Bamboo forest disaster detection method and device based on airborne radar

Abstract

The invention provides a bamboo forest disaster detection method and device based on an airborne radar, and relates to the technical field of remote sensing monitoring, and the method collects three-dimensional point cloud data of a to-be-detected bamboo forest through a laser radar, and the laser radar is carried on an unmanned aerial vehicle, thereby solving the problems of low efficiency and limitation of terrains in traditional manual treading. The data acquisition efficiency is improved; based on the three-dimensional point cloud data, determining the affected area and the normal area of the bamboo forest to be detected, thereby accurately and quickly positioning the affected area according to the three-dimensional point cloud data; determining disaster indexes of the to-be-detected bamboo forest based on the affected area and the normal area, so as to carry out detailed quantification on disaster conditions; and based on the disaster index, obtaining a disaster detection result of the to-be-detected bamboo forest. According to the bamboo forest disaster detection method based on the airborne radar, accurate and efficient detection of bamboo forest disasters is realized, and a decision basis can be provided for post-disaster recovery work, disaster-affected bamboo forest resource reutilization and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing monitoring, and in particular to a bamboo forest disaster detection method and device based on airborne radar. BACKGROUND

[0002] Frequent and severe climate change will bring a huge impact on evergreen vegetation, bamboo forest growth, for example, in a short time in winter and spring concentrated snowfall, causing large area of bamboo forest bending, breaking, breaking and turning, etc. Phenomenon, resulting in a substantial decline in yield of timber bamboo and shoot bamboo, leading to the local forest farmers' greater economic losses. Therefore, bamboo farmers need to investigate the disaster of bamboo forest, judge the disaster degree, so as to take timely post-disaster recovery work, and carry out disaster bamboo forest resource recycling.

[0003] The traditional bamboo forest disaster investigation method is mainly artificial investigation. After the disaster occurs, the bamboo farmer delimits the disaster area in the bamboo forest field, and investigates and records the disaster situation. However, this method is low in efficiency, time-consuming and laborious, and the investigators are easily affected by weather and terrain, so that part of the disaster area cannot be investigated, and therefore, the disaster area information of large area of bamboo forest cannot be accurately and quickly obtained. SUMMARY

[0004] The present application provides a bamboo forest disaster detection method and device based on airborne radar, which can accurately and quickly obtain the disaster area information of large area of bamboo forest, and realize accurate and efficient detection of bamboo forest disaster.

[0005] The present application provides a bamboo forest disaster detection method based on airborne radar, comprising the following steps: Collecting three-dimensional point cloud data of the bamboo forest to be measured by laser radar; the laser radar is carried on the unmanned aerial vehicle; Based on the three-dimensional point cloud data, the disaster area and the normal area of the bamboo forest to be measured are determined; Based on the disaster area and the normal area, the disaster index of the bamboo forest to be measured is determined; Based on the disaster index, the disaster detection result of the bamboo forest to be measured is obtained; The disaster index includes the point cloud angle of the disaster area, the crown layer point cloud volume reduction rate of the disaster area and the disaster area ratio.

[0006] According to the bamboo forest disaster detection method based on airborne radar provided by the present application, the three-dimensional point cloud data is used to determine the disaster area and the normal area of the bamboo forest to be measured, which comprises: The three-dimensional point cloud data is segmented by CHM to obtain the crown layer height model of the bamboo forest to be measured; The crown layer height model is segmented by distance map reconstruction marking to obtain the crown layer point cloud of different areas of the bamboo forest to be measured. The area with the height of the canopy point cloud being less than the under-branch height of the bamboo is the disaster area, and the area with the height of the canopy point cloud being greater than or equal to the under-branch height of the bamboo is the normal area; the under-branch height of the bamboo is determined based on the variety of the bamboo in the bamboo forest to be detected.

[0007] According to the bamboo forest disaster detection method based on the airborne radar provided in the present application, the disaster index of the bamboo forest to be detected is determined based on the disaster area and the normal area, including: The angle between the geometric center line of the canopy point cloud of the disaster area and the geometric center line of the canopy point cloud of the normal area is taken as the point cloud angle of the disaster area.

