Forest resource monitoring system based on airborne lidar data

Abstract

The present invention relates to the technical field of forest monitoring, and in particular to a forest resource monitoring system based on airborne laser radar data. The present invention provides a forest feature acquisition module, an understory feature analysis module, a feature preprocessing module, and an understory monitoring adjustment module. The forest feature acquisition module is used to acquire point cloud data of a forest area to be measured. The understory feature analysis module is used to screen feature monitoring sub-areas. The feature preprocessing module is used to mark tree points to determine the growth coefficient of the trees, or to determine whether there is feature overlap among the marked trees, and to obtain crown morphological parameters. The understory monitoring adjustment module is used to select an adjustment mode of the airborne laser radar. Thus, the system realizes rapid identification of areas affected by laser pulses during the monitoring process, adaptively adjusts the monitoring mode of the laser radar according to the morphological characteristics of the upper vegetation, and improves the monitoring accuracy of forest resources.

Description

Technical Field

[0001] The present invention relates to the technical field of forest monitoring, and in particular to a forest resource monitoring system based on airborne laser radar data. Background Art

[0002] Forest resources play an important role in maintaining ecological balance, providing ecological services, and promoting economic development. It is crucial to accurately and timely grasp the current status and changes of forest resources. Traditional forest resource monitoring often relies on ground surveys, which is time-consuming and labor-intensive, and difficult to implement in areas with complex terrain and inconvenient transportation. Although monitoring methods based on remote sensing images can cover a large area, they are limited by resolution and spectral information, making it difficult to accurately obtain three-dimensional structural information of forests. Airborne LiDAR carries the LiDAR system on an aircraft, which can quickly obtain three-dimensional spatial information of large areas of forests. It is not restricted by terrain and vegetation cover and has high spatial resolution and vertical accuracy.

[0003] However, the forest is a complex ecosystem with a multi-layered vegetation structure. Upper-level plants, such as tall trees, usually have dense crowns with densely distributed branches and leaves. When airborne lidar emits laser pulses from the air to scan the forest, these upper-level plants will affect the laser pulses, so that only part of the laser energy can reach the understory vegetation. Different tree species have different crown shapes, branch and leaf densities, and canopy density, and the degree of their impact on the laser also varies. Therefore, improving the reliability of monitoring understory vegetation is a technical problem that needs to be solved urgently.

[0004] For example, China's patent application publication number: CN119355750A, the invention discloses a lidar data processing method for complex forest environments, which obtains original point cloud data of the target forest area through an airborne lidar system or a backpack lidar system, preprocesses the data to obtain preprocessed point cloud data, filters out ground points in the preprocessed point cloud data to obtain non-ground point cloud data, and classifies the ground objects in the non-ground point cloud data to obtain ground object classification point cloud data; based on the ground object classification point cloud data, a three-dimensional canopy point cloud model is constructed, and based on the single tree parameter extraction method, the single tree spatial position and key forestry parameters of individual trees in the target forest area are obtained.

[0005] The following problems also exist in the prior art:

[0006] The existing technology does not take into account the multi-layered vegetation structure of the forest, resulting in the difficulty in quickly identifying the impact of the upper vegetation on the laser pulse during airborne lidar scanning. During the monitoring process, the existing technology cannot quickly identify the area where the laser pulse is affected, and cannot adaptively adjust the lidar monitoring method according to the morphological characteristics of the upper vegetation, affecting the monitoring accuracy of forest resources. Summary of the Invention

[0007] To this end, the present invention provides a forest resource monitoring system based on airborne lidar data to overcome the problems of the existing technology in that it cannot quickly identify the area affected by the laser pulse during the monitoring process, cannot adaptively adjust the lidar monitoring mode according to the morphological characteristics of the upper vegetation, and affects the monitoring accuracy of forest resources.

[0008] To achieve the above objectives, the present invention provides a forest resource monitoring system based on airborne laser radar data, comprising:

[0009] A forest feature acquisition module, which is used to control the airborne laser radar to obtain point cloud data of the forest area to be measured;

[0010] An understory feature analysis module, connected to the forest feature acquisition module, is used to divide the forest area to be measured into a number of monitoring sub-areas and determine terrain parameters based on the point cloud data of each monitoring sub-area to screen characteristic monitoring sub-areas;

[0011] a feature preprocessing module, connected to the forest feature acquisition module and the understory feature analysis module, respectively, for marking a plurality of tree points based on the point cloud data of the feature monitoring sub-region, determining a distribution coefficient based on the distribution of the tree points to select a growth coefficient for the feature monitoring sub-region determined based on the point cloud data, or determining whether the marked trees have overlapping features based on a comparison between the locations of the marked trees and the understory identification difference region, and obtaining crown morphological parameters of the marked trees with overlapping features;

[0012] an understory monitoring adjustment module, connected to the forest feature acquisition module and the feature preprocessing module, respectively, for selecting an adjustment method for the airborne laser radar: determining a scanning frequency of the airborne laser radar based on the growth coefficient and the distribution coefficient, or determining a laser scanning wavelength for scanning marker trees with overlapping features based on crown morphological parameters;

[0013] The understory identification difference area is determined according to the terrain parameters.

