Vegetation leaf area index inversion method and system based on laser point cloud

Abstract

The embodiment of the invention provides a vegetation leaf area index inversion method and system based on laser point cloud. The method is applied to the technical field of agrometeorological observation and comprises the following steps: acquiring laser point cloud data of a whole vegetation growth period; wherein the laser point cloud data comprises a vegetation canopy echo point cloud and a ground echo point cloud; probability density statistics is carried out on height data in the laser point cloud data at a preset time interval, and the height of a point with the maximum gradient is determined; performing distribution simulation random filtering on the laser point cloud data according to the height of the point with the maximum gradient to remove ground points; and performing inversion on the laser point cloud data after the ground points are removed based on the Beer-Lambert principle to obtain a vegetation leaf area index. In this way, automatic calculation of the vegetation leaf area index LAI can be achieved, and the time-space coverage rate and accuracy of monitoring are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of agricultural meteorological observation, and particularly to a method and system for retrieving the leaf area index of vegetation based on laser point cloud. Background Art

[0002] The leaf area index (LAI) is one of the important parameters describing the canopy structure of vegetation, and is of great significance for monitoring crop growth status, evaluating ecological environment changes, and studying global climate changes, etc. Traditional LAI measurement methods such as direct measurement method, image analysis method, etc., usually consume a large amount of time and manpower, and will have a destructive effect on vegetation during measurement, which is not conducive to continuous monitoring of crops. To solve the above problems, remote sensing observation technology has been widely applied to the quantitative estimation of crop growth parameters.

[0003] In recent years, lidar (Light Detection And Ranging, LiDAR) technology has shown great potential in obtaining vegetation structure parameters due to its characteristics of high precision, high efficiency, and non-destructive measurement. The three-dimensional point cloud data obtained by lidar technology can be used to achieve a detailed description of the vegetation canopy structure, and then be used for the inversion of LAI. For example, using terrestrial laser scanning (TLS) to obtain vegetation structure parameters, and segmenting the vegetation leaf echo point cloud by voxelization method. The introduction of this method has significantly improved the accuracy of LAI inversion. However, the existing LAI inversion methods based on lidar technology still have some deficiencies. For example, some methods have problems such as complex algorithms and large computational amounts in the data processing process, which limit their popularization in practical applications. In addition, for different growth stages and different varieties of vegetation, there are differences in their canopy structure characteristics. How to further improve the accuracy and applicability of LAI inversion is still the focus of current research.

[0004] In summary, the present invention aims to provide a method for retrieving the leaf area index LAI of vegetation based on laser point cloud, which can make full use of the advantages of lidar technology in obtaining vegetation structure parameters, and combine optimized algorithms and models to achieve accurate, rapid, and non-destructive measurement of vegetation LAI, providing strong technical support for precise vegetation growth monitoring and agricultural development. Summary of the Invention

[0005] The present disclosure provides a method and system for retrieving the leaf area index of vegetation based on laser point cloud, which solves the technical problems of untimely and inaccurate vegetation growth monitoring.

[0006] According to the first aspect of the present disclosure, a method for retrieving the leaf area index of vegetation based on laser point cloud is provided. The method includes:

[0007] Obtain the laser point cloud data of the entire growth cycle of vegetation; among them, the laser point cloud data includes vegetation canopy echo point cloud and ground echo point cloud;

[0008] Perform probability density statistics on the height data in the laser point cloud data at a preset time interval and determine the height of the point with the maximum gradient;

[0009] Perform cloth simulation random filtering on the laser point cloud data according to the height of the point with the maximum gradient to remove the ground echo point cloud;

[0010] Invert the laser point cloud data after removing the ground echo point cloud based on the Beer-Lambert principle to obtain the vegetation leaf area index.

[0011] For the above aspects and any possible implementation manners, a further implementation manner is provided. The obtaining of the laser point cloud data of the entire growth cycle of vegetation further includes:

[0012] Perform rotation correction on the laser point cloud data and then remove outliers.

