Method and system for identifying ecological space geographical units based on water-sediment coupling process

Abstract

The present invention discloses a method and system for identifying ecological spatial geographical units based on the water-sediment coupling process, which relates to the technical fields of geomorphology and urban planning. The method includes the following steps: First, obtain the image data of the landscape morphological characteristics; then, based on the water-sediment coupling process, extract the terrain influence factors and hydrological response factors from the image data of the landscape morphological characteristics; finally, identify the landscape-related geographical units according to the terrain influence factors and hydrological response factors, and conduct ecological spatial planning for urban construction according to the identification results. Through the method and system provided by the present invention, the ecological functions can be effectively associated and output, so that in the process of urban construction, the geographical units in the horizontal ecology can be quickly and effectively automatically extracted, so as to improve the accuracy of ecological protection and effectively reduce the cost of ecological restoration.

Description

Technical Field

[0001] The present invention relates to the technical field of geomorphology and urban planning, and particularly to a method and system for identifying ecological spatial geographical units based on the water-sediment coupling process. Background Art

[0002] A geographical unit is a geographical structural unit formed by the combination of geographical factors at a certain level. Different ecological elements of a complete ecological spatial geographical unit are interrelated and interact to output certain ecological functions.

[0003] The interaction between flowing water and sediment is the main driving force for shaping the ecological space. The movement of flowing water transforms the geomorphic form through the erosion, transportation, and deposition of sediment. The slope and boundary of the terrain also affect the curvature, bifurcation coefficient, width-depth ratio, etc. of the water system. The process of water-sediment movement obeys the law of minimum energy loss rate. When the flow field in the river channel changes, the transportation state of sediment changes. Therefore, when terrain mutations such as slopes and mountain edges are formed by sediment, the morphological features such as the curvature, width-depth ratio, and bifurcation of the river channel also respond accordingly, forming geographical units with interconnections between different geographical factors.

[0004] The existing approaches for identifying ecological spatial geographical units mainly rely on the "dual evaluation" (evaluation of resource and environmental carriers and evaluation of the suitability of territorial space development). From the perspective of vertical ecological processes, key ecological spaces are identified by overlaying multiple data in a "thousand-layer cake" manner. This method lacks the identification of the horizontal spatial relationships and functional outputs between different ecological elements from the perspective of horizontal ecological processes, resulting in the inability to effectively associate and output ecological functions among the ecological elements for identifying ecological spaces. Consequently, during the process of urban construction, geographical units in horizontal ecology cannot be effectively extracted, and thus ecological protection cannot be accurately and effectively carried out, leading to an increase in the cost of ecological restoration. Summary of the Invention

[0005] In order to overcome the problem in the prior art that during the process of urban construction, geographical units in horizontal ecology cannot be effectively extracted, and thus ecological protection cannot be accurately and effectively carried out, resulting in an increase in the cost of ecological restoration, the present invention proposes a method and system for identifying ecological spatial geographical units based on the water-sediment coupling process, which can quickly and effectively automatically extract geographical units in horizontal ecology to improve the accuracy of ecological protection and effectively reduce the cost of ecological restoration.

[0006] To achieve the object of the present invention, the present invention is implemented by adopting the following technical solutions:

[0007] A method for identifying ecological spatial geographical units based on the water-sediment coupling process, the method comprising the following steps:

[0008] Obtain image data of the morphological features of mountains and waters;

[0009] Based on the water-sediment coupling process, extract the topographic influence factors and hydrological response factors in the image data of landscape morphological features;

[0010] Identify the landscape-related geographical units according to the topographic influence factors and hydrological response factors, and conduct ecological space planning for urban construction based on the identification results.

[0011] In the above technical solution, through the extracted topographic influence factors and hydrological response factors, it can visually reflect the process of water flowing from high to low, the conversion of gravitational potential energy into kinetic energy and doing work in the water-sediment coupling process from the data, and then obtain the important factors affecting the development of the basin water system, and construct the association system between the topographic influence factors and hydrological response factors, so as to quickly and effectively automatically extract the geographical units in the horizontal ecology, improve the accuracy of ecological protection, and effectively reduce the cost of ecological restoration.

[0012] Furthermore, the process of extracting the topographic influence factors in the image data of landscape morphological features includes:

[0013] Set the data change characteristics of the image data of landscape morphological features in the neural network model information to obtain a topographic influence factor generalization model that grasps the topographic data changes in the image data of landscape morphological features;

[0014] Use the topographic influence factor generalization model to extract the contour features of the terrain in the image data of landscape morphological features, and extract the mountain edge line features by region according to the extracted contour features and topographic characteristics;

[0015] Use the topographic influence factor generalization model to extract the slope features of the terrain in the image data of landscape morphological features, and extract the slope change features according to the extracted slope features;

[0016] Combine the extracted mountain edge line features and slope change features to obtain the topographic influence factors.

