Ecological space geographic unit identification method and system based on water-sediment mutual coupling process

Abstract

The invention discloses an ecological space geographic unit identification method and system based on a water-sediment mutual coupling process, and relates to the technical field of geomorphology and urban planning. The method comprises the following steps: firstly, obtaining morphological feature image data of landscapes; then, based on a water-sediment mutual coupling process, extracting a terrain influence factor and a hydrological response factor in the mountain-water morphological feature image data; and finally, according to the terrain influence factor and the hydrological response factor, carrying out mountain-water associated geographic unit identification, and according to an identification result, carrying out ecological space planning of urban construction. According to the method and the system provided by the invention, the ecological functions can be effectively associated and output, so that the geographic units in the horizontal ecology can be quickly, effectively and automatically extracted in the urban construction process, the accuracy of ecological protection is improved, and the ecological restoration cost is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of geomorphology and urban planning, and in particular to an ecological space geographical unit identification method and system based on water-sand mutual 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 ecological space. The movement of flowing water transforms the landform through erosion, transportation and accumulation of sediment. The slope and boundary of the terrain also affect the curvature, bifurcation coefficient, width-to-depth ratio, etc. of the water system. The process of water and sediment movement obeys the law of minimum energy loss rate. The flow field in the river changes, and the transportation state of sediment changes. Therefore, when the terrain suddenly changes such as slope and mountainside formed by sediment, the curvature, width-to-depth ratio, bifurcation and other forms of the river also respond to form geographical units that are interconnected between different geographical factors.

[0004] The existing identification methods of ecological space geographical units are mainly based on "double evaluation" (resource and environmental carrier evaluation and land space development suitability evaluation), which identifies key ecological spaces from the perspective of vertical ecological processes by superimposing multivariate data in a "thousand-layer pancake" manner. This method lacks the ability to identify the horizontal spatial relationship and functional output between different ecological elements from the perspective of horizontal ecological processes, resulting in the inability to effectively associate and output ecological functions between the ecological elements of the identified ecological space, which leads to the inability to effectively extract geographical units in the horizontal ecology during urban construction, and thus the inability to accurately and effectively carry out ecological protection, resulting in increased costs for ecological restoration. Summary of the invention

[0005] In order to overcome the problem that the existing technology cannot effectively extract the geographical units in the horizontal ecology during urban construction, and thus cannot accurately and effectively carry out ecological protection, resulting in increased ecological restoration costs, the present invention proposes an ecological space geographical unit identification method and system based on the water-sediment mutual coupling process, which can quickly and effectively automatically extract the geographical units in the horizontal ecology, so as to improve the accuracy of ecological protection and effectively reduce the cost of ecological restoration.

[0006] To achieve the purpose of the present invention, the present invention adopts the following technical solutions:

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

[0008] Acquire image data of landscape morphological features;

[0009] Based on the water-sediment coupling process, the terrain influencing factors and hydrological response factors in the landscape morphological characteristic image data are extracted;

[0010] Based on the terrain influencing factors and hydrological response factors, the mountain-water related geographical units are identified, and the ecological space planning of urban construction is carried out according to the identification results.

[0011] In the above technical scheme, by extracting terrain influence factors and hydrological response factors, the data can intuitively reflect the process in which water flows from high to low during the water-sediment coupling process, and the gravitational potential energy is converted into kinetic energy and works, thereby obtaining the important factors affecting the development of the basin water system, and constructing a correlation system between terrain influence factors and hydrological response factors, so as to quickly and effectively automatically extract geographical units in horizontal ecology, so as to improve the accuracy of ecological protection and effectively reduce the cost of ecological restoration.

[0012] Furthermore, the process of extracting terrain influence factors from the landscape morphological feature image data includes:

[0013] 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;

[0014] 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;

[0015] 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;

[0016] The extracted mountain edge features are combined with the slope change features to obtain the terrain influence factor.

[0017] Furthermore, the contour features of the mountainous terrain are extracted, and the expression is:

[0018]

[0019] code=P3×8+P2×4+P1×2+P0×1;

[0020] Extract the slope characteristics of the terrain from the landscape morphological feature image data. The expression is:

[0021]

[0022] According to the extracted slope features, the slope change features are extracted, and the expression is:

[0023]

[0024] 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.

