Method for simulating shading area of farmland protection forest net based on LESS optical radiation transmission model

Abstract

The invention provides a method for simulating the shading area of a farmland protection forest net based on an LESS optical radiation transmission model, and the method comprises the following steps: 1), scanning a farmland protection forest belt through a laser radar, and obtaining the three-dimensional point cloud data of the farmland protection forest belt; step 2) performing point cloud preprocessing on the acquired three-dimensional point cloud data; 3) constructing a single tree voxel space by adopting the preprocessed point cloud data; and 4) performing shading range simulation by adopting a four-component simulation plate of the LESS optical radiation transmission model.

Description

Technical Field

[0001] The invention belongs to the technical field of forestry, and in particular relates to a method for simulating the shaded area of ​​a farmland shelterbelt network based on a LESS light radiation transmission model. Background Art

[0002] Light is the energy source for plant photosynthesis, and the photosynthetic products of plants provide the food, oxygen and energy they need for countless organisms. The distribution characteristics of solar radiation in the forest have a crucial impact on the amount of light radiation received by crops. At present, the determination of the shade range under the forest is mainly divided into two methods: actual measurement and theoretical model. The research on light simulation model mainly focuses on two categories: geometric model and statistical model. The morphology of the tree crown plays a vital role in the construction of the solar radiation transmission model. The quantitative calculation of the projection length of a single tree crown, the qualitative discussion of the projection shape and change, the approximate geometric calculation of the projection area of ​​a single tree crown, and the approximate calculation of the projection area of ​​non-single tree crowns by simple accumulation or rectangular method are all the key points of model construction. However, due to the large heterogeneity of the canopy structure, errors in the above calculations cause errors in the model. In addition, the research on light distribution is limited to the use of empirical formulas for derivation, or the use of regular geometric shapes to simulate the shade state of the forest belt, while ignoring the light-transmitting gaps of the trees themselves, resulting in errors.

[0003] Nowadays, the application of terrestrial laser scanners (TLS) has opened a new chapter in theoretical 3D simulation of shading. Unlike the previous methods that relied on simplified 3D structures, TLS technology can capture detailed and real 3D morphological data of trees. Using these detailed point cloud data, it is possible to create accurate and highly realistic 3D tree models. The development of a series of 3D radiation transfer models also provides the possibility to understand the subtle interaction between the canopy and solar radiation. Among them, the LESS (LargE-Scale remote sensing data and image Simulation framework) light radiation transfer model can simulate the propagation process of photons in the entire scene, thereby simulating the attenuation of solar radiation by shelterbelts. It is a 3D radiation transfer model that can express the real scene structure and efficiently simulate large scenes. Therefore, the LESS light radiation transfer model provides a highly accurate and visual tool for the study of shade area within the forest network, which has broad application prospects and important scientific significance. Summary of the invention

[0004] The present invention aims at the current research on light distribution in forest network, which is limited to the derivation using empirical formulas or the use of regular geometric shapes to simulate the shade state of forest belts, ignoring the light-transmitting gaps of trees themselves, resulting in errors. The present invention proposes to use laser radar technology to obtain forest belt point cloud data, and through point cloud processing, single tree segmentation, model construction and simulation, etc., to accurately and quickly obtain the shade area of ​​farmland shelterbelt network. The present invention adopts the following technical solutions:

[0005] A method for simulating the shade area of ​​a farmland shelterbelt network based on a LESS light radiation transmission model comprises the following steps:

[0006] Step 1) Use laser radar to scan the farmland shelterbelt to obtain three-dimensional point cloud data of the farmland shelterbelt;

[0007] Step 2) performing point cloud preprocessing on the acquired three-dimensional point cloud data;

[0008] Step 3) constructing a single-tree voxel space using the preprocessed point cloud data;

[0009] Step 4) The shading range is simulated in the single tree voxel space using the four-component simulation block of the LESS light radiation transmission model.

[0010] Optionally, in step 1), a ground-based LiDAR scanner is used to scan stations in the forest belt to collect three-dimensional point cloud data of all trees in the farmland shelterbelt.

[0011] Optionally, in the step 2), the preprocessing includes: point cloud stitching, point cloud cropping, denoising, and thinning.

