Forest land light energy utilization efficiency evaluation method and system based on unmanned aerial vehicle laser radar

Abstract

The invention discloses a forest land light energy utilization efficiency evaluation method based on an unmanned aerial vehicle laser radar, and the method comprises the following steps: scanning a forest land through the unmanned aerial vehicle laser radar, obtaining laser point cloud data, and synchronously recording the spatial position and attitude data of the radar; preprocessing the laser point cloud data, and generating a high-precision three-dimensional laser point cloud in combination with the spatial position and attitude data; calculating the canopy transmissivity based on the three-dimensional laser point cloud; acquiring photosynthetically active radiation data; and based on the canopy transmittance and the photosynthetically active radiation data, evaluating the light energy utilization efficiency of the forest land through a light energy utilization efficiency dynamic evaluation model, and outputting an evaluation result. The forest land light energy utilization efficiency dynamic evaluation method can realize high resolution, is not limited by weather, can penetrate through canopies and quantifies the light transmission characteristics of the canopies.

Description

Technical Field

[0001] This invention relates to the field of vegetation ecological monitoring technology, and in particular to a method and system for evaluating the efficiency of forest light energy utilization based on UAV lidar. Background Technology

[0002] Light use efficiency (LUE) is a key physiological and ecological parameter for assessing the photosynthetic capacity of vegetation and the productivity of ecosystems. Its core is to quantify the effectiveness of plants in using light energy for photosynthesis, which is of great significance for accurately assessing precision agricultural management, forest carbon sequestration, and global climate change research.

[0003] Traditional LUE assessment methods primarily rely on two types of technologies: ground-based spectrometer measurements and satellite remote sensing inversion. However, these traditional methods have significant limitations. First, while satellite remote sensing has a wide coverage area, its spatial resolution is low (typically at the kilometer or hundred-meter level), making it difficult to capture differences in light energy utilization within forest stands or even at the individual tree scale. This results in overly macroscopic assessments that fail to meet the needs of refined management. Second, both satellite remote sensing and ground-based spectrometer measurements largely depend on passive optical sensors, i.e., detecting reflected sunlight. This makes the measurement process heavily dependent on weather conditions; data cannot be effectively acquired under adverse weather conditions such as rain, fog, or haze, and the measurement results are inconsistent across different times and solar altitude angles, making dynamic continuous monitoring difficult. Furthermore, ground-based spectrometers cannot penetrate dense vegetation canopies, only acquiring spectral information from the top of the canopy. They lack information on crucial aspects such as light transmission processes within the canopy and the understory light environment, limiting their ability to deeply analyze the overall light energy utilization status of the canopy.

[0004] With the development of lidar technology, especially the application of airborne lidar, new methods have been provided for acquiring three-dimensional structural parameters of vegetation (such as leaf area index (LAI) and canopy height). However, the current applications of lidar technology in vegetation ecological monitoring are mostly concentrated on topographic mapping and structural parameter extraction, and its technological potential has not been fully explored. A prominent bottleneck in its application is that existing methods fail to effectively correlate the three-dimensional structural information acquired by lidar with the light energy transmission model within the canopy. The transmission process of laser pulses in the canopy itself contains rich light transmission information, but existing methods lack the ability to quantify and invert the dynamic changes in light transmittance in real time, and have failed to establish a direct and efficient method for calculating canopy transmittance from laser point cloud data. This results in assessments remaining at the structural level, failing to deeply reveal the physiological and ecological processes related to light energy utilization.

[0005] Therefore, there is an urgent need in this field for a technical solution that can overcome the above-mentioned defects. Summary of the Invention

[0006] This invention provides a method and system for evaluating the efficiency of forest land solar energy utilization based on UAV lidar, aiming to effectively solve the above-mentioned technical problems.

[0007] According to a first aspect of the present invention, the present invention provides a method for evaluating the efficiency of forest land solar energy utilization based on UAV lidar, characterized by comprising the following steps:

[0008] The forest land is scanned by the drone's lidar to obtain laser point cloud data, and the spatial position and attitude data of the laser point cloud are recorded simultaneously.

[0009] The laser point cloud data is preprocessed, and a high-precision three-dimensional laser point cloud is generated by combining the spatial position and attitude data.

[0010] The canopy transmittance is calculated based on the ratio of the three-dimensional laser point cloud reaching the ground.

