Remote sensing coordinate information collection and conversion method for dead standing trees infected with pine wilt

Abstract

This invention discloses a method for collecting and converting remote sensing coordinate information of dead trees infected with pine wilt disease, belonging to the field of information conversion technology. The method comprises the following steps: Step 1: Accessing a pine forest data management database; Step 2: Identifying dead trees based on remote sensing images; Step 3: Coordinate matching; Step 4: Visual marking; and Step 5: Coordinate information conversion: Based on the coordinate information and the pine forest data management database, the remote sensing coordinate information is converted into prevention and control task information. By identifying dead trees based on remote sensing images and converting the coordinate information, the present invention accurately locates and identifies dead trees infected with pine wilt disease, thereby achieving efficient and comprehensive dead tree information collection, effectively addressing the inefficiency and omissions of manual inspections in existing technologies.

Description

Technical Field

[0001] The present invention relates to the field of information conversion technology, and in particular to a method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wood nematodes. Background Art

[0002] Pine wilt disease is a devastating pest in pine forests. Traditional forestry pest monitoring and control efforts rely heavily on manual inspections to locate and collect information on dead standing trees infected with pine wilt. Forestry workers must traverse pine forests, relying on their experience to individually check the health of each tree and record its coordinates and other information.

[0003] This method not only consumes a lot of manpower, material resources and time costs, but also is prone to omissions in inspections when dealing with large areas of pine forests, resulting in some dead standing trees infected with pine wilt disease not being discovered in time, thus delaying the best time for prevention and control. Summary of the Invention

[0004] The embodiments of the present application provide a method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wood nematodes, thereby solving the problem of low efficiency and easy omissions in manual inspections in the prior art and achieving efficient and comprehensive dead standing tree information collection.

[0005] The present application embodiment provides a method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wood nematodes, comprising the following steps: Step 1, accessing a pine forest data management database;

[0006] Step 2: Identify dead trees based on remote sensing images: Obtain remote sensing spectral images of the target pine forest area, identify and locate dead trees and dying trees infected with pine wilt disease from the remote sensing spectral images, and obtain remote sensing coordinate information corresponding to the dead trees and dying trees;

[0007] The identification method of the dying wood is:

[0008] Based on multispectral image analysis of vegetation index and red edge offset, the vegetation index threshold range and red edge offset threshold are obtained, and suspected targets with vegetation index in the faded range and red edge offset exceeding the limit are screened out.

[0009] Extract the texture features of the suspected target, including the gray-level co-occurrence matrix entropy and fractal dimension, obtain the texture entropy threshold and fractal dimension threshold, and retain the target pine trees that meet both spectral and texture anomalies;

[0010] The pixel coordinates of the screened target pine trees are converted into latitude and longitude coordinates in the geographic coordinate system through affine transformation;

[0011] Using the longitude and latitude coordinates as index keys, the pine forest data management database is queried to obtain the historical injury and illness dataset of the target pine trees, including injury type codes, injury severity, and cumulative treatment duration, for infection risk analysis.

[0012] Obtain the infection risk value, mark the target pine trees whose infection risk value is not less than the infection risk threshold as dying trees, and output their latitude and longitude coordinates and infection risk value;

[0013] Step 3, coordinate matching: spatially match and register all remote sensing coordinate information with the geographic base map to obtain the drawn remote sensing scan map;

[0014] Step 4, visual marking: visually mark the dead standing trees and dying trees on the remote sensing scan map;

[0015] Step 5, coordinate information conversion: Based on the coordinate information and the pine forest data management database, the remote sensing coordinate information is converted into control task information, including using the coordinates of the remaining pine trees other than the dead standing trees and dying trees as the spraying points to trigger the spraying control task;

[0016] The steps of the spraying prevention task include:

[0017] Step A1: Aggregate the points to be sprayed into several operation blocks, with the altitude deviation being less than a first threshold and the plane distance being less than a second threshold as constraints;

[0018] Step A2: Calculate the shortest connected path for all the points to be sprayed in each operation block;

[0019] Step A3: Using the coordinates of the center of gravity of the operation block as nodes, use the ant colony algorithm to generate the block operation sequence with the global minimum time cost;

[0020] Step A4: Output the block operation sequence as a waypoint instruction set executable by the drone.