[0013] For the above aspects and any possible implementation manners, a further implementation manner is provided. The performing of cloth simulation random filtering on the laser point cloud data according to the height of the point with the maximum gradient to remove the ground points includes:

[0014] Adopt cyclic cloth simulation random filtering to remove the ground echo point cloud. If the highest removed ground echo point cloud exceeds the height of the point with the maximum gradient, then invert the laser point cloud data after removing the ground echo point cloud based on the Beer-Lambert principle; if not, continue to perform cloth simulation random filtering to remove the ground echo point cloud.

[0015] For the above aspects and any possible implementation manners, a further implementation manner is provided. The performing of cloth simulation random filtering on the laser point cloud data according to the height of the point with the maximum gradient to remove the ground points further includes:

[0016] Perform probability density statistics at a preset time interval and determine the height at which the first gradient drops fastest as the maximum height for dividing the ground echo point cloud, so as to limit the number of cycles of cloth simulation random filtering.

[0017] For the above aspects and any possible implementation manners, a further implementation manner is provided. The calculation of the leaf area index based on the Beer-Lambert principle is expressed by the formula:

[0018]

[0019]

[0020] where f coveris the vegetation canopy coverage, P(θ) is the porosity, μ is the cosine of the solar zenith angle cosθ, θ is the lidar scanning angle / solar zenith angle, G is the average projection of all vegetation leaves per unit area in the plane perpendicular to the solar incidence, taking the empirical value of 0.5, λ 0 is the Nielsen parameter, taking the empirical value of -1, n crop is the vegetation canopy echo point cloud, n cropland is the total point cloud.

[0021] According to the second aspect of the present disclosure, a system for inverting the leaf area index of vegetation based on lidar point clouds is provided. The system includes:

[0022] An acquisition module, configured to acquire lidar point cloud data for the entire growth cycle of vegetation; wherein, the lidar point cloud data includes vegetation canopy echo point clouds and ground echo point clouds;

[0023] A processing module, configured to perform probability density statistics on the height data in the lidar point cloud data at a preset time interval and determine the height of the point with the maximum gradient;

[0024] A processing module, configured to perform cloth simulation random filtering on the lidar point cloud data according to the height of the point with the maximum gradient to remove ground echo point clouds;

[0025] An inversion module, configured to invert the lidar point cloud data after removing ground echo point clouds based on the Beer-Lambert principle to obtain the leaf area index of vegetation.

[0026] According to the third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor, and a computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0027] According to the fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method as according to the first aspect and / or the second aspect of the present disclosure is implemented.

[0028] First, acquire lidar point cloud observation data during the entire growth cycle of vegetation, remove point cloud data with obvious data missing or errors, preprocess the point cloud data, then use probability density statistics and cloth simulation random filtering methods to filter out ground echo point clouds, and finally invert the lidar point cloud data after removing ground echo point clouds based on the Beer-Lambert principle to obtain the leaf area index of vegetation.

[0029] The beneficial effects of the present disclosure include: (1) improving production efficiency: automated monitoring of the leaf area index LAI of vegetation can help accurately monitor and determine the development of vegetation, take appropriate measures, and improve the quality of vegetation;

[0030] (2) Reduce the cost of vegetation monitoring: Traditional observation methods rely on manual observation and recording. Automatically monitoring the leaf area index (LAI) of vegetation using algorithms can greatly reduce the labor burden and cost.

[0031] (3) Promote intelligent development: Observing the leaf area index (LAI) of vegetation based on physical simulation methods is conducive to improving the modernization level and technological content of production, thereby promoting sustainable development.

[0032] (4) Provide data analysis and research: By collecting a large amount of leaf area index (LAI) of vegetation, scientific basis for discovering the growth and development laws of vegetation is obtained, which is conducive to improving planting techniques and management.