[0011] Obtain photosynthetically active radiation data;

[0012] Based on the canopy transmittance and the photosynthetically active radiation data, the light energy utilization efficiency of the forest land is evaluated using a dynamic evaluation model, and the evaluation results are output.

[0013] Furthermore, the calculation process for the canopy transmittance includes:

[0014] The 3D laser point cloud was classified into vegetation points and ground points using machine learning algorithms;

[0015] Based on the classified point cloud, the forest land is divided into multiple analysis units, and the number of vegetation points and ground points in each analysis unit are counted.

[0016] Based on the number of ground points and vegetation points, the canopy transmittance of each analysis unit is calculated.

[0017] Furthermore, the machine learning algorithm is trained and classified based on point cloud features, which include at least the planar coordinate position, height value, relative elevation difference with neighboring points, echo intensity, and point cloud density of the point cloud.

[0018] Furthermore, the formula for calculating the canopy transmittance of each of the analysis units is as follows:

[0019] Ti = N_ground_i / (N_ground_i + N_vegetation_i)

[0020] In the formula, Ti represents the canopy transmittance of the i-th analysis unit, N_ground_i represents the number of ground points, and N_vegetation_i represents the number of vegetation points.

[0021] Furthermore, the dynamic evaluation model for light energy utilization efficiency is as follows:

[0022] LUE = α × T × PAR / (1 + β × T²)

[0023] In the formula, LUE is light energy utilization efficiency, T is canopy transmittance, PAR is photosynthetically active radiation, and α and β are model correction coefficients related to vegetation type.

[0024] Furthermore, it also includes acquiring environmental factor data and dynamically fine-tuning the model correction coefficients based on the environmental factor data.

[0025] According to a second aspect of the present invention, the present invention also provides a forest land light energy utilization efficiency assessment system based on UAV lidar, characterized in that it comprises:

[0026] The data acquisition module is used to collect laser point cloud data, spatial location and attitude data of the laser point cloud in the forest using lidar and sensing devices carried by the drone.

[0027] The data processing module is used to process the laser point cloud data to calculate the canopy transmittance of the forest land, and combine it with the photosynthetically active radiation data to obtain the light energy utilization efficiency of the forest land through the light energy utilization efficiency evaluation model.

[0028] The result output module is used to visualize and apply the light energy utilization efficiency.

[0029] Furthermore, the visualization involves generating a corresponding spatial distribution map of light energy utilization efficiency based on the light energy utilization efficiency assessment results, and generating an assessment report.

[0030] According to a third aspect of the present invention, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement any of the steps of the above-described method for evaluating forest light energy utilization efficiency based on UAV lidar.

[0031] According to a fourth aspect of the invention, the invention also provides a storage medium storing a plurality of instructions adapted for loading by a processor to execute the steps of any of the above-described methods for evaluating forest light energy utilization efficiency based on UAV lidar.

[0032] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements any of the above-mentioned methods for evaluating forest land light energy utilization efficiency based on UAV lidar.

[0033] Through one or more embodiments of the above-described embodiments of the present invention, at least the following technical effects can be achieved: The present invention utilizes a UAV equipped with a multi-line lidar to actively emit laser pulses that penetrate the vegetation canopy, effectively overcoming the limitations of traditional optical sensors constrained by weather. The system achieves high-precision 3D modeling using high-density point cloud data, automatically distinguishes between vegetation and ground point clouds based on machine learning algorithms, and accurately calculates canopy transmittance through gridded statistics, achieving direct quantification of the photosynthetically active radiation transmission process. Simultaneously, the further constructed LUE dynamic evaluation model organically combines transmittance with photosynthetically active radiation, environmental factors (temperature and humidity), and vegetation type correction coefficients, achieving precise mapping from canopy structure to physiological function. Finally, a refined distribution map of forest area light energy utilization efficiency is generated, significantly improving efficiency compared to traditional methods while addressing the shortcomings of existing technologies. Attached Figure Description

[0034] The technical solution and other beneficial effects of the present invention will become apparent from the following detailed description of specific embodiments of the invention, in conjunction with the accompanying drawings.

