River and lake shoreline type identification method and device, electronic equipment and storage medium

Abstract

The invention provides a river-lake shoreline type identification method and device, electronic equipment and a storage medium, and belongs to the technical field of data identification, and the method comprises the steps: determining a river-lake boundary line and a shoreline type of the river-lake shoreline based on satellite image data of the river-lake shoreline; the shoreline type comprises a natural shoreline and / or a non-natural shoreline; generating a vector polygon based on the shoreline type, and calculating density distribution data of the non-natural shoreline based on the vector polygon; dividing the river-lake shoreline ecosystem into a first region and a second region based on the density distribution data; the shoreline type of the second area comprises a natural shoreline and / or a non-natural shoreline; and determining the natural shoreline rate based on the number of the splines of the natural shoreline and the number of the splines of the non-natural shoreline of the second area and the natural shoreline length of the first area. According to the river and lake shoreline type identification method and device, the electronic equipment and the storage medium provided by the invention, the accuracy of evaluation of the natural ecosystem proportion and the natural shoreline rate of the shoreline zone can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of data identification technology, and more specifically, to a river and lake shoreline type identification method and device, electronic equipment, and storage medium. Background Art

[0002] The shoreline of rivers and lakes is an important part of the river ecosystem and is of great significance to maintaining the ecological balance and function of rivers. The proportion of natural ecosystems in the shoreline and the natural shoreline rate are important indicators for assessing the ecological environment of the shoreline. The shoreline classification technology of rivers, lakes and reservoirs based on spatial information technology such as remote sensing is an effective means of monitoring the shoreline of rivers, lakes and reservoirs. Compared with traditional methods such as ground surveys, it improves the efficiency of monitoring the shoreline of rivers, lakes and reservoirs.

[0003] However, the existing remote sensing monitoring methods for river, lake and reservoir shores are mostly used to monitor the distribution range of land use types in the area. When the planar land use types are projected along the length of the river, unreasonable segmentation of river, lake and reservoir shores occurs, resulting in large errors in the classification results, complex calculations and low accuracy. Summary of the invention

[0004] The purpose of the present disclosure is to provide a method and device for identifying river and lake shoreline types, electronic equipment, and storage media to improve the accuracy of the assessment of the proportion of natural ecosystems in the shore zone and the natural shoreline rate.

[0005] A first aspect of an embodiment of the present disclosure provides a method for identifying river and lake shoreline types, comprising: Based on the satellite image data of river and lake shorelines, the river and lake boundary lines and shoreline types of river and lake shorelines are determined. The shoreline types include natural shorelines and / or non-natural shorelines.

[0006] A vector polygon is generated based on the shoreline type, and density distribution data of the non-natural shoreline is calculated based on the vector polygon. The river and lake shoreline ecosystem is divided into a first area and a second area based on the density distribution data. The shoreline type of the first area is a natural shoreline. The shoreline type of the second area includes a natural shoreline and / or a non-natural shoreline.

[0007] The natural shoreline ratio is determined based on the number of splines of the natural shoreline of the second region and the number of splines of the non-natural shoreline, and the length of the natural shoreline of the first region. The splines are equally spaced lines created in the second region.

[0008] A second aspect of an embodiment of the present disclosure provides a device for identifying river and lake shoreline types, including: The first calculation module is used to determine the river-lake boundary line and the shoreline type of the river-lake shoreline based on the satellite image data of the river-lake shoreline. The shoreline type includes natural shoreline and / or non-natural shoreline.

[0009] The second calculation module is used to generate a vector polygon based on the shoreline type, and obtain density distribution data of the non-natural shoreline based on the vector polygon. The river and lake shoreline ecosystem is divided into a first area and a second area based on the density distribution data. The shoreline type of the first area is a natural shoreline. The shoreline type of the second area includes a natural shoreline and / or a non-natural shoreline.

[0010] The third calculation module is used to determine the natural shoreline rate based on the number of spline lines of the natural shoreline and the number of spline lines of the non-natural shoreline in the second area and the length of the natural shoreline in the first area. The spline is an equidistant line created in the second area.

[0011] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned river and lake shoreline type identification method when executing the computer program.

[0012] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned river and lake shoreline type identification method are implemented.

[0013] The beneficial effects of the river and lake shoreline type identification method and device, electronic device, and storage medium provided by the embodiments of the present disclosure are: on the one hand, the embodiments of the present disclosure automatically identify river and lake boundaries and shoreline types through satellite image data, reducing the workload of manual observation and judgment and improving work efficiency. At the same time, by using vector polygons to calculate the density distribution data of non-natural shorelines, a refined division of shoreline types is achieved, providing a scientific basis for the subsequent division of ecosystem regions.

