CN114022536B - Leaf area solving method based on foundation laser radar point cloud data - Google Patents

Leaf area solving method based on foundation laser radar point cloud data Download PDF

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CN114022536B
CN114022536B CN202111207990.2A CN202111207990A CN114022536B CN 114022536 B CN114022536 B CN 114022536B CN 202111207990 A CN202111207990 A CN 202111207990A CN 114022536 B CN114022536 B CN 114022536B
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leaf
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CN114022536A (en
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李世华
尤航凯
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University of Electronic Science and Technology of China
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
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Abstract

The invention belongs to the technical field of ground laser radar point cloud data processing, and particularly relates to a leaf area solving method based on ground laser radar point cloud data. The invention utilizes the Alpha-shape algorithm to construct polygons and polyhedrons from a pile of disordered three-dimensional points, thereby carrying out envelope surface fitting on the blade points with separated branches and leaves, reconstructing the surface of the blade and carrying out accurate blade area solution. The method can get rid of the dependence of the traditional physical model-based leaf area solving method on parameters, overcomes the difficulty of data acquisition caused by the complexity of the model, and has excellent precision of extracting the leaf area.

Description

Leaf area solving method based on foundation laser radar point cloud data
Technical Field
The invention belongs to the technical field of ground laser radar point cloud data processing, and particularly relates to a leaf area solving method based on ground laser radar point cloud data, wherein an Alpha-shape algorithm is used for carrying out envelope surface fitting.
Background
Trees are important components in natural ecology, and the three-dimensional reconstruction of the trees has important application value in aspects of plant digital storage, scene rendering, agriculture and forestry research and the like (Livny et al, 2010;
Figure BDA0003307539460000013
et al, 2012; bremer et al, 2013). The Leaf Area Index (LAI) is one half of the total area of the leaves per unit of surface area, and is one of the most important features of trees. For single woodIn this respect, the solution of the leaf area index is built on the solution of the leaf area of the single tree, and the measurement of the leaf area of the single tree is an indispensable part of the solution. The leaf area solving needs to obtain the area of each leaf from the laser radar point cloud data, and is an important premise for extracting the single-wood scale leaf area data. Due to the complexity of tree structure and leaf surface details, such as asymmetric branches, irregular epidermal shapes, complex-textured leaves, etc., it is difficult to accurately extract leaf information from only two-dimensional images. Therefore, the modern remote sensing technology faces new challenges in accurately and quickly obtaining the three-dimensional structural information of the vegetation.
The LiDAR (Light Detection And Ranging) is an active remote sensing technology which is developed rapidly in recent years, and mainly shows accurate three-dimensional structure information of a target object by measuring the propagation distance of laser Light emitted by a sensor between the sensor And the target object, analyzing information such as the size of reflection energy on the surface of the target object And the amplitude, frequency And phase of a reflection spectrum And the like. The ground-based three-dimensional Laser Scanner (TLS) has unique advantages of acquiring high-density and high-precision geometric structure information of the surface of a target object to be detected by observing the target object from different positions. In recent decades, the inversion and calculation of leaf area have been the hot direction for the research of laser radar forestry application. With the continuous improvement of the branch and leaf separation algorithm, the high spatial resolution of the foundation laser radar data has the capability of directly measuring the area of the leaf.
Scholars at home and abroad make a lot of researches on leaf areas by using ground-based laser radar data, and mainly use two inversion methods of Lambert-beer law and contact frequency. The first applicable scenario of lambert-beer law is a turbid solution, and the process of passing electromagnetic waves of a given frequency through a low-concentration solution is successfully described by using the assumption that extinction factors are randomly distributed.
Figure BDA0003307539460000011
Wherein I is the intensity of the emitted light, I 0 Is the intensity of the incident light, I/I0 is the proportion of light passing through the canopy, k is the extinction coefficient, ρ is the solute density in the solution, and l is the depth of the solution. By adjusting the form of the above formula, further:
Figure BDA0003307539460000012
since k · ρ · L represents the extinction area of a single solute multiplied by the number of solutes per unit area, the solutes are replaced by leaves, and k · ρ · L is the leaf area index LAI. The leaf area index is defined as the area of the blade per unit of ground, and thus multiplying the leaf area index by the ground surface area yields the leaf area. By solving and assuming that the leaf surface direction is the same as the electromagnetic wave propagation direction, the method
Figure BDA0003307539460000021
Is the gap ratio P. Thus, LAI can be solved by the following equation:
LAI＝-ln(P)