[0012] Optionally, the point cloud stitching in step 2) includes: completing automatic stitching for a GPS positioning system using a laser radar.

[0013] Optionally, in step 2), Cloud Compare software is used to perform point cloud cropping, denoising, and thinning;

[0014] The thinning adopts a space-based thinning method, and sets the sampling distance to 0.005 meters.

[0015] Optionally, in step 3), the size of a single voxel in the single voxel space is defined as 10 cm.

[0016] Optionally, in step 3), Cloud Compare software is used to perform single-tree segmentation on the point cloud to obtain a single-tree point cloud, and the single-tree point cloud is used to construct a single-tree voxel space.

[0017] Optionally, constructing a single tree voxel space in step 3) includes: determining which voxel each point belongs to by calculating the offset of each point in the point cloud relative to the minimum coordinate point of the point cloud space, traversing all possible voxel positions, calculating the center point of the voxel, and adding the center point to a new point cloud data set; for each point in the new point cloud, creating cubes centered on these points according to the size of the voxel, and collecting all the cubes to obtain the final tree skeleton.

[0018] Optionally, in the step 4), simulating the shading range of a single tree voxel space using a four-component simulation block of the LESS light radiation transmission model includes: calculating the four components to which each pixel belongs using the four-component simulation block of the LESS light radiation transmission model, the four components including: illuminated soil component; illuminated leaf component; shadowed soil component; shadowed leaf component; judging that the pixel is a shadowed soil pixel when the proportion of the shadowed soil component in the four components of the pixel is the largest; and finally, summing up the areas of all shadowed soil pixels to form the shading range.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] The present invention improves the limitations of traditional forest network shade area observation tools and methods with the help of laser radar technology and computer modeling algorithms, and improves accuracy and comprehensiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a schematic diagram of the process of the present invention;

[0023] Figure 2 Point cloud data collection process: a is the layout of scanning stations; b is the point cloud of the forest belt;

[0024] Figure 3 The preprocessing process of forest belt point cloud data: a is the point cloud splicing target sphere; b is the point cloud before and after denoising;

[0025] Figure 4 It is a partial single-wood voxel model;

[0026] Figure 5 This is a schematic diagram of lighting simulation;

[0027] Figure 6 Comparison chart of simulated and measured values ​​of shading range. DETAILED DESCRIPTION

[0028] The following examples are provided for a better understanding of the present invention, but are not intended to limit the best mode of implementation, nor to limit the content and protection scope of the present invention. Any product identical or similar to the present invention obtained by anyone under the inspiration of the present invention or by combining the features of the present invention with other prior arts shall fall within the protection scope of the present invention.

[0029] If no specific experimental steps or conditions are specified in the examples, the conventional experimental steps or conditions described in the literature in the field can be used. If no manufacturer is specified for the reagents or instruments used, they are all conventional reagent products that can be obtained commercially.

[0030] A method for simulating the shade area of ​​a farmland shelterbelt network based on a LESS light radiation transmission model, the method comprising the following steps:

[0031] (1) Data scanning and acquisition. This embodiment takes the farmland shelterbelt network (Table 1) as the research object, and uses the ground-based LiDAR three-dimensional scanner FARO (its parameters are shown in Table 2) and high-precision GPS to collect three-dimensional point cloud data of the research object. The measuring stations are arranged according to the spacing between trees and rows in the forest belt, and the scanning time of each station is set to the same to ensure that the spatial density and quality of the point cloud of the collected data are consistent. The scanning stations are set in a "Z" shape, and when scanning around the forest belt sample plot, the scanning angle is ensured to cover the entire forest belt height, and the distance between the measuring station and the measured tree is adjusted to obtain clear forest belt point cloud data.

[0032] Table 1 Forest belt structure

[0033]

[0034] Table 2 FARO ground-based laser scanner parameters

[0035]

[0036] (2) Point cloud data preprocessing

[0037] Point cloud stitching. Use SCENE, a desktop software that comes with FARO's ground-based laser radar, to stitch the point cloud. Stitching is divided into two parts: automatic stitching and manual stitching. Automatic stitching is completed by the GPS positioning system of the laser radar. Manual stitching is used for some places that cannot be identified or the stitching is not very good. Manual stitching stitches the point cloud by manually searching for targets. That is, first use the first station as the starting station for stitching, and search for targets at the same position as the three locations in the cloud of the source station at the next station. After the first two stations are stitched together, find their positions with the targets in the scanned point cloud of the third station, and complete the stitching work accordingly.