[0014] On the other hand, the disclosed embodiment divides the river and lake shoreline ecosystem into a first area and a second area through density distribution data, which can more accurately identify the distribution characteristics of natural shorelines and non-natural shorelines, and help to gain a deeper understanding of the structure and function of the coastal ecological environment.

[0015] On the other hand, the embodiment of the present disclosure determines the natural shoreline rate based on the number of intersections between statistical splines and non-natural ecosystem types and the length of natural shorelines, and the calculation process is simple, fast, and highly accurate. This indicator can intuitively reflect the health status and sustainability of the coastal ecological environment, and provides an important basis for ecological protection, restoration, and management.

[0016] In summary, the disclosed embodiments have the advantages of simple calculation, fast and accurate, and wide application range, and are of great significance for evaluating the ecological environment of the shore zone and guiding ecological protection and restoration work. At the same time, the disclosed embodiments also provide new ideas and methods for scientific research and technical applications in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of a flow chart of a method for identifying river and lake shoreline types provided in an embodiment of the present disclosure; Figure 2 A schematic diagram of land use type distribution provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of the distribution of area density of non-natural ecosystems according to an embodiment of the present disclosure; Figure 4 A schematic diagram of spline distribution provided in one embodiment of the present disclosure; Figure 5 A structural block diagram of a device for identifying river and lake shoreline types provided in an embodiment of the present disclosure; Figure 6 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0020] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0021] Please refer to Figure 1 , Figure 1 A schematic flow chart of a method for identifying river and lake shoreline types provided in an embodiment of the present disclosure, the method may include S101 to S103.

[0022] S101: Determine river and lake boundaries and shoreline types of river and lake shorelines based on satellite image data of river and lake shorelines. Shoreline types include natural shorelines and / or non-natural shorelines.

[0023] In this embodiment, the satellite image data of the river and lake shoreline may include remote sensing image information of different bands of the shoreline of the river or lake to be measured, and the river and lake boundary line is the dividing line between the river or lake water body and the surrounding land.

[0024] like Figure 2 As shown, in this embodiment, high-resolution satellite image data covering the river, lake and reservoir area is first obtained, and land use classification data (such as land use type) of the river and lake shore zone is obtained based on the satellite image data. Water system data of the national basic geographic information data is obtained. Based on the satellite image data and the water system data, the inner edge boundary line of the river and the outer edge boundary line of the river are generated on both sides of the river and the lake, respectively, as the river and lake boundary line of the river and lake shoreline to be measured.

[0025] Land use types can include housing, roads, plantations, farmland, natural forests, grasslands, water bodies and bare land. Coastline types can include living coastline, hardened coastline, transportation coastline, port coastline, industrial coastline, agricultural coastline, ecological coastline, estuary coastline, lightly disturbed coastline and unused coastline. Land use types correspond to coastline types one by one. According to the correspondence between land use types and coastline types, river and lake coastlines are divided into natural ecosystems (farmland, natural forests, grasslands, water bodies and bare land) and non-natural ecosystems (housing, roads, plantations, farmland). Natural ecosystems correspond to natural coastlines, and non-natural ecosystems correspond to non-natural coastlines.

[0026] For example, the spatial resolution of high-resolution satellite image data covering river and lake areas is better than 2 meters, and the accuracy of the land use type classification map obtained by interpretation and analysis is also better than 2 meters in spatial resolution. Water system data is vector data representing the centerline of the river. The acquisition methods of waterfront boundary lines and outer edge boundary lines can include: artificial division based on experience or obtaining from the provincial river shoreline protection and utilization planning results of the provincial water affairs departments.

[0027] S102: Generate vector polygons based on the shoreline type, and calculate density distribution data of non-natural shorelines based on the vector polygons. Divide the river and lake shoreline ecosystem into a first area and a second area based on the density distribution data. The shoreline type of the first area is a natural shoreline. The shoreline type of the second area includes a natural shoreline and / or a non-natural shoreline.

