CN111091079A - TLS-based method for measuring dominant single plant structural parameters of vegetation in alpine and fragile regions - Google Patents
TLS-based method for measuring dominant single plant structural parameters of vegetation in alpine and fragile regions Download PDFInfo
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Abstract
The invention discloses a TLS-based method for determining the dominant single plant structural parameters of vegetation in an alpine and fragile area, and belongs to the field of forest resource monitoring and ecological restoration benefit evaluation. And acquiring and processing the point cloud data by adopting a foundation laser radar TLS, performing single plant segmentation on the normalized point cloud data by adopting a semi-automatic segmentation method and a full-automatic segmentation method, and respectively extracting and comparing the number of dominant single plants, the tree height of the dominant single plants and the crown width of the dominant single plants in the single plant scale. By acquiring and processing the three-dimensional point cloud information of the sand artificial vegetation, the method can reduce the field workload of sample plot investigation and simultaneously acquire accurate vegetation structure parameters, thereby better grasping the distribution of sand artificial vegetation resources in alpine and fragile areas and providing technical support for sand vegetation recovery and ecological benefit evaluation expressed by multi-dimensional information.
Description
Technical Field
The invention belongs to the technical field of forest resource monitoring and ecological restoration benefit assessment, and particularly relates to a TLS-based method for determining vegetation superiority single-plant structural parameters in an alpine and fragile area.
Background
The Tibet is the main body of the Qinghai-Tibet plateau, and the unique plateau natural and humanistic environments thereof enable the Tibet desertified land to have the ecological attribute of being crisp, while the land in the Tibet region still retains the original landscape characteristics or traces of the natural region after slight or severe deterioration, and finally generally forms the landscape characteristics of plateau meadows, plateau steppes, semi-desert steppes or mountain shrub steppes and the like. The development change and recovery treatment of the ecosystem of the high and cold vulnerable region of Tibet are receiving global attention, and in recent years, research on the aspect of recovering and reconstructing vegetation of Tibet desertification land is more and more extensive.
The shrub-grassland is not only the main landscape characteristic of the Tibet high-altitude sand land, but also the main vegetation type for ecological restoration of the Tibet sand land. Based on the unique natural environment of the area, the vegetation of the plateau desertified land shows the characteristics of poverty, cushion and sparseness, the height of the sand shrubs is generally low, and only a few populations can reach 3-4m (yellow Qinglin 2011, Li, Shen et al.2013). Therefore, the method has important practical reference significance for accurately extracting the artificial vegetation structure parameters of the sand of the alpine and fragile areas, promoting the ecological service function and value accurate accounting from the full-element angle of the vegetation horizontal and vertical structures and improving the biodiversity monitoring precision. Meanwhile, the vegetation recovery effect under the degraded desertification ecosystem is required to be quantitatively researched, and the future artificial vegetation recovery measures of the degraded grassland are accurately guided, so that the extraction and evaluation of the vegetation structure parameters are key. The conventional single plant structural parameter mode mainly depends on field investigation, and people need to accurately position a single plant, accurately measure the quantity, height and crown parameters of the single plant, and consume a large amount of manpower and material resources.
The appearance and the development of the ground-based laser radar (TLS) technology overcome the adverse effect that the traditional community ecology field work investigation needs to consume a large amount of manpower and material resources, and particularly have obvious data acquisition advantages in alpine and anoxic remote areas of the Qinghai-Tibet plateau. In recent years, rapid development of TLS scanners realizes that scanning errors are controlled to be in the centimeter or even millimeter level (dunsanson, Cook et al 2014; guo qinghua, Liu model et al 2014), and is widely applied to vegetation structure parameter acquisition in the same scale. However, the research on the multidimensional measurement of the sand vegetation structure by utilizing the LiDAR technology is less (Streutker and Glenn 2006; Martinuzzi, Vierling et al 2009). The LiDAR point clouds are regularly distributed in the horizontal direction, the vertical direction is limited by the structural characteristics of ground features, the LiDAR point clouds are complex, scattered and disordered, the multi-dimensional expression and fractal feature calculation of TLS point cloud data on the sand vegetation structural parameters are realized, a new sand vegetation structural parameter extraction method based on the TLS point clouds is established, and the method is a foundation and key problem to be solved for the multi-dimensional measurement of the TLS point cloud vegetation structure.