[0038] Point cloud clipping. The clipping of the sample point cloud is carried out in Cloud Compare software to remove the vegetation point cloud that is not in the sample plot. Finally, the cropped forest belt sample point cloud data is exported and saved as a .txt format file.

[0039] Point cloud denoising. Point cloud denoising is mainly used to process abnormal points or noise points in the point cloud due to various reasons. In the process of collecting point cloud data of farmland shelterbelts, noise points may come from the errors of the lidar itself and environmental factors (such as the position jitter of the leaf point cloud caused by the wind blowing the leaves, and the interference points caused by insects flying over the laser beam path, etc.). The goal of denoising is to identify and remove these points that obviously do not conform to the true geometric shape and spatial distribution of the forest belt. Point cloud noise filtering is performed in Cloud Compare software, and the statistical outlier removal algorithm is used to remove outlier noise points. By comparing the distance between each point and its nearest neighbor, if this distance is significantly greater than the average distance, the point is considered to be a noise point and is removed. The key to the SOR algorithm lies in the setting of two parameters: the number of neighbor points N and the multiple of the standard deviation T. N determines the number of neighboring points considered when calculating the average distance of each point, and T is used to set the distance threshold. Only when the distance between a point and its neighboring points exceeds this threshold will it be considered an outlier and removed. In this study, N is finally set to 20 and T is 2.

[0040] Point cloud thinning was performed in Cloud Compare software, using the secondary sampling (re-sampling) function in the software, selecting the space-based thinning method, and setting the sampling distance to 0.005 meters.

[0041] (3) Construction of three-dimensional model of forest belt:

[0042] Manually segment the single tree point cloud. Use Cloud Compare software to manually segment the single tree, and finally output the cropped and segmented single tree point cloud data and save it as a .txt file.

[0043] Single-tree voxel space construction. The construction of the voxel model is achieved by compiling the code of Matlab software. By calculating the offset of each point in the point cloud relative to the minimum coordinate point of the point cloud space, determine which voxel each point belongs to, traverse all possible voxel positions, calculate the center point of the voxel, and add this center point to the new point cloud data set. For each point in the new point cloud, create a cube centered on these points according to the size of the voxel, and the final tree skeleton is obtained by the collection of all cubes. The size of a single voxel in the single-tree voxel space is defined as 10cm. This 10cm size can effectively characterize the structural characteristics of the forest belt: in the modeling of farmland shelterbelt forest belts, the 10cm voxel size can well capture the key structural information such as the trunk, main branches and the approximate shape of the crown of the trees in the forest belt. For the trunk of the tree, this size can clearly reflect its thickness changes and direction; for the crown, it can depict its approximate outline range, helping us understand the overall morphology and spatial distribution of the forest belt.

[0044] Reduce the amount of computation and memory usage: The amount of forest belt point cloud data is usually large, especially for complex forest belts containing a large number of trees. Smaller voxels will lead to a sharp increase in the number of voxels, and the amount of computation and memory requirements will also increase accordingly. Taking a simple tree canopy as an example, assuming that the canopy is approximately a sphere with a radius of 2m, when the voxel size is 1cm, approximately 4188790 voxels are required; and when the voxel size is 10cm, only about 33510 voxels are required. The 10cm voxel size can greatly reduce the consumption of computing resources while ensuring the availability of the model, so that the modeling process can proceed smoothly on ordinary computer equipment, avoiding modeling failures or long processing times due to exhaustion of computing resources.

