neighboring measuring points is still significant, it is determined that the point cannot be effectively corrected. Otherwise, it is considered that the correction is successful. For example, the original elevation of a certain point deviates by 1.5 meters, and the average value of the five points in the neighborhood is 36.3 meters. After correction, it is set to 36.3 meters, and the elevation difference is controlled within a reasonable tolerance. After completing the elevation replacement of all abnormal points, the corrected data is combined with the original points as the data basis for subsequent interpolation reconstruction, and each point is recorded whether it is corrected, the value before and after correction, and the number of surrounding points, so as to be summarized and analyzed to form the elevation correction data group.

[0095] S113: According to the elevation correction data group, judge the elevation distribution of each region in space, adjust the elevation reference standard of each region, and summarize the elevation adjustment results of the spatial region to obtain the partition elevation adjustment parameter.

[0096] The elevation correction data set is divided into spatial blocks, a plurality of partition units are established, all the measurement points belonging to each block are extracted, including the correction points and the original points, the elevation mean and the variation degree of each block are counted, the elevation mean offset of each block and the whole region is compared to determine whether the block has systematic deviation, if the offset of a block exceeds 0.5 meters, the block is identified as an abnormal region, and the mean value of the block itself is used as a new elevation reference value for subsequent elevation uniform correction, and the DEM grid elevation values in the block are adjusted upward or downward to be consistent with the reference value; for example, the statistical elevation mean of a certain western block is 34.7 meters, and the whole region is 34.2 meters, the deviation between the two is more than 0.5 meters, so the DEM value in the block needs to be increased by 0.5 meters to achieve uniform standardization, and all the processing results form a set of parameters in each block, including the elevation reference value, the adjustment range and the data fluctuation range, which are used as the partition elevation adjustment parameters for subsequent analysis.

[0097] Please refer to Figure 3 The acquisition steps of the connected boundary coordinate sequence are as follows:

[0098] S211: Based on the partition elevation adjustment parameters, the spatial points of the submerged boundary generated by the remote sensing image are analyzed, the actual spatial distance between the spatial points is calculated, the spatial points with close distance are screened, and a set of spatial adjacent point pairs is obtained;

[0099] First, the adjusted elevation reference value in each partitioned region is obtained, and the water submerged boundary line extracted from the remote sensing image is subjected to spatial cross docking operation, the boundary point coordinate information of each submerged boundary line is read, the geographical coordinates corresponding to the boundary point in the grid space are extracted, and the closest elevation point in the partitioned DEM after elevation adjustment is searched, the horizontal and vertical coordinate distance between the two is judged, the Euclidean distance from the boundary point to the nearest DEM point is calculated, and the process is repeated to process all remote sensing boundary points. In all distance data, the boundary point pairing relationship with a distance less than 2.5 meters is selected, and the threshold value is used as the definition value of the spatial points with close distance. The threshold value is set to 2.5 meters with reference to the spatial resolution of 2 meters of the remote sensing image, and is adjusted to 2.5 meters based on the consideration of image distortion error and DEM pixel length tolerance. If a remote sensing boundary point coordinate is (125432.6, 3847212.3), the corresponding DEM point coordinate is (125431.4, 3847210.8), the distance between the two is 1.73 meters, which is less than the threshold value of 2.5 meters, so the point pair is included in the adjacent point pair set, and if the distance is 3.2 meters, it is excluded. The process needs to be executed in batches in the spatial data processing tool, and the coordinate value accuracy is verified after confirmation that there is no error, and the boundary point pair information meeting the conditions is output to obtain the spatial adjacent point pair set.

[0100] S212: Based on the set of spatially adjacent point pairs, compare the DEM elevation of each spatial point with the remote sensing boundary elevation to determine whether the elevation difference is close, identify spatial points with similar elevation information, and adjust the boundary points that do not meet the conditions to invalid points to obtain the set of elevation consistent boundary points.