[0017] Furthermore, the expression for extracting the contour features of the mountainous terrain is:

[0018] ;

[0019] The expression for extracting the slope features of the terrain in the image data of landscape morphological features is:

[0020] ;

[0021] According to the extracted slope features, the expression for extracting the slope change features is:

[0022] ;

[0023] Among them, P0 represents the lower left vertex of the landscape morphological feature image data, P1 represents the lower right vertex of the landscape morphological feature image data, P2 represents the upper right vertex of the landscape morphological feature image data, P3 represents the upper left vertex of the landscape morphological feature image data, z i represents the vertex elevation value, and z c represents the contour value, and respectively represent the elevation change rates in the horizontal x and vertical y directions of the elevation.

[0024] In the above technical solution, during the process of the trained terrain influence factor generalization model extracting the mountain edge line feature and the slope change feature, it can display the water and sediment movement process according to the landscape morphological feature image data, and perform data processing on this process, so as to quickly and effectively extract the mountain edge line feature and the slope change feature from the data, and then obtain the complete terrain influence factor, effectively improving the efficiency and accuracy of data processing.

[0025] Furthermore, the process of extracting the hydrological response factor from the landscape morphological feature image data includes:

[0026] Setting the data change characteristics of the landscape morphological feature image data of the neural network model information to obtain a hydrological response factor generalization model that masters the data change of the river data in the landscape morphological feature image data;

[0027] Using the hydrological response factor generalization model to extract the binary map data of the river channel in the landscape morphological feature image data, and based on the binary map data of the river channel, using the morphological algorithm to extract the river bank line feature, the river center line feature and the river confluence point feature;

[0028] According to the extracted binary map data of the river channel, the hydrological response factor generalization model performs segment processing on the river and extracts the river curvature mutation point feature;

[0029] According to the extracted binary map data of the river channel, the hydrological response factor generalization model calculates the river width and extracts the river width mutation point feature according to the river width;

[0030] Based on the binary map data of the river channel, the hydrological response factor generalization model uses the flood filling method to extract the mid-channel bar feature;

[0031] Combining the extracted river confluence point feature, the river curvature mutation point feature, the river width mutation point feature and the mid-channel bar feature to obtain the hydrological response factor.

[0032] Furthermore, the process of using the morphological algorithm to extract the river bank line feature, the river center line feature and the river confluence point feature includes:

[0033] Smooth and denoise the river data, construct a binary matrix of m×n according to the binary map of the river channel, and calculate the gradient value of the river shoreline. The expression is:

[0034] ;

[0035] Extract the edge of the binary map according to the gradient value, traverse all the element points of the binary matrix, and count all the points with large gradient values to obtain the river shoreline characteristics;

[0036] Use the erosion algorithm to perform iterative erosion calculation on the binary matrix to extract the characteristics of the river center line. The expression is:

[0037] ;

[0038] According to the river shoreline characteristics and the river center line characteristics, use the morphological algorithm to extract the characteristics of the river confluence points;

[0039] Among them, f represents the gradient value, x , y both represent the element points in the binary matrix, represents the binary matrix element.

[0040] Furthermore, the process of extracting the characteristics of the river curvature mutation points includes:

[0041] According to the segmented river, calculate the along - length and the direct length to obtain the river curvature. The expression is:

[0042] ;

[0043] According to the calculated river curvature, use the sliding window technique to extract the characteristics of the river curvature mutation points. The expression is:

[0044] ;

[0045] Among them, represents the curvature of each segmented river.

[0046] Furthermore, the process of calculating the river width by the hydrological response factor generalization model and extracting the characteristics of the river width mutation points includes:

[0047] Use MATLAB to perform distance transformation on the binary map of the river bank line in the river channel binary map data to obtain the distance image. The expression is:

[0048] ;

[0049] According to the river center line characteristics and the distance image, calculate the river width. The expression is:

[0050] ;

[0051] According to the river width, the window sliding technology is adopted to extract the characteristics of river width mutation points, and the expression is:

[0052] ;

[0053] Among them, represents the element in the binary image matrix, represents the non-zero element closest to the element in the binary image matrix, represents the element and the non-zero element the distance between them.

[0054] Furthermore, the process of the generalized model of hydrological response factors using the flood filling method to extract the characteristics of mid-channel bars includes:

[0055] According to the binary image of the river channel, the flood filling algorithm is used to fill the holes in the river, and all elements in the binary image matrix are traversed to find the position where the element value is zero, and check the values of the 4-connected position elements of this element A. When the 4-connected elements have non-zero values, the element at this position is assigned 1 to obtain the filled matrix, and the expression is:

[0056] ;

[0057] Subtract the original river data from the filled data to obtain the mid-channel bar image, and convert the mid-channel bar image result to a vector to obtain the characteristics of the mid-channel bar.