[0025] In the above technical scheme, the trained generalized model of terrain influence factors can display the water and sand movement process according to the mountain and water morphological characteristic image data in the process of extracting the mountain edge line characteristics and slope change characteristics, and digitize this process to quickly and effectively extract the mountain edge line characteristics and slope change characteristics from the data, and then obtain a complete terrain influence factor, which effectively improves the efficiency and accuracy of data processing.

[0026] Furthermore, the process of extracting the hydrological response factor from the landscape morphological feature image data includes:

[0027] 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;

[0028] 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;

[0029] 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.

[0030] 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;

[0031] 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.

[0032] 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.

[0033] Furthermore, the process of extracting river bank features, river centerline features and river intersection features using morphological algorithms includes:

[0034] 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:

[0035]

[0036] 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;

[0037] The erosion algorithm is used to iteratively erode the binary image matrix to extract the river centerline features. The expression is:

[0038] if A(x i ,y j-1 )+A(x i ,y j+1 )+A(x i-1 ,y i )+A(x i+1 ,y j )<4then A(x i ,y j )=0;

[0039] According to the river bankline and river centerline characteristics, the morphological algorithm is used to extract the river intersection features;

[0040] Among them, f represents the gradient value, x and y represent the element points in the binary image matrix, and A(x i ,y j ) represents the binary image matrix element.

[0041] Furthermore, the process of extracting the characteristics of the sudden change point of river curvature includes:

[0042] 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:

[0043] K a =L 直接长度 / L 沿程长度 ;

[0044] 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:

[0045]

[0046] Among them, K a Represents the curvature of each river segment.

[0047] Furthermore, the hydrological response factor generalization model calculates the river width, and the process of extracting the characteristics of the river width mutation point according to the river width includes:

[0048] 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:

[0049]

[0050] According to the river centerline characteristics and distance image, the river width is calculated as follows:

[0051] W=D i,j ×10×2;

[0052] 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:

[0053]

[0054] Among them, (x i ,y j ) represents the elements in the binary image matrix, (x' i , y' j ) indicates that in the binary graph matrix, the same element (x i ,y j ) the nearest non-zero element, D i,j Represents an element (x' i , y' j ) and non-zero elements (x i ,y j ) between the two.

[0055] Furthermore, the process of extracting river island characteristics using the flood filling method in the generalized hydrological response factor model includes:

[0056] According to the binary river map, 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 element A(x i ,y j ) value is zero, check the value of the 4-connected position element of the element, when the 4-connected element has a non-zero value, the position element is assigned 1, and the filling matrix is ​​obtained. The expression is:

[0057] if A(x i ,y j )=0and(A(x i ,y j-1 )orA(x i-1 ,y j )orA(x i+1 ,y j )orA(x i ,y j+1 ))

[0058] then A(x i ,y j )=1;

[0059] 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.

[0060] In the above technical scheme, the trained generalized model of hydrological response factors can perform binary image conversion according to the input image data in the process of extracting river confluence features, river curvature mutation point features, river width mutation point features and river island features, so as to better observe the image data from concise data, thereby extracting the corresponding river confluence features, river curvature mutation point features, river width mutation point features and river island features, so as to improve the accuracy in the process of geographic unit identification, thereby improving the accuracy of ecological protection and effectively reducing the cost of ecological restoration.

[0061] Further, the data change characteristics of the landscape morphological feature image data are set in 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;

[0062] 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.

[0063] 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;

[0064] The geographical units include Shanguan, Shuikou, Shuijiao and Jiangwan.

[0065] 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.

[0066] An ecological space geographical unit identification system based on water-sand mutual coupling process, the system comprising:

[0067] A data acquisition module is used to acquire image data of landscape morphological features;

[0068] 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;

[0069] 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;

[0070] 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.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] The present invention proposes a method and system for identifying geographical units in ecological space based on the water-sediment mutual coupling process. By extracting terrain influence factors and hydrological response factors, the present invention can intuitively reflect from data the process in which water flows from high places to low places, and the gravitational potential energy is converted into kinetic energy and works during the water-sediment mutual coupling process. The present invention also constructs a correlation system between terrain influence factors and hydrological response factors, thereby quickly and effectively automatically extracting geographical units in horizontal ecology, thereby improving the accuracy of ecological protection and effectively reducing the cost of ecological restoration. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A flowchart of the steps of an eco-spatial geographic unit identification method based on the water-sand mutual coupling process provided in an embodiment of the present application;

