Method for determining boundary of specific regional unit, method and device for evaluating value of ecological products

Abstract

The present application provides a method for determining the boundary of a specific regional unit and a method for evaluating the value of an ecological product, by obtaining the DEM grid data and initial boundary line of the target specific regional unit; determining the streamline of the water flow in the target specific regional unit based on the DEM grid data; the streamline is the path of the water flow, including the streamline; dividing the grid cells on all streamlines at the same streamline outlet into the same catchment area, and forming a catchment area matrix from the divided catchments; determining a number of observation points based on the initial boundary line; determining the field of view of each observation point in the target specific regional unit based on the DEM grid data, and forming a field of view matrix from each field of view; determining the grid cells in the intersection of the catchment area matrix and the field of view matrix as the boundary of the target specific regional unit. The present invention first preliminarily determines the boundary of the target specific regional unit through the streamline and the catchment area, and then determines the boundary through the field of view analysis, which greatly improves the accuracy of the boundary.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method for determining the boundary of a specific regional unit, and a method and device for evaluating the value of an ecological product. Background Art

[0002] Ecological value assessment in a specific geographical unit is to quantitatively assess the value of services provided by the ecosystem in a specific geographical unit, such as water conservation, soil conservation, biodiversity protection, etc., so as to rationally allocate environmental resources. Among them, a specific geographical unit is a geographical space delineated for quantitatively assessing the impact of ecosystem services under the interaction between man and nature, such as mountains, rivers, lakes, forests, wetlands, etc. The boundary demarcation of a specific geographical unit is very important for ecological value assessment.

[0003] The existing technology uses project red lines for simple divisions, such as 500 meters or 1000 meters. This division is too rough. Although the division is easy, the boundary accuracy is low and it cannot capture important ecological characteristics. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for determining the boundaries of specific regional units, a method and device for evaluating the value of ecological products, so as to improve the accuracy of boundary division and reduce the difficulty of division.

[0005] In a first aspect, a method for determining a boundary of a specific geographical unit is provided, the method comprising:

[0006] Obtain the DEM (Digital Elevation Model) raster data and initial boundary line of the target specific regional unit; DEM raster data refers to DEM data stored in raster unit format by dividing the target specific regional unit into raster grids; DEM raster data includes the coordinates and elevation of the raster unit;

[0007] Determine the streamline of water flow in the target specific regional unit based on DEM raster data; the streamline is the path of water flow, including the streamline; the starting point and the streamline outlet;

[0008] The grid cells on all streamlines of the same streamline outlet are divided into the same catchment area, and the catchment area matrix is ​​formed by the divided catchment areas;

[0009] Determine a number of observation points based on the initial boundary line;

[0010] Based on the DEM raster data, the viewshed of each observation point in the target specific regional unit is determined, and the viewshed matrix is ​​composed of each viewshed; the viewshed is the area that can be observed at the observation point;

[0011] The grid cells within the intersection of the catchment matrix and the viewshed matrix are determined as the boundaries of the target specific area cells.

[0012] Optionally, determining the streamline of water flow in a target specific regional unit based on DEM raster data includes:

[0013] Based on the DEM grid data, the D8 algorithm is used to calculate the flow direction of each grid cell to obtain the flow direction matrix;

[0014] According to the flow direction in the flow direction matrix, the cumulative flow of each grid cell is determined step by step from the initial grid;

[0015] When the accumulated flow exceeds a preset flow threshold, the grid cell corresponding to the accumulated flow is determined as the starting point of the streamline;

[0016] Determine the streamline outlet based on the streamline starting point and flow direction matrix;

[0017] The grid cells between the streamline starting point and the streamline outlet are determined as the streamline of the water flow.

[0018] Optionally, the viewshed of each observation point in the target specific geographical unit is determined based on the DEM data, and the viewshed matrix composed of each viewshed includes:

[0019] Based on the preset observation height of each observation point, a first horizontal distance of each observation point relative to the target point and a second horizontal distance relative to a grid cell on the sight path of the target point are determined; wherein the grid cell on the sight path between the observation point and the target point is referred to as an intermediate grid point; the target point is any grid cell within the target specific regional unit;

[0020] Calculate the sight height of the middle grid point based on the first horizontal distance, the second horizontal distance and the elevation of the middle grid point;

[0021] When the sight heights of all the intermediate grid points between the target point and any observation point are greater than their respective elevations, the grid cell where the target point is located is determined to be a visible grid cell;

[0022] All visible grid cells are searched by traversal, and a viewing matrix is ​​formed by all visible grid cells.

[0023] Second, a method for evaluating the value of ecological products in a specific geographical unit is provided, the method comprising:

[0024] Divide the flight area of ​​the drone based on the specific geographical unit boundaries determined by any one of the methods of the first aspect;

[0025] Obtain remote sensing data collected by the UAV in each flight area; the remote sensing data includes at least image data and multispectral data;

[0026] Merge and pre-process the remote sensing data of each navigation area;

[0027] Determine the ecosystem type of each grid cell based on the merged image data;

[0028] Determine the NDVI (Normalized Difference Vegetation Index) and NPP (Net Primary Production) data of each grid cell based on the merged multispectral data;

[0029] Generate multiple parameter layers based on the ecosystem type, NDVI and NPP data of each grid unit; the parameter layers at least include the runoff coefficient layer, the vegetation coverage factor layer, and the unit area transpiration heat consumption layer;

[0030] Determine the physical quantity of each ecological product within the boundary of a specific regional unit based on the parameter layer; the physical quantity includes at least the physical quantity of flood regulation, water conservation, and soil conservation;

[0031] The value of each ecological product is determined based on the physical quantity of each ecological product and the current unit cost required for each physical quantity.