but the gap rate P cannot be measured directly by LiDAR but needs to be derived by processing point cloud data. Forest et al (2018) solved the clearance ratio by a bin projection method by projecting the blade points into a unit hemispherical space and rasterizing the unit hemispherical space, the clearance ratio being the ratio of the grid projected by the blade points to the total grid number by statistics. However, the accuracy of this method is very sensitive to the grid size, and it is difficult to determine the optimal grid size for different data sets. Xushu sailing sails and the like (2021) simulate laser beams to detect canopies through a Monte Carlo method to obtain a numerical solution of the clearance rate, the precision of the numerical solution obtained by the method is related to the simulation times, and the method has limitation on obtaining the high-precision clearance rate of a large range of forest stands.
Disclosure of Invention
Aiming at the problems or the defects, the invention provides a leaf area solving method based on foundation laser radar point cloud data, aiming at solving the problem that the existing leaf area solving method has strong dependency on empirical parameters. The method gets rid of the constraint based on statistics and model assumptions in the past, fully utilizes the characteristic of high spatial resolution of the foundation laser radar, and solves the leaf area based on morphology.
A method for solving the leaf area based on foundation laser radar point cloud data comprises the following steps:
step 1, extracting blade point cloud data of foundation laser radar point cloud data:
the point cloud data obtained by the laser radar scanning equipment is a sample of an object in the real world, so the point cloud data comprises different types of points such as tree points, ground points and the like. The leaf area solving method only aims at the leaf point cloud data, so that ground point filtering and branch and leaf separation processing are required to be carried out on the original foundation laser radar point cloud data, and the leaf point cloud data are extracted from the original foundation point cloud data to obtain the leaf points.
Step 2, single-leaf segmentation:
(1) Taking the leaf points obtained in the step 1 as input, setting default codes of the leaf points to be 0 and setting initial values to be 0
Figure BDA0003307539460000022
In which a leaf point with code 0 is arbitrarily found
Figure BDA0003307539460000023
And setting the searched leaf code of the point as K, wherein K is more than or equal to 1, and the leaf codes of all points on the same leaf are the same.
Figure BDA0003307539460000024
The blade code marked as the point in the middle is 0 as each point code is the default code, and when a certain point determines the blade attribution, the blade code becomes
Figure BDA0003307539460000025
The subscript i represents that the point is the ith point in the corresponding code K set, and i is respectively numbered and assigned as the number of points in the K blades increases.
(2) Traversing the leaf point set with the code of 0, and solving each point
Figure BDA0003307539460000026
And points coded as K
Figure BDA0003307539460000027
With a minimum euclidean distance R therebetween min The calculation formula of the euclidean distance R is as follows:
Figure BDA0003307539460000031
wherein (x) a ,y a ,z a ),(x b ,y b ,z b ) Respectively representing the coordinates of a point a and a point b, which are respectively corresponding to the points
Figure BDA0003307539460000032
And
Figure BDA0003307539460000033
if it is not
Figure BDA0003307539460000034
And with
Figure BDA0003307539460000035
Minimum Euclidean distance R between points min Less than a given threshold R th Wherein R is th The value of (A) is set to be 1 to 10 times of the spatial resolution of the ground-based laser radar data, the point is considered to be
Figure BDA0003307539460000036
Belonging to the leaf coded K, will
Figure BDA0003307539460000037
Is set to K and corresponding i, i
Figure BDA0003307539460000038
If it is used
Figure BDA0003307539460000039
And with
Figure BDA00033075394600000310
Is the minimum euclidean distance R between points min Greater than R th If the point does not belong to the K blade, then
Figure BDA00033075394600000311
The encoding of the dots remains unchanged.
Until there is no new point in one traversal
Figure BDA00033075394600000312
And if the number is K, the leaf with the leaf number of K is considered to be completely divided.
(3) And if the set S with the code of 0 in the blade points is not empty, taking the set S at the moment as the input of (1), assigning the value of K to be K + m, wherein m is the cycle execution frequency of the step (2), and continuing to circularly execute the steps (1) to (2) until the set S is empty when the codes of all the blade points are not 0, namely the set S is empty, and executing the step 3.
Step 3, solving the blade area of the blade:
utilizing Alpha-shape algorithm to establish envelope surface for each encoded point set obtained in step 2, and solving surface Area of each envelope surface K (where superscript indicates that the surface area is the surface area of the envelope created by the set of points encoded as K). Taking half of the surface area of the enveloping surface as the area of the blade.
Step 4, solving the total leaf area:
and (4) accumulating the blade areas of all the numbered blades obtained in the step (3) to obtain the total blade area. The specific physical meaning of the total leaf area is determined by the input point cloud data: if the input point cloud data is point cloud data of a single tree, the obtained total leaf area is the total leaf area of the single tree; and if the input point cloud data is point cloud data of the forest stand, the obtained total leaf area is the total leaf area of the forest stand.
Further, R in the step 2 th The spatial resolution of the foundation laser radar point cloud data is set to be 2-3 times, so that the guarantee of high precision of technical results and the consideration of relatively low solving complexity are ensured.