[0035] Figure 1 This is a flowchart of the forest land light energy utilization efficiency evaluation method based on UAV lidar provided in the embodiments of the present invention;

[0036] Figure 2 This is a schematic diagram of the forest land light energy utilization efficiency evaluation system based on UAV lidar provided in an embodiment of the present invention;

[0037] Figure 3 This is a schematic diagram of a drone lidar performing forest scanning according to an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of the flight path of the unmanned aerial vehicle provided in the embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of radar point cloud data provided in an embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of the distribution of forest land light energy utilization efficiency provided in an embodiment of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0042] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the term "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, the character " / " in this document, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0043] To address the aforementioned technical problems, this invention provides a method and system for evaluating the efficiency of forest land solar energy utilization based on UAV lidar. The method will first be described in conjunction with the accompanying drawings.

[0044] like Figure 1 The flowchart shown illustrates the forest land solar energy utilization efficiency assessment method. This embodiment, based on UAV lidar, includes the following steps for assessing forest land solar energy utilization efficiency:

[0045] Step S1: Scan the forest area to be tested using the drone's lidar to obtain laser point cloud data, and simultaneously record the spatial position and attitude data of the laser point cloud.

[0046] In this step, the first step is to plan the UAV's preset flight path based on the survey area and forest characteristics, ensuring overlap between the flight path and lateral directions to achieve full coverage scanning. Then, the UAV automatically flies along the flight path, and the lidar mounted on the UAV continuously scans the vegetation canopy, simultaneously recording the emitted and received laser pulse signals (including reflection intensity and echo count). At the same time, a GPS / IMU system is used to simultaneously record the platform's spatial position and attitude data.

[0047] Step S2: Preprocess the laser point cloud data and generate a three-dimensional laser point cloud of the forest to be tested by combining its spatial location and attitude data.

[0048] This preprocessing step includes at least denoising and stitching operations on the laser point cloud to form a complete point cloud dataset for the survey area. Using POS data (spatial position and attitude data) and laser ranging data, a high-precision three-dimensional laser point cloud is generated through calculation.

[0049] Step S3: Divide the three-dimensional laser point cloud into vegetation point cloud and ground point cloud, and calculate the canopy transmittance based on the ratio of the three-dimensional laser point cloud reaching the ground.

[0050] In this embodiment, machine learning algorithms such as random forest models are used to adaptively classify point clouds. Specifically, typical vegetation points, ground points, and noise points are manually selected from the point cloud as training samples, and features of each point are extracted, including at least: the planar coordinate position of the point cloud, its height value, the relative elevation difference with neighboring points, echo intensity, and point cloud density. Then, a random forest model is trained based on the training samples, thereby automatically classifying the point cloud of the entire survey area through the trained random forest model, outputting two categories: "vegetation points" and "ground points".

[0051] After distinguishing between vegetation points and ground points in the survey area, the entire survey area was divided into a regular grid of 1m × 1m. For each grid, the number of "ground points" and "vegetation points" was counted. The canopy transmittance was calculated grid by grid, and a spatially continuous transmittance raster map was generated. The calculation formula is as follows:

[0052] Ti = N_ground_i / (N_ground_i + N_vegetation_i)

[0053] In the formula, Ti represents the canopy transmittance of the i-th analysis unit, N_ground_i represents the number of "ground points", and N_vegetation_i represents the number of "vegetation points".

[0054] Step S4: Calculate the photosynthetically active radiation data of the survey area by combining the atmospheric radiation transfer model with the geographical location of the flight time.

[0055] Step S5: Construct a dynamic evaluation model for light energy utilization efficiency (LUE) based on the coupling relationship between canopy transmittance and photosynthetically active radiation data. Use this model to evaluate light energy utilization efficiency and output the evaluation results.

[0056] The dynamic evaluation model for light energy utilization efficiency is as follows:

[0057] LUE = α × T × PAR / (1 + β × T²)

[0058] In the formula, LUE is light energy utilization efficiency, T is canopy transmittance, PAR is photosynthetically active radiation, and α and β are model correction coefficients related to vegetation type. These coefficients are obtained through prior knowledge or experimental data for different tree species (such as coniferous forests and broad-leaved forests) and are used to correct for differences in photosynthetic physiology and structure among different vegetation types. The β×T² term is used to simulate nonlinear physiological effects such as light saturation.

[0059] In the above formula, synchronously collected or acquired data such as temperature and humidity are used as auxiliary inputs to the LUE dynamic evaluation model. Environmental factors are used to correct the α and β coefficients to reflect the impact of environmental stresses (such as high temperature and drought) on light energy utilization efficiency.