[0032] Optionally, the flight zone of the drone is divided based on the boundary of a specific geographical unit, including:

[0033] Based on the specific regional unit boundary determined in the first aspect, the area within the boundary is divided into a plurality of remote sensing units;

[0034] Determine the maximum elevation of each remote sensing unit based on the DEM data of each remote sensing unit;

[0035] The relative flight altitude of the UAV in each remote sensing unit is calculated based on the optical sensor parameters carried by the UAV; the calculation formula is:

[0036]

[0037] in, is the relative flight altitude; The focal length of the optical sensor carried by the drone; is the actual ground object range width of the remote sensing unit; is the range width of the optical sensor; is the resolution of the optical sensor; is a pixel of an optical sensor;

[0038] Determine the actual flight altitude of the UAV in each remote sensing unit based on the relative flight altitude and the maximum altitude;

[0039] The flight altitude deviation between each remote sensing unit and the adjacent remote sensing unit is determined based on the actual flight altitude, and the remote sensing units with altitude deviation less than the preset altitude deviation threshold are merged into the same flight area. The calculation formula of the preset altitude deviation threshold is:

[0040]

[0041] in, is the flight altitude deviation threshold, is the resolution range of the optical sensor, is the pixel of the optical sensor, is the range width of the optical sensor.

[0042] Optionally, the remote sensing data of each navigation area are merged and preprocessed including:

[0043] Determine whether there are overlapping areas in the remote sensing data collected in each navigation area;

[0044] For the remote sensing data in the overlapping area, the remote sensing data collected by the drone with higher resolution is determined as the remote sensing data of the overlapping area;

[0045] Merge the remote sensing data of overlapping and non-overlapping areas into one remote sensing dataset;

[0046] The merged remote sensing data set is subjected to data preprocessing, and the data preprocessing at least includes smoothing processing.

[0047] Optionally, determining the ecosystem type of each grid cell based on the merged image data includes:

[0048] The merged image data of each grid unit is input into the pre-trained classification model to obtain the ecosystem type of each grid unit, where the classification model is:

[0049]

[0050] in, Image data for each grid cell; for ecosystem categories; is the index of the category; is the ecosystem category with the highest probability.

[0051] Optionally, multiple parameter layers are generated based on the ecosystem type, NDVI, NPP data and other assessment data for each grid cell, including:

[0052] The ecosystem type, NDVI, NPP data and other assessment data of each grid unit are spatially preprocessed to keep the data in the same spatial range and the same spatial resolution; the spatial preprocessing at least includes clipping the grid and spatial resampling;

[0053] Generate various parameter layers based on the spatially preprocessed data.

[0054] In a third aspect, a device for determining a boundary of a specific geographical unit is provided, the device comprising:

[0055] The first acquisition unit is used to acquire the DEM grid data and the initial boundary line of the target specific regional unit; the DEM grid data refers to the DEM data stored in the grid unit format by dividing the target specific regional unit into grid grids;

[0056] The first determination unit is used to determine the streamline of water flow in the target specific regional unit based on the DEM grid data; the streamline is the path of the water flow, including the streamline starting point and the streamline outlet;

[0057] The first division unit is used to divide the grid cells on all streamlines of the same streamline outlet into the same catchment area, and form a catchment area matrix from the divided catchment areas;

[0058] A second determining unit, used to determine a number of observation points based on the initial boundary line;

[0059] The third determining unit is used to determine the viewshed of each observation point in the target specific regional unit based on the DEM raster data, and form a viewshed matrix from each viewshed; the viewshed is the area range that can be observed at the observation point;

[0060] The fourth determining unit is used to determine the grid cells in the intersection of the catchment area matrix and the viewshed matrix as the boundaries of the target specific area cells.

[0061] In a fourth aspect, a device for evaluating the value of an ecological product in a specific geographical unit is provided, the device comprising:

[0062] A second division unit is used to divide the flight area of ​​the UAV according to the specific geographical unit boundary determined by any method of the first aspect;

[0063] The second acquisition unit is used to acquire remote sensing data collected by the UAV in each flight area; the remote sensing data includes at least image data and multispectral data;

[0064] A merging unit is used to merge and pre-process the remote sensing data of each navigation area;

[0065] A fifth determination unit, used to determine the ecosystem type of each grid cell based on the merged image data;

[0066] A sixth determining unit, used to determine NDVI and NPP data of each grid cell based on the merged multispectral data;

[0067] A generation unit is used to generate a variety of parameter layers based on the ecosystem type, NDVI and NPP data of each grid unit; the parameter layers at least include a runoff coefficient layer, a vegetation coverage factor layer, and a unit area transpiration heat consumption layer;

[0068] The seventh determination unit is used to determine the physical quantity of each ecological product within the boundary of a specific regional unit based on the parameter layer; the physical quantity at least includes the physical quantity of flood storage, the physical quantity of water conservation, and the physical quantity of soil conservation;

[0069] The eighth determination unit is used to determine the value of each ecological product based on the physical quantity of each ecological product and the current unit cost required for each physical quantity.

[0070] In a fifth aspect, an electronic device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0071] Memory, used to store computer programs;

[0072] The processor is used to implement the method steps described in any one of the first aspect or the second aspect when executing the program stored in the memory.

[0073] The method for determining the boundary of a specific regional unit, the method and device for evaluating the value of an ecological product provided by the present invention obtain the DEM grid data and the initial boundary line of the target specific regional unit; determine the streamline of the water flow in the target specific regional unit based on the DEM grid data; the streamline is the path of the water flow, including the streamline; divide the grid cells on all streamlines at the same streamline outlet into the same catchment area, and form a catchment area matrix from the divided catchments; determine a number of observation points based on the initial boundary line; determine the field of view of each observation point in the target specific regional unit based on the DEM grid data, and form a field of view matrix from each field of view; determine the grid cells in the intersection of the catchment area matrix and the field of view matrix as the boundary of the target specific regional unit. The present invention first preliminarily determines the boundary of the target specific regional unit through the streamline and the catchment area, and then further selects the observable boundary through the field of view analysis, so as to determine the final boundary of the target specific regional unit, which greatly improves the accuracy of the boundary.

[0074] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0076] Figure 1 A flow chart showing a method for determining a specific regional unit boundary provided by an embodiment of the present invention is shown;

[0077] Figure 2 A flow chart showing a method for evaluating the value of ecological products in a specific regional unit provided by an embodiment of the present invention;

[0078] Figure 3 A schematic diagram showing the structure of a device for determining a boundary of a specific regional unit provided by an embodiment of the present invention is shown;

[0079] Figure 4 A schematic diagram showing the structure of an ecological product value assessment device for a specific regional unit provided by an embodiment of the present invention;

[0080] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0082] Considering that the specific regional units are simply divided by project red lines, such as 500 meters or 1000 meters, this division is too rough. Although the division is easy, the boundary accuracy of the division is low and it is impossible to capture important ecological features. Based on this, the embodiment of the present invention provides a method and device for determining the boundary of a specific regional unit, which is described below through an embodiment.