[0021] Furthermore, the pine forest data management database includes pine tree numbers, pine tree locations, and historical inspection information;

[0022] The pine tree location refers to the latitude and longitude information of the current pine tree and the felled pine tree;

[0023] The historical inspection information refers to the results of the staff's inspection of the pine trees, including health, injury types and treatment history.

[0024] Furthermore, the method for identifying the dead standing trees is:

[0025] The normalized vegetation index and overgreening index are calculated based on multispectral images, and a binary mask image is generated according to the double threshold conditions to filter out low-activity pixels.

[0026] Perform morphological closing operations on continuous low-activity pixel clusters to fill holes and extract the minimum bounding rectangle of each cluster;

[0027] The center point of the rectangle is used as the initial pixel coordinate of the dead tree, and a set of longitude and latitude coordinates is generated through geographic projection transformation.

[0028] Furthermore, the infection risk value formula is:

[0029] ;

[0030] Among them, R is the infection risk value, is the weight value, , is the historical severity of injuries, is the cumulative treatment time after time-attenuation treatment, is the time decay factor, is the plane distance from the target pine tree to the nearest diseased tree.

[0031] Furthermore, the specific steps of spatially matching and registering all remote sensing coordinate information with the geographic base map to obtain the drawn remote sensing scan map are as follows:

[0032] Use the forest area digital elevation model and vector class boundary map as the geographic base map;

[0033] Convert the remote sensing coordinate information set of dead standing trees and dying trees into the coordinates of the geographic basemap: , is the rotation and scaling matrix, is the translation vector, and are the longitude and latitude of the coordinates in the geographic map, and is the longitude and latitude in remote sensing coordinate information;

[0034] Optimize matrix parameters through measured coordinates of ground control points;

[0035] The coordinates on the optimized geographic map are integrated with the forest area digital elevation model to generate a three-dimensional remote sensing scanning map.

[0036] Furthermore, the visual marking of dead standing trees and dying trees on the remote sensing scan map specifically refers to:

[0037] Mark the coordinate points of the dead standing trees on the remote sensing scan map with colored circles, where the diameter of the circles is proportional to the crown size, and mark the coordinate values ​​of the dead standing trees on the remote sensing scan map;

[0038] The coordinate points of the dying trees on the remote sensing scan map are marked with a double-layer circle frame with a color. The outer ring diameter of the double-layer circle frame is a fixed pixel, and the filling transparency of the inner ring is positively correlated with the infection risk value. The coordinate values ​​and infection risk values ​​of the dying trees on the remote sensing scan map are also marked.

[0039] Furthermore, the converting of remote sensing coordinate information into prevention and control task information further includes:

[0040] Generate a task work order based on the coordinate point type and attributes of the visual mark:

[0041] The coordinates of standing dead trees trigger felling tasks. The standing dead tree task work order includes: positioning coordinates, felling pile processing requirements, and associated small group number;

[0042] The coordinates of the dying tree trigger the injection task. The dying tree task work order includes: positioning coordinates and injection requirements.

[0043] Furthermore, the formula for obtaining the shortest connected path is:

[0044] ;

[0045] in, is the job block number, is the total number of points to be sprayed in the operation block, and Number the points to be sprayed in the operation area. is the binary decision variable for path decision, , is the time cost of wind direction.

[0046] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0047] 1. By identifying dead trees based on remote sensing images and converting their coordinate information, we can accurately locate and identify dead trees infected with pine wilt disease, thereby achieving efficient and comprehensive dead tree information collection, effectively solving the problem of low efficiency and easy omissions in manual inspections in existing technologies.

[0048] 2. By using multispectral imagery to calculate the Normalized Difference Vegetation Index and Overgreening Index and other technologies to identify dead trees, the accuracy and objectivity of dead tree identification are improved. By in-depth analysis of characteristics such as the vegetation index and red edge position offset, dying trees can be screened, thereby more accurately determining the scope and extent of diseased pine trees.