[0060] Based on the forest land solar energy utilization efficiency assessment method based on UAV lidar provided in the above embodiments, this application also provides a specific implementation of a forest land solar energy utilization efficiency assessment system based on UAV lidar. Please refer to the following embodiments:

[0061] like Figure 2 As shown in the embodiments of this application, the forest land light energy utilization efficiency assessment system includes:

[0062] The data acquisition module 21 is used to collect laser point cloud data, spatial location and attitude data of the forest land through the lidar and sensing equipment carried by the UAV.

[0063] In this embodiment, the data acquisition module is a UAV remote sensing platform that integrates perception, positioning and flight control, including a UAV flight platform, a multi-line lidar mounted on it, a high-precision positioning and attitude determination unit, and optional environmental sensors (such as temperature and humidity sensors, ground PAR sensors).

[0064] Specifically, the drone flight platform uses multi-rotor or vertical take-off and landing fixed-wing drones, possessing stable flight control and automated mission execution capabilities. The flight altitude can be dynamically adjusted within a range of 50 to 200 meters according to forest stand height and point cloud density requirements to achieve optimal scanning results.

[0065] A multi-line lidar is used as the core sensor. In this embodiment, the lidar selected has a wavelength range of 905 nm to 1550 nm to ensure good penetration and reflection characteristics through vegetation. Its pulse frequency is ≥ 200 kHz to acquire high-density canopy point cloud data, accurately depicting the spatial distribution of branches and leaves, such as... Figure 3 As shown, it can record multiple echoes (first, intermediate, and final) to analyze the reflection, refraction, and transmission processes of laser light in the canopy.

[0066] The high-precision positioning and attitude determination unit integrates high-precision GPS (such as RTK / PPK) and IMU (inertial measurement unit) to provide centimeter-level precise position information and precise attitude angles for each laser pulse, which is the basis for generating high-precision, measurable 3D point clouds.

[0067] This embodiment also includes an auxiliary data acquisition unit, namely, a miniature environmental sensor deployed synchronously on the UAV or on the ground, for real-time acquisition of temperature and humidity data of the flight area. A ground-based photosynthetically active radiation sensor is also deployed for on-site verification and correction of the estimated PAR values.

[0068] The data processing module 22 is used to process the laser point cloud data to calculate the canopy transmittance of the forest land, and combine it with the photosynthetically active radiation data to obtain the light energy utilization efficiency (LUE) of the forest land through a light energy utilization efficiency evaluation model. The data processing module 22 includes a point cloud data processing unit 221, a canopy transmittance calculation unit 222, and an LUE evaluation unit 223.

[0069] The point cloud data processing unit 221 is used to perform denoising and stitching on the collected 3D laser point cloud data of the survey area, and then, based on machine learning algorithms, distinguish the point cloud data of the survey area into vegetation points and ground points. At the same time, the survey area is divided into multiple analysis units, and the number of vegetation points and ground points in each analysis unit is counted.

[0070] The canopy transmittance calculation unit 222 is used to calculate the canopy transmittance of each analysis unit according to the ratio of the three-dimensional laser point cloud reaching the ground, and generate a spatially continuous transmittance raster map.

[0071] LUE evaluation unit 223 is a dynamic evaluation model for light energy utilization efficiency, which is used to evaluate the light energy utilization efficiency of the test area.

[0072] The result output module 23 is used to output the light energy utilization efficiency evaluation results output by the LUE evaluation unit 223, and generate the corresponding light energy utilization efficiency spatial distribution map and evaluation report based on the evaluation results, so as to realize visualization and application.

[0073] The forest land light energy utilization efficiency assessment system described in this application collects high-density laser point cloud data through an integrated hardware platform, uses a core algorithm to accurately extract key parameters such as canopy transmittance from the point cloud, and combines environmental sensor data to construct a dynamic light energy utilization efficiency assessment model, ultimately achieving refined spatial mapping of forest land productivity.

[0074] The following uses a 10-hectare temperate mixed forest as the monitoring object. Based on the UAV lidar forest light energy utilization efficiency assessment system, the spatial distribution of light energy utilization efficiency during the peak growing season is specifically evaluated.

[0075] The hardware setup for the data acquisition module is as follows:

[0076] Unmanned Aerial Vehicle (UAV) Platform: The Pegasus D2000 multi-rotor UAV was selected. This platform offers stable flight, high payload capacity, and centimeter-level high-precision PPK / RTK positioning, meeting the requirements of this method for both flight platform and positioning accuracy.