[0083] In a first aspect, a method for determining a specific regional unit boundary is provided, the method comprising the following steps:

[0084] Step S101: obtaining DEM grid data and initial boundary lines of a target specific regional unit; the DEM grid data refers to DEM data stored in a grid unit format by dividing the target specific regional unit into grid grids.

[0085] In this step, DEM raster data is collected through remote sensing technology of external equipment such as drones; DEM raster data includes the coordinates and elevation data of each grid point; the initial boundary line can use the project red line, which refers to the boundary line of the plot planned by the functional department, that is, the scope of land use rights.

[0086] After collecting DEM grid data and project redline data, they can be stored in a pre-built system database. Other data related to the target specific regional unit are also stored in the database, such as general basic data: ecosystem distribution and area, precipitation, evaporation; water conservation related data: runoff coefficient, reservoir depreciation rate; soil conservation related data: rainfall erosivity factor, soil erodibility factor, slope length factor, slope factor, soil bulk density, sediment deposition system, etc.; flood storage related data, etc.

[0087] In the subsequent ecological value assessment, relevant data are directly called from the database.

[0088] Step S102: Determine the streamline of water flow in the target specific regional unit based on the DEM raster data; the streamline is the path of the water flow, including the streamline starting point and the streamline outlet.

[0089] DEM grid data reflects the ups and downs of the terrain, which will affect the direction of water flow. Therefore, the streamlines of water flow in the target area unit can be determined through the DEM grid data. The streamlines, as watersheds, are the natural dividing lines between plots.

[0090] The specific determination method will be described in detail in the following embodiments and will not be repeated here.

[0091] Step S103: Divide the grid cells on all streamlines of the same streamline outlet into the same catchment area, and form a catchment area matrix from the divided catchment areas.

[0092] By dividing all grid cells flowing to the same streamline outlet into the same catchment area, more detailed management and analysis of streamlines can be achieved, providing more intuitive data for determining the boundaries of specific regional cells.

[0093] Step S104: Determine a number of observation points based on the initial boundary line.

[0094] In a feasible implementation, when the initial boundary line of the file adopts the project red line, an observation point can be set every 20-50 meters on the project red line boundary, and the observation height is the average human height of 1.55-1.70 meters.

[0095] Step S105: Determine the viewshed of each observation point in the target specific geographical unit based on the DEM grid data, and form a viewshed matrix from each viewshed; the viewshed is the area that can be observed at the observation point.

[0096] The method for determining the viewing area matrix will be described in detail in the following embodiments and will not be described in detail here.

[0097] Step S106: Determine the grid cells in the intersection of the catchment area matrix and the viewshed matrix as the boundaries of the target specific area cells.

[0098] In one example, the boundary of a target specific geographical unit is represented by a boundary matrix, which is expressed as follows:

[0099] (1);

[0100] in:

[0101] (2);

[0102] (3);

[0103] in, Represents a grid cell; is the catchment area matrix; is the view matrix; is a specific regional unit boundary matrix; each grid cell A value of 1 means the cell is within the range, and 0 means it is not.

[0104] After preliminarily determining the boundary of the target specific regional unit through streamlines and watersheds, the present invention further selects observable boundaries through viewshed analysis to determine the final boundary of the target specific regional unit, thereby greatly improving the accuracy of the boundary.

[0105] Based on the above embodiment, determining the streamline of water flow in a target specific regional unit based on DEM grid data includes the following steps:

[0106] Step S102A: Based on the DEM grid data, the flow direction of each grid cell is calculated using the D8 algorithm to obtain a flow direction matrix.

[0107] In this step, the flow direction of each grid cell refers to the direction in which the water flows from the grid cell to one or more of the eight adjacent grid cells around it. The specific implementation process of the D8 algorithm is as follows:

[0108] The first step is to calculate the slope: calculate the slope between each grid cell and the eight adjacent grid cells. The slope is obtained by dividing the elevation difference between two adjacent cells by the distance between them. The slope calculation formula is as follows:

[0109] (4);

[0110] in, represents the coordinates of the center grid cell; is the elevation of the center grid cell; Represents the coordinates of adjacent grid cells; is the elevation of the adjacent grid cell; is the distance from the center cell to the adjacent grid cell. For vertical or horizontal adjacent grid cells, Equal to the side length of the grid , for diagonally adjacent grid cells, equal .

[0111] The central grid cell has eight adjacent grid cells, with elevations of .

[0112] The second step is flow direction calculation: the flow direction is determined by the side with the largest slope value. The flow direction calculation formula is as follows:

[0113] (5);

[0114] in, is the flow direction of the central grid cell; is the slope to the adjacent grid cell.

[0115] In an example, suppose the cell corresponding to the maximum slope is , then the flow direction is from the center cell Flow to the adjacent cell .

[0116] Step S102B: According to the flow direction in the flow direction matrix, the cumulative flow of each grid unit is determined step by step from the initial grid.

[0117] In this step, the cumulative flow of each grid cell is equal to the initial flow of the grid cell plus the sum of the flows of all upstream grid cells flowing to the grid cell. In one example, the initial flow of each grid cell is set to 1.

[0118] In the embodiment of the present invention, the initial grid is the most upstream grid unit, and the calculation starts from the most upstream grid unit step by step, and the calculation formula is as follows: (6);

[0119] in, Grid Cell Flow accumulation value; Is the flow direction The upstream grid cell, the flow direction is determined by the flow direction matrix, is the flow rate of the upstream grid cell; 1 is The initial flow at the grid cell plus the cumulative flow into the grid cell from all its upstream cells.

[0120] In one example, for example is the initial grid, and its flow direction , Flow direction ,So The cumulative flow is 1, The cumulative flow is 2, The cumulative flow is 3.

[0121] Step S102C: When the accumulated flow exceeds a preset flow threshold, the grid unit corresponding to the accumulated flow is determined as the starting point of the streamline.

[0122] In the embodiment of the present invention, the starting point of the streamline represents the source of a river or an area on a hillside where water flows gather.