Disclosure of Invention
The technical problems solved by the invention are as follows: the prior art method has the problems of difficult extraction, low precision and the like in the aspect of extracting vegetation structure parameters in Tibet alpine regions.
The invention aims to provide a TLS-based method for measuring the dominant single plant structural parameters of vegetation in alpine and fragile regions, which is used for accurately extracting the single plant structural parameters of dominant bushes in a single plant scale and evaluating the extraction accuracy of the dominant single plant structural parameters based on methods such as field investigation and the like.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a method for measuring structural parameters of dominant single plants of vegetation in alpine and cold vulnerable regions based on TLS comprises the steps of acquiring point cloud data by a foundation laser radar, processing the point cloud data, carrying out single plant segmentation on the point cloud data after normalization by adopting a semi-automatic segmentation method and a full-automatic segmentation method based on an ecological theory method, and respectively extracting the number of the dominant single plants, the height of the dominant single plant tree and the canopy width of the dominant single plant in a single plant scale and comparing the numbers. The method specifically comprises the following steps:
(1) acquiring LiDAR point cloud data in a region to be detected by adopting a ground-based laser radar scanner; measured data were recorded within the plots: the method comprises the steps of measuring the number of dominant single-plant shrubs, the height of the dominant single-plant shrubs and the crown width of the dominant single-plant shrubs;
(2) data preprocessing: carrying out registration among scanning multi-station Point cloud data, carrying out rough splicing by selecting control points, and then carrying out automatic fine splicing by an Iterative Closest Point (ICP) algorithm;
(3) receiving intensity information and waveform data of the echo by using laser, and acquiring a corresponding high-resolution optical image by combining equipment to perform filtering classification; and interpolating the ground points to form a digital elevation model with a certain resolution.
(4) Normalizing the data according to the digital elevation model, eliminating the influence of terrain on the point cloud data, and performing individual plant segmentation on the normalized point cloud data by adopting a semi-automatic segmentation method and a full-automatic segmentation method;
(5) and extracting dominant single plant structure parameters from the point cloud data obtained after the single plant segmentation, wherein the dominant single plant structure parameters comprise dominant single plant quantity, dominant single plant height and dominant single plant canopy width.
Further, the ground-based laser radar scanner in the step (1) is a Riegl VZ-400iLiDAR sensor, a plurality of sensors are erected to record complete laser pulse information, the laser pulse information mainly comprises echo information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image coloring information,
the base point cloud splicing software in the step (2) is Riscan Pro, and the point cloud filtering analysis processing software in the step (3) is LiDAR 360.
Further, the ICP algorithm in step (2) calculates the optimal translation T and rotation R transformation parameters between the two point sets by finding the corresponding relationship between the target point set and the reference point, and converts the point cloud models in different coordinate systems to the same coordinate system, so that the registration error between the two point sets is minimized, and the three-dimensional point cloud data of the whole sample plot is obtained.
Further, the filtering classification in step (3) includes the following specific steps: firstly, removing noise points and outliers through statistical filtering (SOR); then, separating the ground point from the non-ground point based on a filtering method (PTD) of progressive triangulation network encryption; then, the ground points are subjected to a TIN interpolation method to obtain a digital elevation model DEM (the resolution is 0.1 m).
The principle of the statistical filtering (SOR) is to perform a statistical analysis on all points, calculate the average distance between each point and its neighboring points, and if the distance is not within a certain range, it is regarded as noise and removed. The principle of the filtering method (PTD) of the progressive triangulation network encryption is that initial ground seed points are obtained through morphological open operation, then the seed points with larger residual error values are removed through plane fitting, and a triangulation network is constructed through the remaining ground seed points and is encrypted to obtain final ground points. The principle of irregular Triangulation Interpolation (TIN) is to extract the cell values of the grid from the surface formed by a plurality of triangles composed of nearest-neighbor points using the Delaunay triangulation algorithm.