[0028] In this embodiment, in the field of geographic information systems, a vector polygon is a geometric figure based on a vector data model. A vector polygon is a closed figure formed by connecting the boundaries of shoreline types (such as natural shorelines and unnatural shorelines) through points and lines. Each polygon can represent a shoreline area, and its attributes include information such as shoreline type. For unnatural shorelines, density distribution data can refer to indicators such as the length of unnatural shorelines per unit area or the proportion of unnatural shoreline area. The density distribution data can intuitively reflect the distribution density of unnatural shorelines in the entire river and lake shoreline ecosystem. The first area refers to an area where almost all shoreline types are natural shorelines, and natural shorelines dominate in this area. The second area is an area where natural shorelines and unnatural shorelines coexist, and the shoreline ecosystem here is subject to a high degree of human interference or transformation. Through the above division, the ecological conditions of different shorelines can be better evaluated and managed.

[0029] For example, the above detailed land use types are merged into natural ecosystems and non-natural ecosystems, and the natural ecosystem type is assigned attribute 0, and the non-natural system type is assigned attribute 1, generating vector polygons with attributes 0 and 1 respectively. The area and density distribution of the non-natural ecosystem polygons with attribute 1 are statistically analyzed to obtain a density distribution map ( Figure 3 ), using the natural optimal classification method, the data is classified into a limited number of levels, so that the internal differences of each level are minimized and the differences between levels are maximized. According to the classification results of the density distribution map, the classification areas with values ​​less than the density gradient threshold are selected as natural ecosystem areas (i.e., the first area), and the classification areas with values ​​greater than the density gradient threshold are selected as areas where the length of non-natural coastlines is to be more finely counted (i.e., the second area). The inner edge boundary of the river is divided into two parts: the natural coastline (corresponding to the first area) and the non-natural coastline to be more finely counted (corresponding to the second area), and the length of the natural coastline and the length of the non-natural coastline to be more finely counted are counted.

[0030] S103: Determine the natural shoreline ratio based on the number of spline lines of the natural shoreline and the number of spline lines of the non-natural shoreline in the second area and the length of the natural shoreline in the first area. The spline is an equidistant line created in the second area.

[0031] In this embodiment, the natural shoreline rate is calculated by counting the relevant data of specific shorelines in different regions. In the second region, the number of splines of natural shorelines and non-natural shorelines is counted respectively, and the length of natural shorelines is counted in the first region. These data are combined to measure the proportion of natural shorelines in the entire river and lake shoreline, so as to reflect the natural degree of ecology.

[0032] It can be concluded from the above that, on the one hand, this embodiment automatically identifies river and lake boundaries and shoreline types through satellite image data, reducing the workload of manual observation and judgment and improving work efficiency. At the same time, by using vector polygons to calculate the density distribution data of non-natural shorelines, a refined division of shoreline types is achieved, providing a scientific basis for the subsequent division of ecosystem regions.

[0033] On the other hand, this embodiment divides the river and lake shoreline ecosystem into a first area and a second area through density distribution data, which can more accurately identify the distribution characteristics of natural shorelines and non-natural shorelines, and help to gain a deeper understanding of the structure and function of the coastal ecological environment.

[0034] On the other hand, this embodiment determines the natural coastline rate based on the number of intersections between the statistical spline and the non-natural ecosystem type and the length of the natural coastline, and the calculation process is simple, fast, and highly accurate. This indicator can intuitively reflect the health status and sustainability of the coastal ecological environment, and provides an important basis for ecological protection, restoration, and management.

[0035] In summary, this embodiment has the advantages of simple calculation, fast and accurate, and wide application range, and is of great significance for evaluating the ecological environment of the coastal zone and guiding ecological protection and restoration work. At the same time, this embodiment also provides new ideas and methods for scientific research and technical applications in related fields.

[0036] In one embodiment of the present disclosure, determining the river-lake boundary line and the shoreline type of the river-lake shoreline based on the satellite image data of the river-lake shoreline includes: The boundaries of rivers and lakes are determined based on satellite image data of river and lake shorelines and geographic water system data.

[0037] Determine land use types along river and lake shorelines based on satellite imagery data.

[0038] Determine the corresponding coastline type based on the land use type.

[0039] In this embodiment, the river-lake boundary line includes the river-lake inner edge boundary line and the river-lake outer edge boundary line.

[0040] Determine river and lake boundaries based on satellite imagery data of river and lake shorelines and geographic water system data, including: Determine the inner boundary lines of rivers and lakes based on satellite image data and geographic water system data.

[0041] The outer boundary line of rivers and lakes is determined based on the inner boundary line of rivers and lakes and the width of the buffer zone.

[0042] In this embodiment, the width of the buffer zone varies according to the level or width of the river, and the width of the buffer zone at different locations of the same river is different.

