CN114265036A - Vegetation aggregation index estimation method based on foundation laser radar point cloud - Google Patents
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Abstract
The invention belongs to the technical field of laser radar remote sensing data processing, and particularly relates to a vegetation concentration index estimation method based on foundation laser radar point cloud. According to the method, after the point cloud data of the foundation laser radar is preprocessed, the point cloud data is transformed into a coordinate system and then projected to a hemispherical surface to calculate the clearance rate, in the process, through laser point area reconstruction, the vertical direction information of the three-dimensional point cloud is expressed in a calculation result after the hemispherical projection, the concept that laser points with different projection radiuses are endowed with different areas is introduced, so that three-dimensional elevation information is not ignored and is transformed into two-dimensional available information, and the defect that the third-dimensional information cannot be effectively used in the traditional method is effectively avoided; and finally, counting the clearance rate values in regions, and calculating by using a finite length average method to obtain the aggregation index result of each region. The method can fully consider the vertical structure information of the vegetation when estimating the concentration index, and overcomes the problem of low precision of the traditional method.
Description
Technical Field
The invention belongs to the technical field of laser radar remote sensing data processing, and particularly relates to an vegetation concentration index estimation method based on foundation laser radar point cloud.
Background
The forest is an important influence factor of the change of the earth ecology, is an ecosystem with the largest land area, the widest distribution, the most complex composition structure and the most abundant material resources as a main body of a land ecosystem, is a resource library with the most perfect functions in the nature, exchanges carbon, water and energy with the atmosphere, and plays an important role in the regulation of water circulation, carbon circulation and climate (the world of vicunas, etc., 2012). In the present day that the development speed of the human society is increasing, global environmental problems such as climate warming, biological diversity decline, ecological system function weakening and the like threaten the sustainable development capability of the earth. Therefore, accurate monitoring of various parameters of the forest ecosystem has great reference and research value in assessing the impact of human activities on the environment (guo qinghua et al, 2014). Most of the traditional measuring methods monitor the ecosystem at the sample level, and can obtain the most accurate data. However, with the continuous development and progress of the detection technology in recent years, the traditional field measurement has the limitations that the field measurement cannot adapt to large-scale and dynamic monitoring, and is time-consuming and labor-consuming. The laser radar is a new active remote sensing technology, can well make up for many defects of the traditional measuring mode, and can play an important role in the detection and simulation of the forest ecosystem.
Laser is one of the most important inventions in the human development history of the 20 th century, and the laser radar technology develops rapidly since the birth of the middle and later period of the 20 th century; LiDAR, originally named LASER RADAR (radio detection and ranging), is now commonly referred to as LiDAR, laser detection and ranging. The laser radar has extremely wide application in the earth science, and also has increasingly wide application in the fields of surveying and mapping, three-dimensional cities, city planning, forestry, agriculture and the like. As an active detection means, the laser radar has higher precision, and also has the advantages of high sampling speed, high resolution, no need of contacting with a detection target, strong anti-interference capability and the like. As a non-contact active remote sensing technology. The laser radar technology can obtain spatial three-dimensional information of a target object at high speed and high efficiency without contacting the target object (f.m. danson et al, 2007). The laser radar can be classified into ground-based, vehicle-mounted, airborne, satellite-borne and the like according to different platforms, wherein the ground-based laser radar is the laser radar which is erected on a ground platform and can also be called as ground-based laser scanning (TLS).