[0033] It should be understood that the content described in the Summary of the Invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In combination with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0035] Figure 1 Shows a flowchart of a method for inverting the leaf area index of vegetation based on laser point cloud according to an embodiment of the present disclosure;

[0036] Figure 2 Shows a schematic diagram of missing data according to an embodiment of the present disclosure;

[0037] Figure 3 Shows a schematic diagram before and after data rotation correction according to an embodiment of the present disclosure;

[0038] Figure 4 Shows a schematic diagram of data anomalies according to an embodiment of the present disclosure;

[0039] Figure 5 Shows a schematic diagram of the distribution of maize leaf area index according to an embodiment of the present disclosure;

[0040] Figure 6 Shows a block diagram of a system for inverting the leaf area index of vegetation based on laser point cloud according to an embodiment of the present disclosure;

[0041] Figure 7 Shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the scope of protection of the present disclosure.

[0043] In addition, the term "and / or" in this document is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after.

[0044] In the present disclosure, first, laser point cloud observation data during the entire vegetation growth cycle is obtained, the point cloud data with obvious data missing or errors is removed, the point cloud data is preprocessed, and then the ground echo point cloud is filtered using probability density statistics and cloth simulation random filtering methods. Finally, based on the Beer-Lambert principle, the laser point cloud data after removing the ground echo point cloud is inverted to obtain the vegetation leaf area index. Thus, the automatic calculation of the leaf area index during the vegetation development period can be realized, and the spatio-temporal coverage rate and accuracy of monitoring can be improved.

[0045] Figure 1 The flowchart of a method for inverting the vegetation leaf area index based on laser point cloud according to an embodiment of the present disclosure is shown. As Figure 1 shown, a method for inverting the vegetation leaf area index based on laser point cloud includes:

[0046] S101, obtaining laser point cloud data during the entire vegetation growth cycle; wherein, the laser point cloud data includes vegetation canopy echo point cloud and ground echo point cloud.

[0047] In S101, the obtaining of the laser point cloud data during the entire vegetation growth cycle further includes removing outliers after performing rotation correction on the laser point cloud data.

[0048] In some embodiments, Figure 2 The schematic diagram of data missing according to an embodiment of the present disclosure is shown. For the point cloud data with data missing, the present disclosure uses manual screening to perform quality control on the point cloud data and deletes the data with problems such as data missing, obvious data errors, and data damage in the historical data.

[0049] In some embodiments, after removing data with problems such as file corruption and data corruption in the data, further select a certain day from the remaining data, perform 3D drawing, and save the pictures in the directions of the initial viewing angles of 0°, 30°, 60° horizontally, 90° vertically, and the viewing angles of 0°, 30°, 60° horizontally, 90° vertically. Set the rotation parameters according to the pictures in each viewing angle and each direction so that the underlying surface of the finally obtained point cloud data is parallel to the xoy axis. Figure 3 The schematic diagrams before and after data rotation correction according to the embodiments of the present disclosure are shown, and the specific calculation method of the rotation correction is shown in the following formula:

[0050] (1) Rotate by an angle α around the z-axis:

[0051]

[0052] x′ = cosα·x - sinα·y

[0053] y′ = sinα·x + cosα·y

[0054] z′ = z

[0055] (2) Rotate by an angle β around the y-axis:

[0056]

[0057] x′ = cosβ·x + sinβ·z

[0058] y′ = y

[0059] z′ = -sinβ·x + cosβ·z

[0060] (3) Rotate by an angle θ around the x-axis:

[0061]

[0062] x′ = x

[0063] y′ = cosθ·y - sinθ·z

[0064] z′ = sinθ·y + cosθ·z

[0065] In some embodiments, Figure 4 The schematic diagram of data anomalies according to the embodiments of the present disclosure is shown. Outliers in the point cloud data after rotation correction are removed by means of box plots, threshold monitoring, noise filtering, etc.