[0058] In the above technical solution, the trained generalized model of hydrological response factors can perform binary image conversion according to the input image data during the process of extracting the characteristics of river confluence points, river curvature mutation points, river width mutation points, and mid-channel bar characteristics, so as to better observe the image data from the concise data, and thus extract the corresponding characteristics of river confluence points, river curvature mutation points, river width mutation points, and mid-channel bar characteristics, so as to improve the accuracy in the process of geographical unit recognition, and further improve the accuracy of ecological protection, and effectively reduce the cost of ecological restoration.

[0059] Furthermore, set the data change characteristics of the image data of the landscape morphological characteristics of the neural network model to obtain a landscape-associated geographical unit recognition model that masters the data change of the landscape spatial association in the image data of the landscape morphological characteristics;

[0060] The landscape-related geographic unit identification model spatially superimposes and analyzes the extracted terrain influence factors and hydrological response factors, finds the spatial correlation between the two, and introduces the landscape spatial correlation characteristics and the water and sand movement characteristics in the geographic unit to construct a landscape-related system of terrain influence factors and hydrological response factors.

[0061] Based on the landscape association system, the landscape association geographic unit identification model spatially calculates the terrain influence factors and hydrological response factors to extract the geographic units in the landscape morphological feature image data;

[0062] The geographical units include Shanguan, Shuikou, Shuijiao and Jiangwan.

[0063] In the above technical scheme, the trained mountain-water associated geographic unit recognition model can convert the mountain edge line characteristics and slope change characteristics input by the terrain influencing factor generalization model, as well as the river intersection characteristics, river curvature mutation point characteristics, river width mutation point characteristics, and river island characteristics input by the hydrological response factor generalization model, and extract the characteristics of mountain passes, water mouths, water bays and river bays in the geographic units one by one from the input multi-type data features, and output the extracted geographic units to facilitate urban planning staff to make reasonable urban planning arrangements based on the geographic unit recognition results, thereby improving the accuracy of ecological protection and effectively reducing the cost of ecological restoration.

[0064] An ecological space geographical unit identification system based on water-sand mutual coupling process, the system comprising:

[0065] A data acquisition module is used to acquire image data of landscape morphological features;

[0066] The terrain impact factor generalization module is used to extract the terrain impact factors from the landscape morphological feature image data based on the water-sand mutual coupling process;

[0067] The hydrological response factor generalization module is used to extract the hydrological response factors from the landscape morphological feature image data based on the water-sediment mutual coupling process;

[0068] The mountain-water associated geographic unit identification module is used to identify mountain-water associated geographic units based on terrain influencing factors and hydrological response factors, and to carry out ecological space planning for urban construction based on the identification results.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention provides a method and system for identifying ecological spatial geographical units based on the water-sediment coupling process. By extracting topographic influence factors and hydrological response factors, it can visually reflect from the data the process in which water flows from high to low in the water-sediment coupling process, and the process in which gravitational potential energy is converted into kinetic energy and does work. An association system between topographic influence factors and hydrological response factors is constructed, so as to quickly and effectively automatically extract geographical units in the horizontal ecology, improve the accuracy of ecological protection, and effectively reduce the cost of ecological restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a flowchart of the steps of a method for identifying ecological spatial geographical units based on the water-sediment coupling process provided by an embodiment of the present application;

[0072] Figure 2 It is a schematic diagram of the water-sediment coupling process provided by an embodiment of the present application;

[0073] Figure 3 It is a process diagram of extracting geographical units based on the characteristics of water-sediment movement provided by an embodiment of the present application;

[0074] Figure 4 It is a schematic diagram of the mountain edge line characteristics provided by an embodiment of the present application;

[0075] Figure 5 It is a schematic diagram of the slope change characteristics provided by an embodiment of the present application;

[0076] Figure 6 It is a schematic diagram of the river confluence point characteristics provided by an embodiment of the present application;

[0077] Figure 7 It is a schematic diagram of the river curvature provided by an embodiment of the present application;

[0078] Figure 8 It is a schematic diagram of the characteristics of the river curvature mutation point provided by an embodiment of the present application;

[0079] Figure 9 It is a schematic diagram of the river width provided by an embodiment of the present application;

[0080] Figure 10 It is a schematic diagram of the characteristics of the river width mutation point provided by an embodiment of the present application;

[0081] Figure 11 It is a schematic diagram of the mid-channel bar characteristics provided by an embodiment of the present application;

[0082] Figure 12 It is a spatial overlay analysis diagram of local topographic influence factors and hydrological response factors provided by an embodiment of the present application;

[0083] Figure 13Flow chart of the geographical unit extraction method based on water and sediment movement characteristics provided by the embodiments of the present application;

[0084] Figure 14 Schematic diagram of the correlation system of topographic influence factors and hydrological response factors for constructing the landscape space correlation characteristics provided by the embodiments of the present application;

[0085] Figure 15 Distribution map of geographical units identified based on the landscape correlation system provided by the embodiments of the present application;

[0086] Figure 16 Schematic structural diagram of an ecological space geographical unit identification system based on the water-sediment coupling process provided by the embodiments of the present application. Detailed implementation manners

[0087] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided so that the understanding of the disclosure of the present invention is more thorough and comprehensive.