[0074] Figure 2 A schematic diagram of a water-sand coupling process provided in an embodiment of the present application;

[0075] Figure 3 A diagram of the process of extracting geographical units based on water and sand movement characteristics provided in an embodiment of the present application;

[0076] Figure 4 A schematic diagram of mountain edge features provided in an embodiment of the present application;

[0077] Figure 5 A schematic diagram of slope variation characteristics provided in an embodiment of the present application;

[0078] Figure 6 A schematic diagram of the characteristics of a river intersection provided in an embodiment of the present application;

[0079] Figure 7 A schematic diagram of river curvature provided in an embodiment of the present application;

[0080] Figure 8 A schematic diagram of the characteristics of a sudden change point of river curvature provided in an embodiment of the present application;

[0081] Fig. 9 A schematic diagram of river width provided for an embodiment of the present application;

[0082] Fig.10 A schematic diagram of the characteristics of the river width mutation point provided in the embodiment of the present application;

[0083] Fig.11 A schematic diagram of the characteristics of Jiangxinzhou provided in an embodiment of the present application;

[0084] Fig.12 A spatial superposition analysis diagram of the local terrain influence factor and the hydrological response factor provided in the embodiment of the present application;

[0085] Fig.13 A flow chart of a method for extracting geographical units based on water and sand movement characteristics analysis provided in an embodiment of the present application;

[0086] Fig.14 A system diagram of the association between terrain influence factors and hydrological response factors is constructed for the landscape spatial association characteristics provided in the embodiment of the present application;

[0087] Fig.15 A geographical unit distribution map based on the mountain-water association system identification provided in the embodiment of the present application;

[0088] Fig.16 A structural schematic diagram of an eco-spatial geographic unit identification system based on the water-sand mutual coupling process provided in an embodiment of the present application. DETAILED DESCRIPTION

[0089] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Preferred embodiments of the present invention are provided 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, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0090] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention 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 related listed items.

[0091] Embodiment 1:

[0092] This embodiment provides a method for identifying ecological spatial geographical units based on the water-sediment coupling process. Figure 1 , the method comprises the following steps:

[0093] Step S1: Acquire landscape morphological feature image data;

[0094] Step S2: extracting terrain influence factors and hydrological response factors from the landscape morphological feature image data based on the water-sediment mutual coupling process;

[0095] Step S3: Identify the mountain-water associated geographical units according to the terrain influencing factors and the hydrological response factors, and carry out the ecological space planning of urban construction according to the identification results.

[0096] In step S2, see Figure 2 , the water-sediment mutual coupling process is analyzed to find the key control areas. According to the fact that the water-sediment movement process obeys the law of conservation of mass, Newton's laws of motion, the law of kinetic energy, the law of momentum and the law of minimum energy loss rate of rivers, the areas with sudden changes in river width, sudden changes in river curvature and sudden changes in slope are locked in as the key control areas.

[0097] In step S2, see Figure 3 ,The process of extracting terrain influence factors from landscape morphological feature image data includes:

[0098] S201: Setting the data change characteristics of the landscape morphological feature image data of 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;

[0099] S202: extracting the contour features of the terrain in the landscape morphological feature image data using the generalized model of terrain influence factors, and extracting the mountain edge features by region based on the extracted contour features and terrain characteristics;

[0100] S203: extracting the slope features of the terrain in the landscape morphological feature image data using the terrain influencing factor generalization model, and extracting the slope variation features based on the extracted slope features;

[0101] S204: Combining the extracted mountain edge features with the slope change features to obtain terrain influence factors.

[0102] Specifically, in step S202, the DEM image calculates contour lines based on the Marching Squares algorithm to extract the contour line features of the mountainous terrain. The expression is:

[0103]

[0104] code=P3×8+P2×4+P1×2+P0×1;

[0105] According to the terrain characteristics, the mountain edge lines are extracted in different regions according to different contour lines. The 100-meter contour line in the hilly area is the mountain edge line, and the 30-meter contour line in the plain area is the mountain edge line.

[0106] In some embodiments, see Figure 4 The embodiment of the present invention calculates contour lines for a DEM image with a resolution of 10 meters in Guangdong Province, and extracts mountain edges according to different contour lines in different regions according to the terrain characteristics. The mountain edges in the northern hilly area are extracted according to the 100-meter contour lines, and the mountain edges in the southern plain area are extracted according to the 30-meter contour lines.