[0008] According to the bamboo forest disaster detection method based on the airborne radar provided in the present application, the disaster index of the bamboo forest to be detected is determined based on the disaster area and the normal area, including: The ratio of the canopy point cloud volume of the disaster area to the canopy point cloud volume of the normal area is taken as the canopy point cloud volume reduction rate of the disaster area.

[0009] According to the bamboo forest disaster detection method based on the airborne radar provided in the present application, the disaster index of the bamboo forest to be detected is determined based on the disaster area and the normal area, including: The area of the disaster area is determined based on the canopy point cloud of the disaster area; The total area of the bamboo forest to be detected is determined based on the three-dimensional point cloud data; The ratio of the area of the disaster area to the total area of the bamboo forest to be detected is taken as the disaster area ratio.

[0010] According to the bamboo forest disaster detection method based on the airborne radar provided in the present application, the disaster detection result of the bamboo forest to be detected is obtained based on the disaster index, including: In the case that all the disaster indexes are less than the corresponding preset threshold, the disaster detection result is a mild disaster; In the case that any index in all the disaster indexes is greater than or equal to the corresponding preset threshold, the disaster detection result is a severe disaster.

[0011] The present application also provides a bamboo forest disaster detection device based on airborne radar, including the following modules: The acquisition module is used for acquiring three-dimensional point cloud data of the bamboo forest to be detected by a laser radar; the laser radar is carried on a drone; The division module is used for determining a disaster area and a normal area of the bamboo forest to be detected based on the three-dimensional point cloud data; a quantification module configured to determine a disaster index of the bamboo forest to be detected based on the disaster area and the normal area; a detection module configured to obtain a disaster detection result of the bamboo forest to be detected based on the disaster index. The disaster index comprises a point cloud included angle of the disaster area, a canopy point cloud volume reduction rate of the disaster area, and a disaster area ratio.

[0012] The application further provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the bamboo forest disaster detection method based on airborne radar according to any one of the preceding embodiments when executing the computer program.

[0013] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the bamboo forest disaster detection method based on airborne radar according to any one of the preceding embodiments.

[0014] The application further provides a computer program product comprising a computer program, and the computer program is executed by a processor to implement the bamboo forest disaster detection method based on airborne radar according to any one of the preceding embodiments.

[0015] The bamboo forest disaster detection method and device based on airborne radar provided by the application solve the problems of low efficiency and terrain limitation of traditional manual inspection by collecting three-dimensional point cloud data of the bamboo forest to be detected by a laser radar, and the laser radar is carried on a UAV, thereby improving the data collection efficiency. Based on the three-dimensional point cloud data, the disaster area and the normal area of the bamboo forest to be detected are determined, so as to accurately and quickly locate the disaster area according to the three-dimensional point cloud data for subsequent disaster detection. Based on the disaster area and the normal area, the disaster index of the bamboo forest to be detected is determined, so as to quantitatively analyze the disaster situation. Based on the disaster index, the disaster detection result of the bamboo forest to be detected is obtained, wherein the disaster index comprises a point cloud included angle of the disaster area, a canopy point cloud volume reduction rate of the disaster area, and a disaster area ratio, thereby realizing accurate and efficient detection of the bamboo forest disaster and providing a decision basis for post-disaster recovery work and reutilization of the disaster bamboo forest resources. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort.

[0017] Figure 1 is a flowchart of a bamboo forest disaster detection method based on airborne radar provided by the application.

[0018] Figure 2 The present application provides a schematic diagram of the point cloud angle formed by the crown layer point cloud geometric center line of the disaster area and the crown layer point cloud geometric center line of the normal area.

[0019] Figure 3 The present application provides a schematic diagram of the crown layer point cloud volume of the disaster area and the crown layer point cloud volume of the normal area.

[0020] Figure 4 The present application provides a schematic diagram of the area of the disaster area and the area of the normal area.