[0043] For example, based on the high-resolution satellite image data covering the river, lake and reservoir area and the water system data of the national basic geographic information data, two inner edge boundary lines of the river are generated on both sides of the river, that is, the river boundary, also called the water boundary line, that is, the boundary line close to the river side, also known as the river water boundary line. Based on the inner edge boundary of the water system data of the national basic geographic information data, a buffer boundary line of a certain width is made outward, that is, the buffer boundary line away from the river side is the outer edge boundary line of the river.

[0044] In this embodiment, the correspondence between land use type and shoreline type includes: housing corresponds to living shoreline, port shoreline or industrial shoreline, road corresponds to hardened shoreline or traffic shoreline, farmland corresponds to agricultural shoreline. The shoreline types are divided into natural shoreline and non-natural shoreline, natural shoreline corresponds to ecological shoreline, estuary shoreline, unused shoreline and slightly disturbed shoreline; non-natural shoreline corresponds to hardened shoreline, traffic shoreline, agricultural shoreline, living shoreline, port shoreline and industrial shoreline.

[0045] The recognition conditions for ecological coastlines include: the land use types within the coastline only include natural or ecologically restored woodlands and grasslands; except for hardened embankments, the land use types within the coastline only include natural or ecologically restored woodlands and grasslands.

[0046] The identification conditions of estuary coastline include: at the entrance and exit of the river (lake), the land use type within the coastline is only water area.

[0047] The conditions for identifying unused coastline include: the land use type within the coastline is only unused land; except for hardened embankment slopes, the land use type within the coastline only includes unused land.

[0048] The identification criteria for a lightly disturbed coastline include: the land use types within the coastline include roads along rivers (lakes), and natural or ecologically restored woodlands and grasslands; except for hardened embankments and roads along rivers (lakes), the land use types within the coastline only include natural or ecologically restored woodlands, grasslands or unused land.

[0049] The identification conditions for hardened coastline include: the land use type within the coastline is only hardened embankment slope.

[0050] The identification conditions for transportation coastlines include: the land use types within the coastline include airports, railways, bridges and other roads in transportation land.

[0051] The identification conditions for agricultural coastline include: the land use types within the coastline include agricultural land.

[0052] The identification conditions for living coastline include: the land use types within the coastline include living land and hydraulic construction land.

[0053] The recognition conditions for port coastline include: the land use types within the coastline include port terminal land.

[0054] The identification conditions for industrial coastlines include: the land use types within the coastline include industrial and mining land.

[0055] From the above, it can be concluded that, on the one hand, this embodiment uses high-resolution satellite image data to improve the accuracy of river and lake boundary delineation, making the determination of the boundary closer to the actual situation and reducing human errors. On the other hand, this embodiment sets different buffer widths by considering the river level or river width, which not only reflects the personalized characteristics of the river, but also ensures the rationality and scientificity of the outer boundary of the river and lake. On the other hand, this embodiment combines the land use type to determine the shoreline type, providing a more detailed and accurate classification basis for river and lake management.

[0056] In summary, this embodiment provides an efficient and accurate method for determining river and lake boundaries and dividing shoreline types, improves the efficiency and level of river and lake management, helps achieve sustainable utilization of water resources and protection of the ecological environment, and has important practical significance.

[0057] In one embodiment of the present disclosure, the vector polygons include natural shoreline polygons and non-natural shoreline polygons.

[0058] Generate vector polygons based on shoreline type, including: If the coastline type is a natural coastline, a natural coastline polygon with an attribute of 0 is generated based on the natural ecological area corresponding to the natural coastline.

[0059] If the coastline type is a non-natural coastline, a non-natural coastline polygon with an attribute of 1 is generated based on the non-natural ecological area corresponding to the non-natural coastline.

[0060] In this embodiment, vector polygons with different attribute values ​​are generated for ecological regions corresponding to different types of coastlines. Natural coastline polygons and non-natural coastline polygons are distinguished by attribute values ​​0 and 1, so as to achieve clear division and representation of different coastline types in spatial data.

[0061] For example, when planning the ecological restoration of rivers and lakes, the natural shoreline polygons can be used to determine the natural shoreline areas that need to be protected to avoid development and construction. For non-natural shoreline polygon areas with an attribute of 1, their impact on the ecology can be evaluated, such as considering whether to transform some non-natural shorelines into natural shorelines to enhance ecological functions. When conducting ecological and environmental monitoring, the changes in natural shoreline polygons and non-natural shoreline polygons in different periods are compared to analyze the evolution of the shoreline ecosystem.