With the development of the laser radar technology, the research quantity of vegetation structure parameter reverse by using the ground-based laser radar data is increasing day by day, and many inversion cases for successfully researching forest structure parameters by using the ground-based laser radar data exist. Among them, Gap Fraction (Gap Fraction) and Concentration Index (CI) are two important canopy structure characteristic parameters describing a vegetation interception light process and a canopy radiation transmission process. The aggregative index was proposed by Nilson (1971) during the study of the radiation propagation theory of plant canopy; he obtains a mathematical expression between the clearance rate and the leaf area index in the research process, and introduces a correction parameter lambda based on a Markov model0Therefore, the gathering condition of the canopy is described and is used up to now. Henceforth, Chen (1991) defines the aggregation index as the effective leaf area index divided by the true leaf area index; and then, a gap size distribution method is provided, the hypothesis of the space distribution mode of the blade and the canopy is eliminated, the aggregation effect inside the canopy is quantitatively measured, and the large gap between the crowns is eliminated to a great extent. Lang and Xiang (1986) proposed using finite length averaging to calculate an index of concentration, assuming a random distribution of leaves within a subsample over a finite length; the method is simple, convenient and practical and is widely accepted. The two methods are the main methods for calculating the leaf concentration index at present, namely (1) logarithmic mean method CI based on the clearance rate of the canopyLX(2) CI (dimension distribution method) based on gap sizeCC。
Danson et al (2007) calculated the gap ratio by converting the slice projection of the TLS data xy axis into a two-dimensional image, while comparing the gap ratio calculated with the digital hemisphere photo, showed that the gap ratios obtained with the high resolution point cloud data and the digital hemisphere image were similar, some of which may be due to sun glare seen in the photo or errors associated with manual thresholding of the digital image. Garci ia et al (2015) have shown the concentration index of different vegetation types based on ground and airborne laser radar technical meter respectively, compare the result of inversion with the concentration index result that obtains based on hemisphere photography technique simultaneously, and the result shows that different methods have stronger relevance in the time of inversion concentration index. Bao (2016) inverts the gap rate by using ground-based laser radar (TLS) data and Digital Hemisphere Photography (DHP) respectively, calculates the aggregation index by using a formula proposed by Lang (1986), and the R2 (coefficient of determination) of the inversion result reaches 0.863, which shows that the aggregation indexes of TLS and DHP have good correlation.
At present, in the inversion process of the gap ratio, the two types of methods are mainly divided into a voxel method and a projection method. The voxel method is mainly used for dividing the space where the point cloud is located by using a three-dimensional voxel with a certain size, judging whether the point cloud exists in the voxel or not and counting the number of all voxels containing the point cloud, thereby calculating the gap rate. The principle of projection method is mainly similar to digital hemisphere photography, projecting three-dimensional information to a two-dimensional plane and then calculating the gap rate. When two methods, especially a voxel method, are used for quantitative description of a canopy by using three-dimensional point cloud data, although three-dimensional coordinates are used, the voxel is usually projected into a hemispherical image after the attribute of each voxel is calibrated, three-dimensional information is directly compressed into two dimensions, and the three-dimensional information is not really used when final parameter extraction is performed, so that the method has large information loss.
Disclosure of Invention
Aiming at the problems or the defects, the invention provides a vegetation aggregation index estimation method based on ground laser radar point cloud, which aims to solve the problem that the accuracy of a final result is poor due to the fact that the characteristics of the existing vegetation aggregation index estimation method are selected and the data utilization is insufficient.
A vegetation concentration index estimation method based on foundation laser radar point cloud comprises the following steps:
and (3) registering and denoising the foundation laser radar point cloud data of the target sample plot by using software, exporting the foundation laser radar point cloud data into a text format, and then eliminating points with the height lower than that of a foundation laser radar (such as a scanner).
Because the point cloud data acquired by the ground-based laser scanner generally has large data volume and high density and has optical characteristic information of a scanned object, preprocessing such as multi-survey-station point cloud data registration and point cloud denoising is required before researching the canopy structure parameters. Only under the condition of accurately finishing the preprocessing work, the accurate three-dimensional radar point cloud data can be obtained, so that the canopy structure parameters can be more accurately inverted.
And setting a sample in a research area, selecting a survey station position according to the sample situation, and erecting a foundation laser radar to ensure that a complete forest canopy can be scanned. And at least 3 targets are arranged in the scanning range of the laser radar as the basis of later multi-station registration.
Step 2, point cloud coordinate conversion and area division:
the point cloud data acquired by the ground-based laser radar is a space rectangular coordinate system, and as the path of the solar incidence canopy is generally described by a zenith angle, the point cloud coordinate acquired in the step 1 is converted into a spherical coordinate, namely the point cloud coordinate is converted into P (x, y, z) from P (x, y, z)
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 point cloud data needs to be divided into a plurality of areas equally within the range of 0-90 degrees of the zenith angle and 0-360 degrees of the azimuth angle for data partitioning.
Furthermore, the zenith angle is 5 degrees, the azimuth angle is 45 degrees, and the zenith angle and the azimuth angle are used as basic parameters for equal division, so that later software and data are compatible and the operation efficiency is easy.
