CN112068153B - Crown clearance rate estimation method based on foundation laser radar point cloud - Google Patents
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- 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
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- 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
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/42—Simultaneous measurement of distance and other co-ordinates
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/483—Details of pulse systems
- G01S7/486—Receivers
- G01S7/487—Extracting wanted echo signals, e.g. pulse detection
Abstract
The invention belongs to the technical field of ground-based laser radar remote sensing application, and relates to a method for estimating the canopy clearance rate by using Monte Carlo simulation laser beams based on ground-based laser radar data, in particular to a canopy clearance rate estimation method based on ground-based laser radar point cloud. Converting three-dimensional rectangular coordinates of point cloud data into spherical coordinates, and partitioning the data according to the interval of zenith angles and azimuth angles; according to different space distribution conditions of the canopy layers in different block areas, point cloud distribution of each area is counted, and then the judgment distance is calculated by combining scanning resolution; and finally, converting the clearance rate into a probability model by a Monte Carlo method, and simulating a laser beam to detect the canopy point cloud so as to realize the estimation of the clearance rate. The method fully utilizes the advantages of the three-dimensional structure information provided by the foundation laser radar, and is simple, easy and effective.
Description
Technical Field
The invention belongs to the technical field of ground-based laser radar remote sensing application, and relates to a method for estimating the canopy clearance rate by using Monte Carlo simulation laser beams based on ground-based laser radar data, in particular to a canopy clearance rate estimation method based on ground-based laser radar point cloud.
Background
The forest canopy not only exchanges complex substances and energy with the atmosphere, but also forms the light conditions and soil environment of the vegetation under the forest (li dels et al, 2004; qiu Jiany et al, 2008). Therefore, the research on the forest canopy has important ecological significance. In the research of forest canopy, because the transmission and distribution of canopy radiation are influenced by canopy structure, accurately depicting canopy structure becomes a hot point of research of scholars at home and abroad. Gap Fraction (GF), which is the probability that a photon will reach one point in the canopy in a certain direction to another point without being intercepted by the canopy (Ni et al, 1997), is an important canopy structure parameter. When the light is incident to the canopy from a certain angle and collides with the leaves, the distribution condition of the leaves needs to be considered, the leaves are not randomly distributed in space but are in a certain aggregation state, and the aggregation of the leaves on the branches can enlarge the gaps in the canopy. Estimating the non-randomly distributed gap rate may therefore help to correct the Leaf Area Index (LAI) that is underestimated.
Both the traditional optical remote sensing method and the optical instrument method can only describe the parameters of the canopy structure from a two-dimensional angle, and with the development of quantitative remote sensing, more and more models and computer simulation need to consider or input the three-dimensional structure information of vegetation. Laser Scanning over ground (TLS) is a technology that has been developed rapidly in recent years, and a Laser scanner samples the complete geometry of a scanned object by measuring the travel time of a transmitted Laser pulse reflected by the object and received at the scanner. The laser scanner can quickly obtain high-density tree three-dimensional point cloud data in a forest, and the three-dimensional coordinates of trees can reflect the internal structure information of the canopy, particularly the vertical structure information which is not available in passive optical remote sensing, so more and more learners utilize the point cloud data of the laser radar of the foundation to invert the structural parameters of the canopy.
When describing tree structure information by ground-based laser radar, a voxel-based 3D modeling method is widely applied. Hosoi and Omasa (2006) utilize canopy point cloud data measured by ground-based lidar to build voxel-based single-wood models. Voxels are defined as volume elements in a three-dimensional array. The three-dimensional rectangular coordinates of all point cloud data in the registration dataset are converted to voxel coordinates by the following equation:
where (i, j, k) is the voxel coordinate in the voxel array, int is a function rounded to the nearest integer in one decimal place, (X, Y, Z) represents the three-dimensional rectangular coordinate of the registered lidar point cloud data, (X min ,Y min ,Z min ) Is the minimum value of the three-dimensional rectangular coordinates of the point cloud, and (Δ I, Δ j, Δ k) represents the size of the unit voxel. The attribute of the voxel intercepted by the laser beam is marked as 1, and the rest voxels are marked as 0, namely, the gap is formed.
