CN109948106B - Method for calculating forest stand height accumulated percentage by using laser point cloud - Google Patents
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Abstract
The invention discloses a method for calculating the cumulative percentage of forest stand height by using laser point clouds, which is an improved method for calculating probability distribution characteristic parameters of point clouds on vertical structures under various structures of three-dimensional forest vegetation by using a calculation geometric algorithm and belongs to the research field of forest vertical structure parameter acquisition methods. The method comprises the steps of obtaining and preprocessing three-dimensional laser point cloud data of trees; extracting tree three-dimensional point cloud ground points and generating a digital elevation model DEM; point cloud normalization of a digital elevation model DEM based on an algorithm; and extracting the calculation of the accumulated percentage of the height of the characteristic parameter of the normalized point cloud. Compared with the traditional observation means, the method has the characteristics of small workload, no need of contact observation, no damage to the structure and growth characteristics of the stand trees of the forest stand, objective, high efficiency and accuracy; a method for extracting three-dimensional structure and biophysical diversity information from laser radar data is developed, and the vertical distribution change rule of the canopy and the trunk under various combinations of the tree stands in the forest stand is characterized.
Description
Technical Field
The invention belongs to the technical field of forest structure analysis, and particularly relates to a method for calculating the cumulative percentage of forest stand height by using laser point cloud.
Background
The forest structure parameters reflect the growth state of forest stands and are important parameters for management and management. The vertical structure of the plant is closely related to the characteristics of different tree species and the planting and cultivating modes of forest stand. The vertical structure reflects the change condition in the growth direction, particularly the specific state of the change of the transverse diameter, and the size of the trunk diameter is an important index for guiding the subsequent felling and lumber-making plan.
The vertical structure of the standing tree is expressed by the cut degree of the trunk shape, which means the degree of gradual change of the diameter which becomes thinner along with the increase of the height, and is an index describing the gradual reduction of the diameter of the trunk along with the increase of the height of the tree. The cut degree characteristics in the same community or forest stand are reflected consistently, and the cut degree characteristics are obtained by a trunk analysis method (as shown in fig. 2 a). After felling and standing, sawing the tree into segments according to 2 m, making discs, checking the number of annual rings and the width of the annual rings of the discs at each height, recording the growth data of the annual rings of the analyzed trees, and drawing a dry growth curve graph (shown in figure 2 b).
The method has large workload and strong subjectivity, and more importantly, the method directly destroys the growth of the standing trees and the subsequent utilization of the manufactured materials, so the method is difficult to adopt in practice for the operation and the utilization. Therefore, a new fast, accurate, indirect method is needed to obtain the spatial distribution of the stem shapes on the vertical structure. In recent years, Laser Scanning can efficiently and accurately obtain three-dimensional coordinate information of an object by using a Laser Detection and Ranging (LiDAR) technology, structure information of the object to be measured is completely recorded by point cloud information obtained by multi-station Scanning, and accurate structure information of trees can be obtained by the accurate three-dimensional coordinate information provided by the point cloud information, for example, in the text "designing commercial Volume in display through high precision a calibrated measuring Function from Non-describing in" design "Volume 7 of Sun et al in 2016, trunk curve research of Poplar is performed by using a ground Laser radar, and a high-precision trunk cutting model is established (as shown in fig. 3 b). The method extracts the stumpage dry curve which is highly consistent with the result of the analytical wood method (as shown in the right part of the attached figure 2b and the attached figure 3 b).
The point cloud data does not reflect the structure information of the ground object, and simultaneously reflects the remote sensing information of the ground object to be detected from the characteristic parameters of the point cloud distribution. For example: in the context of "Moving volume method for simulating airborne base height from airborne laser scanner data" by Maguya et al, Remote Sensing, volume 7, 2015, it was noted that the waveform of the Airborne Laser (ALS) could be converted to a corresponding crown height profile, and the Quantile measures for each height calculated to provide such a crown profile that is 25 meters circular compared to the ground. Therefore, the quantification of the distribution information of the point cloud has important significance for the stem shape research of the trunk. The ground laser point cloud data has not been reported yet.