[0066] Specifically, a box plot structure is constructed based on the distribution law of vegetation point cloud data in each dimension. For the spatial coordinate dimension, box plots are drawn with the x, y, and z coordinate values as variables respectively. The box covers the middle 50% of the data, the box lines represent the interquartile range, and the whiskers extend beyond a certain multiple of the interquartile range. Data points outside the whiskers are preliminarily determined as outliers.

[0067] Specifically, threshold monitoring mainly sets a reasonable threshold range for specific attributes of vegetation point cloud data. In terms of reflection intensity, upper and lower thresholds are determined based on the common reflection intensity ranges of vegetation leaves and stems and the performance parameters of the measurement device. When the reflection intensity of the point cloud data exceeds this range, it is marked as an outlier.

[0068] Specifically, noise filtering mainly eliminates noise interference introduced by measurement devices, environmental factors, etc.

[0069] S102, Perform probability density statistics on the height data in the laser point cloud data at a preset time interval and determine the height of the point with the maximum gradient.

[0070] In some embodiments, probability density statistics are performed on the height point cloud data of the vegetation growth cycle, and a probability density map is drawn for each day to determine the height of the point with the maximum gradient.

[0071] S103, According to the height of the point with the maximum gradient, perform cloth simulation random filtering on the laser point cloud data to remove ground echo point clouds.

[0072] In some embodiments, the ground echo point clouds are removed by using cyclic cloth simulation filtering (CSF). If the highest removed ground echo point cloud exceeds the height of the point with the maximum gradient, the laser point cloud data after removing the ground echo point clouds is inverted based on the Beer-Lambert principle; if not, continue to perform cloth simulation random filtering to remove the ground echo point clouds.

[0073] Specifically, imagine the soil as multiple layers of cloth stacked together, and use the multiple-cycle cloth simulation filtering CSF algorithm to remove soil points. During the process of removing ground echo point clouds each time, continuously pay attention to the height of the highest point in the set of removed ground echo point clouds and compare it with the height of the previously determined point with the maximum gradient in real time. If the height of this highest point is less than the height of the point with the maximum gradient, it indicates that there may still be ground echo point clouds that have not been completely removed, and the cloth simulation filtering CSF operation needs to be performed again to ensure that the ground points are removed as thoroughly as possible while avoiding misdeleting vegetation points.

[0074] In some embodiments, probability density statistics are performed at preset time intervals, and the height at which the first gradient descends fastest is determined as the highest height for segmenting ground points, thereby limiting the number of cycles of cloth simulation random filtering.

[0075] Specifically, the number of cycles is limited by probability density statistics. Since as vegetation grows and the plants become denser, the points hitting the vegetation gradually become more numerous than the ground points, and the occurrence probability of ground points drops rapidly. Taking the point cloud of one observation as a sample, a probability density distribution graph is plotted every day, and the height at which the first gradient descends fastest is taken as the highest height for segmenting the ground echo point cloud, which is used to limit the number of cycles of cloth simulation random filtering.

[0076] S104, based on the Beer-Lambert principle, perform inversion on the lidar point cloud data after removing the ground echo point cloud to obtain the vegetation leaf area index.

[0077] In some embodiments, when after multiple iterative filterings, it is found that the height of the highest point of the ground echo point cloud that has been removed is greater than or equal to the height of the point with the maximum gradient, the point cloud data of ground points and non-ground points are statistically analyzed and the leaf area index of the vegetation is calculated based on the Beer-Lambert law.

[0078] In some embodiments, calculating the leaf area index based on the Beer-Lambert principle is expressed by the formula:

[0079]

[0080]

[0081] where f cover is the vegetation canopy coverage, P(θ) is the porosity, μ is the cosine of the solar zenith angle cosθ, θ is the lidar scan angle / solar zenith angle, G is the average projection of all vegetation leaves per unit area in the plane perpendicular to the solar incidence, taking the empirical value 0.5, λ 0 is the Nilson parameter, taking the empirical value -1, n crop is the vegetation canopy echo point cloud, n cropland is the total point cloud.