[0014] Furthermore, the understory feature analysis module is used to determine terrain parameters, wherein:

[0015] The understory feature analysis module is used to construct a real-time terrain model of the monitoring sub-area based on point cloud data;

[0016] for dividing the real-time terrain model into a plurality of sub-model regions, dividing the terrain model samples into a plurality of sample sub-model regions, and establishing an association relationship between the sub-model regions and the sample sub-model regions;

[0017] to respectively calculate the morphological overlap between the sub-model area and the sample sub-model area, and determine the minimum value of the morphological overlap as the topographic parameter of the monitoring sub-area;

[0018] The terrain model sample is determined based on historical monitoring data of the monitoring sub-area.

[0019] Furthermore, the understory feature analysis module is used to screen feature monitoring sub-areas, wherein:

[0020] If the terrain parameters of the monitoring sub-area meet the characteristic determination conditions, the understory characteristic analysis module selects the monitoring sub-area as a characteristic monitoring sub-area;

[0021] The feature determination condition is that the terrain parameter does not exceed a preset terrain threshold.

[0022] Furthermore, the feature preprocessing module is used to mark a number of tree points and determine the distribution coefficient, wherein,

[0023] The feature preprocessing module determines the height values ​​of a plurality of preset acquisition points in the feature monitoring sub-area in the geodetic coordinate system based on the point cloud data, and marks the acquisition points whose height values ​​exceed a preset height threshold as the tree points;

[0024] The feature preprocessing module calculates the minimum distance between any feature tree point and the remaining tree points in the feature monitoring sub-area, and determines the minimum distance as the distribution factor of the feature tree point;

[0025] The distribution factor variances of a plurality of characteristic tree points are calculated, and the distribution factor variances are determined as the distribution coefficients of the characteristic monitoring sub-areas.

[0026] Furthermore, if the distribution coefficient of the feature monitoring sub-area meets the uniform distribution determination condition, the feature preprocessing module selects to determine the growth coefficients of several trees in the feature monitoring sub-area based on the point cloud data;

[0027] If the distribution coefficient of the feature monitoring sub-area does not meet the uniform distribution judgment condition, the feature preprocessing module determines whether the feature of the marker trees overlaps based on the comparison between the location of the marker trees and the understory identification difference area, and obtains the crown morphological parameters of the marker trees with overlapping features;

[0028] The uniform distribution determination condition is that the distribution coefficient does not exceed a preset distribution threshold.

[0029] Furthermore, the feature preprocessing module is used to determine the growth coefficients of several trees in the feature monitoring sub-area, wherein:

[0030] The feature preprocessing module determines the diameters of trees at different height layers based on the point cloud data, determines the average diameter as the growth factor of the trees, calculates the variance based on the growth factors of several trees in the feature monitoring sub-area, and determines the variance as the growth coefficient.

[0031] Furthermore, the feature preprocessing module is used to determine whether the landmark trees have overlapping features, wherein:

[0032] The feature preprocessing module marks the feature tree points whose distribution factors do not exceed a preset distribution reference value as landmark trees, and determines the area corresponding to the terrain parameters in the feature monitoring sub-area as the understory identification difference area;

[0033] If the marker trees are within the understory identification difference area, the feature preprocessing module determines that there is feature overlap between the marker trees and obtains crown morphological parameters.

[0034] Furthermore, it is characterized in that the feature preprocessing module is used to determine the crown morphological parameters, wherein,

[0035] The feature preprocessing module obtains the crown point cloud of each marked tree, divides the crown point cloud into a number of voxels, determines the number of point clouds in the voxels, calculates the variance based on the number of point clouds in the voxels, and determines the variance as the crown morphological parameter.

[0036] Furthermore, the understory monitoring adjustment module is used to select an adjustment method for the airborne laser radar, wherein:

[0037] If the growth coefficients of several trees in the characteristic monitoring sub-area are obtained, the understory monitoring adjustment module selects an adjustment method for the airborne laser radar to determine the scanning frequency of the airborne laser radar according to the growth coefficient and the distribution coefficient;

[0038] If it is determined whether there is feature overlap among the marker trees and the crown morphological parameters are determined, the understory monitoring adjustment module selects an adjustment method for the airborne laser radar to determine the laser scanning wavelength for scanning each marker tree according to the crown morphological parameters.

[0039] Furthermore, the scanning frequency is negatively correlated with the growth coefficient and the distribution coefficient, respectively, and the laser scanning wavelength is positively correlated with the crown morphological parameters.

[0040] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention sets a forest feature acquisition module, an understory feature analysis module, a feature preprocessing module, and an understory monitoring adjustment module, obtains point cloud data of the forest area to be measured through the forest feature acquisition module, divides a number of monitoring sub-areas through the understory feature analysis module, and determines terrain parameters based on the point cloud data of each monitoring sub-area to screen the feature monitoring sub-area, marks a number of tree points through the feature preprocessing module, selects the growth coefficients of a number of trees in the feature monitoring sub-area determined according to the distribution of the number of tree points according to the point cloud data, or, determines whether there is feature overlap of the marked trees according to the comparison between the location of the marked trees and the understory identification difference area, and obtains the crown morphological parameters of the area with feature overlap, selects the adjustment method of the airborne laser radar through the understory monitoring adjustment module, and thus realizes the rapid identification of the area where the laser pulse is affected during the monitoring process, and adapts the monitoring method of the laser radar according to the morphological characteristics of the upper vegetation, thereby improving the monitoring accuracy of forest resources.