[0021] Figure 5 The present application provides a structure schematic diagram of a bamboo forest disaster detection device based on airborne radar.

[0022] Figure 6 The present application provides a structure schematic diagram of an electronic device. DETAILED DESCRIPTION

[0023] To make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with the drawings in the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0024] The present application provides a structure schematic diagram of an electronic device. Figures 1 to 6 The present application provides a structure schematic diagram of an electronic device.

[0025] Figure 1 The present application provides a structure schematic diagram of an electronic device. Figure 1 The present application provides a structure schematic diagram of an electronic device. Step 101, collecting three-dimensional point cloud data of the bamboo forest to be measured by laser radar; the laser radar is carried on the unmanned aerial vehicle.

[0026] Specifically, the three-dimensional point cloud data of the bamboo forest to be measured is collected by flying the unmanned aerial vehicle carrying the laser radar sensor above the bamboo forest to be measured.

[0027] For example, the DJI M300 RTK unmanned aerial vehicle is carried with a gimbal, the unmanned aerial vehicle platform has good flight performance, and the load capacity and endurance time can meet the needs of multispectral data collection. The laser radar sensor adopts LiAir X3-H light and small unmanned aerial vehicle laser radar system, which integrates light and small laser radar, self-developed inertial navigation system, 2600W high-resolution mapping camera and high-power edge computing platform.

[0028] In the data acquisition process, the laser radar system controls the device to acquire data by receiving and executing the commands sent by the hardware switch or Liplan flight assistant. The acquired raw data is all stored in the TF card, and after the data acquisition is completed, it is copied to the local computer for data processing. The position and angle information in the laser radar system coordinate system and the POS data are fused and solved by using Ligeoreference software to obtain accurate geographic coordinates, so that the measured bamboo forest is accurately data-acquired, and the specific positioning of the data of different areas collected is clear.

[0029] In order to ensure the quality of acquisition, it is preferred to carry out unmanned aerial vehicle data acquisition in sunny weather from 10:00 to 14:00 to ensure the consistency of light conditions.

[0030] The embodiment of the application solves the problems of satellite images that cannot acquire data in real time, difficult data acquisition, limited data dimension or high use cost, and improves the efficiency and accuracy of data acquisition.

[0031] Step 102, determining the disaster area and normal area of the measured bamboo forest based on the three-dimensional point cloud data.

[0032] Further, the determination of the disaster area and normal area of the measured bamboo forest based on the three-dimensional point cloud data comprises: CHM segmentation is performed on the three-dimensional point cloud data to obtain a canopy height model of the measured bamboo forest. The canopy height model is subjected to distance map reconstruction and label segmentation to obtain canopy point clouds of different areas of the measured bamboo forest. The area with a canopy point cloud height less than the under-branch height of the bamboo is the disaster area, and the area with a canopy point cloud height greater than or equal to the under-branch height of the bamboo is the normal area. The under-branch height of the bamboo is determined based on the variety of the bamboo in the measured bamboo forest.

[0033] Specifically, in order to improve the accuracy and quality of the three-dimensional point cloud data, relevant pretreatment can be performed before the three-dimensional point cloud data is segmented, including: Flight strip splicing - based on a strict geometric model, automatically match the three-dimensional point cloud data collected by different flight strips, and display the splicing result in real time to generate high-precision point cloud data; Data management - data format conversion, point cloud denoising, normalization, raster band operation and other operations are performed to improve the data quality; Statistical analysis - the number of points, point density, Z value and other information in the three-dimensional point cloud data are counted and analyzed to evaluate the data quality.