Step 3, hemispherical projection:
and (3) projecting all the point cloud data obtained in the step (2) onto a hemispherical surface so as to facilitate the subsequent calculation of the clearance rate. The radius L of the hemispherical projection plane is 1m above and below the maximum tree height of the target sample plot, and each laser point in the point cloud data obtains a projection radius D related to the distance from the laser point to the projection plane.
Step 4, calculating the area of the laser spot:
the sampling interval when considering ground laser radar scanning blade to and different tree species blade area size have great difference, and it is also different at the growth condition of the inside different positions of same day top ring, consequently need consider these all factors simultaneously when setting up the laser spot area.
Assuming that the radius of the laser spot is e, the distance between the laser spot and the origin of the spherical coordinate is r, and the radius of the hemispherical projection surface is L, the projection radius of the laser spot D can be expressed as (hereinafter referred to as projection radius):
D＝|L-r| (2)
for the used ground laser radar with the resolution of F meters, two laser points with the distance of F meters can be identified, and the relation between the radius e of the laser points and the radius D of the projection satisfies the following formula:
e＝k*D*f/F (3)
wherein k is an adjusting coefficient and the value range is 2-5.
Finally, the area s of the single laser spot projected on the hemisphere can be expressed as:
s＝pi*e2 (4)
wherein pi is a circumferential ratio
the total laser spot area projected in all directions of different zenith rings is counted, and then the ratio of the area sum Se of the vacant parts which are not projected on the hemispherical surface to the total surface level St of the projected hemispherical surface is calculated to obtain the clearance rate GF of all directions of all zenith rings, which can be expressed as:
GF＝Se/St (5)
calculating the clearance rate data of each zenith ring in different directions by a finite length averaging method to obtain the concentration index result of the canopy, which can be expressed as:
wherein ,
denotes the zenith angle theta and the azimuth angle theta
The time gap rate, omega (theta), represents the aggregation index when the zenith angle is theta, the numerator is the logarithm of the average gap rate of the canopy, and the denominator is the logarithmic average of the gap rate.
In the invention, for selecting the radius L of the projection surface, a plurality of groups of radius values are generally required to be compared to find a suitable value. Through multiple experiments, the radius of the optimal projection plane is 1m above and below the maximum tree height in the range of the sample plot. The adjustment coefficient in the laser spot area formula is usually selected to be a constant between 2 and 5, and the size of the adjustment coefficient can be adjusted according to the tree species in the applied field. Finally, the mode of estimating the aggregation index is obtained by calculating the clearance rate data by a finite length averaging method. The method assumes that each area where the processed data is located has a gap, and when the gap rate of a certain area is 0, a gap element needs to be added into the area; in addition, the method also assumes that the data collected from the canopy is randomly distributed in finite length units, which is also the name of finite length averaging. The method based on the logarithmic mean of the clearance rate of the canopy is proposed by Lang and Xiang in 1986, namely, the ratio of the logarithm of the mean of the clearance rate and the mean of the logarithm of the clearance rate is used for solving the aggregation index, and the formula is shown as the formula (6).
According to the method, the forest vegetation canopy three-dimensional laser point cloud data are obtained by using the foundation laser radar, and the convergence index is calculated by using a finite length averaging method through preprocessing processes such as registration, point cloud normalization, denoising and elevation filtering, hemispherical projection, laser spot area reconstruction and clearance rate estimation by combining the tree canopy three-dimensional structure characteristics and the blade distribution form, so that the convergence index is established based on TLS point cloud data from the forest canopy.
The method comprises the steps of firstly, carrying out data preprocessing of registration, denoising and filtering on point cloud data of the foundation laser radar in sequence; then, the processed point cloud data is transformed into a coordinate system and projected to a hemispherical surface, so that the clearance rate is calculated, the vertical direction information of the three-dimensional point cloud is expressed in the calculation result after the hemispherical projection through laser spot area reconstruction in the process, and the concept that laser spots with different projection radiuses are endowed with different areas is introduced, so that the three-dimensional elevation information is not ignored and is transformed into two-dimensional available information, and the defect that the third-dimensional information cannot be effectively used in the traditional method is effectively avoided; finally, counting the clearance rate values in regions, and calculating by using a finite length average method to obtain a convergence index result of each region; the flow is shown in figure 1.