[0088] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0089] Embodiment 1:

[0090] This embodiment provides an ecological space geographical unit identification method based on the water-sediment coupling process. Refer to Figure 1 , the method includes the following steps:

[0091] Step S1: Obtain the image data of the landscape morphological characteristics;

[0092] Step S2: Based on the water-sediment coupling process, extract the topographic influence factors and hydrological response factors from the image data of the landscape morphological characteristics;

[0093] Step S3: Identify the landscape-related geographical units according to the topographic influence factors and hydrological response factors, and conduct ecological space planning for urban construction according to the identification results.

[0094] In step S2, refer to Figure 2, analyze the water-sediment coupling process to find the key control areas. According to the law of conservation of mass, Newton's law of motion, kinetic energy law, momentum law, and the law of minimum energy loss rate of rivers, lock the areas where the river width changes suddenly, the river curvature changes suddenly, and the slope changes abruptly as the key control areas.

[0095] In step S2, refer to Figure 3 , the process of extracting the terrain influence factors from the mountain and water form feature image data includes:

[0096] S201: Set the data change characteristics of the mountain and water form feature image data in the neural network model information to obtain a terrain influence factor generalization model that grasps the terrain data changes in the mountain and water form feature image data;

[0097] S202: Use the terrain influence factor generalization model to extract the contour features of the terrain in the mountain and water form feature image data, and according to the extracted contour features and terrain characteristics, extract the mountain edge line features by region;

[0098] S203: Use the terrain influence factor generalization model to extract the slope features of the terrain in the mountain and water form feature image data, and according to the extracted slope features, extract the slope change features;

[0099] S204: Combine the extracted mountain edge line features with the slope change features to obtain the terrain influence factors.

[0100] Specifically, in step S202, the DEM image calculates the contour lines based on the Marching Squares algorithm to extract the contour features of the mountainous terrain. The expression is:

[0101] ;

[0102] Extract the mountain edge lines by region according to different contour lines according to the terrain characteristics. The 100-meter contour line is the mountain edge line in the hilly area, and the 30-meter contour line is the mountain edge line in the plain area.

[0103] In some embodiments, refer to Figure 4 , the embodiments of the present invention calculate the contour lines for the DEM image with a 10-meter resolution in Guangdong Province, and extract the mountain edge lines by region according to different contour lines according to the terrain characteristics. The mountain edge lines are extracted according to the 100-meter contour line in the northern hilly area, and the mountain edge lines are extracted according to the 30-meter contour line in the southern plain area.

[0104] Specifically, in step S203, extract the slope features of the terrain in the mountain and water form feature image data. The expression is:

[0105] ;

[0106] Based on the extracted slope features, the DEM image extracts the slope change features, and the expression is:

[0107] ;

[0108] where P0 represents the lower left vertex of the landscape morphological feature image data, P1 represents the lower right vertex of the landscape morphological feature image data, P2 represents the upper right vertex of the landscape morphological feature image data, P3 represents the upper left vertex of the landscape morphological feature image data, z i represents the vertex elevation value, and z c represents the contour value. and respectively represent the elevation change rates in the horizontal x and vertical y directions of the elevation.

[0109] Exemplarily, referring to Figure 5 , in the embodiment of the present invention, the DEM image with a resolution of 10 meters in Guangdong Province is calculated to obtain a slope image. The slope is equal to the elevation increment divided by the horizontal increment. In MATLAB, a sliding window (window size is 10) is used to find the maximum and minimum values of pixels in the slope image, and the extreme values are calculated and re-assigned to the window to obtain the slope change rate. The slope change rate map is converted into a vector, the elements with a change rate greater than 2 are screened, the number of points with a change rate greater than 2 within a 5-kilometer fishing net grid is counted, and the outliers at the outer edge of the study area are removed.

[0110] It can be understood that the terrain impact factor generalization model obtained by training can display the water and sediment movement process according to the landscape morphological feature image data during the process of extracting the mountain edge line feature and the slope change feature, and perform data processing on this process, so as to quickly and effectively extract the mountain edge line feature and the slope change feature from the data, and then obtain a complete terrain impact factor, effectively improving the efficiency and accuracy of data processing.