[0049] 3. By spatially matching and registering remote sensing coordinate information with the geographic base map and visually marking it, the distribution of dead and dying trees infected with pine wilt disease in the pine forest can be intuitively displayed. By converting the coordinate information into prevention and control task information, targeted prevention and control task allocation can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of the method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wood nematodes provided in the embodiments of the present application. DETAILED DESCRIPTION

[0051] The embodiments of the present application solve the problems of low efficiency and easy omissions in manual inspections in the prior art by providing a method for collecting and converting remote sensing coordinate information of dead trees infected with pine wilt disease. By identifying dead trees based on remote sensing images and converting coordinate information, dead trees infected with pine wilt disease can be accurately located and identified, thereby achieving efficient and comprehensive dead tree information collection.

[0052] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0053] like Figure 1 As shown, the embodiment of the present application provides a method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wood nematodes, the method comprising the following steps: Step 1, accessing a pine forest data management database;

[0054] Step 2: Identify dead trees based on remote sensing images: Obtain remote sensing spectral images of the target pine forest area, identify and locate dead trees and dying trees infected with pine wilt disease from the remote sensing spectral images, and obtain remote sensing coordinate information corresponding to the dead trees and dying trees;

[0055] The identification method of the dying wood is:

[0056] Based on multispectral image analysis of vegetation index and red edge offset, the vegetation index threshold range and red edge offset threshold are obtained, and suspected targets with vegetation index in the faded range and red edge offset exceeding the limit are screened out.

[0057] Extract the texture features of the suspected target, including the gray-level co-occurrence matrix entropy and fractal dimension, obtain the texture entropy threshold and fractal dimension threshold, and retain the target pine trees that meet both spectral and texture anomalies;

[0058] The pixel coordinates of the screened target pine trees are converted into latitude and longitude coordinates in the geographic coordinate system through affine transformation;

[0059] Using the longitude and latitude coordinates as index keys, the pine forest data management database is queried to obtain the historical injury and illness dataset of the target pine trees, including injury type codes, injury severity, and cumulative treatment duration, for infection risk analysis.

[0060] Obtain the infection risk value, mark the target pine trees whose infection risk value is not less than the infection risk threshold as dying trees, and output their latitude and longitude coordinates and infection risk value;

[0061] Step 3, coordinate matching: spatially match and register all remote sensing coordinate information with the geographic base map to obtain the drawn remote sensing scan map;

[0062] Step 4, visual marking: visually mark the dead standing trees and dying trees on the remote sensing scan map;

[0063] Step 5, coordinate information conversion: Based on the coordinate information and the pine forest data management database, the remote sensing coordinate information is converted into control task information, including using the coordinates of the remaining pine trees other than the dead standing trees and dying trees as the spraying points to trigger the spraying control task;

[0064] The steps of the spraying prevention task include:

[0065] Step A1: Aggregate the points to be sprayed into several operation blocks, with the altitude deviation being less than a first threshold and the plane distance being less than a second threshold as constraints;

[0066] Step A2: Calculate the shortest connected path for all the points to be sprayed in each operation block;

[0067] Step A3: Using the coordinates of the center of gravity of the operation block as nodes, use the ant colony algorithm to generate the block operation sequence with the global minimum time cost;

[0068] Step A4: Output the block operation sequence as a waypoint instruction set executable by the drone, including three-dimensional coordinates and spraying parameters.

[0069] Furthermore, the pine forest data management database includes pine tree numbers, pine tree locations, and historical inspection information;

[0070] The pine tree location refers to the latitude and longitude information of the current pine tree and the felled pine tree;

[0071] The historical inspection information refers to the results of the staff's inspection of the pine trees, including health, injury types and treatment history.

[0072] Furthermore, the method for identifying the dead standing trees is:

[0073] The normalized vegetation index and overgreening index are calculated based on multispectral images, and a binary mask image is generated according to the double threshold conditions to filter out low-activity pixels.