[0045] The specific code for voxel model construction and optimization is as follows:

[0046] strPath = 'E:\xhj';

[0047] strFile=[strPath'xjy+jgy-xjy-1.xyz'];

[0048] fid = fopen(strFile,'rt');

[0049] points=fscanf(fid,'%f',[3,inf])';

[0050] fclose(fid);

[0051] gridsize=0.15;

[0052] mins = min(points,[],1);

[0053] xnew=floor((points(:,1)-mins(1)) / gridsize);

[0054] ynew=floor((points(:,2)-mins(2)) / gridsize);

[0055] znew=floor((points(:,3)-mins(3)) / gridsize);

[0056] maxs=max(points,[],1);

[0057] ns=floor((maxs-mins). / gridsize);

[0058] newpts=zeros(sum(xnew==0&ynew==0&znew==0),3);

[0059] k1=0;

[0060] for i=0:ns(1)-1

[0061] xmin=i*gridsize+mins(1);

[0062] xmax=xmin+gridsize;

[0063] for j=0:ns(2)-1

[0064] ymin=j*gridsize+mins(2);

[0065] ymax=ymin+gridsize;

[0066] for k=0:ns(3)-1

[0067]

[0068] pt1=pt_cen+[-gridsize / 2,-gridsize / 2,-gridsize / 2];

[0069] pt2=pt_cen+[-gridsize / 2,-gridsize / 2,gridsize / 2];

[0070] pt3=pt_cen+[-gridsize / 2,gridsize / 2,gridsize / 2];

[0071] pt4=pt_cen+[-gridsize / 2,gridsize / 2,-gridsize / 2];

[0072] pt_back=[pt1;pt2;pt3;pt4];

[0073] pt_front=pt_back;

[0074] pt_front(:,2)=pt_back(:,2)+gridsize;

[0075] pt1=pt_cen+[-gridsize / 2,-gridsize / 2,-gridsize / 2];

[0076] pt2=pt_cen+[-gridsize / 2,-gridsize / 2,gridsize / 2];

[0077] pt3=pt_cen+[gridsize / 2,-gridsize / 2,gridsize / 2];

[0078] pt4=pt_cen+[gridsize / 2,-gridsize / 2,-gridsize / 2];

[0079] pt_left=[pt1;pt2;pt3;pt4];

[0080] pt_right=pt_left;

[0081] pt_right(:,1)=pt_left(:,1)+gridsize;

[0082] pt_all=[pt_top;pt_bottom;pt_back;pt_front;pt_left;pt_right];

[0083] for j=1:6

[0084] fprintf(fid,'v%f%f%f\n',pt_all((j-1)*4+1:j*4,:)');

[0085] fprintf(fid,'f-1 -2-3 -4\n');

[0086] endend

[0087] fclose(fid);

[0088] (4) Light distribution simulation

[0089] The simulation of the shading range uses the four-component simulation block of LESS. Band 1 is the four-component category to which each pixel belongs: 1-illuminated soil; 2-illuminated leaves; 3-shaded soil; 4-shaded leaves. The category here is determined by the component with the largest proportion of the four components in the pixel.

[0090] When the sun's rays can directly illuminate the soil voxel and the area is not blocked by tree voxels (mainly leaves and branches), the corresponding pixel is judged as illuminated soil. This means that the light from the sun's direction can reach the area corresponding to the soil voxel without obstacles. For example, in places where there are large gaps between forest belts, soil voxels are more likely to be judged as illuminated soil.

[0091] When light directly hits a leaf voxel, and the leaf voxel is not severely blocked by other leaves or branches, so that the leaf can receive sufficient light, the corresponding pixel is judged as an illuminated leaf. This may happen in the outer layer of the canopy or in the part with sparse branches, where the leaves can be fully exposed to the sun.

[0092] When light is blocked by tree voxels (mainly leaves and branches) so that the soil voxels below cannot receive direct light, the pixels corresponding to these soil voxels are determined to be shadow soil. Most of the soil voxels within the canopy projection range are determined to be shadow soil.

[0093] Inside the canopy, some leaf voxels are blocked by other leaves or branches and cannot receive direct light. The pixels corresponding to these blocked leaf voxels are classified as shadow leaves. This usually occurs in areas with dense canopies, where the leaves inside are in the shadow of other leaves.

[0094] Ratio calculation and final judgment: In the single tree voxel space, for each pixel corresponding to the area, calculate the number of voxels or area ratio of the four components of illuminated soil, illuminated leaves, shaded soil and shaded leaves. According to the calculated ratio, the pixel is classified into the category with the largest proportion among the four components. In this way, the category of the pixel is determined from the single tree voxel space, thus providing a basis for the subsequent simulation of the shade area.