[0107] Specifically, in step S203, the slope feature of the terrain in the landscape morphological feature image data is extracted, and the expression is:

[0108]

[0109] The DEM image extracts the slope change characteristics based on the extracted slope characteristics. The expression is:

[0110]

[0111] 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.

[0112] For example, see Figure 5 The embodiment of the present invention calculates the slope image for the DEM image of Guangdong Province with a resolution of 10 meters, and the slope is equal to the elevation increment divided by the horizontal increment. The slope image uses a sliding window (window size is 10) in MATLAB to find the maximum and minimum pixel values, and calculates the extreme value and reassigns it to the window to obtain the slope change rate. The slope change rate map is converted to a vector, and the elements with a change rate greater than 2 are screened. The number of points with a change rate greater than 2 in the 5-kilometer fishing net grid is counted, and the outliers outside the study area are eliminated.

[0113] It can be understood that the trained generalized model of terrain influence factors can display the water and sand movement process according to the mountain and water morphological characteristic image data in the process of extracting the mountain edge line characteristics and slope change characteristics, and digitize this process to quickly and effectively extract the mountain edge line characteristics and slope change characteristics from the data, and then obtain a complete terrain influence factor, which effectively improves the efficiency and accuracy of data processing.

[0114] In this embodiment, by extracting terrain influence factors and hydrological response factors, the data can intuitively reflect the process in which water flows from high to low during the water-sediment coupling process, and the gravitational potential energy is converted into kinetic energy and works, thereby obtaining important factors affecting the development of the water system in the basin, and constructing a correlation system between terrain influence factors and hydrological response factors, so as to quickly and effectively automatically extract geographical units in horizontal ecology, so as to improve the accuracy of ecological protection and effectively reduce the cost of ecological restoration.

[0115] Embodiment 2: Based on Embodiment 1, this embodiment further describes steps S2 to S3, as follows:

[0116] In step S2, see Figure 3 ,The process of extracting the hydrological response factors from the landscape morphological feature image data includes:

[0117] 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;

[0118] 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;

[0119] 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;

[0120] 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;

[0121] 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;

[0122] 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.

[0123] Specifically, in step S206, the process of extracting river bank features, river centerline features, and river intersection features using a morphological algorithm includes:

[0124] 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:

[0125]

[0126] 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.

[0127] The erosion algorithm is used to iteratively erode the binary image matrix to extract the river centerline features. The expression is:

[0128] if A(x i ,y j-1 )+A(x i ,y j+1 )+A(x i-1 ,y i )+A(x i+1 ,y j )<4then A(x i ,y j )=0;

[0129] Among them, the focus of the center line calculation is to calculate the number of transitions of pixels from non-zero values ​​to zero values, and iteratively and gradually extract other pixels outside the skeleton, that is, the last iteration process before the elements are 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), the element value of the four fields of the element is searched.

[0130] According to the characteristics of river bankline and river centerline, the morphological algorithm in MATLAB is used to extract the river intersection features, and the extraction result is converted from raster to vector;

[0131] Among them, f represents the gradient value, x and y represent the element points in the binary image matrix, and A(x i ,y j ) represents the binary image matrix element.

[0132] For example, see Figure 6 In the embodiment of the present invention, the Guangdong Provincial water system data is preprocessed in MATLAB. The uneven edge of the original river will affect the subsequent river calculation results. The river data needs to be noise-reduced and smoothed by closing operation. After preprocessing, the river data is calculated and extracted using the MATLAB morphological algorithm. The intersection of the center line is the intersection of the river.

[0133] In step S207, the process of extracting the characteristics of the sudden change point of river curvature includes:

[0134] 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:

[0135] K a =L 直接长度 / L 沿程长度 ;

[0136] 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:

[0137]

[0138] Among them, K a Represents the curvature of each river segment.

[0139] For example, see Figure 7 In the embodiment of the present invention, the center line obtained by MATLAB morphological calculation is segmented according to a 5-kilometer grid. The Euclidean distance between all the center line points of the segmented river in MATLAB is the length of the river along the river. The coordinates of the first and last breakpoints are extracted and the Euclidean distance between the two points is calculated as the direct length of the river. The length of each section of the river along the river is divided by the direct length to obtain the curvature. Figure 8 As shown in the figure, the river curvature dataset is used in MATLAB to find the curvature mutation points using a 10 km sliding window.