[0041] In particular, the present invention determines the terrain parameters based on the point cloud data of each monitoring sub-area through the understory feature analysis module to screen the characteristic monitoring sub-area. It can be understood that the laser pulse of the airborne lidar is easily affected by the upper vegetation, thereby affecting the accuracy of the point cloud data. A real-time terrain model is constructed based on the point cloud data actually obtained by the lidar, and the accuracy of the point cloud data obtained by the current lidar is characterized according to the overlap between the real-time terrain model and the terrain model sample. The higher the overlap, the higher the accuracy of the point cloud data, and the smaller the impact of the upper vegetation on the current monitoring sub-area when the airborne lidar is detected. The lower the overlap, the lower the accuracy of the point cloud data. The greater the impact of the upper vegetation on the current monitoring sub-area when the airborne lidar is detected, the more the upper vegetation is affected when the airborne lidar is detected. Screening out the areas that are greatly affected by the upper vegetation during airborne lidar detection, that is, the characteristic monitoring sub-areas, will help to take more targeted data collection for these areas in subsequent monitoring, thereby realizing the rapid identification of areas where laser pulses are affected during the monitoring process and improving the monitoring accuracy of forest resources.

[0042] In particular, the present invention determines a distribution coefficient based on the distribution of several tree points through a feature preprocessing module to select a growth coefficient for a feature monitoring sub-region determined based on point cloud data, or determines whether there is feature overlap among the marker trees based on a comparison of the location of the marker trees with the identified difference area under the forest, and obtains crown morphological parameters of the marker trees with feature overlap. It can be understood that different tree distributions represent different forest structures and ecological environments. By selecting an appropriate method, forest diversity can be monitored more meticulously. Whether it is the overall characteristics of a uniformly distributed area or the special characteristics of a locally concentrated area, accurate data can be collected, thereby more comprehensively and realistically reflecting the actual condition of the forest. Selecting different parameters for different tree distributions helps to adopt more targeted monitoring measures for different areas. For areas with uniform tree distribution, a relatively unified monitoring strategy can be implemented, while for areas with uneven trees and localized concentrations, a more targeted monitoring method can be adopted, providing a more accurate basis for scientific monitoring of forest resources. Furthermore, it is possible to quickly identify areas affected by laser pulses during the monitoring process, adaptively adjust the lidar monitoring method according to the morphological characteristics of the upper vegetation, and improve the monitoring accuracy of forest resources.

[0043] In particular, the present invention determines the growth coefficients of several trees in the feature monitoring sub-area based on point cloud data under the condition that the tree points are relatively evenly distributed. It can be understood that when the trees are evenly distributed, the tree diameters obtained based on the point cloud data can more accurately represent the size of the trees in the entire area. Based on these accurate diameter data, the degree of occlusion of the understory vegetation by the tree crown can be more accurately evaluated, because the even distribution of tree points makes the influence range of each tree relatively stable and predictable, unlike the uneven distribution where there is local dense occlusion or sparse no occlusion. This provides a more accurate basis for adjusting the lidar parameters to ensure that the laser can effectively penetrate the gaps between the tree crowns to reach the understory vegetation. Obtaining the tree diameters under the condition of even distribution of tree points and adjusting the lidar parameters accordingly helps to ensure that the understory vegetation data obtained in the entire monitoring area has high reliability. Furthermore, it realizes the rapid identification of the areas where the laser pulses are affected during the monitoring process, adaptively adjusts the lidar monitoring method according to the morphological characteristics of the upper vegetation, and improves the monitoring accuracy of forest resources.

[0044] In particular, under the condition that the tree points are relatively unevenly distributed, the present invention determines whether there is feature overlap of the marker trees based on the comparison between the location of the marker trees and the forest understory identification difference area, and obtains the crown morphological parameters of the area where there is feature overlap. It can be understood that the marker trees are trees with relatively concentrated tree point distribution, and the forest understory identification difference area is the area where the overlap between the terrain obtained by the lidar and the historical terrain is low. The marker trees are located in the forest understory identification difference area, which represents the deviation of the terrain obtained by the airborne lidar in real time, and there is a possibility of influence by the upper vegetation. By obtaining the crown morphological parameters in these areas, the degree of occlusion of the understory vegetation by the upper tree crown can be evaluated. The regular tree crown occlusion is relatively uniform. The parameters of the lidar are adjusted according to the actual occlusion situation to make the laser energy more reasonably distributed, improve the ability to penetrate the crown to reach the understory vegetation, and thus obtain more reliable understory vegetation data. Furthermore, it is realized that the area where the laser pulse is affected is quickly identified during the monitoring process, and the lidar monitoring method is adaptively adjusted according to the morphological characteristics of the upper vegetation, thereby improving the monitoring accuracy of forest resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a structural block diagram of a forest resource monitoring system based on airborne lidar data according to an embodiment of the present invention;

[0046] Figure 2 This is a logic flow chart of the understory feature analysis module screening feature monitoring sub-areas according to an embodiment of the present invention;

[0047] Figure 3 This is a logic flow chart of the feature preprocessing module of an embodiment of the present invention for determining whether there is feature overlap between marked trees;

[0048] Figure 4 This is a logic flow chart for selecting an adjustment method for an airborne laser radar in an understory monitoring adjustment module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0050] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0051] It should be noted that, in the description of the present invention, terms such as "upper", "lower", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0052] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted" and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0053] See also Figure 1 As shown in FIG, which is a structural block diagram of a forest resource monitoring system based on airborne laser radar data according to an embodiment of the present invention, the forest resource monitoring system based on airborne laser radar data according to the present invention includes:

[0054] A forest feature acquisition module, which is used to control the airborne laser radar to obtain point cloud data of the forest area to be measured;

[0055] Specifically, the present invention does not limit the specific structure of the forest feature acquisition module. Preferably, it can be a processor used by a computer to control an airborne laser radar to obtain point cloud data of the forest area to be measured, which will not be repeated here.