[0101] For each pair of points in the set, extract the elevation value in the DEM and the corresponding elevation value of the remote sensing water boundary point. If the remote sensing boundary point itself has no additional elevation data, the water level corresponding to the hydrological station at the time of shooting of the remote sensing image is used as its elevation value. On this basis, the elevation difference between the pair of points is compared, the elevation difference of all point pairs is counted, the mean and standard deviation of the entire set are calculated, and the judgment standard for consistent elevation difference is set to be within ±0.4 meters of the mean value, that is, the elevation difference within 0.4 meters is considered close. If the remote sensing point represents a water level of 32.1 meters and the DEM point elevation is 31.85 meters, the difference between the two is 0.25 meters, which is within the close range and is marked as elevation consistent point. 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, which exceeds the threshold. It is determined that the elevations are not consistent, and the boundary point is marked as invalid and will not be used for subsequent boundary connectivity analysis. The threshold is set by referring to the resolution of the remote sensing water level and the tolerance of the DEM elevation error. The combined value of the remote sensing elevation error ±0.2 meters and the DEM tolerance ±0.2 meters is selected as the upper limit value for judgment. All elevation consistent point pairs retain the original boundary mark and are uniformly output to form the set of elevation consistent boundary points.

[0102] S213: Call the set of elevation consistent boundary points, analyze the spatial interval and elevation float of each continuous boundary node, optimize the boundary information through the spatial and elevation characteristics of the continuous nodes, and use the formula:

[0103] ;

[0104] Calculate the spatial connectivity level of the boundary segment , obtain the connected boundary coordinate sequence, wherein, represents the spatial interval between the th node and its adjacent node in the set of elevation consistent boundary points, represents the elevation difference between the th node and its adjacent node, represents the elevation float of the th node in the boundary segment, represents the spatial continuity degree of the th node in the boundary segment, is the total number of continuous boundary segment nodes.

[0105] The spatial connectivity level refers to the closeness and consistency of the nodes on a boundary line in space. In the analysis of the relationship between the water level and the water surface of a lake, the spatial connectivity level reflects the integrity, non-fracture and actual physical connection degree between the nodes of the boundary in space. In simple terms, the higher the spatial connectivity level, the more continuous the boundary in space, the more complete the whole, the smaller the spatial jump and the elevation fluctuation, and the more consistent the spatial and elevation characteristics of the nodes.

[0106] First, connect the nodes in the boundary point set in spatial order to determine the spatial distance between adjacent nodes in each boundary segment , extract the elevation difference and the elevation floating amplitude of the corresponding nodes, and quantify the arrangement continuity degree according to the angle change between adjacent nodes , calculate the spatial connectivity level of the boundary segment. If there are four groups of continuous nodes in a boundary segment, the original parameters are as follows:

[0107] The first group: the distance is 2.6 meters, the normalized elevation difference is 1.1 meters, the normalized elevation floating is 0.8 meters, the normalized continuity degree is 1.2, and the normalized ;

[0108] The second group: the distance is 3.0 meters, the normalized elevation difference is 0.9 meters, the normalized elevation floating is 0.7 meters, the normalized continuity degree is 1.4, and the normalized ;

[0109] The third group: the distance is 2.4 meters, the normalized elevation difference is 1.3 meters, the normalized elevation floating is 1.0 meter, the normalized continuity degree is 1.6, and the normalized ;

[0110] The fourth group: the distance is 2.2 meters, the normalized elevation difference is 1.0 meter, the normalized elevation floating is 0.6 meters, the normalized continuity degree is 1.1, and the normalized .

[0111] Substitute the parameters into the formula to calculate the four items one by one:

[0112] The first item:

[0113] ;

[0114] Item 2:

[0115] ;

[0116] Item 3:

[0117] ;

[0118] Item 4:

[0119] ;

[0120] Summing up all the items to get the total connectivity index:

[0121] ;

[0122] The results show that the spatial connectivity index calculated reflects the comprehensive stability level of the current boundary segment in the spatial structure, and the higher the value represents the more coordinated the node in the spatial distance, the consistency of the height and the arrangement continuity, and the stronger the overall coherence of the boundary. The numerical result is directly used to determine which continuous boundary segment has a usable spatial order structure, and according to this, the node coordinates in the paragraph are extracted according to the original spatial arrangement order to form a two-dimensional coordinate sequence.