[0123] In one example, the starting point of the streamline is determined by the following equation 7: (7);

[0124] in, is the preset flow threshold.

[0125] Step S102D: Determine the streamline outlet based on the streamline starting point and the flow direction matrix.

[0126] Starting from the starting point, we move gradually to the downstream cells according to the flow matrix until we reach the outlet of the pipeline, which is a river, lake or other water body. The cumulative flow at the outlet of the pipeline is the grid cell with the largest cumulative flow on the streamline, which can be expressed as (8);

[0127] Step S102E: Determine the grid cells between the streamline starting point and the streamline outlet as the streamline of the water flow.

[0128] In an example, the streamline expression is as follows:

[0129] (9);

[0130] in, For streamline, is the grid cell on the streamline.

[0131] Based on the above embodiment, determining the viewshed of each observation point in the target specific geographical unit based on DEM data, and forming a viewshed matrix from each viewshed includes the following steps:

[0132] Step S105A: Determine the first horizontal distance of each observation point relative to the target point and the second horizontal distance relative to the grid cell on the line of sight path with the target point based on the preset observation height of each observation point; wherein the grid cell on the line of sight path between the observation point and the target point is referred to as the intermediate grid point; the target point is any grid cell within the target specific geographical unit.

[0133] In the embodiment of the present invention, all grid cells are taken as target points in a traversal manner to determine whether they can be observed.

[0134] In one example, the preset observation height is the average human height of 1.55-1.70 meters.

[0135] The first horizontal distance can be calculated based on the coordinates between the observation point and the target point, and the second horizontal distance can also be calculated based on the coordinates between the observation point and the middle grid point.

[0136] It should be noted that the first horizontal distance cannot be greater than the maximum visible distance of the observation point. The maximum visible distance is affected by the visibility and altitude of the observation point, and its calculation formula is as follows:

[0137] (10);

[0138] (11);

[0139] in, is the maximum viewing distance; is the viewing distance, To preset the observation height, is the radius of the earth (6371 km), is the average visibility (KM) of a specific geographical unit.

[0140] Step S105B: Calculate the sight height of the middle grid point based on the first horizontal distance, the second horizontal distance and the elevation of the middle grid point.

[0141] In one example, the eye level is calculated by the following equation 12:

[0142] (12);

[0143] in, It is an observation point The elevation of It is an observation point The observation height, It is the target point The elevation of It is the target point the target height (usually zero, i.e. the ground), From the observation point To any intermediate grid point The horizontal distance It is an observation point To the destination The horizontal distance of Less than the maximum viewing distance .

[0144] Step S105C: When the sight heights of all intermediate grid points between the target point and any observation point are greater than their respective elevations, the grid cell where the target point is located is determined to be a visible grid cell.

[0145] If any point between the target point and the observation point Elevation Above or equal to eye level , then the line of sight is blocked, the target point For observation point The target point can only be seen when the elevation of all the intermediate grid points is less than the sight height.

[0146] From the observation point To the destination Vision , which can be expressed by a binary function:

[0147] (13);

[0148] Step S105D: searching all visible grid cells by traversal, and forming a viewing matrix with all visible grid cells.

[0149] In the embodiment of the present invention, for an observation point , the process of viewshed analysis needs to be extended to all possible target points, that is, for each target point The above sight height comparison is performed to generate a binary view matrix :

[0150] (14);

[0151] in, The middle grid point.

[0152] for Observation points , each observation point has a location and height , the viewshed analysis of multiple observation points is a reuse of the basic formula of the above viewshed analysis. That is, for each observation point , the view matrix from this observation point can be calculated , the view matrices of multiple observation points need to be integrated. Ability to see a grid cell , then the unit in the overall viewing matrix Marked as visible. The overall view matrix expression is as follows:

[0153] (15);

[0154] in: It is The view matrix of observation points; It is the integrated overall view matrix, where each element in the matrix is ​​1, indicating that the point is visible from at least one observation point.

[0155] In a second aspect, an embodiment of the present invention provides a method for evaluating the value of an ecological product in a specific geographical unit, such as Figure 2 As shown, the method comprises the following steps:

[0156] Step S201: Divide the flight area of ​​the UAV based on the boundaries of specific geographical units.

[0157] In the embodiment of the present invention, the reasons why flight area planning is required are: 1. The altitude difference in mountainous areas is large, for the sake of drone safety; 2. The optical equipment carried by the drone often does not have a large-scale zoom capability and does not support terrain-following flight. Therefore, among the data obtained by the drone at the lowest flight altitude, the data accuracy in the low-altitude area is poor.

[0158] By dividing the flight areas and setting different flight altitudes in each area, the safety of the drone is ensured on the one hand, and the accuracy of data collection is improved on the other.

[0159] The specific method of dividing the flight zones will be described in detail in the following embodiments and will not be described in detail here.

[0160] Step S202: Acquire remote sensing data collected by the UAV in each flight area; the remote sensing data includes at least image data and multispectral data.

[0161] Step S203: pre-processing the remote sensing data of each navigation area.

[0162] In this step, since the UAV collects remote sensing data according to each flight area, the remote sensing data of each flight area is separate. By merging and integrating them into one data set, it is convenient for processing in subsequent steps.

[0163] Step S204: Determine the ecosystem type of each grid unit based on the merged image data.

[0164] In a feasible implementation, the merged image data of each grid unit is input into a pre-trained classification model to obtain the ecosystem type of each grid unit, wherein the classification model is:

[0165] (16);

[0166] in, Image data for each grid cell; for ecosystem categories; is the index of the category; is the ecosystem category with the highest probability.

[0167] In one example, the ecosystem types and their classification codes are as follows:

[0168]

[0169] In this table, each ecosystem type includes subdivided ecosystem types, and each subdivided ecosystem type corresponds to a category index.

[0170] Step S205: Determine the NDVI and NPP data of each grid cell based on the merged multispectral data.

[0171] NDVI refers to the Normalized Difference Vegetation Index, which is an important parameter to reflect the health of vegetation. NDVI values ​​close to 1 usually indicate healthy vegetation, while NDVI values ​​close to 0 or negative values ​​indicate sparse vegetation, soil, or water.