The ground laser radar scanner can acquire high-precision (millimeter-scale) Forest canopy structure information from a three-dimensional angle, the distribution condition of the obtained point cloud can also acquire and reflect Forest vertical structure information, and the existing research shows that the characteristic parameters of the laser radar can well invert Forest structure parameters and can be widely applied, for example, in 2014, Srinivasan et al, in the ' Multi-temporal periodic laser scanning for modeling biological change ' of Forest Ecology and Management ' volume 15, introduces the application of the point cloud characteristic parameters extracted by the ground laser radar in the biomass of inversion standing trees. However, the characteristics of the parameters reflecting the dry shape change and the vertical structure change after accumulating the dry shape vertical structure, particularly the point clouds in the vertical direction are not reported.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention aims to provide a method for calculating the cumulative percentage of the forest stand height by using laser point cloud.
The technical scheme is as follows: in order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
a method for calculating the cumulative percentage of forest stand height by using laser point cloud mainly comprises the following steps:
(1) acquiring and preprocessing three-dimensional laser point cloud data of forest vegetation;
(2) and separating the ground points and the non-ground points by using elevation filtering, and removing miscellaneous points in the non-ground points by combining visual interpretation to obtain the three-dimensional laser point cloud of the forest vegetation.
(3) Extracting and generating a point cloud digital elevation model DEM based on forest vegetation;
(4) normalizing the forest vegetation three-dimensional laser point cloud obtained in the step (2) according to the generated digital elevation model DEM;
(5) and (4) obtaining normalized forest vegetation three-dimensional laser point clouds according to the step (4), obtaining point clouds of single plants from forest stand point clouds by utilizing single tree segmentation as a data source, obtaining tree community point clouds of a sample circle level from forest stands as a data source, and taking three-dimensional laser data of the whole sample plot as the data source.
(6) Sorting the three-dimensional laser point clouds of the data types of the normalized dimensions of the singlewood, the sample circle or the sample plot according to the heights of the point clouds from small to large;
(7) and accumulating the elevations of all the sorted forest vegetation point clouds, accumulating the sorted point clouds from the first point from bottom to top point by point, and calculating the percentage relation between the sorted point clouds and the total elevation of the vegetation point clouds, wherein the percentage relation is the accumulated percentage of the heights.
Preferably, in the step (1), the three-dimensional laser point cloud data of the forest vegetation is acquired by a ground multi-echo three-dimensional laser scanner, and the three-dimensional laser point cloud data comprises the space geometry of a scanning target point, multi-time wave splitting information returned by a laser beam and space position coordinate information of each point.
Preferably, in the step (1), the preprocessing of the three-dimensional laser point cloud data includes: and performing multi-station splicing on the obtained point cloud, and removing noise points and wire drawing points in the point cloud after the multi-station splicing.
Preferably, after acquiring and preprocessing three-dimensional laser point cloud data, performing elevation filtering in step (2), roughly separating ground points from the forest vegetation point cloud after multi-site splicing, and further removing miscellaneous points in the ground points by combining visual interpretation to obtain the ground points.
Preferably, in the step (3), the ground point cloud data from which the impurity points are removed is stored in an LAS1.2 format, is imported into LIDAR 360 software, and a TIN difference method is adopted to generate a digital elevation model DEM in a TIFF format.
Preferably, in the step (7), the height values of the first point to the mth point of the sorted point cloud are accumulated according to the sequence from bottom to top, the height percentage z% obtained by dividing the height values of the first point to the mth point by the accumulated heights of all the n points is the height accumulated percentage of the point cloud, and the height h corresponding to the mth point is usedmCalled the Hz height percentage of the point cloud, the formula for which is as follows:
Hz＝hm
in the formula: hi is the height of the ith point, m is the mth point,
n is the total number of point clouds, hmThe height corresponding to the mth point.
Has the advantages that: compared with the prior art, the invention has the following advantages:
the method for calculating the cumulative percentage of the forest stand height by using the laser point cloud provides an effective method for calculating the cumulative percentage of the forest vegetation characteristic parameter height, and the method can be directly used for calculating the cumulative percentage of the forest vegetation characteristic parameter height no matter the cumulative percentage of the height of a single tree or a forest sample canopy. The extraction of the parameters enriches the types of the characteristic parameters of the ground laser used for estimating the biophysical parameters of the vegetation, enhances the popularity and the effectiveness of the application of the three-dimensional laser scanning technology, and can better serve resource environment research projects such as forest resource investigation, vegetation ecological remote sensing and the like.