[0034] After preprocessing, in order to further improve the accuracy and efficiency of the three-dimensional point cloud data segmentation, and obtain accurate bamboo canopy information, the embodiment of the present application first carries out canopy height model (CHM) segmentation on the three-dimensional point cloud data, and obtains the canopy height model of the measured bamboo forest, wherein the CHM segmentation is the process of converting LiDAR data into CHM; then the CHM is further segmented by distance map reconstruction marking segmentation algorithm to extract the bamboo canopy information of different regions. The specific steps include the following: Data preparation - first, using the three-dimensional point cloud data after preprocessing as the data source, a CHM three-dimensional model is constructed to reflect the vertical structure of the bamboo forest above the ground; CHM segmentation - in view of the problem of more pores in the high-resolution CHM crown area, enhanced Frost local filtering processing is adopted to optimize the CHM, so as to effectively suppress the canopy noise and better retain the image detail information; Further segmentation - the CHM is segmented by using the distance map reconstruction marking segmentation algorithm to improve the segmentation accuracy of the bamboo classification in different regions, and the canopy point cloud of single bamboo is accurately extracted; Comparative analysis - by comparing the segmentation effects under different resolutions, it is found that the CHM segmentation effect under 0.2m resolution is the best, so the canopy point cloud corresponding to the CHM under 0.2m resolution is preferably selected to improve the data accuracy; Data output - after the distance map reconstruction marking segmentation of the CHM under 0.2m resolution, each input CHM data will generate a corresponding CSV file and SHP file, which respectively correspond to the canopy point cloud of single bamboo, and these files are superimposed and displayed to present detailed bamboo attribute information, so as to obtain the canopy point cloud of different regions of the measured bamboo forest.

[0035] The canopy point cloud in different regions is visually segmented (i.e. manually distinguished) by using the Labelme tool, and then the region with the height of the canopy point cloud less than the underbranch height of the bamboo is taken as the disaster area, and the region with the height of the canopy point cloud greater than or equal to the underbranch height of the bamboo is taken as the normal region, wherein the underbranch height of the bamboo is determined based on the bamboo variety of the measured bamboo forest.

[0036] The underbranch height of the bamboo refers to the vertical height from the ground of the bamboo to the lowest branch point of the canopy. Since the underbranch height of different bamboo species is quite different, for example, the underbranch height of Phyllostachys pubescens is about 3m, and the underbranch height of Pleioblastus amarus is about 2.5m, therefore, in order to improve the judgment accuracy, the corresponding underbranch height threshold is determined according to the bamboo variety of the measured bamboo forest.

[0037] And in the disaster area, due to the bending, breaking, breaking or turning of the bamboo, the corresponding crown layer point cloud height will be significantly reduced, when the height of the crown layer point cloud is observed to be less than the height of the bamboo branch, it can be judged that the area is a disaster area.

[0038] In some embodiments, the high-definition camera carried by the unmanned aerial vehicle can also be used to capture high-definition images of the bamboo forest below, so as to identify the disaster area and the normal area according to the high-definition images, and verify the three-dimensional point cloud segmentation result.

[0039] For example, according to the area and shape of the bamboo sample plot, the flight height of the unmanned aerial vehicle can be set to 100 meters, the flight speed can be set to 10 m / s, the heading overlap degree can be set to 30%, and the side overlap degree parameter can be set to 75%, and the camera can be carried to ensure that complete sample plot images are obtained and data redundancy is minimized, and then the disaster area and the normal area are identified according to the high-definition images, and the three-dimensional point cloud segmentation result is verified.

[0040] The embodiment of the present application combines CHM segmentation and distance map reconstruction marker segmentation algorithm to perform high-precision segmentation on three-dimensional point cloud data, improves the recognition accuracy of single bamboo, and uses the bamboo branch height as the disaster area judgment threshold according to the difference between bamboo varieties, so as to accurately identify and quickly locate the disaster area and the normal area in the bamboo forest to be tested, thereby improving the accuracy and efficiency of bamboo forest disaster detection.

[0041] Step 103, based on the disaster area and the normal area, determining the disaster index of the bamboo forest to be tested; the disaster index includes the point cloud angle of the disaster area, the crown point cloud volume reduction rate of the disaster area, and the disaster area ratio.

[0042] Specifically, the disaster situation of the bamboo forest mainly includes the disaster area, and the disaster area can be reflected by a variety of specific disaster indexes, such as the disaster area ratio, the crown point cloud volume reduction rate of the disaster area, and the point cloud angle of the disaster area.