[0123] Please refer to Figure 4 , the steps for obtaining the shoreline time sequence correction result are as follows:

[0124] S311: Based on the connected boundary coordinate sequence, analyze the spatial distribution of the multi-temporal remote sensing water boundary, calculate the actual spatial variation range of the point by comparing the coordinate difference of each spatial point in the different remote sensing phases, and obtain the multi-temporal spatial offset data;

[0125] The water body boundary line corresponding to each remote sensing image phase is extracted and converted into a spatial coordinate point set, a water body boundary point database of multi-temporal images is established, the phase identifiers are sequentially numbered according to the image acquisition time, and the coordinate identifier code of each spatial point in the corresponding phase is set. The spatial trajectory pairing operation is performed on the spatial points at the same position in all phases. Specifically, each boundary point is matched and searched in other phases according to a fixed spatial window. If the same point exists in multiple remote sensing images, the horizontal and vertical coordinate difference values of the point in each phase are recorded. Then, the point position change amount in different time intervals is determined based on the spatial distance calculation method. For example, in the April 2020 image, the coordinates of a certain boundary point are (324523.6, 3704211.2), and in the April 2021 image, the coordinates of the same numbered point are (324526.1, 3704213.8). The horizontal and vertical difference values between the two are 2.5 meters and 2.6 meters, respectively. The spatial offset of the point in the time period is about 3.6 meters. The cross-phase coordinate offset table is established for all points by such processing, the displacement change of each point in each phase is recorded, the spatial offset discrimination reference value is set to 2.0 meters, the spatial resolution of the reference remote sensing image is 2 meters, the spatial error allowable value is ±0.5 meters, and the maximum offset recognition threshold is set to 2.0 meters. If the spatial distance between a point in any two phases exceeds the threshold, the point is marked as an “abnormal offset point”, otherwise it is a “stable point”. The spatial displacement set of all points in different phases is output to form multi-temporal spatial offset data.

[0126] S312: Based on the multi-temporal spatial offset data, the points with prominent spatial position changes are screened, the coordinate points in the neighborhood of each abnormal change point that have not changed are judged, the elevation data of the points are optimized, and the spatial mean value is calculated to obtain the elevation mean value of the non-offset points;

[0127] The spatial offset values of all points are sorted and screened, and points with offset greater than a set threshold value are extracted to form an "abnormal change point set". Meanwhile, the spatial range of each abnormal point is recorded. Then, for each abnormal change point, a fixed buffer radius search is performed around it, and the spatial point set within a radius of 10 meters is extracted. It is determined whether the offset values of the points in this range are less than the set threshold value of 2.0 meters. If the offset values of most points are less than the threshold value, it is determined that the area where the abnormal point is located is stable, and the abnormal point can be considered as a single point disturbance. The elevation values of the stable points in the buffer range of the abnormal point are averaged, and the average value is taken as the elevation optimization correction value of the abnormal point. For example, the elevation of an abnormal point is 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. The corresponding average value is 35.35 meters. Therefore, the elevation of the abnormal point is corrected to 35.35 meters. If there are not enough stable points around the abnormal point or the elevation fluctuation of each point is greater than 0.8 meters, the point is marked as "unmodifiable" and excluded from subsequent analysis. The elevation set of all corrected abnormal points and their adjacent stable points is output as the elevation mean value of the unoffset points.

[0128] S313: According to the elevation mean value of the unoffset points, the spatial difference between the elevation mean value and the elevation of the abnormal point is compared using the formula:

[0129] ;

[0130] The elevation of the abnormal change point is updated to obtain the shoreline time correction result, wherein, represents the spatial elevation correction difference of the th abnormal change point in the th time phase, represents the elevation mean value of the adjacent unchanging points of the abnormal change point in the th time phase, represents the original elevation of the th abnormal change point in the th time phase, is the number of adjacent unchanging points, and represent the spatial horizontal coordinate and vertical coordinate of the th unchanging point, respectively, and represent the spatial horizontal coordinate and vertical coordinate of the th abnormal change point, respectively.

[0131] 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.

[0132] 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:

[0133] ;

[0134] 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:

[0135] ;

[0136] 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:

[0137] ;

[0138] 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.