Similar to the 3D voxel modeling method, Moorthy (2008) et al slices XYZ point cloud data of trees according to scanning the point cloud range of an artificial tree, extracts laser pulse echo density distribution, and estimates the canopy gap rate according to measured pulse density and theoretical pulse density. Seidel (2012) models singletrees using a voxel method and assigns volumes to data points of a three-dimensional point cloud, which facilitates comparison with digital hemispherical images, although the resolution of the model is reduced compared to the point cloud. Zheng (2016) proposes a novel method of radial hemispherical point cloud slicing (RHPS) for the spatial distribution of leaf elements, firstly, the rectangular coordinates of the point cloud are converted into spherical coordinates, the point cloud is divided into trapezoidal voxels according to the zenith angle azimuth angle, then the density of the point cloud is counted according to the segmentation in the radius direction, and most of the leaf elements are found to be distributed in the spherical tangential plane with the radius of 5-15 meters. Regarding the difference in angular gap rates of TLS data and the inversion of the hemisphere photographs, the authors believe that it is due to the accurately segmented TLS range versus the hemisphere photographs without well-defined ranges.
Although three-dimensional point cloud data based on ground-based laser radars, many methods convert three-dimensional data into two-dimensional images when estimating GF, and do not fully utilize three-dimensional structural information provided by the point cloud data. Danson et al (2007) calculated GF by converting the slice projection of TLS data xy-axis into a two-dimensional image, while comparing GF calculated from digital hemisphere photographs, showed that GF using high resolution point cloud data and digital hemisphere images was similar, some of which differences may be due to sun glare seen in photographs or errors associated with manual thresholding of digital images. Cifuentes et al (2014) collect ground lidar point cloud data in young, middle and mature forests, respectively, and also collect digital hemispherical images for comparison. They apply different voxel sizes to the point cloud data to perform voxel modeling, and convert the voxel data into a hemispherical image using open-source ray-tracing software to calculate GF. Finally, it is found that in forest stands of different age classes, the voxel size has different degrees of influence on the estimation of the clearance rate and also corresponds to different optimal voxel sizes. Hancock et al (2014) also use ray tracing techniques to convert lidar data into hemispherical images in conjunction with the laser beam separation of each echo. Li (2017) estimates GF by voxel-transforming the canopy TLS data and projecting it onto a 1-meter hemisphere.
In conclusion, with the development of the laser radar technology, the research of inverting forest parameters by using the laser radar is more and more. The ground-based laser radar can provide accurate three-dimensional structure information inside a forest canopy, and TLS point cloud data is utilized to carry out gap ratio inversion mainly through voxel modeling, or 3D data is converted into a two-dimensional image. The voxel-based method generally discusses the optimal voxel size, and the size of the study range, the resolution of the scanner, and the tree type all affect the optimal voxel size of the model. The method of converting the point cloud data into the hemispherical image through the projection or ray tracing technology does not fully utilize the three-dimensional structure information provided by the laser radar, and loses the advantages of using the laser radar. Therefore, how to fully utilize the three-dimensional point cloud data of the laser radar and provide a simpler clearance rate algorithm still needs further efforts of scholars.
Disclosure of Invention
Aiming at the problems or the defects, the invention provides a canopy clearance rate estimation method based on ground-based laser radar point cloud, which fully utilizes the three-dimensional coordinate information of point cloud data, converts the clearance rate into a probability model through a Monte Carlo method, and simulates a laser beam to detect the canopy point cloud so as to realize the estimation of the clearance rate.
The specific technical scheme is as follows:
and setting a sample in the research area, setting a measuring station according to the condition of the sample to erect the three-dimensional laser scanner, and scanning the forest canopy of the complete sample in the research area. At least 3 targets are arranged in the scanning range of the scanner to be used as the basis of the later multi-station registration. And (4) registering and denoising the scanned TLS point cloud data by using software, exporting the TLS point cloud data into a text format, and removing the point cloud data lower than the scanner height.
Step 2, converting and blocking the coordinates of the point cloud data;
the coordinate system of the point cloud data acquired by the ground-based laser radar is a space rectangular coordinate system, and the path of the sun incident on the canopy is usually described by a zenith angle, so that the three-dimensional rectangular coordinates of all the point cloud data obtained in the step 1 are converted into spherical coordinates, namely the point cloud coordinates are converted from P (x, y, z) into spherical coordinates
Wherein theta is the zenith angle,
and r is the distance from the canopy point cloud to the origin. The formula is as follows:
the quantity of point clouds acquired by the foundation laser radar is large, and in order to improve the running speed of a program, the converted point cloud data needs to be subjected to data partitioning according to the interval of a zenith angle of 2-10 degrees and an azimuth angle of 10-60 degrees.