[0043] According to the crown layer point cloud data corresponding to the disaster area and the normal area, a variety of specific disaster indexes can be calculated, so as to quantify the disaster degree of the bamboo forest from multiple dimensions and provide a reliable basis for disaster detection.

[0044] Step 104, obtaining the disaster detection result of the bamboo forest to be tested based on the disaster index.

[0045] Further, the disaster detection result of the bamboo forest to be tested based on the disaster index includes: In the case that all indexes in all disaster indexes are less than the corresponding preset threshold, the disaster detection result is a light disaster; In a case where any one of all disaster indexes is greater than or equal to a corresponding preset threshold value, the disaster detection result is a severe disaster.

[0046] Specifically, according to the calculated disaster indexes, a specific threshold value can be further set, in a case where all indexes in the disaster indexes are less than corresponding preset threshold values, a mild disaster can be judged; in a case where any one of the disaster indexes is greater than or equal to a corresponding preset threshold value, a severe disaster can be judged.

[0047] For example, a canopy point cloud volume reduction rate of a disaster area can be set to be less than 30%, a point cloud angle of the disaster area can be set to be less than 15°, and a disaster area ratio can be set to be less than 20%, in this case, a mild disaster is judged, and in a case where any one of the indexes is greater than or equal to a corresponding preset threshold value, a severe disaster is judged.

[0048] The embodiment of the present application avoids the defects of low efficiency and high human influence factors caused by experience judgment in the traditional investigation method, improves the monitoring efficiency and quantization accuracy of the bamboo forest disaster, and can accurately and quickly obtain the disaster detection result of a large area of bamboo forest.

[0049] The bamboo forest disaster detection method based on airborne radar provided by the present application collects three-dimensional point cloud data of the to-be-detected bamboo forest by a laser radar, and the laser radar is carried on a drone, thereby solving the problem of low efficiency and terrain limitation of traditional manual inspection, and improving the data collection efficiency; based on the three-dimensional point cloud data, the disaster area and the normal area of the to-be-detected bamboo forest are determined, so as to accurately and quickly locate the disaster area according to the three-dimensional point cloud data for subsequent disaster detection; based on the disaster area and the normal area, the disaster indexes of the to-be-detected bamboo forest are determined, so as to quantize the disaster situation in detail; based on the disaster indexes, the disaster detection result of the to-be-detected bamboo forest is obtained, wherein the disaster indexes include the point cloud angle of the disaster area, the canopy point cloud volume reduction rate of the disaster area, and the disaster area ratio, thereby realizing accurate and efficient detection of the bamboo forest disaster, and providing a decision basis for post-disaster recovery work and reutilization of the disaster bamboo forest resources.

[0050] Further, the determination of the disaster indexes of the to-be-detected bamboo forest based on the disaster area and the normal area comprises: The angle between the canopy point cloud geometric centerline of the disaster area and the canopy point cloud geometric centerline of the normal area is taken as the point cloud angle of the disaster area.

[0051] Specifically, Figure 2 The schematic diagram of the formation of the point cloud angle between the canopy point cloud geometric centerline of the disaster area and the canopy point cloud geometric centerline of the normal area provided by the present application is shown as Figure 2 .

[0052] In the disaster area, due to the bending, breaking, breaking or turning of the bamboo forest, the height of the crown layer point cloud is reduced, while the bamboo forest in the normal area remains upright, and the height of the corresponding crown layer point cloud is higher, so the height difference between the point clouds can form the point cloud angle. The intersection of the corresponding crown layer point cloud geometry center lines of the disaster area and the normal area can calculate the value of the point cloud angle. The larger the point cloud angle, the more serious the disaster, and the larger the point cloud angle, the lighter the disaster. Through the point cloud angle, the disaster situation of the bamboo forest can be further quantified from the spatial angle of the geometric center line.

[0053] Further, the disaster index of the bamboo forest to be measured is determined based on the disaster area and the normal area, comprising: The ratio of the crown layer point cloud volume of the disaster area to the crown layer point cloud volume of the normal area is taken as the crown layer point cloud volume reduction rate of the disaster area.