[0139] Please see Figure 5 The specific steps for obtaining the spatial coverage area index are as follows:

[0140] S411: Call the shoreline timing correction result, analyze the spatial correspondence relationship between the shoreline point and the remote sensing water body boundary, compare the coordinate consistency and elevation change of each coincident node, calculate the elevation change amplitude of the spatial node in the difference period, identify the key spatial node of change, and obtain the elevation change intensity sequence;

[0141] Each shoreline spatial point is numbered and classified according to the acquisition phase of the remote sensing image it belongs to, and the corresponding matching relationship between the shoreline point and the remote sensing water body boundary point is established. The matching operation is based on coordinate similarity as the criterion, and the spatial distance value between the shoreline point and the remote sensing water body boundary point is calculated in turn. The point pairs with a distance within 2.0 meters are selected as coincident nodes, and the water level data of the remote sensing image at each coincident node is further extracted. The elevation value of the same position point is extracted from the DEM, and the elevation change amplitude of the point is recorded through the elevation comparison between the two phases. If the corresponding water level of a point in the remote sensing image in 2020 is 33.1 meters, and the corresponding DEM elevation of the same spatial position in the remote sensing image in 2021 is 33.9 meters, then the elevation change is +0.8 meters. After processing all the matching point pairs, the absolute value of the elevation change value is processed and sorted, and the change discrimination threshold is set to 0.5 meters. Nodes above the threshold are marked as "change key nodes", and nodes below the threshold are marked as "stable nodes". The discrimination value is set based on the remote sensing water level extraction error control of ±0.2 meters and the DEM interpolation error tolerance of ±0.3 meters. The combined tolerance is 0.5 meters. If the water level of a coincident point in the 2021 image is 34.2 meters and in the 2022 image is 34.8 meters, the difference is +0.6 meters, which exceeds the 0.5 meter threshold, then the point is determined as a change key node. The change information of each node is output according to the point number, spatial position and elevation change amplitude, and the elevation change intensity sequence is obtained.

[0142] S412: According to the elevation change intensity sequence, filter the adjacent spatial nodes with close change amplitude, judge whether the nodes constitute a spatial connected region, optimize the spatial relationship of each group of connected nodes, and obtain the overlapping boundary connected node set;

[0143] 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.

[0144] 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:

[0145] ;

[0146] 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.

[0147] 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.

[0148] 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 extract the original elevation of the corresponding node The difference between the reference elevation in the correction elevation band , build an elevation change index, and use the product of the two indices to build a node spatial influence factor , and build a spatial coverage area index by accumulating the influence factors of all nodes;

[0149] The different dimensional data involved in the calculation are normalized, and the following is the calculation process of each node (after normalization):

[0150] Node A1: , , ; , , , calculation:

[0151] ;

[0152] Node A2: , ; , , , calculation:

[0153] ;

[0154] Node A3: , ; , , , calculation:

[0155] ;

[0156] Node A4: , ; , , , calculation:

[0157] ;

[0158] Node A5: , ; , , , calculation:

[0159] ;

[0160] Add all node calculation results: ​

[0161] ;

[0162] The total number of nodes is The average influence degree is:

[0163] ;

[0164] The results show that the lateral spatial distribution of nodes in the current overlapping boundary connected region deviates from the average axis direction, and the elevation change has a negative correlation trend, that is, the elevation rises mainly on the side of the horizontal coordinate below the average value. This trend constitutes the basis for the slope of the regional elevation surface. The results show that the slope of the regional elevation surface is As a quantitative expression of the change trend of multiple nodes, it can be used to determine whether there is a spatial asymmetry feature or a concentrated distribution of shoreline adjustment belts in the region.

[0165] Please refer to Figure 6 The steps for obtaining the water level area change sequence are as follows:

[0166] S511: According to the spatial coverage area index, analyze the elevation distribution of DEM data under the condition of different water levels, calculate the terrain undulation in the region corresponding to each water level, screen the region with an elevation lower than or equal to the current water level, and determine whether the region is connected to obtain the submerged region pixel group;