[0082] In some embodiments, various parameters need to be adjusted and improved to improve the accuracy of vegetation leaf area index calculation.

[0083] Specifically, since there is a certain angle between the ground in the original data and the z-axis, the original data needs to be rotationally corrected before segmenting the ground points and crop points. However, since the rotational correction is performed manually, the setting of the rotation angle has some subjectivity. Therefore, after obtaining the final result, the lidar scan angle and the rotation angle will be adjusted according to the result.

[0084] Specifically, since the threshold obtained using the probability density is a relatively small range, the number of loops of the cloth simulation random filtering CSF algorithm will be adjusted according to the result to make the result more reasonable.

[0085] Next, a specific embodiment is combined to detail a method 100 for inverting the leaf area index of vegetation based on laser point cloud provided by the embodiments of the present disclosure, which is specifically as follows:

[0086] First, obtain the laser point cloud data of the entire growth cycle of corn in 2023 from an agricultural website, including the echo point cloud of the corn canopy and the echo point cloud of the ground.

[0087] After rotating and correcting the corn laser point cloud data, outliers are removed. First, manually screen out the data with obvious errors or data damage, etc., and then rotate and correct the remaining data so that the underlying surface of the finally obtained point cloud data is parallel to the xoy axis. Finally, use methods such as box plots, threshold monitoring, and noise filtering to remove outliers from the rotated and corrected point cloud data.

[0088] Perform probability density statistics on the height data in the corn laser point cloud data and draw the probability density map for each day. Determine the height of the point with the maximum gradient, and perform cloth simulation random filtering on the corn laser point cloud data based on the determined height of the maximum point to remove the ground echo point cloud. Among them, if the highest removed ground echo point cloud exceeds the height of the point with the maximum gradient, perform inversion on the corn laser point cloud data after removing the ground echo point cloud based on the Beer-Lambert principle; if not, continue to perform cloth simulation random filtering to remove the ground echo point cloud.

[0089] Finally, perform inversion on the corn laser point cloud data after removing the ground echo point cloud based on the Beer-Lambert principle to obtain the corn leaf area index. Figure 5 Shows a schematic diagram of the distribution of the corn leaf area index according to an embodiment of the present disclosure.

[0090] According to the embodiments of the present disclosure, the following technical effects are achieved:

[0091] (1) Improve production efficiency: Automated monitoring of the leaf area index LAI of vegetation can help accurately monitor and determine the growth of vegetation, and take appropriate measures to improve the quality of vegetation.

[0092] (2) Reduce the cost of vegetation monitoring: Traditional observation methods rely on manual observation and recording. Using algorithms for automated monitoring of the leaf area index LAI of vegetation can greatly reduce the labor burden and labor costs.

[0093] (3) Promote intelligent development: Observing the leaf area index LAI of vegetation based on physical simulation methods is conducive to improving the modernization level and technological content of production, thereby promoting sustainable development;

[0094] (4) Provide data analysis and research: By collecting a large amount of leaf area index LAI of vegetation, discovering the scientific basis for the growth and development laws of vegetation is conducive to improving planting techniques and management.

[0095] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0096] The above is the introduction of method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0097] Figure 6 The block diagram of a vegetation leaf area index inversion system 600 based on laser point cloud according to an embodiment of the present disclosure is shown. As Figure 6 shown, the system 600 includes:

[0098] An acquisition module, configured to acquire laser point cloud data of the entire growth cycle of vegetation; wherein, the laser point cloud data includes vegetation canopy echo point cloud and ground echo point cloud;

[0099] A processing module, configured to perform probability density statistics on the height data in the laser point cloud data at a preset time interval and determine the height of the point with the maximum gradient;

[0100] A processing module, configured to perform cloth simulation random filtering on the laser point cloud data according to the height of the point with the maximum gradient to remove the ground echo point cloud;

[0101] An inversion module, configured to invert the laser point cloud data after removing the ground echo point cloud based on the Beer-Lambert principle to obtain the vegetation leaf area index.