Step 3, simulating a laser beam;
and converting the estimation of the gap rate into a probability model based on a Monte Carlo method. The working principle of the three-dimensional laser scanner is simulated through a computer program, rays are randomly emitted from the scanner (namely a point cloud coordinate origin) to serve as simulated laser beams, and the emission range of the simulated laser beams is consistent with the zenith angle and azimuth angle range of converted point cloud data. The structural information of the canopy is described by detecting the point cloud in the canopy by emitting a simulated laser beam, and the distribution of the gaps is quantified.
Step 4, determining a judgment distance;
a certain sampling interval exists between canopy point clouds obtained by scanning a canopy with a ground-based laser radar (as shown in fig. 2a), so that a proper discrimination distance needs to be determined when a simulated laser beam explores spatial attributes in a corresponding direction. Because the growth and development conditions of the forest trees are different, and the distribution conditions of the canopy layers with different azimuth angles in the same sky ring are also different (as shown in fig. 2b), the determination of the discrimination distance is not only related to the scanning resolution, but also needs to consider the spatial distribution of the canopy layers.
And (3) counting the distance distribution from the point clouds to the origin in each space region divided in the step (2) by taking 0.1-1 meter as a step length, wherein the distribution condition of the canopy of the region is represented by the expectation of the distance from all the point clouds to the origin. As shown in fig. 3, the relationship between the sampling interval and the point cloud-to-origin distance is as follows:
Where R and R are the distances of the target point cloud to the origin, and D and D are the sampling intervals to which R and R correspond. Therefore, the discrimination distance is calculated according to the distribution of the point cloud in each area and the sampling interval of the scanner:
where s is the discriminant distance, D and R are derived from the scanner resolution, and R is the expected value of the distance from all point clouds in the region to the origin.
Step 5, estimating the clearance rate GF;
and emitting a simulated laser beam in each space region by using a computer program, and judging the Euclidean distance between the simulated laser beam and all point clouds in the corresponding space region. If the Euclidean distance is smaller than or equal to the judgment distance, determining that the cloud point collides with the canopy point, counting and re-emitting the simulation ray for next detection; if the canopy point cloud within the judgment distance is not detected in the whole data traversal, the next simulated laser beam is detected, and the principle is shown in fig. 4. And finally, estimating the gap rate according to the relation between the number of times of collision to the point cloud and the total number of the emitted simulated laser beams, wherein the formula is as follows:
the principle involved in steps (3) and (4):
monte Carlo is a statistical simulation method guided by probability statistical theory, firstly, the geometric quantity and geometric characteristics of the movement of an object are grasped, the problem to be solved is converted into a probability model, and the probability of the problem is estimated according to the frequency of the occurrence of random events by a random sampling method. The laser radar detects a target by emitting laser beams, the rotary laser radar is arranged in a vertical row by a plurality of laser beams and rotates 360 degrees around an axis, each laser beam scans a plane, and a three-dimensional figure is displayed after longitudinal superposition.
Therefore, the gap rate is converted into a probability model according to the Monte Carlo method, and all three-dimensional point cloud data records the structural information of the canopy, and if a certain direction is observed from a scanner and no point cloud blocks, the position is the gap of the canopy. The gap profile of each segmented region can be detected by simulating a large number of shots of the laser beam in that region.
In the working process of the three-dimensional laser scanner, certain point cloud intervals are brought by scanning resolution and leaf inclination angles, and gaps among all the point clouds do not correspond to real gaps. It is necessary to determine the detection distance at the time of the analog laser beam detection. And because the space distribution conditions of the canopy in different block areas are different, the canopy point cloud distribution of each area needs to be counted, and then the discrimination distance is calculated by combining the scanning resolution.
In conclusion, the method fully utilizes the advantages of three-dimensional structure information provided by the foundation laser radar, converts the clearance rate into a probability model by the Monte Carlo method, simulates the laser beam to detect the canopy point cloud so as to realize the estimation of the clearance rate, and is simple, easy and effective.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic of data; wherein (a) is canopy point cloud data acquired by a laser radar, and (b) is a canopy point cloud distribution top view of a zenith ring at 50-55 degrees;
FIG. 3 is a schematic diagram showing a relationship between a sampling interval and a distance from a point cloud to an origin;
FIG. 4 is a schematic diagram of a ray detection canopy point cloud;
FIG. 5 is a comparison of the results of the present invention and voxel method estimation of the gap rate versus a line graph;
FIG. 6 is a comparative analysis of the results of the present invention and voxel method estimated gap rates.