[0172] In a feasible implementation, NDVI calculation mainly uses the spectral difference between the red band and the near-infrared band to characterize the activity of vegetation, and its calculation formula is:

[0173] (17);

[0174] in, is the reflectivity in the near-infrared band, is the reflectivity of the red band.

[0175] NPP refers to net primary productivity, which measures the amount of organic matter fixed by vegetation through photosynthesis. NPP is usually calculated using NDVI:

[0176] (18);

[0177] in, is photosynthetically active radiation, obtained through meteorological stations or satellite remote sensing data. For light energy utilization efficiency, it can be preset according to different plant types and climatic conditions.

[0178] Step S206: Generate multiple parameter layers based on the ecosystem type, NDVI and NPP data of each grid unit; the parameter layers at least include a runoff coefficient layer, a vegetation coverage factor layer, and a unit area transpiration heat consumption layer.

[0179] In this step, the ecosystem type, NDVI, NPP data, etc. of each grid unit are first spatially preprocessed to keep the data in the same spatial range and the same spatial resolution; the spatial preprocessing at least includes clipping the grid and spatial resampling.

[0180] Different data may have different spatial resolutions. In order to make all data have the same resolution, a resampling operation is used. Common resampling methods include nearest neighbor, bilinear interpolation, cubic convolution, etc.

[0181] In one example, the raster can be clipped using the Clip Raster by Layer tool so that the data is within the same spatial extent.

[0182] Generate various parameter layers based on the spatially preprocessed data.

[0183] In one example, the corresponding runoff coefficient is assigned according to the ecosystem type, and the runoff coefficient layer RC is generated.

[0184] ( i =1 2 n; j =1 2 … m) (19);

[0185] in, Grid cells of the same ecosystem type.

[0186] In one example, the corresponding vegetation coverage factor is assigned according to the ecosystem type and vegetation coverage, and the vegetation coverage factor layer C is generated.

[0187] In one example, according to the ecosystem type, the corresponding NEP and NPP conversion coefficients are assigned to generate the NEP and NPP conversion coefficient layer α.

[0188] ( i =1 2 … n; j =1,2,…,m) (20);

[0189] in, Grid cells of the same ecosystem type.

[0190] Step S207: Determine the physical quantity of each ecological product within the boundary of a specific regional unit based on the parameter layer; the physical quantity includes at least the physical quantity of flood regulation, the physical quantity of water conservation, and the physical quantity of soil conservation.

[0191] In one example, the physical quantity of water conservation is calculated. Select the precipitation layer P, the runoff coefficient layer RC, and the evaporation layer ET, and calculate the physical quantity according to the following formula: .

[0192] = - )(twenty one);

[0193] in, is the area of ​​the grid cell; is the precipitation layer; is the runoff coefficient layer; For the evaporation layer.

[0194] In one example, the physical quantity of soil conservation is calculated. Select the rainfall erosion factor layer R, soil erodibility factor layer K, slope length factor layer L, slope factor layer S, and vegetation cover factor layer C, and calculate the physical quantity according to the following formula: .

[0195] (twenty two);

[0196] in, is the area of ​​the grid cell; is the rainfall erosivity factor layer; is the soil erodibility factor layer; is the slope length factor layer; is the slope factor layer; It is the vegetation cover factor layer.

[0197] In one example, the physical quantity of flood storage is calculated. Select the ecosystem type ES, the storm rainfall layer RS, the storm runoff coefficient layer SRC, the inflow QI, and the outflow QO, and calculate the physical quantity as follows: .

[0198] (twenty three);

[0199] in, is the area of ​​the grid cell; This is the storm rainfall layer; is the storm runoff coefficient layer; is the flow into the lake; For outflow from the lake.

[0200] In one example, the physical amount of water environment purification is calculated. Select the COD annual purification amount per unit area layer P COD 、Annual purification of ammonia nitrogen layer P NHN 、Total phosphorus annual purification layer P TP , calculate the actual amount of COD purification according to the following formula , Physical quantity of ammonia nitrogen purification , Actual amount of total phosphorus purification .

[0201] (twenty four);

[0202] (25);

[0203] (26);

[0204] in, is the area of ​​the grid cell; This is the annual purification volume layer; It is the layer of annual ammonia nitrogen purification capacity; This is the annual total phosphorus purification layer.

[0205] In one example, the actual amount of air purification is calculated. Select the annual purification amount of SO2 per unit area layer P SO2 、Annualized nitrogen oxides layer P NOX , Annual dust purification capacity layer P PM , calculate the actual amount of SO2 purification according to the following formula 、Nitrous oxide purification quantity , Actual amount of dust purification .

[0206] (27);

[0207] (28);

[0208] (29);

[0209] in, is the area of ​​the grid cell; This is the annual SO2 purification amount per unit area layer; Annual nitrogen oxides level layer; This is the layer showing the annual dust purification amount.

[0210] In one example, the physical amount of carbon sequestration is calculated. Select the primary productivity layer NPP, the NPP and NEP conversion coefficient layer α, and calculate the physical amount according to the following formula .

[0211] (30);

[0212] In one example, is the molar mass of carbon dioxide; is the molar mass of carbon; is the primary productivity layer; This is the NPP and NEP conversion coefficient layer.

[0213] In one example, the amount of oxygen released is calculated. The amount of oxygen released is based on the amount of carbon fixed, and the amount of oxygen released is calculated as follows: .

[0214] (31);

[0215] In one example, the physical quantity of climate regulation is calculated. Select the evaporation layer ET, the unit area transpiration heat consumption layer EPP, and the number of days the air conditioner is open Days, and calculate the physical quantity according to the following formula: .

[0216] (32);

[0217] in, is the area of ​​the grid cell; This is the layer of heat consumption per unit area by transpiration; This is the evaporation layer.

[0218] Step S208: Determine the value of each ecological product based on the physical quantity of each ecological product and the current unit cost required for each physical quantity.

[0219] In one example, the soil conservation value is calculated. Obtaining the soil conservation price , sediment deposition coefficient , soil bulk density , calculate the value according to the following formula .

[0220] (33);

[0221] In an example, the value of air purification is calculated by selecting the unit treatment cost C of SO2, nitrogen oxides and dust. SO2 , C NOX , C PM , calculate the value according to the following formula .