Compared with the traditional observation means, the method has the characteristics of small workload, no need of contact observation, no damage to the structure and growth characteristics of the stand trees of the forest stand, objective, high efficiency and accuracy; a method for extracting three-dimensional structure and biophysical diversity information from laser radar data is developed, and the vertical distribution change rule of the canopy and the trunk under various combinations of the tree stands in the forest stand is characterized.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a disk and tree height curve of a trunk resolution method;
a. obtaining and manufacturing a trunk resolving disc;
b. resolving a wood disc measurement to draw a tree height curve
FIG. 3 is the upper diameter of the ground laser point cloud extraction;
a. a trunk point cloud slice schematic;
b. extracting a tree height curve drawn by the upper diameter of the point cloud;
FIG. 4 is a digital elevation model DEM of a ground point cloud of forest vegetation;
a. extracting a ground three-dimensional point cloud schematic diagram;
b. a digital elevation model DEM is produced according to the ground three-dimensional point cloud;
FIG. 5 is normalized gown three-dimensional point cloud data;
a. three-dimensional point cloud data of the individual Chinese gown;
b.5 forming three-dimensional point cloud data of a sample circle by using the Chinese gowns;
c. three-dimensional point cloud data of the Chinese gown sample plot;
FIG. 6 is a schematic diagram of a calculation method of cumulative percentage of height of an unlined long gown;
a. a method for calculating the cumulative percentage of the height of the single Chinese unlined long gown;
b. the cumulative percentage curve of the height of the single Chinese unlined long gown;
c. a three-dimensional cross-sectional view of the cumulative percentage of individual gown heights;
FIG. 7 is a schematic diagram showing the calculation method of cumulative percentage of height of a circle formed by 5 gowns;
a.5 a calculation method of the cumulative percentage of the height of the Chinese liriodendrons;
b.5 cumulative percentage curve of the height of the Chinese liriodendrons;
c.5 three-dimensional cross-sectional view of cumulative percentage of the height of the Chinese gown;
FIG. 8 is a schematic illustration of a calculation of cumulative percentage height of the gown plot;
a. calculating the cumulative percentage of the height of the unlined long gown round;
b. the cumulative percentage curve of the height of the unlined long gown-like circle;
c. a three-dimensional cross-sectional view of the cumulative percentage of the height of the gown-like circle;
FIG. 9 is a cumulative percentage comparison of the height of the gown sheet, the sample circle and the plot.
Detailed Description
The present invention will be further illustrated by the following specific examples, which are carried out on the premise of the technical scheme of the present invention, and it should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention.
The principle of the invention is as follows: by utilizing a newer remote sensing technical means (a ground three-dimensional laser radar scanning system) and combining with a technical means of calculating geometry, the point cloud distribution parameter-height cumulative percentage of the single tree and forest sample size scale is directly obtained from a three-dimensional angle, and then the structure parameters of the single tree, forest sample circle and forest sample size scale are analyzed and inverted. Firstly, ground points and non-ground points in point cloud data are separated by utilizing elevation filtering, and mixed points in the separated ground points are deleted by combining visual interpretation on the basis. The method comprises the steps of setting the size of a grid by adopting a TIN difference method, converting ground points to generate a digital Elevation model DEM (digital Elevation model), abbreviated as DEM, of a TIFF format, normalizing point clouds of individual trees and forest sample dimensions by using the obtained digital Elevation model DEM to eliminate Elevation difference caused by ground fluctuation, sequencing the point clouds after normalization from small to large according to Elevation information, accumulating the elevations of all the sequenced forest vegetation point clouds, accumulating the sequenced point clouds from a first point by point upwards, and calculating the percentage relationship between the point clouds and the total Elevation of the vegetation point clouds, wherein the percentage relationship is the height accumulated percentage.
As shown in the flowchart of fig. 1, the method for calculating the cumulative percentage of forest stand heights by using laser point clouds mainly includes the following steps:
(1) acquiring and preprocessing three-dimensional laser point cloud data of forest vegetation;
the method comprises the steps that three-dimensional laser point cloud data of forest vegetation are acquired through a ground multi-echo three-dimensional laser scanner, bottom surface laser radar scanning sites are distributed before data acquisition, then multi-site scanning is carried out, and sites are automatically spliced/registered to obtain the multi-echo multi-site point cloud data. The three-dimensional laser point cloud data of the forest vegetation comprises the space geometry of a scanning target point, multiple wave division information returned by a laser beam and space position coordinate information of each point.