[0056] An understory feature analysis module, connected to the forest feature acquisition module, is used to divide the forest area to be measured into a number of monitoring sub-areas and determine terrain parameters based on the point cloud data of each monitoring sub-area to screen characteristic monitoring sub-areas;

[0057] Specifically, the division size of the monitoring sub-area can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the division size of the monitoring sub-area. Preferably, the division size of the monitoring sub-area can be 200m×200m.

[0058] Specifically, airborne lidar is usually installed in specific parts of the aircraft, such as the belly of the aircraft, under the wings, and the tail of the aircraft. Preferably, it can be installed on the belly of the aircraft so that the laser beam is emitted vertically downward or nearly vertically downward, which can directly obtain vertical point cloud data of the target area, which is conducive to the accurate measurement of information such as terrain and vegetation height. Moreover, the space on the belly of the aircraft is relatively large, which is convenient for installing and fixing lidar equipment, and can also better protect the equipment from airflow interference. It will not be repeated here.

[0059] Specifically, the lidar system emits laser pulses to the forest area to be measured, digitizes the received reflected light information, and calculates the coordinates of each reflection point in three-dimensional space based on the flight time of the laser pulse and the position and attitude information of the aircraft, thereby generating point cloud data of the forest area. I will not go into details here.

[0060] Specifically, the present invention does not limit the specific structure of the forest understory feature analysis module. Preferably, it can be a microprocessor for dividing monitoring sub-areas, determining terrain parameters, and screening feature monitoring sub-areas, which will not be repeated here.

[0061] a feature preprocessing module, connected to the forest feature acquisition module and the understory feature analysis module, respectively, for marking a plurality of tree points based on the point cloud data of the feature monitoring sub-region, determining a distribution coefficient based on the distribution of the tree points to select a growth coefficient for the feature monitoring sub-region determined based on the point cloud data, or determining whether the marked trees have overlapping features based on a comparison between the locations of the marked trees and the understory identification difference region, and obtaining crown morphological parameters of the marked trees with overlapping features;

[0062] Specifically, the present invention does not limit the specific structure of the feature preprocessing module. Preferably, it can be a programmable logic controller for marking several tree points, determining the distribution coefficient, determining the growth coefficient, and determining whether there is feature overlap to obtain crown morphological parameters, which will not be repeated here.

[0063] an understory monitoring adjustment module, connected to the forest feature acquisition module and the feature preprocessing module, respectively, for selecting an adjustment method for the airborne laser radar: determining a scanning frequency of the airborne laser radar based on the growth coefficient and the distribution coefficient, or determining a laser scanning wavelength for scanning marker trees with overlapping features based on crown morphological parameters;

[0064] Specifically, the present invention does not limit the specific structure of the understory monitoring and adjustment module. Preferably, it can be a microprocessor for selecting the adjustment method of the airborne laser radar, determining the scanning frequency of the airborne laser radar, and determining the laser scanning wavelength for scanning each marker tree. It will not be repeated here.

[0065] The understory identification difference area is determined based on the terrain parameters.

[0066] Specifically, the understory feature analysis module is used to determine terrain parameters, wherein:

[0067] The understory feature analysis module is used to construct a real-time terrain model of the monitoring sub-area based on point cloud data;

[0068] Specifically, the point cloud data collection points when constructing a real-time terrain model can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the interval distance between the preset collection points in the feature monitoring sub-area. The value range of the interval distance can be [3, 10], and the interval unit is m. Preferably, the interval distance can be 5m.

[0069] Specifically, the terrain slope is determined based on the point cloud data to construct a real-time terrain model of the monitoring sub-area. The acquired point cloud data is preprocessed. The data preprocessing includes data import, filtering, and denoising. The filtering algorithm is used to remove non-ground points such as vegetation, and statistical filtering and other methods are used to remove noise points. Based on the filtered ground point cloud data, a suitable interpolation method such as inverse distance weighted interpolation is selected to generate a digital elevation model (DEM). The first-order difference algorithm is used to calculate the elevation change rate of each grid cell in the DEM and its adjacent cells to obtain the terrain slope. The DEM and slope map are combined, and a three-dimensional modeling software is used to construct a terrain model of the monitoring sub-area that can reflect the real-time terrain conditions. I will not go into details here.

[0070] The understory feature analysis module is used to divide the real-time terrain model into a plurality of sub-model areas, divide the terrain model samples into a plurality of sample sub-model areas, and establish an association relationship between the sub-model areas and the sample sub-model areas;

[0071] The understory feature analysis module is used to calculate the morphological overlap between the sub-model area and the sample sub-model area, and determine the minimum value of the morphological overlap as the terrain parameter of the monitoring sub-area;

[0072] The terrain model sample is determined based on historical monitoring data of the monitoring sub-area.