[0102] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0103] In the technical solution of the present disclosure, the acquisition, storage, and application of user personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0104] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium, and a computer program product.

[0105] Figure 7 FIG. shows a schematic block diagram of an electronic device 700 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0106] The electronic device 700 includes a computing unit 701 that can perform various appropriate actions and processes according to a computer program stored in the ROM 702 or a computer program loaded from the storage unit 708 into the RAM 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. The I / O interface 705 is also connected to the bus 704.

[0107] A plurality of components in the electronic device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a magnetic disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the electronic device 700 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0108] The computing unit 701 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the method 100 described above may be executed. Alternatively, in other embodiments, the computing unit 701 may be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0109] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0112] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0114] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship of the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, can also be a server of a distributed system, or a server incorporating a blockchain.

[0115] It should be understood that the various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of this disclosure can be achieved, and no limitation is imposed herein.

[0116] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A vegetation leaf area index inversion method based on laser point cloud, comprising: Obtain laser point cloud data of the entire vegetation growth cycle; wherein the laser point cloud data includes vegetation canopy echo point cloud and ground echo point cloud; Performing probability density statistics on the height data in the laser point cloud data at preset time intervals and determining the height of the maximum gradient point; According to the height of the maximum gradient point, the laser point cloud data is subjected to cloth simulation random filtering to eliminate the ground echo point cloud; Based on the Beer-Lambert principle, the laser point cloud data after removing the ground points are inverted to obtain the vegetation leaf area index.

2. The method according to claim 1, characterized in that The laser point cloud data for obtaining the whole cycle of vegetation growth also includes: The laser point cloud data is rotated and corrected before outliers are removed.

3. The method according to claim 1, characterized in that The performing cloth simulation random filtering on the laser point cloud data to remove ground points according to the height of the maximum gradient point comprises: The ground echo point cloud is eliminated by using a cyclic cloth simulation random filter. If the highest point of the eliminated ground echo point cloud exceeds the height of the maximum gradient point, the laser point cloud data after eliminating the ground echo point cloud is inverted based on the Beer-Lambert principle; if not, the ground echo point cloud is eliminated by continuing to use cloth simulation random filtering.

4. The method according to claim 3, characterized in that The performing cloth simulation random filtering on the laser point cloud data to remove the ground echo point cloud according to the height of the maximum gradient point also includes: Probability density statistics are performed at preset time intervals and the first height with the fastest gradient descent is determined as the highest height for segmenting the ground echo point cloud, thereby limiting the number of cycles of random filtering for cloth simulation.

5. The method according to claim 1, characterized in that The leaf area index calculated based on the Beer-Lambert principle is expressed by the formula: Among them, f cover is the vegetation canopy coverage, P(θ) is the porosity, μ is the cosine of the solar zenith angle cosθ, θ is the lidar scanning angle / solar zenith angle, G is the average projection of all vegetation leaves per unit area on the plane perpendicular to the sun’s incidence, taking an empirical value of 0.5, λ0 is the Nelson parameter, taking an empirical value of -1, n crop is the vegetation canopy echo point cloud, n cropland is the total point cloud.

6. A vegetation leaf area index inversion system based on laser point cloud, comprising: An acquisition module is used to acquire laser point cloud data of the entire vegetation growth cycle; wherein the laser point cloud data includes vegetation canopy echo point cloud and ground echo point cloud; A processing module, used for performing probability density statistics on the height data in the laser point cloud data at preset time intervals and determining the height of the maximum gradient point; A processing module, used for performing cloth simulation random filtering on the laser point cloud data according to the height of the maximum gradient point to eliminate the ground echo point cloud; The inversion module is used to invert the laser point cloud data after removing the ground echo point cloud based on the Beer-Lambert principle to obtain the vegetation leaf area index.

7. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

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