[0111] In this embodiment, through the extracted terrain impact factor and hydrological response factor, it can visually reflect from the data the process of water flowing from high to low, the conversion of gravitational potential energy into kinetic energy and doing work during the water and sediment coupling process, and then obtain important factors affecting the development of the basin water system, and construct an association system between the terrain impact factor and the hydrological response factor, so as to quickly and effectively automatically extract geographical units in the horizontal ecology, improve the accuracy of ecological protection, and effectively reduce the cost of ecological restoration.

[0112] Embodiment 2. This embodiment is based on Embodiment 1 and further illustrates steps S2 to S3 as follows:

[0113] In step S2, referring to Figure 3 , the process of extracting the hydrological response factor from the landscape morphological feature image data includes:

[0114] S205: Setting the data change characteristics of the landscape morphological feature image data of the neural network model information to obtain a generalized model of hydrological response factors for grasping the changes of river data in the landscape morphological feature image data;

[0115] S206: extracting river binary map data from the landscape morphological feature image data using the hydrological response factor generalization model, and extracting river bankline features, river centerline features, and river intersection features using a morphological algorithm based on the river binary map data;

[0116] S207: According to the extracted river binary map data, the hydrological response factor generalization model performs segmentation processing on the river and extracts the characteristics of the sudden change points of the river curvature;

[0117] S208: Calculate the river width using the generalized hydrological response factor model based on the extracted river binary map data, and extract the river width mutation point characteristics based on the river width;

[0118] S209: Based on the binary river map data, the hydrological response factor generalization model uses the flood filling method to extract the characteristics of the river island;

[0119] S210: The extracted river intersection features, river curvature mutation point features, river width mutation point features and river island features are combined to obtain a hydrological response factor.

[0120] Specifically, in step S206, the process of extracting river bank features, river centerline features, and river intersection features using a morphological algorithm includes:

[0121] The river data is smoothed and denoised using MATLAB closed operations, and an m×n binary image matrix is ​​constructed based on the river binary image to calculate the river bank gradient value. The expression is:

[0122] ;

[0123] The edge of the binary image is extracted according to the gradient value, all the element points of the binary image matrix are traversed, and all the points with large gradient values ​​are counted to obtain the river shoreline characteristics; a large image gradient value indicates that the pixel points of the element on the x-axis and y-axis change quickly, and the element is located at the edge, that is, all points with large gradient values ​​are the river shoreline.

[0124] The erosion algorithm is used to iteratively erode the binary image matrix to extract the river centerline features. The expression is:

[0125] ;

[0126] Among them, the key to the centerline calculation is to calculate the number of times the pixel transitions from a non-zero value to a zero value, and iteratively remove other pixels outside the skeleton. That is, it is the last iteration process before the element is eroded into an empty set; the logic of the erosion calculation is to traverse all elements of the matrix (m×n), and for each element A(x, y), find the values of the four neighboring elements of this element.

[0127] According to the characteristics of the river shoreline and the river centerline, the morphological algorithm in MATLAB is used to extract the characteristics of the river confluence point, and the extraction result is rasterized to vector.

[0128] Among them, f represents the gradient value, x 、 y both represent the element points in the binary image matrix, represents the element of the binary image matrix.

[0129] Exemplarily, referring to Figure 6 , in the embodiment of the present invention, the water system data of Guangdong Province is preprocessed in MATLAB. The unevenness of the original river edge will affect the subsequent river calculation results, and the river data needs to be denoised and smoothed on the river edge by using the closing operation first. After preprocessing, the MATLAB morphological algorithm is used to calculate and extract the river shoreline and the centerline from the river data, and the intersection point of the centerline is the confluence point of the river.

[0130] In step S207, the process of extracting the characteristics of the river curvature mutation points includes:

[0131] For the segmented river, calculate the along - path length and the direct length to obtain the river curvature, and the expression is:

[0132] ;

[0133] According to the calculated river curvature, adopt the sliding window technique to extract the characteristics of the river curvature mutation points, and the expression is:

[0134] ;

[0135] Among them, represents the curvature of each segmented river.

[0136] Exemplarily, referring to Figure 7 , in the embodiment of the present invention, the centerline obtained by MATLAB morphological calculation is segmented according to a 5 - kilometer grid. For the segmented river, the Euclidean distance between all the inflection points of the centerline is traversed in MATLAB as the along - path length of the river. Extract the coordinates of the first and the last inflection points and calculate the Euclidean distance between the two points as the direct length of the river. The along - path length of each segment of the river is divided by the direct length to obtain the curvature. As Figure 8As shown, in MATLAB, the river curvature dataset is used to find the mutation points of river curvature using a 10-kilometer sliding window.