[0077] LiDAR sensor: Equipped with Pegasus Lidar500 multi-line lidar, this device has a laser wavelength of 905 nm and a maximum pulse frequency of 320 kHz, which fully meets the technical requirements of laser wavelength and pulse frequency ≥200 kHz.

[0078] System Integration and Inspection: Rigidly connect and precisely calibrate the Lidar500 and the D2000 UAV's high-precision POS system (integrating GNSS and IMU) to ensure time synchronization. Before the mission begins, perform a power-on check on all equipment to confirm that the LiDAR, GPS signal, and IMU are in normal initialization status.

[0079] Survey area and flight route planning:

[0080] Import the boundary vector file of the target forest area into the flight control software. Based on the average stand height (approximately 15 meters) and the required point cloud density (≥50 points / square meter), set the flight altitude to 100 meters. Set the flight path speed to 8 meters / second and the heading overlap to 80%, generating a vector file as shown below. Figure 4 The parallel routes shown are subjected to full-coverage scanning.

[0081] Experimental environment: The operation was carried out in a time window with clear weather, no rain, and wind speed less than level 4, in order to reduce the impact of the environment on flight stability and GNSS signal.

[0082] Data Acquisition: The UAV flies automatically along a preset route. The Lidar500 synchronously emits laser pulses and receives echo signals returning from the vegetation canopy and the ground, recording multiple echo information and intensity values, including primary, secondary, and tertiary echoes. The onboard POS system continuously records the platform's latitude, longitude, elevation, roll, pitch, and heading angle data.

[0083] Other auxiliary data collection:

[0084] Before and after the flight operation, five ground checkpoints were evenly distributed within the survey area using handheld GPS devices for subsequent point cloud accuracy verification. Simultaneously, a small automatic weather station was deployed in an open area of ​​the forest to record temperature, humidity, and photosynthetically active radiation data during the flight.

[0085] The data processing module processes the collected data through the following steps:

[0086] Step 1: Point cloud data preprocessing and generation.

[0087] The acquired laser data and POS data are tightly coupled and jointly processed to obtain a trajectory file with centimeter-level accuracy using PPK technology. Then, using point cloud processing software, a high-precision 3D laser point cloud is generated by combining the trajectory file and the original laser data. Figure 5As shown in the .las format, each point is assigned accurate 3D coordinates and echo information. The preprocessed point cloud of the entire survey area contains approximately 300 million points.

[0088] Step 2: Use the trained random forest model to automatically classify the point cloud of the entire survey area, and output two categories: "vegetation points" and "ground points".

[0089] Step 3: Divide the entire survey area into a regular grid of 1m×1m, calculate the canopy-ground point cloud classification and transmittance for each grid, and generate a spatially continuous transmittance raster map.

[0090] Step 4: Calculate the light energy utilization efficiency of the survey area based on the light energy utilization efficiency model.

[0091] The transmittance raster map and photosynthetically active radiation generated in step three are input into the light energy utilization efficiency model LUE = α × T × PAR / (1 + β × T²). The raster calculation tool is used to perform pixel-by-pixel calculation for each grid i. At the same time, the model dynamically adjusts the vegetation type correction coefficients α and β based on environmental factors. For example, when the temperature is higher than 30°C, the value of α is reduced by a certain proportion according to a preset rule to simulate high temperature stress.

[0092] Specifically, in this embodiment, the instantaneous photoactive radiation (PAR) value for each grid is approximately 1800 μmol / m² / s. This value has been verified and corrected using PAR data measured by a ground-based meteorological station. Environmental factors obtained from airborne or ground-based meteorological stations include a temperature of 28°C and a relative humidity of 65%. Furthermore, based on the dominant tree species in this survey area (such as camphor trees), corresponding coefficients are retrieved from a pre-built parameter library. In this embodiment, α = 0.05 and β = 1.2.

[0093] Step 5: Generate a corresponding forest land light energy utilization efficiency distribution map based on the output of the light energy utilization efficiency model.

[0094] like Figure 6 As shown in the figure, rendered in pseudocolor, the spatial heterogeneity of LUE within the forest area is clearly displayed: areas with sparse canopy and high light transmittance have higher LUE values, while areas with dense canopy or under stress have relatively lower LUE values.