In conclusion, the vertical structure information of the vegetation can be fully considered when the concentration index is estimated, and the problem that the conventional digital hemispherical photography method is limited by factors such as service time and weather and cannot carry out data acquisition all the day is solved; and the gap rate result is underestimated in the canopy sparse area due to the influence of the exposure parameter setting. The method gives different area values to the laser points with different projection radiuses under the condition of considering the difference of the growth distribution conditions of the canopy layers with different heights, and effectively estimates the concentration indexes of various broadleaf forest canopy layers.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram illustrating example data;
FIG. 3 is a schematic diagram showing the relationship between the sampling interval and the distance from the point cloud to the origin;
FIG. 4 is a schematic view of a hemispherical laser spot projection;
FIG. 5 is a comparison line graph of the results of the clearance ratio estimated by the present method and the digital hemisphere projection method;
FIG. 6 is a comparative line graph of the concentration index results estimated by the present method and the digital hemisphere projection method;
Detailed Description
The invention is described in further detail below by way of an example of the same and with reference to the accompanying drawings:
the development environment is Microsoft Visual Studio 2013, and the programming language is C.
A25-30 meter sample is arranged in a nanmu forest beside a main building in a clear water river school district of electronic science and technology university, foundation laser radar data is acquired by a Leica Scanstation C10 at 4 measuring stations, one measuring station is arranged in the center of a sample plot, and the other 3 measuring stations are arranged at edge positions. 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 spacing) were set up on Leica C10, combined with considerations of the condition of the plot and the total number of point clouds, the resolution should not be too high to avoid data redundancy, and the remaining parameters are listed in the table below. And utilizing Cyclone software to be used for registering, denoising and normalizing the multi-station TLS data, and removing the point cloud with the z-axis smaller than 0 after exporting the data.
TABLE 1 three-dimensional laser scanner Leica Scanstation C10 parameter
And 2, establishing a space rectangular coordinate system for the preprocessed point cloud data by taking a laser radar scanner station as a coordinate origin, and recording the coordinate of each point as P (X, Y, Z), wherein the X axis is the east-righting direction, the Y axis is the north-righting direction, and the Z axis is the vertical direction. After a coordinate system is established, the laser point coordinates are converted into spherical coordinates from space rectangular coordinates, and data after the coordinates are converted are partitioned according to the interval of a zenith angle of 5 degrees and an azimuth angle of 45 degrees, so that the cost is saved. According to the relation between the tree height and the radius of the sample plot, the range of the effective zenith angle 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 degrees, 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.
And 3, selecting 15 meters as the radius of the hemispherical surface according to the size of the sample, and projecting the point cloud onto the hemispherical surface.
And 4, converting according to different relative distances between the laser point and the projection plane in the point cloud data through the resolution ratio of the laser radar scanner to obtain the radius of the laser point, and further calculating the area of the laser point.
And 5, adding the laser spot areas in each area to calculate the total area, and subtracting the total area of the laser spot by using the projected hemispherical area to obtain the area of the gap area so as to obtain the clearance rate. And solving the logarithmic mean of the clearance rate data in each direction, and calculating to obtain the aggregation index of each zenith ring.
Comparative experiments were carried out using the method according to the invention and the digital hemispherical projection method, respectively, on the target panels of the examples. Specific experimental curves are shown in fig. 5 and 6. FIG. 5 is a plot of the results of the standoff ratio versus fold line estimated by the present method and the digital hemisphere projection method; FIG. 6 is a comparative line graph of the concentration index results estimated by the present method and the digital hemisphere projection method.
By carrying out hemispherical projection and laser spot area reconstruction on data in all directions after point cloud areas are divided, and then calculating the clearance rate of each area, it is not difficult to find that the same laser spot is endowed with different areas under different projection radiuses or different adjustment coefficients, and then different clearance rate and concentration index calculation results can be obtained, so that the influence of the projection radiuses and the adjustment coefficients on clearance rate estimation can be researched.