[0073] Specifically, the area covered by the terrain model can be divided into sub-model areas according to rectangular grids. The number of sub-model areas is the product of the area of ​​the monitoring sub-area and the division factor. The division factor can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the division factor. The value range of the division factor can be [0.2, 0.4]. Preferably, the division factor can be 0.3. For example, a specific embodiment of determining the number of sub-model areas is given here. The division factor is set to 0.3. When the area of ​​the monitoring sub-area is obtained to be 40,000 square meters, the number of sub-model areas is 12,000, and the area of ​​each sub-model area is 3.33 square meters.

[0074] Specifically, the terrain model sample is determined based on the historical detection data obtained by the surveying vehicle within a preset time period before the airborne lidar monitoring. The preset time period can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the change in the terrain itself, and the shorter the preset time period. The value range of the preset time period can be [5, 10], and the interval unit is day. Preferably, the preset time period can be 8 days.

[0075] Specifically, the morphological overlap between the sub-model area and the sample sub-model area can be calculated based on the point cloud data. First, the point cloud data is matched, and the number of overlapping points and the total number of points of each are counted. The formula is substituted into the following formula: number of overlapping points / (total number of points in the sub-model area point cloud data + total number of points in the sample sub-model area point cloud data - number of overlapping points) × 100% to calculate the morphological overlap. This will not be repeated here.

[0076] See also Figure 2 As shown, it is a logic flow chart of the understory feature analysis module screening feature monitoring sub-areas according to an embodiment of the present invention. The understory feature analysis module is used to screen feature monitoring sub-areas, wherein:

[0077] If the terrain parameters of the monitoring sub-area meet the characteristic determination conditions, the understory characteristic analysis module selects the monitoring sub-area as a characteristic monitoring sub-area;

[0078] If the terrain parameters of the monitoring sub-area do not meet the feature determination conditions, the understory feature analysis module does not screen the monitoring sub-area;

[0079] The feature determination condition is that the terrain parameter does not exceed a preset terrain threshold.

[0080] Specifically, the preset terrain threshold can be set by those skilled in the art according to the terrain fluctuation rate of the monitored sub-area. The faster the terrain fluctuation rate, the smaller the set terrain threshold. The value range of the terrain threshold can be [0.6, 0.8]. Preferably, the terrain threshold can be 0.7.

[0081] Specifically, the present invention determines the terrain parameters based on the point cloud data of each monitoring sub-area through the understory feature analysis module to screen the characteristic monitoring sub-area. It can be understood that the laser pulse of the airborne lidar is easily affected by the upper vegetation, thereby affecting the accuracy of the point cloud data. A real-time terrain model is constructed based on the point cloud data actually obtained by the lidar, and the accuracy of the point cloud data obtained by the current lidar is characterized according to the overlap between the real-time terrain model and the terrain model sample. The higher the overlap, the higher the accuracy of the point cloud data, and the smaller the impact of the upper vegetation on the current monitoring sub-area when the airborne lidar is detected. The lower the overlap, the lower the accuracy of the point cloud data. The greater the impact of the upper vegetation on the current monitoring sub-area when the airborne lidar is detected, the areas that are greatly affected by the upper vegetation during airborne lidar detection, that is, the characteristic monitoring sub-areas, are screened out, which will help to take more targeted data collection for these areas in subsequent monitoring, thereby realizing the rapid identification of areas where laser pulses are affected during the monitoring process and improving the monitoring accuracy of forest resources.

[0082] Specifically, the feature preprocessing module is used to mark a number of tree points and determine the distribution coefficient, wherein:

[0083] The feature preprocessing module determines the height values ​​of a plurality of preset acquisition points in the feature monitoring sub-area in the geodetic coordinate system based on the point cloud data, and marks the acquisition points whose height values ​​exceed a preset height threshold as the tree points;

[0084] The feature preprocessing module calculates the minimum distance between any feature tree point and the remaining tree points in the feature monitoring sub-area, and determines the minimum distance as the distribution factor of the feature tree point;

[0085] The distribution factor variances of a plurality of characteristic tree points are calculated, and the distribution factor variances are determined as the distribution coefficients of the characteristic monitoring sub-areas.

[0086] Specifically, the setting of several preset collection points in the feature monitoring sub-area can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the interval distance of the preset collection points in the feature monitoring sub-area. The value range of the interval distance can be [3, 10], and the interval unit is m. Preferably, the interval distance can be 5m.

[0087] Specifically, the spacing distance is the distance between two tree points in the horizontal direction.

[0088] Specifically, the preset height threshold is the average height of the point cloud in the feature monitoring sub-area + (standard deviation × preset height factor), the standard deviation is the square root of the variance of the height values ​​of the point cloud in the feature monitoring sub-area, and the preset height factor can be set by those skilled in the art according to the size of the standard deviation. The smaller the standard deviation, the smaller the preset height factor. The value range of the height factor can be [1, 3]. Preferably, the height factor can be 2.

[0089] For example, a specific embodiment of determining the height threshold is given here. The average height of the point cloud of the feature monitoring sub-area is obtained to be 10m, the standard deviation is 2m, and the set height factor is 2. Therefore, the height threshold is 14m.