[0222] + + (34);

[0223] The physical quantity is based on the same principle. The unit cost of each physical quantity is obtained, and the value is calculated using the unit cost and the physical quantity.

[0224] After determining the value of each ecological product, in a feasible implementation, an evaluation report can be generated based on the value.

[0225] The present invention improves the assessment accuracy of the value of the ecological products within the specific geographical unit boundaries by calculating the value of the ecological products within the specific geographical unit boundaries based on the accurately divided specific geographical unit boundaries.

[0226] Based on the above embodiment, dividing the flight area of ​​the drone based on the boundary of a specific geographical unit includes the following steps:

[0227] Step S201A: Based on the specific geographical unit boundary, the area within the boundary is divided into a number of remote sensing units.

[0228] In this step, the boundary line of the specific geographical unit is determined based on the method of the above embodiment.

[0229] In one example, a specific geographical unit is divided into several remote sensing units according to the boundary length of the specific geographical unit and the accuracy to be divided. The expression of the divided remote sensing units is as follows:

[0230] (35);

[0231] in, For remote sensing unit collection, For each remote sensing unit, is the width of the remote sensing unit; is the sensor range width, The DEM data matrix corresponding to each remote sensing unit.

[0232] Step S201B: Determine the maximum elevation of each remote sensing unit according to the DEM data of each remote sensing unit.

[0233] In the embodiment of the present invention, the maximum altitude is the minimum safe flight altitude of the UAV, and the flight altitude of the UAV must be at least higher than the maximum altitude to ensure the flight safety of the UAV.

[0234] Step S201C: Calculate the relative flight altitude of the drone in each remote sensing unit based on the optical sensor parameters carried by the drone; the calculation formula is:

[0235] (36);

[0236] in, is the relative flight altitude; is the focal length of the optical sensor carried by the drone; is the actual ground object range width of the remote sensing unit; is the range width of the optical sensor; is the resolution of the optical sensor; is the pixel of the optical sensor.

[0237] The relative height is calculated through the optical sensor parameters carried by the drone, which can ensure the collection accuracy of the drone.

[0238] Step S201D: Determine the actual flight altitude of the UAV in each remote sensing unit based on the relative flight altitude and the maximum altitude.

[0239] In one example, the actual flight altitude of each remote sensing unit can be obtained by adding the relative flight altitude and the maximum altitude. The calculation formula is as follows:

[0240] (37);

[0241] in, is the actual flight altitude within the remote sensing unit, is the relative flight altitude; is the maximum elevation.

[0242] Step S201E: Determine the flight altitude deviation between each remote sensing unit and the adjacent remote sensing unit based on the actual flight altitude, and merge the remote sensing units with altitude deviation less than a preset altitude deviation threshold into the same flight area, wherein the calculation formula of the preset altitude deviation threshold is:

[0243] (38);

[0244] in, is the flight altitude deviation threshold, is the resolution range of the optical sensor, is the pixel of the optical sensor, is the range width of the optical sensor.

[0245] In the embodiment of the present invention, the remote sensing units that can be merged are determined according to the flight altitude deviation, that is, for each remote sensing unit, the adjacent remote sensing units are determined. Are the following conditions met:

[0246] (39);

[0247] in, is the actual flight altitude of the remote sensing unit 1, is the actual flight altitude of the remote sensing unit 2, wherein the remote sensing unit 1 and the remote sensing unit 2 are two adjacent remote sensing units.

[0248] Then, by gradually checking the flight altitude differences of adjacent remote sensing units, the remote sensing units that meet the flight altitude variation range are merged into the same flight area.

[0249] Among them, after each merger, the updated flight altitude of the navigation area is the average value of all actual flight altitudes in the area, and the flight altitude of each navigation area is obtained. The update calculation formula is:

[0250] (40);

[0251] in, Indicates A flight area; Indicates A flight area.

[0252] Continue to iterate this process until no further merging of flight zones is possible, that is, the difference in flight altitude of all adjacent flight zones is greater than .

[0253] Finally, the result of the flight zone division is obtained:

[0254] (41);

[0255] That is, the entire area It is divided into N flight zones, each of which has a corresponding flight altitude, and these flight zones do not overlap.

[0256] The embodiment of the present invention plans a reasonable flight path for the UAV by merging remote sensing units with similar flight altitudes, thereby ensuring both the safety of the UAV flight and the acquisition accuracy.

[0257] Based on the above embodiment, the merging preprocessing of the remote sensing data of each navigation area includes the following steps:

[0258] Step S203A: Determine whether there is any overlapping area in the remote sensing data collected in each navigation area.

[0259] In this step, although the flight areas do not overlap, the drone will not strictly fly according to the divided flight areas in actual flight, especially when flying at the boundary between adjacent flight areas. The collected data may involve two flight areas. Therefore, it is necessary to determine whether there are overlapping areas in the collection.

[0260] In a feasible implementation, it can be judged according to the number of remote sensing data acquired by the grid cell. If a grid cell acquires two sets of remote sensing data, it means that the grid cell is a data overlapping area.

[0261] Step S203B: For the remote sensing data in the overlapping area, the remote sensing data collected by the drone with a higher resolution is determined as the remote sensing data in the overlapping area.

[0262] Step S203C: merging the remote sensing data of the overlapping area and the non-overlapping area into one remote sensing data set.

[0263] In this step, the data of non-overlapping areas directly use the response data collected by the drone.

[0264] Step S203D: performing data preprocessing on the merged remote sensing data set, where the data preprocessing at least includes smoothing processing.

[0265] In one example, the merged remote sensing data is smoothed using a Gaussian filter to obtain data that can be used for the next step of calculation. :

[0266] (42);

[0267] in, is the Gaussian kernel function, is the filter window size; The combined remote sensing data.

[0268] After obtaining the evaluation results of the value of ecological products in a specific geographical unit, it can provide a reference for the rational allocation of ecological resources; or apply the evaluation results to business scenarios of ecological asset repricing.

[0269] In the application scenario of ecological asset re-pricing, ecological assets can be re-priced by different calculation methods. For example, ecological assets are the price of a plot of land in a specific geographical unit.