[0140] In step S208, the hydrological response factor generalization model calculates the river width, and the process of extracting the river width mutation point characteristics according to the river width includes:

[0141] 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:

[0142]

[0143] According to the river centerline characteristics and distance image, the river width is calculated as follows:

[0144] W=D i,j ×10×2;

[0145] 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:

[0146]

[0147] Among them, (x i ,y j ) represents the elements in the binary image matrix, (x' i , y' j ) indicates that in the binary graph matrix, the same element (x i ,y j ) the nearest non-zero element, D i,jRepresents an element (x' i , y' j ) and non-zero elements (x i ,y j ) between the two.

[0148] For example, Fig. 9 As shown, the embodiment of the present invention uses the distance conversion algorithm to calculate the distance image from the binary image of the river shoreline obtained by MATLAB morphological calculation (the shoreline is 1, and the others are 0). The distance at the shoreline is 0, and the other elements are reassigned to their Euclidean distance to the nearest shoreline. In MATLAB, the location of the centerline of the river is searched, and the distance image is indexed with the location to obtain the distance result (the centerline is the Euclidean distance to the nearest shoreline, and the others are zero). The centerline distance matrix is ​​multiplied by 2 and the data resolution (10 meters in the present invention) to obtain the river width. As shown in FIG. Fig.10 As shown, the river width matrix calculated above is used in MATLAB to find the width mutation point using a 10-kilometer sliding window.

[0149] In step S209, the process of extracting river island characteristics using the flood filling method in the hydrological response factor generalization model includes:

[0150] According to the binary river map, 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 element A(x i ,y j ) value is zero, check the value of the 4-connected position element of the element, when the 4-connected element has a non-zero value, the position element is assigned 1, and the filling matrix is ​​obtained. The expression is:

[0151] if A(x i ,y j )=0and(A(x i ,y j-1 )orA(x i-1 ,y j )orA(x i+1 ,y j )orA(x i ,y j+1 ))

[0152] then A(x i ,y j )=1;

[0153] 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.

[0154] For example, see Fig.11In the embodiment of the present invention, the river binary map (river is 1, others are 0) is used to fill the holes in the river using the 4-connectivity flood filling algorithm in MATLAB. The filled matrix is ​​subtracted from the original river matrix to obtain the river island image, and the river island matrix result is converted into a vector to obtain the river island.

[0155] It can be understood that the trained generalized model of hydrological response factors can perform binary image conversion according to the input image data in the process of extracting river confluence features, river curvature mutation point features, river width mutation point features and river island features, so as to better observe the image data from concise data, thereby extracting the corresponding river confluence features, river curvature mutation point features, river width mutation point features and river island features, so as to improve the accuracy in the process of geographic unit identification, thereby improving the accuracy of ecological protection and effectively reducing the cost of ecological restoration.

[0156] In step S3, see Figure 3 The process of identifying mountain-water associated geographical units based on terrain influence factors and hydrological response factors includes:

[0157] S31: 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;

[0158] S32: The model for identifying geographical units associated with landscapes spatially superimposes and analyzes the extracted terrain influence factors and hydrological response factors, finds the spatial correlation between the two, and introduces the spatial correlation characteristics of landscapes and the characteristics of water and sand movement in geographical units to construct a landscape correlation system of terrain influence factors and hydrological response factors;

[0159] S33: Based on the landscape association system, the landscape association geographic unit identification model spatially calculates the terrain influence factors and the hydrological response factors to extract the geographic units in the landscape morphological feature image data;

[0160] The geographical units include Shanguan, Shuikou, Shuijiao and Jiangwan.

[0161] Specifically, in step S32, see FIG. Fig.12 , Fig.13 and Fig.14 The embodiment of the present invention starts from the formation mechanism of each landform, scientifically selects generalized terrain influencing factors and hydrological response factors to describe the morphological characteristics of typical landscapes, and constructs a terrain and hydrological factor system for landscape shaping.

[0162] Shanguan is close to the mountain edge and the sudden change point of slope change rate, and the basin morphology changes significantly; Shuikou is located in the ridge and valley area, close to the mountain edge, and the basin morphology changes significantly; Shuijiao is the controlling node of the river network, often accompanied by river islands; Jiangwan is distributed in wide basins or open plains, and the river channel is curved.