[0090] Specifically, the present invention determines a distribution coefficient based on the distribution of several tree points through a feature preprocessing module to select a growth coefficient for a feature monitoring sub-area determined based on point cloud data, or determines whether there is feature overlap between the marker trees based on the comparison between the location of the marker trees and the identified difference area under the forest, and obtains the crown morphological parameters of the marker trees with feature overlap. It can be understood that different tree distributions represent different forest structures and ecological environments. By selecting an appropriate method, forest diversity can be monitored more carefully. Whether it is the overall characteristics of a uniformly distributed area or the special characteristics of a locally concentrated area, accurate data can be collected, thereby more comprehensively and realistically reflecting the actual condition of the forest. Selecting different parameters for different tree distributions helps to adopt more targeted monitoring measures for different areas. For areas with uniform tree distribution, a relatively unified monitoring strategy can be implemented, while for areas with uneven trees and local concentrations, a more targeted monitoring method can be adopted, providing a more accurate basis for scientific monitoring of forest resources. Furthermore, it is achieved that the area affected by the laser pulse is quickly identified during the monitoring process, and the lidar monitoring method is adaptively adjusted according to the morphological characteristics of the upper vegetation, thereby improving the monitoring accuracy of forest resources.

[0091] Specifically, if the distribution coefficient of the feature monitoring sub-area meets the uniform distribution judgment condition, the feature preprocessing module selects to determine the growth coefficients of several trees in the feature monitoring sub-area based on the point cloud data;

[0092] Specifically, the present invention determines the growth coefficients of several trees in the feature monitoring sub-area based on point cloud data under the condition that the tree points are relatively evenly distributed. It can be understood that when the trees are evenly distributed, the tree diameters obtained based on the point cloud data can more accurately represent the size of the trees in the entire area. Based on these accurate diameter data, the degree of occlusion of the understory vegetation by the tree crown can be more accurately evaluated, because the uniform distribution of tree points makes the influence range of each tree relatively stable and predictable, unlike the uneven distribution where there is local dense occlusion or sparse no occlusion. This provides a more accurate basis for adjusting the lidar parameters to ensure that the laser can effectively penetrate the gaps between the tree crowns to reach the understory vegetation. Obtaining the tree diameters under the condition of even distribution of tree points and adjusting the lidar parameters accordingly helps to ensure that the understory vegetation data obtained in the entire monitoring area has high reliability. Furthermore, it realizes the rapid identification of the areas where the laser pulses are affected during the monitoring process, adaptively adjusts the lidar monitoring method according to the morphological characteristics of the upper vegetation, and improves the monitoring accuracy of forest resources.

[0093] If the distribution coefficient of the feature monitoring sub-area does not meet the uniform distribution judgment condition, the feature preprocessing module determines whether the feature of the marker trees overlaps based on the comparison between the location of the marker trees and the understory identification difference area, and obtains the crown morphological parameters of the marker trees with overlapping features;

[0094] Specifically, under the condition that the tree points are relatively unevenly distributed, the present invention determines whether there is feature overlap of the marker trees based on the comparison between the location of the marker trees and the forest understory identification difference area, and obtains the crown morphological parameters of the area where there is feature overlap. It can be understood that the marker trees are trees with relatively concentrated tree point distribution, and the forest understory identification difference area is the area where the overlap between the terrain obtained by the lidar and the historical terrain is low. The marker trees are located in the forest understory identification difference area, which characterizes the deviation of the terrain obtained by the airborne lidar in real time, and may be affected by the upper vegetation. By obtaining the crown morphological parameters in these areas, the degree of occlusion of the understory vegetation by the upper crown can be evaluated. The regular crown occlusion is relatively uniform. The parameters of the lidar are adjusted according to the actual occlusion situation to make the laser energy more reasonably distributed, improve the ability to penetrate the crown to reach the understory vegetation, and thus obtain more reliable understory vegetation data. Furthermore, it is realized that the area where the laser pulse is affected is quickly identified during the monitoring process, and the lidar monitoring method is adaptively adjusted according to the morphological characteristics of the upper vegetation, thereby improving the monitoring accuracy of forest resources.

[0095] The uniform distribution determination condition is that the distribution coefficient does not exceed a preset distribution threshold.

[0096] Specifically, the preset distribution threshold can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the preset distribution threshold. The value range of the distribution threshold can be [5, 10]. Preferably, the distribution threshold can be 8.

[0097] Specifically, the feature preprocessing module is used to determine the growth coefficients of several trees in the feature monitoring sub-area, where:

[0098] The feature preprocessing module determines the diameters of trees at different height layers based on the point cloud data, determines the average diameter as the growth factor of the trees, calculates the variance based on the growth factors of several trees in the feature monitoring sub-area, and determines the variance as the growth coefficient.

[0099] Specifically, the interval distance of the height layer is the product of the height value of the tree and the diameter value factor. The height value of the tree can be determined by point cloud data, and the diameter value factor can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the diameter value factor is set. The value range of the diameter value factor can be [0.05, 0.2]. Preferably, the diameter value factor can be 0.1.

[0100] Specifically, the diameter of trees at different heights can be obtained by preprocessing the original point cloud data to separate ground points and tree points, using a clustering algorithm to segment the point cloud of a single tree, setting horizontal sections at different heights, extracting the point cloud on the section, and using fitting methods such as the least squares method to fit the points on the section into a circle. The diameter of the tree at that height is calculated based on the parameters of the fitted circle. This will not be repeated here.