After verification, when the projection radius is slightly higher than the upper limit of the tree height in the sample prescription, the obtained results of the clearance rate and the concentration index are most in line with expectations, and the projection radius selected in the sample prescription is 16; and the adjustment coefficient is more satisfactory when the adjustment coefficient is 2.5. By comparing the clearance rates obtained by the method and the digital hemispherical photography method, the correlation of the clearance rates obtained by the two methods in a reverse manner is higher, the determination coefficient is 0.8104, but the clearance rate values obtained by the two methods in a lower zenith angle region are different, and the result obtained by the laser point area projection method is larger, because the projection direction of the method during laser point projection is different from the shooting direction of the fish-eye lens to surrounding scenery in the digital hemispherical photography, the difference between the two methods in a lower zenith angle region is larger, and the method fully considers the blade distribution of the canopy in the vertical direction in a high zenith angle region, so the problem that the clearance rate is often underestimated by the digital hemispherical photography technology when the blade density is larger is solved.
According to the experimental data, the aggregation index results obtained by the method and the digital hemispherical photography method provided by the invention are still high in correlation, and the decision coefficient is 0.7865, so that the method is feasible and effective in calculating the aggregation index of the vegetation canopy.
According to the method, through laser point area reconstruction, the vertical direction information of the three-dimensional point cloud is expressed in the calculation result after the hemispherical projection, and the concept that laser points with different projection radiuses are endowed with different areas is introduced, so that the three-dimensional elevation information is not ignored and converted into two-dimensional available information, and the defect that the traditional method cannot effectively use the third-dimensional information is effectively avoided; and finally, statistically calculating the clearance rate value in different areas, and calculating by using a finite length average method to obtain the aggregation index result of each area. The method can fully consider the vertical structure information of the vegetation when estimating the aggregation index, and overcomes the problem of low precision of the traditional method.
Claims (2)
1. A vegetation concentration index estimation method based on foundation laser radar point cloud is characterized by comprising the following steps:
step 1, point cloud data acquisition and pretreatment:
registering and denoising the foundation laser radar point cloud data of the target sample plot by using software, and removing points lower than the height of the foundation laser radar after the point cloud data is exported into a text format;
step 2, point cloud coordinate conversion and area division:
converting the point cloud coordinates obtained in the step 1 into spherical coordinates, namely converting the point cloud coordinates from P (x, y, z) to spherical coordinates
Wherein 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:
equally dividing the point cloud data into a plurality of areas in the range of 0-90 degrees of zenith angle and 0-360 degrees of azimuth angle, and partitioning the data to improve the running speed of a program;
step 3, hemispherical projection:
projecting all the point cloud data obtained in the step (2) onto a hemispherical surface, wherein the radius L of the hemispherical projection surface is the range of the maximum tree height of a target sample plot by 1m from top to bottom, and each laser point in the point cloud data can obtain a projection radius D related to the distance between the laser point and the projection surface;
step 4, calculating the area of the laser spot:
if the radius of the laser spot is e, the distance between the laser spot and the origin of the spherical coordinate is r, and the radius of the hemispherical projection surface is L, the projection radius D of the laser spot can be expressed as:
D＝|L-r| (2)
for the used ground laser radar with the resolution of F meters, two laser points with the distance of F meters can be identified, and the relation between the radius e of the laser points and the radius D of the projection satisfies the following formula:
e＝k*D*f/F (3)
wherein k is an adjusting coefficient and the value range is 2-5;
finally, the area s of the single laser spot projected on the hemisphere can be expressed as:
s＝pi*e2 (4)
wherein pi is a circumference ratio;
step 5, estimating the clearance rate and calculating the aggregation index:
through counting the total laser spot area projected in each direction of different zenith rings, and further calculating the ratio of the sum Se of the area of the non-projected vacant part on the hemispherical surface to the total surface level St of the projected hemispherical surface, the clearance rate GF of each zenith ring in each direction can be obtained, and can be expressed as:
GF＝Se/St (5)
calculating the clearance rate data of each zenith ring in different directions by a finite length averaging method to obtain the concentration index result of the canopy, which can be expressed as:
wherein ,
denotes the zenith angle theta and the azimuth angle theta
The time gap rate, omega (theta), represents the aggregation index when the zenith angle is theta, the numerator is the logarithm of the average gap rate of the canopy, and the denominator is the logarithmic average of the gap rate.
2. The method of claim 1, wherein the vegetation concentration index estimation method based on ground-based lidar point cloud comprises: and in the step 2, the zenith angle is 5 degrees, the azimuth angle is 45 degrees, and the zenith angle and the azimuth angle are used as basic parameters for equal division, so that the compatibility and the operation efficiency of later software and data are facilitated.
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