Further, the point cloud normalization in the step (4) specifically comprises the following steps: identifying the three-dimensional coordinates (X, Y, Z) of each point, identifying the ground points (X, Y, Z) corresponding to horizontal positions0) Subtracting each Z value from the corresponding Z0The values are given to new three-dimensional coordinates (X, Y, Z)1) Namely, the normalized point cloud is obtained. The method for dividing the individual plants is mainly based on an ecological theory, and the core idea is that the plants tend to use the shortest path to achieve the optimization of resource allocation in the transmission of water and nutrients, so that the most important point from each point to the adjacent trunk is calculatedThe method is divided into a semi-automatic division method and a full-automatic division method.
In this section, a method of artificially identifying stem seed points and then dividing individual plants is referred to as a semi-automatic division method, and a method of automatically identifying stem seed points and then dividing individual plants is referred to as a full-automatic division method. The effects of the two segmentation methods were evaluated by using two indices of recognition rate and accuracy rate in combination with a visual interpretation method. The identification rate represents the ratio of the number of correct stem seed points to the number of all seed points. The accuracy indicates the ratio of the number of correct individual divisions to the number of visually interpreted individuals.
Further, in the step (5), the number of extracted dominant single plants is defined as the number of single plant shrubs with the height exceeding a height threshold, the height threshold H of the dominant shrubs is reasonably set according to the heights of all vegetation points in the sample plot, the height threshold H is added into the parameter setting of single plant segmentation, only if the height of the segmented shrubs is higher than the height threshold H is regarded as the dominant shrubs, and the number of the dominant single plants is counted; the dominant single plant irrigation height is defined as the highest point Z of the normalized dominant single plant1A value; the crown width of the dominant single plant is defined as the average value of the crown widths of the dominant single plant in the north-south direction and the east-west direction after the single plant is divided.
The method comprises the steps of scanning a sand artificial vegetation sample plot through a foundation laser radar to obtain and process three-dimensional point cloud information, accurately extracting single-plant-level structural parameters of the obtained point cloud data, and forming a sand artificial vegetation structural parameter extraction method system by obtaining a digital elevation model and combining point cloud analysis technologies such as a single-plant segmentation algorithm and the like. The method has the advantages that the method extracts parameters of the dominant single-plant shrubs, is favorable for better grasping the dominant vegetation resource distribution of the sand of the alpine and fragile areas, reduces field investigation work, reduces investigation cost, and provides data and technical support for the recovery of the sand vegetation and the ecological function of multi-dimensional information expression.
The invention obtains a structural parameter extraction method suitable for irrigation and shrub vegetation in Tibet sandy land by referring to and analogy various LiDAR point cloud data processing methods in the prior art, and fills up vegetation combined parameter extraction and analysis blind areas of the individual plants in the sandy land in the alpine and fragile area. Meanwhile, the multidimensional measurement method for the single-plant vegetation structure can meet all requirement indexes required by traditional investigation, fills the blank of obtaining single-plant vegetation structure parameters of the optical passive remote sensing in the sand of the alpine and fragile area, reduces the requirement of traditional ground measurement, improves the measurement precision requirement and reduces the field workload. The method extracts the structural parameters of the single plant of the artificial vegetation in the sand of the alpine and fragile region, and provides data and technical support for the later-stage artificial sand vegetation recovery and ecological benefit evaluation research.
Drawings
FIG. 1 is a flow chart of the assay method of the present invention;
FIG. 2 is a topographical view of a plot;
FIG. 3 is a schematic diagram of a point cloud of a ground-based lidar research sample;
FIG. 4 is a graph showing plots in elevation after various normalization;
FIG. 5 is a point cloud effect diagram of a segmented single plant shrub;
FIG. 6 is a graph of results of semi-automatic and fully automatic segmentation;
FIG. 7 is a graph of precision evaluation of dominant individual plant height and actual measurement values after semi-automatic and full-automatic segmentation;
FIG. 8 is a diagram of precision evaluation of dominant single canopy widths and actual measurement values after semi-automatic and full-automatic segmentation.