[0101] See also Figure 3 As shown, it is a logic flow chart of the feature pre-processing module of an embodiment of the present invention for determining whether there is feature overlap between the marker trees. The feature pre-processing module is used to determine whether there is feature overlap between the marker trees, wherein:

[0102] The feature preprocessing module marks the feature tree points whose distribution factors do not exceed a preset distribution reference value as landmark trees, and determines the area corresponding to the terrain parameters in the feature monitoring sub-area as the understory identification difference area;

[0103] If the marker trees are within the understory identification difference area, the feature preprocessing module determines that there is feature overlap between the marker trees and obtains crown morphological parameters;

[0104] If the marker trees are not within the understory identification difference area, the feature preprocessing module determines that there is no feature overlap between the marker trees.

[0105] Specifically, the preset distribution reference value can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the preset distribution reference value. The value range of the distribution reference value can be [2, 4]. Preferably, the distribution reference value can be 3.

[0106] Specifically, the feature preprocessing module is used to determine the crown morphological parameters.

[0107] The feature preprocessing module obtains the crown point cloud of each marked tree, divides the crown point cloud into a number of voxels, determines the number of point clouds in the voxels, calculates the variance based on the number of point clouds in the voxels, and determines the variance as the crown morphological parameter.

[0108] Specifically, voxel is the abbreviation of volume element. Voxel is a small cubic unit divided in a three-dimensional data field. It represents a specific position and attribute in three-dimensional space. It is widely used in computer graphics, medical imaging, geological exploration and other fields, and will not be repeated here.

[0109] Specifically, the crown point cloud can be divided regularly in three-dimensional space according to a fixed voxel size. The voxel size can be set by technical personnel in this field according to the accuracy requirements of forest resource monitoring. The higher the accuracy requirement, the smaller the voxel size. Preferably, the voxel size can be 0.5m×0.5m×0.5m.

[0110] Specifically, to obtain the crown point cloud of each landmark tree, we can use lidar to collect the original point cloud data, preprocess it, use the ground filtering algorithm to eliminate ground points, highlight the tree point cloud, and segment different trees through clustering or region growing algorithms to obtain the crown point cloud. I will not go into details here.

[0111] Specifically, the number of point clouds within a voxel can be determined based on the position and size range of each voxel in three-dimensional space. All point cloud data within a single voxel can be traversed, and a region judgment algorithm based on point cloud coordinates and voxel boundary coordinates can be used to determine whether each point cloud is located within the voxel space range. Each time a point cloud is determined to belong to the voxel, the number of point clouds within each voxel is counted. This will not be repeated here.

[0112] See also Figure 4 As shown, it is a logic flow chart of the forest monitoring and adjustment module selecting the adjustment method of the airborne laser radar according to an embodiment of the present invention. The forest monitoring and adjustment module is used to select the adjustment method of the airborne laser radar, wherein:

[0113] If the growth coefficients of several trees in the characteristic monitoring sub-area are obtained, the understory monitoring adjustment module selects an adjustment method for the airborne laser radar to determine the scanning frequency of the airborne laser radar according to the growth coefficient and the distribution coefficient;

[0114] If it is determined whether there is feature overlap among the marker trees and the crown morphological parameters are determined, the understory monitoring adjustment module selects an adjustment method for the airborne laser radar to determine the laser scanning wavelength for scanning each marker tree according to the crown morphological parameters.

[0115] Specifically, the scanning frequency is negatively correlated with the growth coefficient and the distribution coefficient, respectively, and the laser scanning wavelength is positively correlated with the crown morphological parameters.

[0116] Specifically, when the growth coefficient is 0.3 and the distribution coefficient is 2, the scanning frequency is 2800Hz; when the growth coefficient is 0.7 and the distribution coefficient is 3.5, the scanning frequency is 2200Hz; when the growth coefficient is 1.2 and the distribution coefficient is 4.8, the scanning frequency is 1500Hz. The larger the growth coefficient and the distribution coefficient, the smaller the scanning frequency. When the crown morphological parameter is 50, the laser scanning wavelength is 808nm; when the crown morphological parameter is 90, the laser scanning wavelength is 1064nm; when the crown morphological parameter is 120, the laser scanning wavelength is 1550nm. The larger the crown morphological parameter, the smaller the laser scanning wavelength.

[0117] Specifically, it is understandable that regular tree crowns usually have a more uniform distribution of branches and leaves and a relatively simple geometric shape, and the scattering and occlusion patterns of lasers are relatively easy to predict. Shorter wavelength lasers have higher resolution and stronger reflection signals, and can more accurately capture the boundaries and detailed information of the tree crowns, thereby more accurately identifying the spatial relationship between the tree crowns and understory vegetation, which helps to improve the recognition and classification accuracy of understory vegetation.