Step 2 principle of single leaf segmentation: the premise of the single-leaf segmentation is that a gap exists between the leaves, and the size of the gap is larger than the spatial resolution of the ground-based laser radar point cloud data. Because the spatial resolution of the ground-based laser radar point cloud data can reach or be superior to millimeter level and is far smaller than the size of the gap between the blades, a distance threshold R larger than the spatial resolution of the ground-based laser radar point cloud data can be set th But not larger than the minimum inter-blade gap size. The distance between the two points once determined is equal to or less than R th Then the two points are considered to belong to the same blade.
Step 4, solving the leaf area principle by the envelope surface area: for a single blade, the default blade has the same area for the female and male surfaces. And because the surface area of the envelope surface of the blade is the sum of the areas of the female surface and the male surface of the blade, half of the surface area of the envelope surface of the blade is directly taken as the blade area of the blade according to the definition of the blade area index.
Firstly, extracting leaf points from point cloud data, and separating leaf points and non-leaf points from original foundation laser radar point cloud; then, single-leaf segmentation is carried out to obtain a point set of each leaf; then carrying out envelope surface fitting on the blade point data obtained after the single-blade segmentation to obtain the surface area of an envelope surface, and taking half of the surface area as the blade area of the blade; finally, the total leaf area of the whole sample is obtained by accumulating the leaf areas of the leaves, and the flow is shown in fig. 1.
The method comprises the steps of obtaining tree blade three-dimensional laser point cloud data by using a foundation laser radar, analyzing gaps among blades according to morphological characteristics of the blades, extracting blade points, segmenting single blades, fitting an envelope surface, solving blade area and solving total blade area according to the relation between the points, and establishing the method for extracting the total blade area from the blade points based on the original foundation laser point cloud. The invention utilizes the Alpha-shape algorithm to construct polygons and polyhedrons from a pile of unordered three-dimensional points, so that the envelope surface fitting is carried out on the blade points after the branches and the leaves are separated, the surface of the blade is reconstructed, and the accurate area of the blade is solved, which has very important significance for forest management.
In conclusion, the method can get rid of the dependence of the traditional physical model-based leaf area solving method on parameters, overcome the difficulty in data acquisition caused by the complexity of the model, and has excellent precision in leaf area extraction.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a graph showing the results of single-leaf segmentation in the example;
FIG. 3 is a diagram of envelope surface fitting of each blade based on Alpha-shape algorithm according to the embodiment.
Detailed Description
The invention is described in further detail below by way of example with reference to the accompanying drawings:
the development environment is MATLAB and Dev-C + +5.11, and the programming language is MATLAB and C + + mixed programming.
Step 1, a purchased money tree with the height of about one meter and two is placed on a square at the front door of an innovation center in a clear water river school district of electronic science and technology university, and the ground-based laser radar data of the tree is obtained by scanning through a Leica Scanstation C10, wherein the scanning time is 2021 year, 9 month and 17 days. For the convenience of verification, partial data of the tree is intercepted and verified with field sampling. The following steps are detailed steps of solving for the leaf area.
And 2, according to the first step of the technical scheme, extracting the blade points after the original foundation laser point cloud is obtained, removing noise points in the point cloud data, and dividing the blade points and non-blade points from the point cloud data.
And 3, according to the step one of the technical scheme, performing single-leaf segmentation on the leaf points obtained in the step 2, and obtaining 7 leaves by total segmentation (as shown in fig. 2).
And 4, carrying out envelope surface fitting (shown in figure 3) based on an Alpha-shape algorithm on each leaf, and solving the area of the envelope surface. The area of the envelope surface of each blade is shown in table 1, and the area of each blade is measured by manual cutting.
TABLE 1 blade surface results
Figure BDA0003307539460000051
And 5, accumulating the areas of the blades to obtain the total blade area, wherein the total blade area of the seven blades in the experiment is 402.3 square centimeters, the total blade area of the seven blades obtained through measurement is 394.7 square centimeters, and the relative error is 2 percent.
According to the embodiments, the three-dimensional laser point cloud data of the tree leaves are obtained by using the foundation laser radar, gaps among the leaves are analyzed according to morphological characteristics of the leaves, and the total leaf area is extracted from the leaf points based on the original foundation laser point cloud established through leaf point extraction, single leaf segmentation, envelope surface fitting, leaf area solving and total leaf area solving according to the relation between points. The method can get rid of the dependence of the traditional physical model-based leaf area solving method on parameters, overcomes the difficulty of data acquisition caused by the complexity of the model, and has excellent precision of extracting the leaf area.