Detailed Description
The invention is explained in further detail below by way of examples and figures:
the development environment is Microsoft Visual Studio 2017, and the programming language is C + +.
According to the technical scheme, in the step 1, a sample side of 40 × 30 meters is set in a magnolia forest in a university of electronic technology, foundation laser radar data is acquired by a Leica Scanstation C10 at 4 measuring stations, one measuring station is arranged in the center of the magnolia forest sample side, and the other 3 measuring stations are arranged around. Laying 3 targets in the forest as a common point in multi-station data registration. High resolution scans (100 meters apart, 0.05 meters horizontal and vertical separation) were set up on Leica C10, taking into account the condition of the plot and the total number of point clouds, with the remaining parameters listed in the table below. And performing multi-station registration, denoising, normalization and the like on the TLS data by using Cyclone software, and exporting the data into a text format only containing 3-dimensional coordinates, wherein the origin of coordinates (0,0,0) is positioned at the scanner erection position of the central survey station. And removing the point cloud with the z-axis smaller than 0 after the data is exported.
TABLE 1 three-dimensional laser scanner Leica Scanstation C10 parameter
According to the technical scheme of step 2, the coordinates of the three-dimensional point cloud data acquired by the Leica Scanstation C10 are a space rectangular coordinate system, and the coordinates of each point are denoted as P (x, y, z). And converting the point cloud coordinates from a space rectangular coordinate system into a spherical coordinate system.
Because the number of point clouds obtained after scanning in the research area reaches 2600 ten thousand, in order to improve the speed of processing data by a program, data after coordinate conversion is subjected to data partitioning according to the interval of a zenith angle of 5 degrees and an azimuth angle of 45 degrees. The effective zenith angle range obtained according to the relation between the tree height and the radius of the sample plot is 0-70 degrees, so the range of the 70-degree zenith angle is divided into 14 circular rings, the azimuth angle is divided into 8 areas by taking 0 degree, 45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees and 315 degrees as central scales, and the data is divided into 112 areas in total.
According to the technical scheme, in the step 3, in each space area divided in the step 2, a computer is utilized to simulate the working principle of a three-dimensional laser scanner, one hundred thousand simulated laser beams in random directions are emitted from the scanner (namely, a coordinate origin) according to the ranges of zenith angles and azimuth angles of block data, and the simulated laser beams are utilized to detect whether the canopy layer point cloud exists in the directions.
According to the technical scheme of step 4, the method comprises the steps of firstly carrying out statistics on the distribution of the canopy point clouds in each area by the step length of 0.5 m, then representing the distribution situation of the canopy in the area by the expected value of the distance from all the point clouds in the area to the origin, and then determining the judgment distance by the formula (4).
According to the technical scheme, in the step 5, when the point cloud is detected by the simulated laser beam, the point cloud is detected according to the distinguishing distance of the area. Every time a simulated laser beam in a random direction is emitted, calculating the Euclidean distance from a point cloud around the laser beam to the laser beam, if a point with the Euclidean distance smaller than or equal to the judgment distance from the point cloud to the laser beam is detected, defining that the laser beam hits the point cloud of the canopy layer, and then re-emitting the simulated laser beam for next detection; and if the Euclidean distance from the point cloud to the laser beam is greater than the judgment distance after traversing all the point clouds, determining that the laser beam does not detect the canopy, and defining the tree crown structure in the direction as a gap. And finally, obtaining the collision times and the total times of the simulated laser beam emission, and estimating the clearance rate by a formula (5).
According to the method provided by the invention, according to the steps of the technical scheme, the statistics of the distribution of the canopy point clouds is carried out on the 112 groups of partitioned data, then the expectation of the distance from the point cloud in each group of data to the origin is calculated, and the corresponding distinguishing distance of each group of data is obtained according to the relation between the expectation of the distance from the point cloud to the origin and the scanning resolution. And detecting the point cloud by combining the judgment distance and using a laser beam in a program simulation random direction to obtain the clearance rate of each area, and then averaging the clearance rates of 8 different azimuth angle areas in one zenith ring to obtain the clearance rate of the zenith ring. Comparing the result of the gap rate of the method with the gap rate obtained by projecting the voxel to a spherical surface to obtain figures 5 and 6; the clearance rate obtained by the two methods shows a descending trend along with the increase of the zenith angle, and the correlation of the results obtained by the two methods from FIG. 6 is strong and reaches 0.82.