[0054] Specifically, Figure 3 It is a contrast diagram of the crown layer point cloud volume of the disaster area and the crown layer point cloud volume of the normal area provided by the application, as shown in Figure 3 .

[0055] In the disaster area, the number of bamboo forests will usually decrease, and the number of points in the corresponding crown layer point cloud will also decrease, so the crown layer point cloud volume will also decrease. By calculating the crown layer point cloud volume reduction rate of the disaster area, the bamboo forest disaster situation can be further quantified from the perspective of volume change. The calculation expression is as follows: Further, the disaster index of the bamboo forest to be measured is determined based on the disaster area and the normal area, comprising: Based on the crown layer point cloud of the disaster area, the area of the disaster area is determined; Based on the three-dimensional point cloud data, the total area of the bamboo forest to be measured is determined; The ratio of the area of the disaster area to the total area of the bamboo forest to be measured is taken as the disaster area ratio.

[0056] Specifically, Figure 4 It is a contrast diagram of the area of the disaster area and the area of the normal area provided by the application, as shown in Figure 4 .

[0057] According to the coverage of the crown layer point cloud of the disaster area and the crown layer point cloud of the normal area, the area of the disaster area and the area of the normal area can be directly observed. At the same time, according to the three-dimensional point cloud data collected by the laser radar, the total area of the bamboo forest to be measured can be calculated.

[0058] According to the disaster area ratio of the to-be-tested bamboo forest, the bamboo forest disaster can be further quantified from the macro perspective of the area ratio, and the calculation expression is as follows: Based on any of the above embodiments, compared with the artificial inspection of the conventional method, the number of disaster plants in each investigation sample plot is counted, the percentage of the number of disaster plants in the total number of plants is calculated, and the disaster rate is obtained. The present application calculates a plurality of disaster indicators (including point cloud angle, crown point cloud volume reduction rate, and disaster area ratio) according to the laser radar point cloud data, quantifies the bamboo forest disaster, represents the bamboo forest disaster from multiple aspects, thereby enhancing the precision and interpretability of the bamboo forest disaster detection result, making the detection result more accurate and reliable, and being less affected by the weather and terrain of the bamboo forest, and having higher detection efficiency, so that large-area bamboo forest disaster monitoring can be performed at low cost, high precision, and high efficiency, and scientific and objective, thereby providing important technical support for fine bamboo forest planting management.

[0059] The bamboo forest disaster detection device based on airborne radar provided by the present application is described below, and the bamboo forest disaster detection device based on airborne radar described below can be correspondingly referred to the bamboo forest disaster detection method based on airborne radar described above.

[0060] Based on any of the above embodiments, Figure 5 is a structural schematic diagram of a bamboo forest disaster detection device based on airborne radar provided by the present application, as Figure 5 shown. The embodiment of the present application provides a bamboo forest disaster detection device based on airborne radar, which comprises an acquisition module 501, a division module 502, a quantification module 503, and a detection module 504, wherein: The acquisition module 501 is used for acquiring three-dimensional point cloud data of a to-be-tested bamboo forest by a laser radar; the laser radar is carried on a drone; the division module 502 is used for determining a disaster area and a normal area of the to-be-tested bamboo forest based on the three-dimensional point cloud data; the quantification module 503 is used for determining a disaster indicator of the to-be-tested bamboo forest based on the disaster area and the normal area; and the detection module 504 is used for obtaining a disaster detection result of the to-be-tested bamboo forest based on the disaster indicator; the disaster indicator comprises a point cloud angle of the disaster area, a crown point cloud volume reduction rate of the disaster area, and a disaster area ratio.