[0062] This embodiment achieves accurate division and representation of coastline types by generating vector polygons with different attribute values ​​for natural coastlines and non-natural coastlines.

[0063] In one embodiment of the present disclosure, density distribution data of non-natural shorelines is obtained based on vector polygon calculations, including: The density distribution data of the non-natural coastline is calculated based on the vector polygon and the first calculation formula.

[0064] The first calculation formula is:

[0065] in, represents the density of non-natural coastlines in the target area x, n represents the number of samples of vector polygons, h represents the width of the buffer zone, Represents the area value of the i-th non-natural coastline polygon.

[0066] In this embodiment, the area and density distribution of the non-natural ecosystem polygons with an attribute of 1 are statistically analyzed, and the polygons are converted into points. , , ..., , f(x) is the density estimate at point x. The first calculation formula calculates the density of non-natural shorelines in the target area by weighted summing the area value of each non-natural shoreline polygon and the position relationship of the target area. The exponential term in the first calculation formula measures the influence of the i-th polygon on the target area x. As the distance from the target area increases (by (x- ) / h), the impact decreases exponentially.

[0067] For example, first determine the target area x, count the number of vector polygon samples n, and set the buffer width h. Then obtain the area value of each non-natural shoreline polygon , substitute into the formula to calculate f(x), and obtain the density distribution data of non-natural coastlines in the target area.

[0068] This embodiment accurately reflects the distribution of unnatural shorelines in different areas by comprehensively considering the polygonal area of ​​unnatural shorelines and their distance relationship with the target area. In addition, this embodiment also helps to monitor the changing trend of unnatural shorelines, timely warn of potential ecological risks, and lay a solid foundation for the sustainable development of river and lake ecological environment.

[0069] In one embodiment of the present disclosure, dividing the river and lake shoreline into a first area and a second area based on density distribution data includes: If the density distribution data is less than or equal to the density gradient threshold, the corresponding area is determined as the first area.

[0070] If the density distribution data is greater than the density gradient threshold, the corresponding area is determined as the second area.

[0071] like Figure 3 As shown, in this embodiment, the density distribution data of the non-natural coastline is compared with the set density gradient threshold to distinguish different areas. When the density is less than or equal to the threshold, the natural coastline in the area is dominant and is divided into the first area; when the density is greater than the threshold, the non-natural coastline accounts for a large proportion and is divided into the second area.

[0072] Exemplarily, the number of levels can be set according to the historical shoreline type ratio data, so that the natural shoreline ratio divided after setting the number of levels reaches a preset similarity with the natural shoreline ratio in the historical data. Specifically, the appropriate number of levels and density gradient threshold can be gradually determined by trying different numbers of levels and observing the interpretability and practicality of the results. For example, start with a smaller number of levels, such as 3 or 4, and then gradually increase until the best visual effect and interpretation strength are found. Finally, the natural shoreline areas representing most of the natural shoreline are merged as part of the natural shoreline (the coverage range is the length of the natural shoreline), and the other levels are merged into non-natural areas (the coverage range is the length of the non-natural shoreline to be more finely counted).

[0073] In this embodiment, the existing remote sensing monitoring methods for river, lake and reservoir shores are mostly used to monitor the distribution range of land use types in the area. When the planar land use type is projected on the length direction of the river, unreasonable segmentation of river, lake and reservoir shores will occur, resulting in large errors in the classification results. This embodiment takes into account the distribution density of the non-natural ecosystem area. Places with lower density do not participate in spline segmentation. Places with higher density are divided into intervals according to multiples. The calculation range of the spline is adjusted to save computing resources, and the determination and calculation of river and lake shoreline types are completed automatically, quickly and effectively.

[0074] This embodiment achieves a refined division of river and lake shorelines by density classification and division of regions, effectively avoiding the classification errors caused by unreasonable segmentation of river and lake shore zones in traditional remote sensing monitoring methods. This embodiment can automatically and quickly determine the type of river and lake shorelines, and reasonably divide the first area and the second area according to the density gradient threshold, thereby improving the accuracy and efficiency of shoreline classification. At the same time, by optimizing the spline segmentation strategy, computing resources are saved, and efficient monitoring and management of shoreline ecosystems are achieved.