However, compared with the gap rate of the voxel method, the gap rate calculated by the Monte Carlo simulated ray method is lower before the zenith angle is 30 degrees, and is higher after the zenith angle is 30 degrees. The reason is that when the canopy point cloud is subjected to voxel formation, the distance between the point cloud and the origin of coordinates is not considered, and the whole point cloud is divided by using the uniform voxel size, so that when the canopy point cloud is projected to a spherical surface with a unit radius, the corresponding actual sizes of voxels with different distances from the origin of coordinates on the spherical surface are not consistent, but when the clearance is counted, a slice with a fixed size is used, so that one part of data is excessively divided, and the other part of data is insufficiently divided, and the method for estimating the clearance rate is effective and feasible.
Claims (3)
1. A canopy clearance estimation method based on ground-based laser radar point cloud is characterized by comprising the following steps:
step 1, TLS point cloud data is obtained and preprocessed;
setting a sample in a research area, setting a measuring station according to the condition of the sample to erect a three-dimensional laser scanner, and scanning a forest canopy of the sample in the research area completely; setting at least 3 targets in the scanning range of a scanner, registering and denoising scanned TLS point cloud data by using software, then exporting the TLS point cloud data into a text format, and removing the point cloud data lower than the height of the scanner;
Step 2, coordinate conversion and blocking of point cloud data;
converting the three-dimensional rectangular coordinates of all point cloud data obtained in the step 1 into spherical coordinates, namely converting the point cloud coordinates from P (x, y, z) into spherical coordinates
Wherein theta is the zenith angle, and theta is the zenith angle,
for azimuth, r is the distance from the canopy point cloud to the origin, and the formula is as follows:
partitioning the converted point cloud data into blocks according to the interval of a zenith angle of 2-10 degrees and an azimuth angle of 10-60 degrees;
step 3, simulating a laser beam;
based on a Monte Carlo method, converting the estimation of the clearance rate into a probability model; simulating the working principle of a three-dimensional laser scanner through a computer program, randomly emitting rays from the scanner as simulated laser beams, wherein the emission range of the simulated laser beams is consistent with the zenith angle and azimuth angle ranges of the converted point cloud data; describing the structural information of the canopy by emitting simulation laser beams to detect the point cloud in the canopy, and quantifying the distribution of gaps;
step 4, determining a judgment distance;
counting the distance distribution from the point clouds to the origin in each space area divided in the step 2 by taking 0.1-1 m as a step length, wherein the distribution condition of the canopy of the area is represented by the expectation of the distance from all the point clouds to the origin, and the relation between the sampling distance and the distance from the point clouds to the origin is as follows:
D and D are sampling intervals corresponding to R and R, and the distinguishing distance is calculated according to the distribution of the point cloud in each area and the sampling intervals of the scanner:
where s is the discrimination distance, D and R are derived from the resolution of the scanner, and R i Is the expected value of the distance from all point clouds in the ith area to the origin;
step 5, estimating the clearance rate GF;
emitting a simulated laser beam in each space region by using a computer program, and judging the Euclidean distance between the simulated laser beam and all point clouds in the corresponding space region; if the Euclidean distance is smaller than or equal to the judgment distance, determining that the cloud point collides with the canopy point, counting and re-emitting the simulation ray for next detection; if the integral data is traversed and no canopy point cloud within the judgment distance is detected, detecting the next simulated laser beam;
and finally, estimating the gap rate according to the relation between the number of times of collision to the point cloud and the total number of the emitted simulated laser beams, wherein the formula is as follows:
2. the method of claim 1 for canopy clearance estimation based on ground based lidar point cloud, wherein: and step 4, determining that the step length adopted by the judgment distance is 0.5 meter.
3. The method of claim 1 for canopy clearance estimation based on ground based lidar point cloud, wherein: and in the step 2, data partitioning is carried out according to the interval of a zenith angle of 5 degrees and an azimuth angle of 45 degrees.
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