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 implementation place of the embodiment is located on the north side (90.889 degrees E, 29.337 degrees N) of a road from Zedang to Ye town in the south of Yanuguengbu river, and the altitude is about 3590m, so that the embodiment belongs to a high-altitude temperate zone semiarid monsoon climate zone. The climate is characterized by warm and semi-arid climate, annual precipitation is 300-450 mm, the annual average temperature is 6.3-8.7 ℃, the climate is cold and windy in winter and spring, and the climate is warm and humid in summer and is rainy and hot in the same season. The size of the sample plot is 100m multiplied by 100m, and since 2008, the forestry department fences the sample plot, the main population of the sample plot is Tibetan sand sagebrush, a small sand dune is arranged beside the sample plot, the sample plot is mainly provided with a floral rod and sand-fixing grass, and the north of the sample plot is a large-area flowing sand dune.
The method specifically comprises the following steps:
(1) acquiring LiDAR point cloud data of a sample plot by using a ground-based laser radar scanner, and recording measured data in the sample plot: including the number of dominant individual shrubs, the height of the dominant individual shrubs, and the canopy width of the dominant individual shrubs. In 2017, in 6 and 30 months, a ground-based laser radar scanner Riegl VZ-400i LiDAR sensor is adopted, 1 station is erected in the center of a sample plot, and 4 stations are erected on gentle slopes at four corners respectively, so that information shielded by tall shrubs cannot appear, and the integrity of the whole point cloud data is ensured. The sensor records complete laser pulse information, which mainly comprises echo information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image coloring information.
(2) Data preprocessing: firstly, registering scanned multi-station Point cloud data, performing rough splicing by selecting control points, and then performing automatic fine splicing by an Iterative Closest Point (ICP) algorithm; the ICP algorithm calculates the optimal translation T and rotation R transformation parameters between two point sets by finding the corresponding relationship between a target point set and a reference point, converts point cloud models in different coordinate systems to the same coordinate system, minimizes the registration error between the two point sets, and performs fine stitching to obtain the three-dimensional point cloud data of the whole sample plot (fig. 3).
(3) And (3) filtering classification: classifying, namely receiving intensity information and waveform data of echoes by using laser, and acquiring corresponding high-resolution optical images by combining equipment to perform filtering classification; and interpolating the ground points obtained by filtering to generate a digital elevation model.
The specific steps of filtering classification are as follows: first, noise and outliers are removed by statistical filtering (SOR), and then ground points and non-ground points are separated by a filtering method (PTD) based on progressive triangulation network encryption. And then, respectively obtaining a digital elevation model DEM (the resolution is 0.1m) by the ground points and the denoised data points according to a TIN interpolation method.
The principle of statistical filtering (SOR) is to perform statistical analysis on all points, calculate the average distance between each point and its neighboring points, and if the distance is not within a certain range, it is considered as noise and removed. The principle of the filtering method (PTD) of the progressive triangulation network encryption is that initial ground seed points are obtained through morphological open operation, then the seed points with larger residual error values are removed through plane fitting, and a triangulation network is constructed through the remaining ground seed points and is encrypted to obtain final ground points. The principle of irregular Triangulation Interpolation (TIN) is to extract the cell values of the grid from the surface formed by a plurality of triangles composed of nearest-neighbor points using the Delaunay triangulation algorithm.
(4) And normalizing the data according to a digital elevation model DEM (digital elevation model), eliminating the influence of the terrain on the point cloud data (figure 4), and performing individual plant segmentation on the obtained point cloud data (figure 5 is a point cloud schematic diagram after the individual plant segmentation).
The normalization comprises the following specific steps: identifying the three-dimensional coordinates (X, Y, Z) of each point, identifying the ground points (X, Y, Z) corresponding to horizontal positions0) Subtracting each Z value from the corresponding Z0The values are given to new three-dimensional coordinates (X, Y, Z)1) Namely, the normalized point cloud is obtained.