[0163] Based on the mountain-water correlation system, the terrain response factors and hydrological response factors are spatially calculated and accurately extracted to obtain the geographical units - mountain pass, water mouth, water jiao and river bay.

[0164] Shanguan: It is mainly located at the junction of mountains (tectonic uplift area) and basins (tectonic subsidence area), and is a key section where the basin morphology undergoes a significant turning point.

[0165] Shuikou: Located in the ridge and valley area, the earth's crust rises, the river cuts down, and the river valley is gradually eroded and embedded to become a canyon. The river flows through the hard rock to form a checkpoint. There is a wide section between the two checkpoints, and the river is narrowed at the checkpoint, and the river channel is shaped like a lotus root node.

[0166] Shuijiao: It is the junction where the river enters the hills and open basin from the estuary and is a controlling node of the river network.

[0167] River Bay: Distributed in wide basins or open plain areas, the river loses lateral constraints and will be affected by the Coriolis force and geological environment, causing it to deviate to one side, develop floodplains, accumulate on convex banks, erode on concave banks, and make the river channel curved.

[0168] Specifically, in step S33, see Fig.15 And Table 1, Table 2, Table 3 and Table 4:

[0169] Table 1: Identification of mountain pass geographical units. A pass is an area with a large slope drop and large changes in river width and depth.

[0170]

[0171] Table 2: Identification of water mouth geographical units. The mouth is an area with a small slope and large changes in river width and depth, which generally forms a river island.

[0172]

[0173] Table 3: Identification of Shuijiao geographical units. Shuijiao is the area where rivers converge and branch, and there are often river islands upstream and downstream.

[0174]

[0175] Table 4: Identification of river bay geographical units. A bay is an area where a river bends.

[0176]

[0177] 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.

[0178] Embodiment three:

[0179] This embodiment provides an ecological space geographical unit identification system based on the water-sand mutual coupling process, see Fig.16 , the system comprising:

[0180] A data acquisition module is used to acquire image data of landscape morphological features;

[0181] 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;

[0182] 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;

[0183] 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.

[0184] 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; Based on the terrain influencing factors and hydrological response factors, the mountain-water related geographical units are identified, and the ecological space planning of urban construction is carried out according to the identification results.

2. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 1 is characterized in that: 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.

3. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 2 is characterized in that: Extract the contour features of mountainous terrain, the expression is: code=P3×8+P2×4+P1×2+P0×1; 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.

4. 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 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 generalized model of hydrological response factor is used to extract the binary image data of river channels from the image data of landscape morphological characteristics. Based on the binary image data of river channels, the morphological algorithm is used to extract the river bankline 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.

5. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 4 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: if A(x i ,y j-1 )+A(x i ,y j+1 )+A(x i-1 ,y i )+A(x i+1 ,y j )<4then A(x i ,y j )=0; According to the river bankline characteristics and river centerline characteristics, the morphological algorithm is used to extract the river intersection features; Among them, f represents the gradient value, x and y represent the element points in the binary image matrix, and A(x i ,y j ) represents the binary image matrix element.

6. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 5 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: K a =L 直接长度 / L 沿程长度 ; 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: Among them, K a Represents the curvature of each river segment.

7. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 5 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: E=D i,j ×10×2; 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: Among them, (x i ,y j ) represents the elements in the binary image matrix, (x' i , y' j ) indicates that in the binary graph matrix, the same element (x i ,y j ) the nearest non-zero element, D i,j Represents an element (x' i , y' j ) and non-zero elements (x i ,y j ) between the two.

8. The method for identifying eco-spatial geographical units based on water-sand coupling process according to claim 5 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 river map, 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 element A(x i ,y j ) value is zero, check the value of the 4-connected position element of the element, when the 4-connected element has a non-zero value, the position element is assigned 1, and the filling matrix is ​​obtained. The expression is: if A(x i ,and j )=0and(A(x i ,and j-1 )orA(x i-1 ,and j )orA(x i+1 ,and j )orA(x i ,and j+1 )) then A(x i ,y j )=1; 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.

9. The method for identifying eco-spatial geographical units based on water-sediment coupling process according to any one of claims 1 to 8, 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.

10. An ecological space geographical unit identification system based on water-sediment coupling process, 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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