[0118] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0119] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A forest resource monitoring system based on airborne lidar data, characterized in that: include: A forest feature acquisition module, which is used to control the airborne laser radar to obtain point cloud data of the forest area to be measured; An understory feature analysis module, connected to the forest feature acquisition module, is used to divide the forest area to be measured into a number of monitoring sub-areas and determine terrain parameters based on the point cloud data of each monitoring sub-area to screen characteristic monitoring sub-areas; The understory feature analysis module is used to construct a real-time terrain model of the monitoring sub-area based on point cloud data; for dividing the real-time terrain model into a plurality of sub-model regions, dividing the terrain model samples into a plurality of sample sub-model regions, and establishing an association relationship between the sub-model regions and the sample sub-model regions; for respectively calculating the morphological overlap between the sub-model area and the sample sub-model area, and determining the minimum value of the morphological overlap as the terrain parameter of the monitoring sub-area, wherein the terrain model sample is determined based on the historical monitoring data of the monitoring sub-area; a feature preprocessing module, connected to the forest feature acquisition module and the understory feature analysis module, respectively, for marking a plurality of tree points based on the point cloud data of the feature monitoring sub-region, determining a distribution coefficient based on the distribution of the tree points to select a growth coefficient for the feature monitoring sub-region determined based on the point cloud data, or determining whether the marked trees have overlapping features based on a comparison between the locations of the marked trees and the understory identification difference region, and obtaining crown morphological parameters of the marked trees with overlapping features; an understory monitoring adjustment module, connected to the forest feature acquisition module and the feature preprocessing module, respectively, for selecting an adjustment method for the airborne laser radar: determining a scanning frequency of the airborne laser radar based on the growth coefficient and the distribution coefficient, or determining a laser scanning wavelength for scanning marker trees with overlapping features based on crown morphological parameters; The understory identification difference area is determined according to the terrain parameters.

2. The forest resource monitoring system based on airborne laser radar data according to claim 1, characterized in that: The understory feature analysis module is used to screen feature monitoring sub-areas, wherein: If the terrain parameters of the monitoring sub-area meet the characteristic determination conditions, the understory characteristic analysis module selects the monitoring sub-area as a characteristic monitoring sub-area; The feature determination condition is that the terrain parameter does not exceed a preset terrain threshold.

3. The forest resource monitoring system based on airborne laser radar data according to claim 2, characterized in that: The feature preprocessing module is used to mark a number of tree points and determine the distribution coefficient, wherein, The feature preprocessing module determines the height values ​​of a plurality of preset acquisition points in the feature monitoring sub-area in the geodetic coordinate system based on the point cloud data, and marks the acquisition points whose height values ​​exceed a preset height threshold as the tree points; The feature preprocessing module calculates the minimum distance between any feature tree point and the remaining tree points in the feature monitoring sub-area, and determines the minimum distance as the distribution factor of the feature tree point; The distribution factor variances of a plurality of characteristic tree points are calculated, and the distribution factor variances are determined as the distribution coefficients of the characteristic monitoring sub-areas.

4. The forest resource monitoring system based on airborne laser radar data according to claim 3 is characterized in that: If the distribution coefficient of the feature monitoring sub-area meets the uniform distribution judgment condition, the feature preprocessing module selects to determine the growth coefficients of several trees in the feature monitoring sub-area based on the point cloud data; If the distribution coefficient of the feature monitoring sub-area does not meet the uniform distribution judgment condition, the feature preprocessing module determines whether the feature of the marker trees overlaps based on the comparison between the location of the marker trees and the understory identification difference area, and obtains the crown morphological parameters of the marker trees with overlapping features; The uniform distribution determination condition is that the distribution coefficient does not exceed a preset distribution threshold.

5. The forest resource monitoring system based on airborne laser radar data according to claim 4 is characterized in that: The feature preprocessing module is used to determine the growth coefficients of several trees in the feature monitoring sub-area, wherein: The feature preprocessing module determines the diameters of trees at different height layers based on the point cloud data, determines the average diameter as the growth factor of the trees, calculates the variance based on the growth factors of several trees in the feature monitoring sub-area, and determines the variance as the growth coefficient.

6. The forest resource monitoring system based on airborne laser radar data according to claim 5, characterized in that: The feature pre-processing module is used to determine whether the landmark trees have overlapping features, wherein: The feature preprocessing module marks the feature tree points whose distribution factors do not exceed a preset distribution reference value as landmark trees, and determines the area corresponding to the terrain parameters in the feature monitoring sub-area as the understory identification difference area; If the marker trees are within the understory identification difference area, the feature preprocessing module determines that there is feature overlap between the marker trees and obtains crown morphological parameters.

7. The forest resource monitoring system based on airborne laser radar data according to claim 6, characterized in that: The feature preprocessing module is used to determine the crown morphological parameters, wherein: The feature preprocessing module obtains the crown point cloud of each marked tree, divides the crown point cloud into a number of voxels, determines the number of point clouds in the voxels, calculates the variance based on the number of point clouds in the voxels, and determines the variance as the crown morphological parameter.

8. The forest resource monitoring system based on airborne laser radar data according to claim 7, characterized in that: The understory monitoring adjustment module is used to select the adjustment method of the airborne laser radar, wherein: If the growth coefficients of several trees in the characteristic monitoring sub-area are obtained, the understory monitoring adjustment module selects an adjustment method for the airborne laser radar to determine the scanning frequency of the airborne laser radar according to the growth coefficient and the distribution coefficient; If it is determined whether there is feature overlap among the marker trees and the crown morphological parameters are determined, the understory monitoring adjustment module selects an adjustment method for the airborne laser radar to determine the laser scanning wavelength for scanning each marker tree according to the crown morphological parameters.

9. The forest resource monitoring system based on airborne laser radar data according to claim 8, characterized in that: The scanning frequency is negatively correlated with the growth coefficient and the distribution coefficient, respectively, and the laser scanning wavelength is positively correlated with the crown morphological parameters.

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