[0075] In one embodiment of the present disclosure, the natural shoreline rate of the river and lake shoreline is determined based on the number of splines of the natural shoreline of the second area, the number of splines of the non-natural shoreline, and the length of the natural shoreline of the first area, including: Determine the land use type for areas connected to the spline in the second region.

[0076] The connection results are determined based on the land use type. The connection results include dividing the splines into natural shoreline splines and non-natural shoreline splines.

[0077] The natural shoreline length and the non-natural shoreline length of the second area are calculated based on the number of natural shoreline splines and the number of non-natural shoreline splines of the second area.

[0078] The natural shoreline ratio is determined based on the natural shoreline length, the unnatural shoreline length, and the natural shoreline length of the first area in the second area.

[0079] like Figure 4 As shown, in this embodiment, before determining the land use type of the area connected to the spline, the method further includes: generating a spline in the second area based on the buffer width and the first interval.

[0080] In this embodiment, generating a spline in the second area based on the buffer width and the first interval may include: generating a vertical spline with a length n times the spatial resolution of the land use type pixel as the first interval and a distance between the inner and outer edges of the river (i.e., the buffer width) as the length on the center line of the coastal zone (within the non-natural shoreline area to be more finely counted). Where n=d / (10*D), d is the buffer width, and D is the spatial resolution of the land use type pixel (better than 2 meters).

[0081] In this embodiment, the connection result is determined based on the land use type, including: taking a length of n / 2 times the spatial resolution of the land use type pixel as the search radius, counting the spatial intersection connection results of each spline and the land use type data, retaining the maximum value of the land use type max as the shoreline type of the area represented by the spline, max=0 represents a natural shoreline, and max=1 represents an unnatural shoreline. Specifically, the vector polygon (surface file of the land use type) with attributes of 0 and 1 respectively is spatially intersected and connected with the spline, and a line file is obtained (the attribute is the maximum attribute value of the surface file intersecting with it, that is, 0 or 1).

[0082] The setting of the search radius means that when performing spatial intersection and connection analysis, the land use type pixels within a certain range around the spline will be considered. Specifically, the length of n / 2 times the spatial resolution of the land use type pixel is used as the search radius, which means that the search range is the length extending outward from the spline, which is half the spatial resolution of the land use type pixel. If the distance between the connecting feature and the target feature is within this range, it is possible to perform a spatial connection. For example, when the search radius is set to 100 meters and a spatial relationship within a certain distance range is used, features within 100 meters of the target feature will be connected. If the value of the search radius is not specified, the distance will be 0.

[0083] For example, the shoreline type is determined by statistically analyzing the spatial intersection and connection results of the spline and the land use type data. The search radius is set with the spline as the center to consider the surrounding land use types because the area represented by the spline will be affected by the surrounding land use conditions. The maximum land use type is selected as the shoreline type of the area represented by the spline based on a dominance principle, that is, it is believed that the dominant land use type within the search range can reflect the main characteristics of the shoreline in the area.

[0084] For example, if the land use type around the spline (within the search radius) is natural (such as wetlands, grasslands, etc., with an attribute of 0), then the coastline type of the area represented by the spline is a natural coastline; if the land use type around the spline (within the search radius) is artificially developed (such as construction land, docks, etc., with an attribute of 1), then it is a non-natural coastline.

[0085] Setting the search radius to a length n / 2 times the spatial resolution of the land use type pixel is a consideration to balance accuracy and computational complexity. If the search radius is too large, too many irrelevant land use type pixels will be included, increasing the computational complexity and possibly introducing erroneous judgments; if the search radius is too small, the land use types that affect the shoreline type of the area represented by the spline cannot be fully considered, resulting in inaccurate results.

[0086] In this embodiment, the number of natural coastline and non-natural coastline spline lines is counted respectively, and the length of natural coastline, non-natural coastline and total length of coastline are calculated: Ln=Ln0+La0*Nn / (Nn+Na) La=Na*La0*Nn / (Nn+Na) Lt=Ln+La Pn=Ln / Lt*100% Among them, Ln0 represents the length of the natural coastline in the first area, La0 represents the length of the non-natural coastline in the second area to be more precisely counted, Nn represents the number of natural coastline splines, Na represents the number of non-natural coastline splines, Ln represents the total natural coastline length, La represents the non-natural coastline length, and Lt represents the total coastline length. Pn represents the natural coastline rate, that is, the proportion of the natural coastline length to the total coastline length.