[0061] The bamboo forest disaster detection device based on airborne radar provided by the application can solve the problems of low efficiency and terrain limitation of traditional manual inspection, improve the data collection efficiency, determine the disaster area and normal area of the bamboo forest to be detected based on the three-dimensional point cloud data, accurately and quickly locate the disaster area according to the three-dimensional point cloud data, and then perform disaster detection, determine the disaster index of the bamboo forest to be detected based on the disaster area and the normal area, and then perform detailed quantification on the disaster situation, obtain the disaster detection result of the bamboo forest to be detected based on the disaster index, wherein the disaster index includes the point cloud included angle of the disaster area, the canopy point cloud volume reduction rate of the disaster area and the disaster area ratio, and the accurate and efficient detection of the bamboo forest disaster is realized, and a decision basis is provided for post-disaster recovery work and disaster bamboo forest resource recycling.

[0062] Figure 6 An example of a schematic diagram of a physical structure of an electronic device is shown in Figure 6 The electronic device can include a processor 610, a communications interface 620, a memory 630 and a communications bus 640, wherein the processor 610, the communications interface 620 and the memory 630 can communicate with each other through the communications bus 640. The processor 610 can call the logic instructions in the memory 630 to execute the bamboo forest disaster detection method based on airborne radar, and the method includes: Collecting three-dimensional point cloud data of the bamboo forest to be detected by a laser radar; the laser radar is carried on a UAV; Based on the three-dimensional point cloud data, the disaster area and the normal area of the bamboo forest to be detected are determined; Based on the disaster area and the normal area, the disaster index of the bamboo forest to be detected is determined; Based on the disaster index, the disaster detection result of the bamboo forest to be detected is obtained; The disaster index includes the point cloud included angle of the disaster area, the canopy point cloud volume reduction rate of the disaster area and the disaster area ratio.

[0063] Moreover, the logic instructions in the memory 630 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0064] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the bamboo forest disaster detection method based on airborne radar provided by the above-mentioned methods, the method comprising: acquiring three-dimensional point cloud data of the bamboo forest to be detected by a laser radar; the laser radar is carried on a UAV; determining a disaster area and a normal area of the bamboo forest to be detected based on the three-dimensional point cloud data; determining a disaster index of the bamboo forest to be detected based on the disaster area and the normal area; obtaining a disaster detection result of the bamboo forest to be detected based on the disaster index; the disaster index comprises a point cloud included angle of the disaster area, a crown layer point cloud volume reduction rate of the disaster area, and a disaster area ratio.

[0065] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the bamboo forest disaster detection method based on airborne radar provided by the above-mentioned methods, the method comprising: acquiring three-dimensional point cloud data of the bamboo forest to be detected by a laser radar; the laser radar is carried on a UAV; determining a disaster area and a normal area of the bamboo forest to be detected based on the three-dimensional point cloud data; determining a disaster index of the bamboo forest to be detected based on the disaster area and the normal area; obtaining a disaster detection result of the bamboo forest to be detected based on the disaster index; The disaster index includes a point cloud included angle of a disaster area, a point cloud volume reduction rate of a canopy of the disaster area, and a disaster area ratio.

[0066] The device embodiments described above are merely illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment scheme according to actual needs. Those skilled in the art can understand and implement without creative labor.

[0067] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods described in each embodiment or some parts of the embodiments.

[0068] It should be noted that in this document, the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, method, article, or device including a series of elements does not only include those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such a process, method, article, or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article, or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to the order of performing functions as shown or discussed, but can also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method can be performed in an order different from the described order, and various steps can also be added, omitted, or combined. In addition, the features described with reference to certain examples can be combined in other examples.

[0069] The "determining B based on A" in the embodiments of the present application means that A is considered as a factor when determining B. It is not limited to "determining B based on A only", but also includes "determining B based on A and C", "determining B based on A, C and E", "determining C based on A, and determining B based on C further", and the like. In addition, it can also include A as a condition for determining B, for example, "when A meets the first condition, determining B using the first method"; for example, "when A meets the second condition, determining B"; for example, "when A meets the third condition, determining B based on the first parameter"; and the like. Of course, A can also be a condition for determining B, for example, "when A meets the first condition, determining C using the first method, and determining B further based on C"; and the like.

[0070] The term "plurality" in the present application refers to two or more, and other quantifiers are similar.

[0071] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications 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 application.