[0074] Perform morphological closing operations on continuous low-activity pixel clusters to fill holes and extract the minimum bounding rectangle of each cluster;

[0075] The step of performing morphological closing operation on the continuous low-activity pixel clusters to fill the holes comprises:

[0076] Step 1: Constructing the circular structural element , its diameter satisfy , 、 is the pixel mapping value of the minimum and maximum crown diameters of pine trees;

[0077] Step 2: Add structural elements Traverse the mask image and for the target pixel implement ; Expand the boundaries of pixel clusters outwards to fill internal holes and gaps;

[0078] Step 3: Use the same structural element on the expanded image implement ;Shrink the expanded boundary to the original cluster geometry;

[0079] Step 4: Extract the connected domain after the closed operation and filter out noise points whose area is smaller than the minimum projected area of ​​a single pine tree;

[0080] The center point of the rectangle is used as the initial pixel coordinate of the dead tree, and a set of longitude and latitude coordinates is generated through geographic projection transformation.

[0081] Early dead tree identification technology relied primarily on simple visual interpretation of remote sensing imagery. Staff visually observed changes in the spectral characteristics of pine trees in remote sensing images to determine if a tree was dead. However, this method is highly subjective and subject to significant differences in personnel skill level and experience, making identification accuracy difficult to guarantee. Furthermore, relying solely on spectral information makes it difficult to accurately distinguish dead trees in complex forest environments and diverse vegetation types. It can be easy to mistakenly identify healthy pine trees or vegetation with similar spectral characteristics as dead trees, making subsequent prevention and control efforts less targeted.

[0082] By using multispectral images to calculate the normalized vegetation index and overgreening index and other technologies to identify dead trees, the accuracy and objectivity of dead tree identification are improved. By in-depth analysis of characteristics such as the vegetation index and red edge position offset, dying trees can be screened, making it possible to more accurately determine the scope and extent of diseased pine trees, effectively solving the problem of low accuracy in dead tree and dying tree identification in existing technologies and susceptibility to interference from subjective factors.

[0083] Furthermore, the infection risk value formula is: ;

[0084] Among them, R is the infection risk value, is the weight value, , is the historical severity of injuries, is the cumulative treatment time after time-attenuation treatment, is the time decay factor, is the plane distance from the target pine tree to the nearest diseased tree.

[0085] Calculating historical injury severity :

[0086] ;

[0087] in, is the injury type weight, , For the The number of days the injury lasts, m is the total number of injuries, is the maximum effective statistical period;

[0088] Cumulative treatment duration After aging treatment, :

[0089] ;

[0090] in, is the treatment time attenuation coefficient, e is a natural constant;

[0091] Retrieve the radius of the coordinate The location and felling time of the diseased trees within the area, and the plane distance from the target pine tree to the nearest diseased tree are calculated. , and the time decay factor , The number of months since the last diseased tree was felled.

[0092] Furthermore, the specific steps of spatially matching and registering all remote sensing coordinate information with the geographic base map to obtain the drawn remote sensing scan map are as follows:

[0093] Use the forest area digital elevation model and vector class boundary map as the geographic base map;

[0094] Convert the remote sensing coordinate information set of dead standing trees and dying trees into the coordinates of the geographic basemap: , is the rotation and scaling matrix, is the translation vector, and are the longitude and latitude of the coordinates in the geographic map, and is the longitude and latitude in remote sensing coordinate information;

[0095] Optimize matrix parameters through measured coordinates of ground control points;

[0096] The coordinates on the optimized geographic map are integrated with the forest area digital elevation model to generate a three-dimensional remote sensing scanning map.

[0097] Furthermore, the visual marking of dead standing trees and dying trees on the remote sensing scan map specifically refers to:

[0098] Mark the coordinate points of the dead standing trees on the remote sensing scan map with colored circles, where the diameter of the circles is proportional to the crown size, and mark the coordinate values ​​of the dead standing trees on the remote sensing scan map;

[0099] The coordinate points of dying trees on the remote sensing scan map are marked with double-layer colored circles. The outer ring diameter of the double-layer circle is fixed pixels, and the transparency of the inner ring is positively correlated with the infection risk value. , and mark the coordinate values ​​and infection risk values ​​of dying trees on the remote sensing scanning map.