[0095] Band 2 is the proportion of illuminated soil in each pixel, and its value is a number between [0,1]; Band 3 is the proportion of illuminated leaves in each pixel; Band 4 is the proportion of shadowed soil in each pixel; Band 5 is the proportion of shadowed leaves in each pixel. The final shadow area is calculated by Matlab software compilation code. The detailed code is as follows:

[0096]

[0097]

[0098] (5) Simulation accuracy evaluation:

[0099] Field observations were conducted using a DJI Phantom 4 at a height of more than 20m above the highest position of the forest stand with a heading overlap rate of more than 60%. The experiment was conducted in clear weather during different phenological periods of the crop, and photographed above the sample plots at 8:00-10:00, 10:00-12:00, 12:00-14:00, 14:00-16:00, and 16:00-18:00. The photos were verified, screened, and aligned, and some photos that could not be subsequently synthesized were eliminated to generate orthophotos with natural tones and no obvious joints. ImageJ image processing software was used to measure the area of ​​irregular shaded areas in the pictures.

[0100] Compare the simulated value of the shaded area with the measured value to verify the accuracy of the shaded area simulation. Figure 6 As shown in Table 3, the results show that there is a strong linear relationship between the simulated value (Y) and the measured value (X), which can be described by the linear equation Y = 0.9154X + 31.31. The coefficient of determination (R 2 ) is 0.99, indicating that the model can explain most of the variation in the measured values ​​(99%) very well, indicating that the consistency between the simulated values ​​and the measured values ​​is very high. The bias is -433.27m 2 , indicating that the simulated values ​​underestimate the measured values ​​overall, but the degree of underestimation is relatively small.

[0101] Table 3 Simulation accuracy of shading range based on voxel modeling and LESS light radiation transfer model

[0102]

[0103] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model, characterized in that: The steps include: Step 1) Use laser radar to scan the farmland shelterbelt to obtain three-dimensional point cloud data of the farmland shelterbelt; Step 2) performing point cloud preprocessing on the acquired three-dimensional point cloud data; Step 3) constructing a single-tree voxel space using the preprocessed point cloud data; Step 4) The shading range is simulated in the single tree voxel space using the four-component simulation block of the LESS light radiation transmission model.

2. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 1 is characterized in that: In the step 1), a ground-based laser radar scanner is used to scan stations arranged in the forest belt to collect three-dimensional point cloud data of all trees in the farmland shelterbelt.

3. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 1 is characterized in that: In the step 2), the preprocessing includes: point cloud stitching, point cloud cropping, denoising, and thinning.

4. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 3 is characterized in that: The point cloud stitching in step 2) includes: completing automatic stitching for a GPS positioning system using a laser radar.

5. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 3 is characterized in that: In step 2), Cloud Compare software is used to perform point cloud cropping, denoising, and thinning; The thinning adopts a space-based thinning method, and sets the sampling distance to 0.005 meters.

6. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 1, characterized in that: In the step 3), the size of a single voxel in the single voxel space is defined as 10 cm.

7. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 6 is characterized in that: In the step 3), Cloud Compare software is used to perform single-tree segmentation on the point cloud to obtain a single-tree point cloud, and the single-tree point cloud is used to construct a single-tree voxel space.

8. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 7 is characterized in that: The step 3) of constructing a single tree voxel space includes: determining which voxel each point belongs to by calculating the offset of each point in the point cloud relative to the minimum coordinate point of the point cloud space, traversing all possible voxel positions, calculating the center point of the voxel, and adding the center point to a new point cloud data set. For each point in the new point cloud, cubes centered on these points are created according to the size of the voxel, and all cubes are collected to obtain the final tree skeleton.

9. The method for simulating the shade area of ​​a farmland shelterbelt network based on the LESS light radiation transmission model according to claim 1, characterized in that: In the step 4), the shading range simulation of the single tree voxel space using the four-component simulation block of the LESS light radiation transmission model includes: using the four-component simulation block of the LESS light radiation transmission model to calculate the four components belonging to each pixel, the four components including: illuminated soil component; illuminated leaf component; shadowed soil component; shadowed leaf component; when the proportion of the shadowed soil component in the four components of the pixel is the largest, the pixel is determined to be a shadowed soil pixel; finally, the areas of all the shadowed soil pixels are summed to form the shading range.

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