[0087] This embodiment can accurately distinguish and quantify the natural and non-natural parts of river and lake shorelines through refined spline generation and land use type analysis. This embodiment not only improves the accuracy of natural shoreline rate calculation, but also provides a scientific basis for river and lake shoreline management, ecological protection and planning. By considering the buffer width and the spatial resolution of land use type pixels, the rationality of spline setting is ensured, and the reliability of the evaluation results is further improved. In addition, this embodiment is easy to operate and easy to promote, which is of great significance for promoting the sustainable development of river and lake shorelines.

[0088] Corresponding to the river and lake shoreline type identification method in the above embodiment, Figure 5 This is a structural block diagram of a device for identifying river and lake shoreline types provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 5 The river and lake shoreline type identification device 20 includes: a first calculation module 21, a second calculation module 22 and a third calculation module 23.

[0089] The first calculation module 21 is used to determine the river-lake boundary line and the shoreline type of the river-lake shoreline based on the satellite image data of the river-lake shoreline. The shoreline type includes natural shoreline and / or non-natural shoreline.

[0090] The second calculation module 22 is used to generate a vector polygon based on the shoreline type, and obtain density distribution data of the non-natural shoreline based on the vector polygon calculation. The river and lake shoreline ecosystem is divided into a first area and a second area based on the density distribution data. The shoreline type of the first area is a natural shoreline. The shoreline type of the second area includes a natural shoreline and / or a non-natural shoreline.

[0091] The third calculation module 23 is used to determine the natural coastline rate based on the number of spline lines of the natural coastline and the number of spline lines of the non-natural coastline in the second area and the length of the natural coastline in the first area. The spline is an equidistant line created in the second area.

[0092] In one embodiment of the present disclosure, the first calculation module 21 is specifically used to determine the river and lake boundary line based on the satellite image data of the river and lake shoreline and the geographical water system data.

[0093] Determine land use types along river and lake shorelines based on satellite imagery data.

[0094] Determine the corresponding coastline type based on the land use type.

[0095] In one embodiment of the present disclosure, the river-lake boundary line includes the inner edge boundary line of the river-lake and the outer edge boundary line of the river-lake. The first calculation module 21 is also specifically used to determine the inner edge boundary line of the river-lake based on satellite image data and geographic water system data.

[0096] The outer boundary line of rivers and lakes is determined based on the inner boundary line of rivers and lakes and the width of the buffer zone.

[0097] In one embodiment of the present disclosure, the vector polygon includes a natural coastline polygon and a non-natural coastline polygon. The second calculation module 22 is specifically configured to generate a natural coastline polygon with an attribute of 0 based on the natural ecological area corresponding to the natural coastline if the coastline type is a natural coastline.

[0098] If the coastline type is a non-natural coastline, a non-natural coastline polygon with an attribute of 1 is generated based on the non-natural ecological area corresponding to the non-natural coastline.

[0099] In one embodiment of the present disclosure, the second calculation module 22 is further configured to calculate density distribution data of the non-natural shoreline based on the vector polygon and the first calculation formula.

[0100] The first calculation formula is:

[0101] in, represents the density of non-natural coastlines in the target area x, n represents the number of samples of vector polygons, h represents the width of the buffer zone, Represents the area value of the i-th non-natural coastline polygon.

[0102] In one embodiment of the present disclosure, the second calculation module 22 is further configured to determine the corresponding area as the first area if the density distribution data is less than or equal to the density gradient threshold.

[0103] If the density distribution data is greater than the density gradient threshold, the corresponding area is determined as the second area.

[0104] In one embodiment of the present disclosure, the third calculation module 23 is specifically used to determine the land use type of the area connected to the spline in the second area.

[0105] The connection results are determined based on the land use type. The connection results include dividing the splines into natural shoreline splines and non-natural shoreline splines.

[0106] The natural shoreline length and the non-natural shoreline length of the second area are calculated based on the number of natural shoreline splines and the number of non-natural shoreline splines of the second area.

[0107] The natural shoreline ratio is determined based on the natural shoreline length, the unnatural shoreline length, and the natural shoreline length of the first area in the second area.

[0108] See also Figure 6 , Figure 6 Schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 6The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules in the above-mentioned device embodiments, such as Figure 5 The functions of modules 21 to 23 are shown.

[0109] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0110] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0111] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0112] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the river and lake shoreline type identification method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device 300 described in the embodiments of the present disclosure, which will not be repeated here.