The preprocessing of the three-dimensional laser point cloud data comprises the following steps: and performing multi-station splicing on the obtained point cloud, and removing noise points and wire drawing points in the point cloud after the multi-station splicing.
(2) And separating the ground points and the non-ground points by using elevation filtering, and removing miscellaneous points in the non-ground points by combining visual interpretation to obtain the three-dimensional laser point cloud of the forest vegetation.
After three-dimensional laser point cloud data are obtained and preprocessed, elevation filtering is carried out, ground points are roughly separated from forest vegetation point clouds after multi-site splicing, and miscellaneous points in the ground points are further removed by combining visual interpretation to obtain the ground points.
(3) Extracting and generating a point cloud digital elevation model DEM based on forest vegetation;
and (3) storing the ground point cloud data with the miscellaneous points removed in the step (2) into LAS1.2 format, importing the ground point cloud data into LIDAR 360 software, and generating a digital elevation model DEM in TIFF format by adopting a TIN difference method (the grid is set to be 0.5mx0.5 m).
(4) And (3) normalizing the forest vegetation three-dimensional laser point cloud obtained in the step (2) according to the generated digital elevation model DEM, and eliminating the influence of the terrain on the forest vegetation elevation.
(5) And (4) obtaining normalized forest vegetation three-dimensional laser point clouds according to the step (4), obtaining point clouds of single plants from forest stand point clouds by utilizing single tree segmentation as a data source according to requirements, obtaining tree community point clouds at a sample circle level from forest stands as a data source, and simultaneously taking three-dimensional laser data of the whole sample plot as a data source.
(6) Sorting the three-dimensional laser point clouds of the data types of the normalized dimensions of the singlewood, the sample circle or the sample plot according to the heights of the point clouds from small to large;
(7) and accumulating the elevations of all the sorted forest vegetation point clouds, accumulating the sorted point clouds from the first point from bottom to top point by point, and calculating the percentage relation between the sorted point clouds and the total elevation of the vegetation point clouds, wherein the percentage relation is the accumulated percentage of the heights.
Accumulating the height values of the first point to the mth point of the sorted point cloud according to the sequence from bottom to top, dividing the height values by the accumulated heights of all the n points to obtain the height percentage z%, namely the accumulated percentage of the height of the point cloud, and determining the height h corresponding to the mth pointmThe Hz height percentage of the point cloud is calculated asThe following:
Hz＝hm
in the formula: hi is the height of the ith point, m is the mth point,
n is the total number of point clouds, hmThe height corresponding to the mth point.
The invention is further explained below by way of examples:
a sample circle consisting of one Chinese gown (Liriodendron Chinese), 5 Chinese gowns and a Chinese gown sample plot are taken as research objects, and a ground three-dimensional laser scanner RIEGL VZ-400i (the parameters are shown in table 1) and a high-precision GPS are used for carrying out multi-site acquisition of three-dimensional point cloud data on the research objects. And performing multi-site splicing by using software RiSCAN PRO 2.6, and removing noise points and wire drawing points in the point cloud after the multi-site splicing to obtain the three-dimensional point cloud data of the single-plant Chinese jacket gown.
TABLE 1 three-dimensional laser scanner RIEGL VZ-400i parameters
After point cloud data of the Chinese unlined gown is obtained and preprocessed, elevation filtering is carried out by using software RiSCAN PRO 2.6, ground points are roughly separated from forest vegetation point clouds after multi-site splicing, miscellaneous points such as short shrubs and the like in the ground points are further removed by combining visual interpretation to obtain the ground points, the ground point cloud data after the miscellaneous points are removed are stored in an LAS1.2 format and are led into LIDAR 360 software, and a TIN difference value method (the grid is set to be 0.5mx0.5m) is adopted to generate a digital elevation model DEM in a TIFF format. The ground three-dimensional point cloud and the generated digital elevation model DEM are shown in the attached figure 4.
After the digital elevation model DEM of the gown is generated, the software LIDAR 360 is used for carrying out normalization processing on the point cloud data of the poplar single plant, the sample circle and the sample plot according to the generated digital elevation model DEM, and three-dimensional point cloud data of the gown single plant, the sample circle and the sample plot are normalized, as shown in an attached figure 5.