[0095] The forest light energy utilization efficiency assessment system in this embodiment can automatically output statistical reports, including the average LUE value of the whole area and the LUE statistical values ​​of different forest stand types, providing forestry managers with accurate carbon sequestration capacity assessment and growth status diagnosis basis.

[0096] Based on any of the above embodiments, another embodiment of the present invention provides an electronic device, which may include: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor may invoke logical instructions in the memory to execute the above method.

[0097] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0098] On the other hand, embodiments of the present invention also provide a storage medium storing a plurality of instructions adapted for loading by a processor to execute the forest land light energy utilization efficiency evaluation method based on UAV lidar provided in the above embodiments.

[0099] On the other hand, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0100] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0102] In summary, although the present invention has been disclosed above with reference to preferred embodiments, the above preferred embodiments are not intended to limit the present invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the scope defined in the claims.

Claims

1. A method for evaluating the efficiency of forest land solar energy utilization based on UAV lidar, characterized in that, Includes the following steps: The forest land is scanned by the drone's lidar to obtain laser point cloud data, and the spatial position and attitude data of the laser point cloud are recorded simultaneously. The laser point cloud data is preprocessed, and a high-precision three-dimensional laser point cloud is generated by combining the spatial position and attitude data. The canopy transmittance is calculated based on the ratio of the three-dimensional laser point cloud reaching the ground. Obtain photosynthetically active radiation data; Based on the canopy transmittance and the photosynthetically active radiation data, the light energy utilization efficiency of the forest land is evaluated using a dynamic evaluation model, and the evaluation results are output.

2. The method for evaluating forest land solar energy utilization efficiency based on UAV lidar according to claim 1, characterized in that, The calculation process for the canopy transmittance includes: The 3D laser point cloud was classified into vegetation points and ground points using machine learning algorithms; Based on the classified point cloud, the forest land is divided into multiple analysis units, and the number of vegetation points and ground points in each analysis unit are counted. Based on the number of ground points and vegetation points, the canopy transmittance of each analysis unit is calculated.

3. The method for evaluating forest land solar energy utilization efficiency based on UAV lidar according to claim 2, characterized in that, The machine learning algorithm is trained and classified based on point cloud features, which include at least the planar coordinates of the point cloud, its height value, the relative elevation difference with neighboring points, the echo intensity, and the point cloud density.

4. The method for evaluating forest land solar energy utilization efficiency based on UAV lidar according to claim 2, characterized in that, The formula for calculating the canopy transmittance of each analysis unit is as follows: Ti = N_ground_i / (N_ground_i + N_vegetation_i); In the formula, Ti represents the canopy transmittance of the i-th analysis unit, N_ground_i represents the number of ground points, and N_vegetation_i represents the number of vegetation points.

5. The method for evaluating forest land solar energy utilization efficiency based on UAV lidar according to claim 1, characterized in that, The dynamic evaluation model for light energy utilization efficiency is as follows: LUE = α × T × PAR / (1 + β × T²); In the formula, LUE is light energy utilization efficiency, T is canopy transmittance, PAR is photosynthetically active radiation, and α and β are model correction coefficients related to vegetation type.

6. The method for evaluating forest land solar energy utilization efficiency based on UAV lidar according to claim 5, characterized in that, It also includes acquiring environmental factor data and dynamically fine-tuning the model correction coefficients based on the environmental factor data.

7. A forest land solar energy utilization efficiency assessment system based on UAV lidar, characterized in that, include: The data acquisition module is used to collect laser point cloud data, spatial location and attitude data of the laser point cloud in the forest using lidar and sensing devices carried by the drone. The data processing module is used to process the laser point cloud data to calculate the canopy transmittance of the forest land, and combine it with the photosynthetically active radiation data to obtain the light energy utilization efficiency of the forest land through the light energy utilization efficiency evaluation model. The result output module is used to visualize and apply the light energy utilization efficiency.

8. The forest land solar energy utilization efficiency assessment system based on UAV lidar according to claim 7, characterized in that, The visualization involves generating a spatial distribution map of light energy utilization efficiency based on the light energy utilization efficiency assessment results, and generating an assessment report.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A storage medium, characterized in that, The storage medium stores a plurality of instructions adapted for loading by a processor to perform the steps of the method according to any one of claims 1 to 6.

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