[0137] In step S208, the process of the hydrological response factor generalization model calculating the river width and extracting the characteristics of the river width mutation points based on the river width includes:

[0138] Using MATLAB to perform distance transformation on the binary image of the riverbank line in the binary image data of the river channel to obtain a distance image, and the expression is:

[0139] ;

[0140] According to the characteristics of the river centerline and the distance image, calculate the river width, and the expression is:

[0141] ;

[0142] According to the river width, adopt the window sliding technology to extract the characteristics of the river width mutation points, and the expression is:

[0143] ;

[0144] Among them, represents the element in the binary image matrix, represents the non-zero element closest to the element in the binary image matrix, represents the element and the non-zero element the distance between them.

[0145] Exemplarily, as Figure 9 shown, in the embodiment of the present invention, the binary image of the riverbank line obtained by MATLAB morphological calculation (the riverbank line is 1 and others are 0) is used to calculate the distance image using the distance transformation algorithm. The distance at the riverbank line of the result is 0, and other elements are re-assigned their Euclidean distance to the nearest riverbank line. In MATLAB, find the position of the river centerline, and use this position to index the distance image to obtain the distance result (the distance at the centerline is the Euclidean distance to the nearest riverbank line, and others are zero). Multiply the centerline distance matrix by 2 and the data resolution (10 meters in the present invention) to obtain the river width. As Figure 10 shown, in MATLAB, the river width matrix calculated above is used to find the width mutation points using a 10-kilometer sliding window.

[0146] In step S209, the process of the hydrological response factor generalization model extracting the characteristics of the mid-channel bar using the flood filling method includes:

[0147] According to the binary image of the river channel, use the flood filling algorithm to fill the holes in the river, and traverse all the elements in the binary image matrix to find the element At the position with a value of zero, check the values of the 4-connected position elements of this element A. When the 4-connected elements have non-zero values, the element at this position is assigned a value of 1 to obtain a filled matrix. The expression is:

[0148] ;

[0149] Subtract the original river data from the filled data to obtain an image of the mid-channel bar, and convert the result of the mid-channel bar image into a vector to obtain the characteristics of the mid-channel bar.

[0150] Exemplarily, refer to Figure 11 , in the embodiment of the present invention, a binary image of the river (the river is 1 and others are 0) is used to fill the holes in the river by using the 4-connected flood filling algorithm in MATLAB. Then subtract the original river matrix from the filled matrix to obtain an image of the mid-channel bar, and convert the result of the mid-channel bar matrix into a vector to obtain the mid-channel bar.

[0151] It can be understood that the generalized model of the hydrological response factor obtained through training can perform binary image conversion according to the input image data during the process of extracting the characteristics of river confluence points, sudden change points of river curvature, sudden change points of river width, and characteristics of mid-channel bars, so as to better observe the image data from the concise data, thereby extracting the corresponding characteristics of river confluence points, sudden change points of river curvature, sudden change points of river width, and characteristics of mid-channel bars, so as to improve the accuracy in the process of geographical unit recognition, and further improve the accuracy of ecological protection and effectively reduce the cost of ecological restoration.

[0152] In step S3, refer to Figure 3 , the process of identifying the mountain-water associated geographical unit according to the topographic influence factor and the hydrological response factor includes:

[0153] S31: Set the data change characteristics of the image data of the mountain-water morphological characteristics in the neural network model information to obtain a mountain-water associated geographical unit recognition model that masters the data change of the mountain-water spatial association in the image data of the mountain-water morphological characteristics;

[0154] S32: The mountain-water associated geographical unit recognition model performs spatial superposition and analysis on the extracted topographic influence factor and hydrological response factor, searches for the spatial association between the two, and introduces the mountain-water spatial association characteristics and the water-sediment movement characteristics in the geographical unit to construct a mountain-water associated system of the topographic influence factor and the hydrological response factor;

[0155] S33: Based on the mountain-water associated system, the mountain-water associated geographical unit recognition model performs spatial calculation on the topographic influence factor and the hydrological response factor to extract the geographical units in the image data of the mountain-water morphological characteristics;

[0156] Among them, the geographical unit includes mountain passes, water inlets, water cellars, and river bays.

[0157] Specifically, in step S32, referring to Figure Figure 12 、 Figure 13 and Figure 14 , starting from the formation mechanism of each landform, the embodiments of the present invention scientifically select generalized topographic influence factors and hydrological response factors to depict the morphological characteristics of typical landscapes, and construct a system of topographic and hydrological factors for landscape shaping.

[0158] The mountain pass is near the mountain edge line and the mutation point of the slope change rate, and the basin morphology changes significantly; the water inlet is located in the ridge-valley area, near the mountain edge line, and the basin morphology changes significantly; the water pond is a control node of the river network and is often accompanied by mid-channel bars; the river bend is distributed in a broad basin or an open plain area, and the river channel is curved.

[0159] Based on the mountain-water association system, the topographic response factors and hydrological response factors are accurately extracted through spatial calculation to obtain geographical units - mountain passes, water inlets, water ponds, and river bends.