[0270] In one example, the residual method can be used to reprice ecological assets:

[0271] (43);

[0272] in: is the land price of a specific geographical unit to be estimated, is the total development value or the total price of the land after development is completed, is the annual growth rate of total development value, The development cost of the entire development project, The cost of ecological restoration, is the annual growth rate of ecological restoration cost, For operation and maintenance costs, is the annual growth rate of operation and maintenance costs, For the additional risk cost, is a risk factor, For the reasonable profit of developers, is the annual growth rate of developer profits.

[0273] Based on the same technical concept, a device for determining the boundary of a specific regional unit is provided, such as Figure 3 As shown, the device comprises:

[0274] The first acquisition unit 301 is used to acquire DEM grid data and initial boundary lines of a target specific regional unit; the DEM grid data refers to DEM data stored in a grid unit format by dividing the target specific regional unit into grid grids;

[0275] The first determining unit 302 is used to determine the streamline of the water flow in the target specific regional unit based on the DEM grid data; the streamline is the path of the water flow, including the streamline starting point and the streamline outlet;

[0276] The first division unit 303 is used to divide the grid cells on all streamlines of the same streamline outlet into the same catchment area, and form a catchment area matrix from the divided catchment areas;

[0277] A second determining unit 304 is used to determine a number of observation points based on the initial boundary line;

[0278] The third determining unit 305 is used to determine the viewshed of each observation point in the target specific regional unit based on the DEM data, and form a viewshed matrix with each viewshed; the viewshed is the area range that can be observed at the observation point;

[0279] The fourth determining unit 306 is used to determine the grid cells in the intersection of the catchment area matrix and the viewshed matrix as the boundaries of the target specific area cells.

[0280] Based on the same technical concept, a device for evaluating the value of ecological products in a specific regional unit is provided, such as Figure 4 As shown, the device comprises:

[0281] A second division unit 401 is used to divide the flight area of ​​the UAV according to the specific geographical unit boundary determined by any method of the first aspect;

[0282] The second acquisition unit 402 is used to acquire remote sensing data collected by the UAV in each flight area; the remote sensing data includes at least image data and multispectral data;

[0283] A merging unit 403 is used to perform merging preprocessing on the remote sensing data of each navigation area;

[0284] A fifth determining unit 404 is used to determine the ecosystem type of each grid cell based on the merged image data;

[0285] A sixth determining unit 405, configured to determine NDVI and NPP data of each grid cell based on the merged multispectral data;

[0286] The generating unit 406 is used to generate a plurality of parameter layers based on the ecosystem type, NDVI, NPP data and other evaluation data of each grid unit; the parameter layers at least include a runoff coefficient layer, a vegetation coverage factor layer, and a unit area transpiration heat consumption layer;

[0287] The seventh determination unit 407 is used to determine the physical quantity of each ecological product within the boundary of the specific regional unit based on the parameter layer; the physical quantity at least includes the physical quantity of flood storage, the physical quantity of water conservation, and the physical quantity of soil conservation;

[0288] The eighth determination unit 408 is used to determine the value of each ecological product based on the physical quantity of each ecological product and the current unit cost required for each physical quantity.

[0289] Based on the same technical concept, an embodiment of the present invention further provides an electronic device, such as Figure 5 As shown, it includes a processor 501 , a communication interface 502 , a memory 503 and a communication bus 504 , wherein the processor 501 , the communication interface 502 , and the memory 503 communicate with each other via the communication bus 504 .

[0290] Memory 503, used for storing computer programs;

[0291] The processor 501 is used to implement the steps of the method for determining the boundary of a specific regional unit and the steps of the method for evaluating the value of ecological products of a specific regional unit when executing the program stored in the memory 503.

[0292] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0293] The communication interface is used for communication between the above electronic device and other devices.

[0294] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0295] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0296] The computer program product for determining the boundaries of specific geographical units and evaluating the value of ecological products of specific geographical units provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be repeated here.

[0297] The device for determining the boundary of a specific geographical unit and evaluating the value of an ecological product of a specific geographical unit provided in the embodiment of the present invention may be specific hardware on the device or software or firmware installed on the device. The implementation principle and technical effects of the device provided in the embodiment of the present invention are the same as those of the aforementioned method embodiment. For the sake of brief description, reference may be made to the corresponding contents in the aforementioned method embodiment for matters not mentioned in the device embodiment. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0298] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0299] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0300] In addition, each functional unit in the embodiment provided by the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0301] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0302] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and are not to be understood as indicating or implying relative importance.

[0303] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating the value of ecological products in a specific geographical unit, characterized in that: The method comprises: Divide the flight area of ​​the drone based on the pre-determined boundaries of specific geographical units; The flight zones of drones divided based on the boundaries of specific geographical units include: Based on the specific regional unit boundary, the area within the boundary is divided into several remote sensing units; Determine the maximum elevation of each remote sensing unit based on the DEM data of each remote sensing unit; The relative flight altitude of the UAV in each remote sensing unit is calculated based on the optical sensor parameters carried by the UAV; the calculation formula is: in, is the relative flight altitude; The focal length of the optical sensor carried by the drone; is the actual ground object range width of the remote sensing unit; is the range width of the optical sensor; is the resolution of the optical sensor; is a pixel of an optical sensor; Determine the actual flight altitude of the UAV in each remote sensing unit based on the relative flight altitude and the maximum altitude; The flight altitude deviation between each remote sensing unit and an adjacent remote sensing unit is determined based on the actual flight altitude, and remote sensing units with altitude deviation less than a preset altitude deviation threshold are merged into the same flight area, wherein the calculation formula of the preset altitude deviation threshold is: in, is the flight altitude deviation threshold, is the resolution range of the optical sensor, is the pixel of the optical sensor, is the range width of the optical sensor; Acquire remote sensing data collected by the UAV in each flight area; the remote sensing data includes at least image data and multispectral data; Merge and pre-process the remote sensing data of each navigation area; Determine the ecosystem type of each grid cell based on the merged image data; Determine the NDVI and NPP data of each grid cell based on the merged multispectral data; Generate multiple parameter layers based on the ecosystem type, NDVI and NPP data of each grid unit; the parameter layers at least include a runoff coefficient layer, a vegetation coverage factor layer, and a unit area transpiration heat consumption layer; Determine the physical quantity of each ecological product within the boundary of a specific regional unit based on the parameter layer; the physical quantity of each ecological product at least includes the physical quantity of flood regulation, the physical quantity of water conservation, and the physical quantity of soil conservation; The value of each ecological product is determined based on the physical quantity of each ecological product and the current unit cost required for each physical quantity.