[0113] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0114] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0115] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0119] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0120] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for identifying river and lake shoreline types, characterized in that: include: Determine river and lake boundary lines and shoreline types of the river and lake shorelines based on satellite image data of river and lake shorelines; The coastline types include natural coastlines and / or non-natural coastlines; Generate a vector polygon based on the shoreline type, and calculate density distribution data of the non-natural shoreline based on the vector polygon; Dividing the river and lake shoreline ecosystem into a first area and a second area based on the density distribution data; the shoreline type of the first area is a natural shoreline; the shoreline type of the second area includes a natural shoreline and / or a non-natural shoreline; The natural shoreline ratio is determined based on the number of spline lines of the natural shoreline and the number of spline lines of the non-natural shoreline of the second area, and the length of the natural shoreline of the first area; the spline lines are equally spaced lines created in the second area.

2. The method for identifying river and lake shoreline types according to claim 1, characterized in that: The method of determining the river and lake boundary line and the shoreline type of the river and lake shoreline based on the satellite image data of the river and lake shoreline includes: Determine the river and lake boundary line based on the satellite image data of the river and lake shoreline and the geographical water system data; Determine the land use type of the river and lake shoreline based on the satellite image data; A corresponding shoreline type is determined based on the land use type.

3. The method for identifying river and lake shoreline types according to claim 2, characterized in that: The river and lake boundary lines include the inner edge boundary lines of rivers and lakes and the outer edge boundary lines of rivers and lakes; The determining of the river and lake boundary line based on the satellite image data and geographic water system data of the river and lake shoreline includes: Determine the inner edge boundary of rivers and lakes based on the satellite image data and the geographic water system data; The outer boundary line of the river and lake is determined based on the inner boundary line of the river and lake and the width of the buffer zone.

4. The method for identifying river and lake shoreline types according to claim 1, characterized in that: The vector polygons include natural shoreline polygons and non-natural shoreline polygons; The generating of a vector polygon based on the shoreline type comprises: If the coastline type is a natural coastline, a natural coastline polygon with an attribute of 0 is generated based on the natural ecological area corresponding to the natural coastline; If the coastline type is a non-natural coastline, a non-natural coastline polygon with an attribute of 1 is generated based on the non-natural ecological area corresponding to the non-natural coastline.

5. The method for identifying river and lake shoreline types according to claim 4, characterized in that: The density distribution data of the non-natural coastline is obtained by calculating based on the vector polygon, including: Calculate the density distribution data of the non-natural shoreline based on the vector polygon and the first calculation formula; The first calculation formula is: in, represents the density of non-natural coastlines in the target area x, n represents the number of samples of vector polygons, h represents the width of the buffer zone, Represents the area value of the i-th non-natural coastline polygon.

6. The method for identifying river and lake shoreline types according to claim 1, characterized in that: The dividing the river and lake shoreline into a first area and a second area based on the density distribution data comprises: If the density distribution data is less than or equal to the density gradient threshold, the corresponding area is determined as the first area; If the density distribution data is greater than the density gradient threshold, the corresponding area is determined as the second area.

7. The method for identifying river and lake shoreline types according to claim 1, characterized in that: The method of determining the natural shoreline rate of the river and lake shoreline based on the number of spline lines of the natural shoreline of the second area, the number of spline lines of the non-natural shoreline and the length of the natural shoreline of the first area comprises: determining a land use type in a second region for an area connected to the spline; Determining the connection result based on the land use type; the connection result includes dividing the spline into a natural shoreline spline and a non-natural shoreline spline; Calculate the natural shoreline length and the non-natural shoreline length of the second area respectively based on the number of natural shoreline splines and non-natural shoreline splines of the second area; The natural shoreline ratio is determined based on the natural shoreline length, the non-natural shoreline length, and the natural shoreline length of the first area in the second area.

8. A device for identifying river and lake shoreline types, characterized in that: include: A first calculation module is used to determine the river and lake boundary line and the shoreline type of the river and lake shoreline based on the satellite image data of the river and lake shoreline; The coastline types include natural coastlines and / or non-natural coastlines; A second calculation module is used to generate a vector polygon based on the shoreline type, and calculate the density distribution data of the non-natural shoreline based on the vector polygon; Dividing the river and lake shoreline ecosystem into a first area and a second area based on the density distribution data; the shoreline type of the first area is a natural shoreline; the shoreline type of the second area includes a natural shoreline and / or a non-natural shoreline; The third calculation module is used to determine the natural coastline rate based on the number of spline lines of the natural coastline and the number of spline lines of the non-natural coastline in the second area and the length of the natural coastline in the first area; the spline lines are equidistant lines created in the second area.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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