[0160] Mountain pass: It is mainly located at the junction of mountains (tectonic uplift areas) and basins (tectonic subsidence areas), and is a key section where the basin morphology undergoes an obvious turning point.

[0161] Water inlet: Located in the ridge-valley area, with the uplift of the crust and the river incision, the river valley is gradually eroded and embedded to become a canyon. When the river flows through hard lithology, a constriction is formed. The section between two constrictions is a wide section, and the constriction is narrowed, and the river channel is in the shape of a lotus root joint.

[0162] Water pond: It is the junction where the river enters from the estuary into the hilly area and the open basin, and is a control node of the river network.

[0163] River bend: Distributed in a broad basin or an open plain area, the river loses its lateral restraint, and will be affected by the Coriolis force and geological environment, etc., and deflects to one side. The floodplain develops, the convex bank accumulates, the concave bank is eroded, and the river channel is curved.

[0164] Specifically, in step S33, referring to Figure 15 and Table 1, Table 2, Table 3 and Table 4:

[0165] Table 1: Identification of the mountain pass geographical unit. The pass is an area with a large slope drop and a large change in the width and depth of the river.

[0166]

[0167] Table 2: Identification of the water inlet geographical unit. The inlet is an area with a small slope drop and a large change in the width and depth of the river, and generally forms mid-channel bars.

[0168]

[0169] Table 3: Identification of the water pond geographical unit. The pond is an area where the river converges and bifurcates, and mid-channel bars often exist upstream and downstream of it.

[0170]

[0171] Table 4: Identification of river bay geographical units. A bay is an area where a river bends.

[0172]

[0173] In this embodiment, the trained mountain-water associated geographic unit recognition model can convert the mountain edge line characteristics and slope change characteristics input by the terrain influencing factor generalization model, as well as the river intersection characteristics, river curvature mutation point characteristics, river width mutation point characteristics, and river island characteristics input by the hydrological response factor generalization model, and extract the characteristics of mountain passes, water mouths, water bays and river bays in the geographic units one by one from the input multi-type data characteristics, and output the extracted geographic units to facilitate urban planning staff to make reasonable urban planning arrangements based on the geographic unit recognition results, thereby improving the accuracy of ecological protection and effectively reducing the cost of ecological restoration.

[0174] Embodiment three:

[0175] This embodiment provides an ecological space geographical unit identification system based on the water-sand mutual coupling process, see Figure 16 , the system comprising:

[0176] A data acquisition module is used to acquire image data of landscape morphological features;

[0177] The terrain impact factor generalization module is used to extract the terrain impact factors from the landscape morphological feature image data based on the water-sand mutual coupling process;

[0178] The hydrological response factor generalization module is used to extract the hydrological response factors from the landscape morphological feature image data based on the water-sediment mutual coupling process;

[0179] The mountain-water associated geographic unit identification module is used to identify mountain-water associated geographic units based on terrain influencing factors and hydrological response factors, and to carry out ecological space planning for urban construction based on the identification results.

[0180] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying eco-spatial geographical units based on water-sediment coupling process, characterized in that: The method comprises the following steps: Acquire image data of landscape morphological features; Based on the water-sediment coupling process, the terrain influencing factors and hydrological response factors in the landscape morphological characteristic image data are extracted; Identify the mountain-water related geographical units according to the terrain influencing factors and hydrological response factors, and carry out the ecological space planning of urban construction based on the identification results; The process of extracting terrain influence factors from landscape morphological feature image data includes: Setting the data change characteristics of the landscape morphological feature image data in the neural network model information to obtain a generalized model of terrain influencing factors for grasping the terrain data changes in the landscape morphological feature image data; The terrain influencing factor generalization model is used to extract the contour features of the terrain in the landscape morphological feature image data, and the mountain edge features are extracted by region based on the extracted contour features and terrain characteristics; The terrain influencing factor generalization model is used to extract the slope characteristics of the terrain in the landscape morphological feature image data, and the slope change characteristics are extracted based on the extracted slope characteristics; The extracted mountain edge features are combined with the slope change features to obtain the terrain influence factor; The process of extracting hydrological response factors from landscape morphological feature image data includes: Setting the data variation characteristics of the landscape morphological characteristic image data of the neural network model information, and obtaining the generalized model of the hydrological response factor for grasping the river data variation in the landscape morphological characteristic image data; The hydrological response factor generalization model is used to extract the river binary map data from the landscape morphological feature image data, and based on the river binary map data, the morphological algorithm is used to extract the river bank line characteristics, river centerline characteristics and river intersection characteristics; According to the extracted river binary map data, the hydrological response factor generalization model divides the river into sections and extracts the characteristics of the sudden change points of the river curvature. According to the extracted river binary map data, the hydrological response factor generalization model calculates the river width, and the characteristics of the river width mutation point are extracted according to the river width; Based on the binary river map data, the generalized hydrological response factor model uses the flood filling method to extract the characteristics of the river island. The extracted river confluence features, river curvature mutation point features, river width mutation point features and river island features are combined to obtain the hydrological response factor.

2. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 1 is characterized in that: Extract the contour features of mountainous terrain, the expression is: ； Extract the slope characteristics of the terrain from the landscape morphological feature image data. The expression is: ； According to the extracted slope features, the slope change features are extracted, and the expression is: ； Among them, P0 represents the lower left corner vertex of the landscape morphological feature image data, P1 represents the lower right corner vertex of the landscape morphological feature image data, P2 represents the upper right corner vertex of the landscape morphological feature image data, P3 represents the upper left corner vertex of the landscape morphological feature image data, and z i Indicates the vertex elevation value, z c represents the contour value, and They represent the rate of change of elevation in the horizontal x and vertical y directions respectively.

3. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 2 is characterized in that: The process of extracting river bank features, river centerline features and river intersection features using morphological algorithms includes: The river data is smoothed and denoised, and an m×n binary image matrix is ​​constructed based on the river binary image to calculate the river bank gradient value. The expression is: ； Extract the binary image edge according to the gradient value, traverse all the element points of the binary image matrix, count all the points with large gradient values, and obtain the river bankline characteristics; The erosion algorithm is used to iteratively erode the binary image matrix to extract the river centerline features. The expression is: ； According to the river bankline characteristics and river centerline characteristics, the morphological algorithm is used to extract the river intersection features; in, f represents the gradient value, x , y All represent the element points in the binary image matrix. Represents a binary image matrix element.

4. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 3 is characterized in that: The process of extracting the characteristics of the sudden change point of river curvature includes: According to the segmented river, the length along the way and the direct length are calculated to obtain the river curvature, which is expressed as: ； According to the calculated river curvature, the sliding window technology is used to extract the characteristics of the river curvature mutation point, and the expression is: ； in, Represents the curvature of each river segment.

5. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 3 is characterized in that: The hydrological response factor generalization model calculates river width and the process of extracting river width mutation point characteristics based on river width includes: MATLAB is used to perform distance conversion on the river bank binary map in the river binary map data to obtain the distance image. The expression is: ； According to the river centerline characteristics and distance image, the river width is calculated as follows: ； According to the river width, the window sliding technology is used to extract the characteristics of the river width mutation point, and the expression is: ； in, represents the elements in the binary image matrix, Represents the binary image matrix with the element The nearest non-zero element, Representation elements With non-zero elements The distance between.

6. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 3 is characterized in that: The process of extracting river island characteristics using the flood filling method in the generalized hydrological response factor model includes: According to the binary map of the river channel, the flood filling algorithm is used to fill the holes in the river, and all elements in the binary map matrix are traversed to find the elements For the position where the value is zero, check the value of the 4-connected position element of the element A. When the 4-connected element has a non-zero value, the position element is assigned 1 to obtain the filled matrix. The expression is: ； The original river data is subtracted from the filled data to obtain the river island image, and the river island image result is converted into a vector to obtain the river island feature.

7. The method for identifying eco-spatial geographical units based on water-sediment coupling process according to any one of claims 1 to 6, characterized in that: The process of identifying mountain-water associated geographical units based on terrain influence factors and hydrological response factors includes: Setting the data change characteristics of the landscape morphological feature image data of the neural network model information to obtain a landscape-related geographic unit recognition model that grasps the landscape spatial correlation data changes in the landscape morphological feature image data; The landscape-related geographic unit identification model spatially superimposes and analyzes the extracted terrain influence factors and hydrological response factors, finds the spatial correlation between the two, and introduces the landscape spatial correlation characteristics and the water and sand movement characteristics in the geographic unit to construct a landscape-related system of terrain influence factors and hydrological response factors. Based on the landscape association system, the landscape association geographic unit identification model spatially calculates the terrain influence factors and hydrological response factors to extract the geographic units in the landscape morphological feature image data; The geographical units include Shanguan, Shuikou, Shuijiao and Jiangwan.

8. An ecological space geographical unit identification system based on water-sand mutual coupling process, the system is used to implement the method according to any one of claims 1 to 7, characterized in that: The system comprises: A data acquisition module is used to acquire image data of landscape morphological features; The terrain impact factor generalization module is used to extract the terrain impact factors from the landscape morphological feature image data based on the water-sand mutual coupling process; The hydrological response factor generalization module is used to extract the hydrological response factors from the landscape morphological feature image data based on the water-sediment mutual coupling process; The mountain-water associated geographic unit identification module is used to identify mountain-water associated geographic units based on terrain influencing factors and hydrological response factors, and to carry out ecological space planning for urban construction based on the identification results.

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