Claims (2)

1. A method for solving a leaf area based on foundation laser radar point cloud data is characterized by comprising the following steps:
step 1, extracting blade point cloud data of foundation laser radar point cloud data:
carrying out ground point filtering and branch and leaf separation processing on original foundation laser radar point cloud data of a target area, and extracting leaf point cloud data from the original foundation laser radar point cloud data to obtain leaf points;
step 2, single-leaf segmentation:
(1) Taking the leaf points obtained in the step 1 as the outputIn, default code of leaf point is 0 and all set initial value as
Figure FDA0003307539450000011
In which a leaf point with code 0 is arbitrarily found
Figure FDA0003307539450000012
Setting the searched leaf code of the point as K, wherein K is more than or equal to 1, and the leaf codes of all points on the same leaf are the same;
Figure FDA0003307539450000013
the blade code marked as the point in the middle is 0 as each point code is the default code, and when a certain point determines the blade attribution, the blade code becomes
Figure FDA0003307539450000014
Subscript i represents that the point is the ith point in the corresponding coding K set, and i is respectively numbered and assigned with the increasing of the number of points in the K blades;
(2) Traversing the leaf point set with the code of 0 and solving each point
Figure FDA0003307539450000015
And points coded as K
Figure FDA0003307539450000016
With a minimum euclidean distance R therebetween min The calculation formula of the euclidean distance R is as follows:
Figure FDA0003307539450000017
wherein (x) a ，y a ，z a )，(x b ，y b ，z b ) Respectively represent the coordinates of a point a and a point b, and the points a and b respectively correspond to the points
Figure FDA0003307539450000018
And
Figure FDA0003307539450000019
if it is not
Figure FDA00033075394500000110
And
Figure FDA00033075394500000111
minimum Euclidean distance R between points min Less than a given threshold R th Wherein R is th The value of (A) is set to be 1 to 10 times of the spatial resolution of the ground-based laser radar data, the point is considered to be
Figure FDA00033075394500000112
Belonging to the leaf coded K, will
Figure FDA00033075394500000113
Is set to K and corresponding i, i
Figure FDA00033075394500000114
If it is not
Figure FDA00033075394500000115
And
Figure FDA00033075394500000116
is the minimum euclidean distance R between points min Greater than R th If the point is not the K blade, then
Figure FDA00033075394500000117
The occupied code remains unchanged;
until there is no new point in one traversal
Figure FDA00033075394500000118
If the blade is coded as K, the blade coded as K is considered to be completely divided;
(3) If the set S with the code of 0 in the blade points is not empty, taking the set S at the moment as the input of (1), assigning the value of K to be K + m, wherein m is the cycle execution frequency of the step (2), and continuing to circularly execute the steps (1) to (2) until the set S is empty when the codes of all the blade points are not 0, and executing the step 3;
step 3, solving the blade area of the blade:
utilizing Alpha-shape algorithm to establish envelope surface for each encoded point set obtained in step 2, and solving surface Area of each envelope surface K Wherein the superscript indicates that the surface area is the surface area of an envelope surface established by a point set encoded as K, and half of the surface area of the envelope surface is taken as the blade area of the blade;
step 4, solving the total leaf area:
accumulating the leaf areas of all numbered leaves obtained in the step (3) to obtain the total leaf area;
the specific physical meaning of the total leaf area is determined by the input point cloud data: if the input point cloud data is point cloud data of a single tree, the obtained total leaf area is the total leaf area of the single tree; and if the input point cloud data is point cloud data of the forest stand, the obtained total leaf area is the total leaf area of the forest stand.
2. The method of claim 1 for solving leaf area based on ground-based lidar point cloud data, wherein:
r in the step 2 th Setting the spatial resolution of the foundation laser radar point cloud data to be 2-3 times.
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