[0167] Read the DEM elevation raster data and determine the spatial position and corresponding elevation value of each pixel, and set a group of target water level values as the judgment basis, such as setting the current water level to 32.5 meters. Compare the elevation value of all pixels in the DEM with the current water level benchmark one by one, mark the pixels with an elevation value less than or equal to the water level as "potential submerged pixels", generate a Boolean value array representing whether the pixel meets the submerged condition, then extract the spatial position coordinates of all pixels that meet the condition and construct a preliminary set of submerged regions, and then analyze the connectivity of the pixels in the set. The 4-neighbor connection principle is used for connectivity judgment. Each pixel that meets the condition is checked in turn to see if there is a pixel that also meets the condition in the upper, lower, left and right directions. If at least one adjacent direction has a connection, it is marked as connected, and the connectivity of all pixels is continuously checked until a complete connected block is formed. If the region pixel numbers (102, 204), (102, 205), and (102, 206) are consecutive 3 points that meet the elevation requirement and are horizontally adjacent to each other, they are determined as the same connected region. If a pixel that meets the condition is surrounded by pixels that do not meet the condition, it will form an isolated region and can be marked as a non-main connected region. From the set, select the connected blocks with a connected size greater than 20 pixels as the effective submerged region, and output all pixels that meet the connectivity condition to obtain the submerged region pixel group.

[0168] S512: Based on the submergence area pixel group, the position relationship of the remote sensing water body boundary is compared, the pixels with spatial overlap are identified, the distribution characteristics of the overlapping pixels in the difference area are analyzed, the pixels with spatial matching relationship are marked, and the spatial overlapping pixel identification is obtained;

[0169] The spatial coordinates of each submerged pixel are overlapped with the water body vector layer formed by the remote sensing water body boundary, and the specific operation is to traverse the center point coordinates of each submerged pixel and search whether it falls into the polygon closed area formed by the remote sensing water body boundary. If it falls into, it is marked as "spatial overlapping pixel", otherwise it is marked as "non-overlapping pixel". The process is based on the principle of point-in-face determination. After the determination is completed, all overlapping pixels are summarized and a spatial index table is constructed. The distribution difference of overlapping pixels in different remote sensing image water body boundaries is further analyzed. By comparing the water body boundary vector structure in different remote sensing periods, it is identified whether the overlapping pixels fall into the water body boundary of each period repeatedly. If a pixel coincides with the water body boundary in 4 periods out of 6 remote sensing images, it can be determined as "stable water body overlapping pixel". Otherwise, if it coincides in only one period, it is considered as "occasional water body overlapping pixel". The reference value for stable water body coincidence determination is set to appear in at least 3 period image water body boundary, to ensure that the identification result has multi-time consistency. Finally, all pixels that meet the spatial coincidence and reach the number of time overlapping are numbered and spatially labeled to form the spatial overlapping pixel identification.

[0170] S513: According to the spatial overlapping pixel identification, the spatial coverage range of the overlapping area under each water level condition is calculated, the boundary structure of each area is analyzed, the area change trend is judged, the difference coverage area is arranged according to the water level condition, and the water level area change sequence is obtained.

[0171] The corresponding overlapping pixel sets under each water level condition are counted, and the corresponding spatial coverage area values are obtained by multiplying the pixel number by the unit area. The pixel resolution is set to 5m*5m, and the area of each pixel is 25m2. If the overlapping pixels under a certain water level are 22000, the total coverage area is 550000m2, i.e. 0.55km2. After the area calculation under each water level condition is completed, the boundary pixels of each coverage area are extracted, the boundary line is reconstructed according to the spatial coordinate connectivity, and whether the boundary line is closed, whether there is a broken structure, and whether it shows an expansion trend are analyzed. The complexity of the boundary structure can be indirectly determined by the ratio of the boundary line length to the area. If the area is 0.55km2 and the total length of the boundary line is 3200m, the shape is relatively broken. On the contrary, if the area is 1.3km2 and the length of the boundary line is 2800m, the boundary shape is relatively concentrated. Then the coverage areas corresponding to each water level are arranged in ascending order according to the water level, and the trend of the coverage area with the water level is observed. If the water level rises from 32.5m to 33.5m, the area increases from 0.55km2 to 1.8km2, and the trend is increasing. Finally, the area change result is formed in one-to-one correspondence sequence according to the water level value, and the water level area change sequence is output.

[0172] The above is only a preferred embodiment of the present application, and does not limit the form of the present application. Any skilled person in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made according to the technical essence of the present application to the above embodiments without departing from the technical solution content of the present application still belongs to the protection scope of the present application.

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. 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.

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 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.

Images