[0100] Furthermore, the converting of remote sensing coordinate information into prevention and control task information further includes:

[0101] Generate a task work order based on the coordinate point type and attributes of the visual mark:

[0102] The coordinates of standing dead trees trigger felling tasks. The standing dead tree task work order includes: positioning coordinates, felling pile processing requirements, and associated small group number;

[0103] The coordinates of the dying tree trigger the injection task. The dying tree task work order includes: positioning coordinates, injection requirements , is the average injection requirement, is the infection risk value.

[0104] Traditional methods lack a systematic and intelligent process for converting coordinate information into prevention and control tasks. Typically, manual planning of prevention and control areas and measures is done based on a small amount of collected coordinate information and empirical experience. This approach fails to fully consider factors such as the overall distribution of the pine forest, the specific condition of individual pine trees, and the geographical environment. This leads to irrational allocation of prevention and control tasks. For example, prevention and control efforts may be insufficient in severely affected areas, while relatively healthy areas may be over-treated. This wastes prevention and control resources and compromises effectiveness.

[0105] By spatially matching and registering remote sensing coordinate information with the geographic base map and visually marking them, the distribution of dead and dying trees infected with pine wilt disease in the pine forest can be intuitively displayed. By converting the coordinate information into prevention and control task information, targeted prevention and control task allocation can be achieved, effectively solving the problem in the existing technology that the conversion of coordinate information into prevention and control tasks is unreasonable and cannot fully consider multiple factors.

[0106] Furthermore, the formula for obtaining the shortest connected path is:

[0107] ;

[0108] in, is the job block number, is the total number of points to be sprayed in the operation block, and Number the points to be sprayed in the operation area. is the binary decision variable for path decision, , Indicates each point to be sprayed There is only one entrance. Indicates each point to be sprayed There is only one exit. is the time cost of wind direction, , is the weight, , From the point to be sprayed arrive The surface distance, From the point to be sprayed arrive The elevation cost, From the point to be sprayed arrive The wind direction time cost, , is the angle between the flight direction and the wind direction, is the drag coefficient, Calibrate the speed of the drone.

[0109] In summary, the embodiment of the present application accurately locates and identifies dead trees infected with pine wood nematode disease by identifying dead trees based on remote sensing images and converting coordinate information, thereby achieving efficient and comprehensive dead tree information collection.

[0110] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0114] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0115] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease, characterized in that: The following steps are involved: Step 1: Access the Songlin Forest Data Management Database; Step 2: Identify dead trees based on remote sensing images: Obtain remote sensing spectral images of the target pine forest area, identify and locate dead trees and dying trees infected with pine wilt disease from the remote sensing spectral images, and obtain remote sensing coordinate information corresponding to the dead trees and dying trees; The identification method of the dying wood is: Based on multispectral image analysis of vegetation index and red edge offset, the vegetation index threshold range and red edge offset threshold are obtained, and suspected targets with vegetation index in the faded range and red edge offset exceeding the limit are screened out. Extract the texture features of the suspected target, including the gray-level co-occurrence matrix entropy and fractal dimension, obtain the texture entropy threshold and fractal dimension threshold, and retain the target pine trees that meet both spectral and texture anomalies; The pixel coordinates of the screened target pine trees are converted into latitude and longitude coordinates in the geographic coordinate system through affine transformation; Using the longitude and latitude coordinates as index keys, the pine forest data management database is queried to obtain the historical injury and illness dataset of the target pine trees, including injury type codes, injury severity, and cumulative treatment duration, for infection risk analysis. Obtain the infection risk value, mark the target pine trees whose infection risk value is not less than the infection risk threshold as dying trees, and output their latitude and longitude coordinates and infection risk value; Step 3, coordinate matching: spatially match and register all remote sensing coordinate information with the geographic base map to obtain the drawn remote sensing scan map; Step 4, visual marking: visually mark the dead standing trees and dying trees on the remote sensing scan map; Step 5, coordinate information conversion: Based on the coordinate information and the pine forest data management database, the remote sensing coordinate information is converted into control task information, including using the coordinates of the remaining pine trees other than the dead standing trees and dying trees as the spraying points to trigger the spraying control task; The steps of the spraying prevention task include: Step A1: Aggregate the points to be sprayed into several operation blocks, with the altitude deviation being less than a first threshold and the plane distance being less than a second threshold as constraints; Step A2: Calculate the shortest connected path for all the points to be sprayed in each operation block; Step A3: Using the coordinates of the center of gravity of the operation block as nodes, use the ant colony algorithm to generate the block operation sequence with the global minimum time cost; Step A4: Output the block operation sequence as a waypoint instruction set executable by the drone.

2. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 1, wherein: The pine forest data management database includes pine tree numbers, pine tree locations, and historical inspection information; The pine tree location refers to the latitude and longitude information of the current pine tree and the felled pine tree; The historical inspection information refers to the results of the staff's inspection of the pine trees, including health, injury types and treatment history.

3. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 1, wherein: The identification method of the dead standing tree is: The normalized vegetation index and overgreening index are calculated based on multispectral images, and a binary mask image is generated according to the double threshold conditions to filter out low-activity pixels. Perform morphological closing operations on continuous low-activity pixel clusters to fill holes and extract the minimum bounding rectangle of each cluster; The center point of the rectangle is used as the initial pixel coordinate of the dead tree, and a set of longitude and latitude coordinates is generated through geographic projection transformation.

4. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 1, wherein: The infection risk value formula is: ； Among them, R is the infection risk value, is the weight value, , is the historical severity of injuries, is the cumulative treatment time after time-attenuation treatment, is the time decay factor, is the plane distance from the target pine tree to the nearest diseased tree.

5. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 1, wherein: The specific steps of spatially matching and registering all remote sensing coordinate information with the geographic base map to obtain the drawn remote sensing scan map are as follows: Use the forest area digital elevation model and vector class boundary map as the geographic base map; Convert the remote sensing coordinate information set of dead standing trees and dying trees into the coordinates of the geographic basemap: , is the rotation and scaling matrix, is the translation vector, and are the longitude and latitude of the coordinates in the geographic map, and is the longitude and latitude in remote sensing coordinate information; Optimize matrix parameters through measured coordinates of ground control points; The coordinates on the optimized geographic map are integrated with the forest area digital elevation model to generate a three-dimensional remote sensing scanning map.

6. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 1, wherein: The visual marking of dead standing trees and dying trees on the remote sensing scan map specifically refers to: Mark the coordinate points of the dead standing trees on the remote sensing scan map with colored circles, where the diameter of the circles is proportional to the crown size, and mark the coordinate values ​​of the dead standing trees on the remote sensing scan map; The coordinate points of the dying trees on the remote sensing scan map are marked with a double-layer circle frame with a color. The outer ring diameter of the double-layer circle frame is a fixed pixel, and the filling transparency of the inner ring is positively correlated with the infection risk value. The coordinate values ​​and infection risk values ​​of the dying trees on the remote sensing scan map are also marked.

7. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 6, wherein: The converting of remote sensing coordinate information into prevention and control task information further includes: Generate a task work order based on the coordinate point type and attributes of the visual mark: The coordinates of standing dead trees trigger felling tasks. The standing dead tree task work order includes: positioning coordinates, felling pile processing requirements, and associated small group number; The coordinates of the dying tree trigger the injection task. The dying tree task work order includes: positioning coordinates and injection requirements.

8. The method for collecting and converting remote sensing coordinate information of dead standing trees infected with pine wilt disease according to claim 7, wherein: The formula for obtaining the shortest connected path is: ； in, is the job block number, is the total number of points to be sprayed in the operation block, and Number the points to be sprayed in the operation area. is the binary decision variable for path decision, , is the time cost of wind direction.

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