And arranging the three-dimensional point clouds of the individual plant of the gown, the sample circle and the sample plot according to the height information of the three-dimensional point clouds of the individual plant of the gown, the sample circle and the sample plot from small to large by utilizing the three-dimensional coordinate information of the normalized point clouds of the gown. And accumulating the heights of the sorted point clouds from the first point to the mth point from bottom to top, and dividing the height by the accumulated heights of all the n points to obtain the total height percentage z%, namely the cumulative percentage of the heights of the point clouds, wherein the height corresponding to the mth point is called the Hz height percentage of the point clouds. The calculation formula is as follows.
Hz＝hm
In the formula: hi is the height of the ith point, m is the mth point,
n is the total number of point clouds, hmThe height corresponding to the mth point.
Finally obtaining an example of the cumulative percentage of the height of a unlined gown, as shown in figure 6; an example of the cumulative percentage of the height of the gown-like circle is obtained, as shown in fig. 7. An example of the cumulative percentage of the height of the resulting gown site is shown in fig. 8; a comparison of the cumulative percentage heights of individual unlined gowns, circles and plots is shown in FIG. 9.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (3)
1. A method for calculating the cumulative percentage of forest stand height by using laser point cloud is characterized by comprising the following steps:
(1) acquiring and preprocessing three-dimensional laser point cloud data of forest vegetation, wherein the three-dimensional laser point cloud data of the forest vegetation is acquired through a ground multi-echo three-dimensional laser scanner, and the three-dimensional laser point cloud data comprises the space geometry of a scanning target point, multi-time wave splitting information returned by a laser beam and space position coordinate information of each point; the preprocessing of the three-dimensional laser point cloud data comprises the following steps: performing multi-station splicing on the obtained point cloud, and removing noise points and wire drawing points in the point cloud after the multi-station splicing;
(2) separating ground points and non-ground points by using elevation filtering, and removing miscellaneous points in the non-ground points by combining visual interpretation to obtain three-dimensional laser point clouds of forest vegetation;
(3) extracting and generating a point cloud digital elevation model DEM based on forest vegetation;
(4) normalizing the forest vegetation three-dimensional laser point cloud obtained in the step (2) according to the generated digital elevation model DEM;
(5) obtaining normalized forest vegetation three-dimensional laser point clouds according to the step (4), obtaining point clouds of single plants from forest stand point clouds by utilizing single tree segmentation as a data source, obtaining tree community point clouds of a sample circle level from forest stands as a data source, and taking three-dimensional laser data of the whole sample plot as the data source;
(6) sorting the three-dimensional laser point clouds of the data types of the normalized dimensions of the singlewood, the sample circle or the sample plot according to the heights of the point clouds from small to large;
(7) accumulating the elevations of all the sorted forest vegetation point clouds, accumulating the sorted point clouds from the first point from bottom to top point by point and upwards, and calculating the percentage relation between the sorted point clouds and the total elevation of the vegetation point clouds, wherein the percentage relation is the accumulated percentage of the heights;
accumulating the height values of the first point to the mth point of the sorted point clouds from bottom to top, dividing the height values by the accumulated heights of all the n points to obtain the height percentage, wherein z% is the accumulated height percentage of the point clouds, and calculating the height h corresponding to the mth pointmCalled the Hz height percentage of the point cloud, the formula for which is as follows:
Hz＝hm
in the formula: hi is the height of the ith point, m is the mth point,
n is the total number of point clouds, hmThe height corresponding to the mth point.
2. The method for calculating the cumulative percentage of forest stand heights using a laser point cloud of claim 1, wherein: after acquiring and preprocessing three-dimensional laser point cloud data, performing elevation filtering in step (2), roughly separating out ground points from the forest vegetation point cloud after multi-site splicing, and further removing miscellaneous points in the ground points by combining visual interpretation to obtain the ground points.
3. The method for calculating the cumulative percentage of forest stand heights using a laser point cloud of claim 1, wherein: and (3) storing the ground point cloud data with the impurity points removed into an LAS1.2 format, importing the ground point cloud data into LIDAR 360 software, and generating a digital elevation model DEM in a TIFF format by adopting a TIN difference method.
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