2. The method according to claim 1, characterized in that The process of determining the boundary of the specific geographical unit includes: Acquire the DEM grid data and initial boundary line of the target specific regional unit; the DEM grid data refers to the DEM data stored in the grid unit format by dividing the target specific regional unit into grid grids; the DEM grid data includes the coordinates and elevation of the grid unit; Determine the streamline of the water flow in the target specific regional unit based on the DEM raster data; the streamline is the path of the water flow, including the streamline starting point and the streamline outlet; The grid cells on all streamlines of the same streamline outlet are divided into the same catchment area, and the catchment area matrix is ​​formed by the divided catchment areas; Determining a number of observation points based on the initial boundary line; Determine the view of each observation point in the target specific regional unit based on the DEM raster data, and form a view matrix with each view; the view is the area that can be observed at the observation point; The grid cells within the intersection of the catchment area matrix and the view matrix are determined as the boundaries of the target specific area cells.

3. The method according to claim 2, characterized in that Determining the streamline of water flow in the target specific regional unit based on the DEM grid data includes: Based on the DEM grid data, the flow direction of each grid cell is calculated using the D8 algorithm to obtain a flow direction matrix; According to the flow direction in the flow direction matrix, the cumulative flow of each grid unit is determined step by step from the initial grid; When the accumulated flow exceeds a preset flow threshold, the grid unit corresponding to the accumulated flow is determined as the starting point of the streamline; Determine a streamline outlet based on the streamline starting point and the flow direction matrix; The grid cells between the streamline starting point and the streamline outlet are determined as the streamline of the water flow.

4. The method according to claim 2, characterized in that: Determining the viewshed of each observation point in the target specific geographical unit based on the DEM data, and forming a viewshed matrix from each viewshed includes: Determine the first horizontal distance of each observation point relative to the target point and the second horizontal distance relative to the grid unit on the line of sight path between the observation point and the target point based on the preset observation height of each observation point; wherein the grid unit on the line of sight path between the observation point and the target point is referred to as the intermediate grid point; the target point is any grid unit within the target specific regional unit; Calculate the sight height of the middle grid point based on the first horizontal distance, the second horizontal distance and the elevation of the middle grid point; When the sight heights of all the intermediate grid points between the target point and any observation point are greater than their respective elevations, the grid cell where the target point is located is determined to be a visible grid cell; All visible grid cells are searched by traversal, and a viewing matrix is ​​formed by all visible grid cells.

5. The method according to claim 1, characterized in that The merging preprocessing of the remote sensing data of each navigation area includes: Determine whether there are overlapping areas in the remote sensing data collected in each navigation area; For the remote sensing data in the overlapping area, the remote sensing data collected by the drone with higher resolution is determined as the remote sensing data of the overlapping area; Merge the remote sensing data of overlapping and non-overlapping areas into one remote sensing dataset; The merged remote sensing data set is subjected to data preprocessing, wherein the data preprocessing at least includes smoothing processing.

6. The method according to claim 1, characterized in that Determining the ecosystem type of each grid unit based on the merged image data includes: The merged image data of each grid unit is input into the pre-trained classification model to obtain the ecosystem type of each grid unit, wherein the classification model is: in, Image data for each grid cell; for ecosystem categories; is the index of the category; is the ecosystem category with the highest probability.

7. The method according to claim 1, characterized in that The generation of multiple parameter layers based on the ecosystem type, NDVI, NPP data and other assessment data of each grid unit includes: Performing spatial preprocessing on the ecosystem type, NDVI, NPP data and other assessment data of each grid unit so that each data is kept in the same spatial range and the same spatial resolution; the spatial preprocessing at least includes clipping the grid and spatial resampling; Generate various parameter layers based on the spatially preprocessed data.

8. An ecological product value assessment device for a specific geographical unit, characterized in that: The device comprises: The second division unit is used to divide the flight area of ​​the UAV based on the predetermined specific geographical unit boundary; The second division unit is specifically used for: Based on the specific regional unit boundary, the area within the boundary is divided into several remote sensing units; Determine the maximum elevation of each remote sensing unit based on the DEM data of each remote sensing unit; The relative flight altitude of the UAV in each remote sensing unit is calculated based on the optical sensor parameters carried by the UAV; the calculation formula is: in, is the relative flight altitude; The focal length of the optical sensor carried by the drone; is the actual ground object range width of the remote sensing unit; is the range width of the optical sensor; is the resolution of the optical sensor; is a pixel of an optical sensor; Determine the actual flight altitude of the UAV in each remote sensing unit based on the relative flight altitude and the maximum altitude; The flight altitude deviation between each remote sensing unit and an adjacent remote sensing unit is determined based on the actual flight altitude, and remote sensing units with altitude deviation less than a preset altitude deviation threshold are merged into the same flight area, wherein the calculation formula of the preset altitude deviation threshold is: in, is the flight altitude deviation threshold, is the resolution range of the optical sensor, is the pixel of the optical sensor, is the range width of the optical sensor; A second acquisition unit is used to acquire remote sensing data collected by the UAV in each flight area; the remote sensing data at least includes image data and multispectral data; A merging unit is used to merge and pre-process the remote sensing data of each navigation area; A fifth determination unit, used to determine the ecosystem type of each grid cell based on the merged image data; A sixth determining unit, used to determine NDVI and NPP data of each grid cell based on the merged multispectral data; A generating unit, used to generate a plurality of parameter layers based on the ecosystem type, NDVI and NPP data of each grid unit; the parameter layers at least include a runoff coefficient layer, a vegetation coverage factor layer, and a unit area transpiration heat consumption layer; A seventh determination unit is used to determine the physical quantity of each ecological product within the boundary of a specific regional unit based on the parameter layer; the physical quantity of each ecological product at least includes the physical quantity of flood regulation, the physical quantity of water conservation, and the physical quantity of soil conservation; The eighth determination unit is used to determine the value of each ecological product based on the physical quantity of each ecological product and the current unit cost required for each physical quantity.

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