Claims

1. A bamboo forest disaster detection method based on airborne radar, characterized in that: include: Collect 3D point cloud data of the bamboo forest to be measured using LiDAR; The laser radar is mounted on a drone; Determining a disaster-stricken area and a normal area of ​​the bamboo forest to be measured based on the three-dimensional point cloud data; Determining a disaster index of the bamboo forest to be tested based on the disaster-stricken area and the normal area; Obtaining disaster detection results of the bamboo forest to be tested based on the disaster indicators; The disaster indicators include the point cloud angle of the disaster-stricken area, the canopy point cloud volume reduction rate of the disaster-stricken area, and the disaster-stricken area ratio.

2. The method for detecting bamboo forest disasters based on airborne radar according to claim 1, characterized in that: The determining of the affected area and the normal area of ​​the bamboo forest to be measured based on the three-dimensional point cloud data includes: Performing CHM segmentation on the three-dimensional point cloud data to obtain a canopy height model of the bamboo forest to be measured; Performing distance map reconstruction and label segmentation on the canopy height model to obtain canopy point clouds of different areas of the bamboo forest to be measured; The area where the height of the canopy point cloud is less than the height under the bamboo branches is the disaster-stricken area, and the area where the height of the canopy point cloud is greater than or equal to the height under the bamboo branches is the normal area; the height under the bamboo branches is determined based on the bamboo species of the bamboo forest to be tested.

3. The method for detecting bamboo forest disasters based on airborne radar according to claim 2, characterized in that: The determining of the disaster index of the bamboo forest to be measured based on the disaster-stricken area and the normal area includes: The angle between the geometric midline of the canopy point cloud of the disaster-stricken area and the geometric midline of the canopy point cloud of the normal area is used as the point cloud angle of the disaster-stricken area.

4. The method for detecting bamboo forest disasters based on airborne radar according to claim 2, characterized in that: The determining of the disaster index of the bamboo forest to be measured based on the disaster-stricken area and the normal area includes: The ratio of the canopy point cloud volume of the disaster-stricken area to the canopy point cloud volume of the normal area is used as the canopy point cloud volume reduction rate of the disaster-stricken area.

5. The method for detecting bamboo forest disasters based on airborne radar according to claim 2, characterized in that: The determining of the disaster index of the bamboo forest to be measured based on the disaster-stricken area and the normal area includes: Determining the area of ​​the disaster-stricken area based on the canopy point cloud of the disaster-stricken area; Determining the total area of ​​the bamboo forest to be measured based on the three-dimensional point cloud data; The ratio of the area of ​​the disaster-stricken area to the total area of ​​the bamboo forest to be measured is used as the disaster-stricken area ratio.

6. The method for detecting bamboo forest disasters based on airborne radar according to claim 1, characterized in that: The obtaining of a disaster detection result of the bamboo forest to be tested based on the disaster indicator includes: When all disaster indicators are less than the corresponding preset thresholds, the disaster detection result is a mild disaster; When any one of all disaster indicators is greater than or equal to the corresponding preset threshold, the disaster detection result is a severe disaster.

7. A bamboo forest disaster detection device based on airborne radar, characterized in that: include: The acquisition module is used to collect three-dimensional point cloud data of the bamboo forest to be measured through lidar; The laser radar is mounted on a drone; a partitioning module, configured to determine a disaster-stricken area and a normal area of ​​the bamboo forest to be measured based on the three-dimensional point cloud data; A quantification module, configured to determine a disaster index of the bamboo forest to be tested based on the disaster-stricken area and the normal area; A detection module, configured to obtain a disaster detection result of the bamboo forest to be tested based on the disaster indicator; The disaster indicators include the point cloud angle of the disaster-stricken area, the canopy point cloud volume reduction rate of the disaster-stricken area, and the disaster-stricken area ratio.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the method for detecting bamboo forest disasters based on airborne radar according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for detecting bamboo forest disasters based on airborne radar according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the method for detecting bamboo forest disasters based on airborne radar according to any one of claims 1 to 6 is implemented.

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