The individual plant segmentation is mainly based on an ecological theory segmentation method, and the core idea is that plants tend to use the shortest path to achieve the optimization of resource allocation in the transmission of water and nutrients, so that the segmentation is performed by calculating the shortest path distance from each point to the adjacent trunk. The method of manually determining the stem seed points and then segmenting the individual plants is called a semi-automatic segmentation method, and the method of automatically determining the stem seed points and then segmenting the individual plants is called a full-automatic segmentation method. The effects of the two segmentation methods were evaluated by using two indices of recognition rate and accuracy rate in combination with a visual interpretation method. The identification rate represents the ratio of the number of correct stem seed points to the number of all seed points. The accuracy indicates the ratio of the number of correct individual divisions to the number of visually interpreted individuals.
(5) Extracting dominant individual plant structure parameters from the point cloud data after individual plant segmentation, and defining the number of dominant individual plantsThe number of single shrubs with the height exceeding the height threshold value. Reasonably setting a height threshold H of the dominant bushes according to the heights of all vegetation points in the sample plot, adding the height threshold H into the parameter setting of single plant division, regarding the divided bushes with the heights above the height threshold H as the dominant bushes, and counting the number of the dominant single plants (figure 6); the dominant single plant irrigation height is defined as the highest point Z of the normalized dominant single plant1Values (FIG. 7); the canopy width of the dominant individual is defined as the average value of the canopy width of the dominant individual in the north-south direction and the east-west direction after the individual is divided (fig. 8).
In this example, the height threshold of the dominant shrub was set to 0.5m as determined by visual interpretation and field investigation, 216 dominant shrubs were set in total in 12 plots, and individual division was performed on 12 plots based on TLS data by two methods, namely semi-automatic division for artificially determining the main stem seed points and full-automatic division for automatically determining the main stem seed points, with the results shown in fig. 6. The full-automatic segmentation method identifies 417 trunk points in total, wherein only 87 trunk points are correct, the identification rate is 20.9%, and the correction rate is 40.3%. And the semi-automatic division is based on the artificial determination of the trunk seed points, so that the identification of the dominant individual plant is greatly improved.
As shown in FIG. 7, for the dominant shrubs extracted by semi-automatic and full-automatic segmentation, the dominant individual shrubs have their dominant individual shrubs height and R of the measured value20.9965 and 0.9961 respectively, and the extraction precision is relatively high. The collection accuracy of TLS data and the low sand vegetation are the main reasons. And respectively extracting the crown breadth from the dominant bushes extracted by the two segmentation methods, and performing precision verification on the extracted crown breadth and the actually measured crown breadth. As shown in FIG. 8, the extracted crown width and R of the measured value are semi-automatically divided2Is 0.6389, and the extracted R is fully automatically segmented2Is 0.3114. Most of single canopy widths extracted by the two segmentation methods have an overestimation phenomenon, and from the segmentation result, the reason is that relatively short shrubs around the canopy are also counted.
The method comprises the steps of scanning a sand artificial vegetation sample plot through a foundation laser radar to obtain and process three-dimensional point cloud information, accurately extracting single plant level structural parameters of obtained point cloud data, and forming a sand artificial vegetation structural parameter extraction method system by obtaining a digital elevation model and combining point cloud analysis technologies such as a single plant segmentation algorithm and the like. According to the results of the scheme, for the artificial vegetation in the sandy land, the effect of single plant segmentation is obviously superior to full-automatic segmentation by artificially determining the trunk point to perform semi-automatic segmentation. The extraction precision is extremely high in the two parameters of the number of the dominant shrubs of the single plant and the tree height. For the crown width parameter of the dominant shrub of the single plant, the parameter value extracted by semi-automatic segmentation is generally large, and 63.89% of the information of the actual crown width can be extracted. Can basically meet the measurement requirements.
The invention obtains the single plant segmentation method and the structural parameter extraction method suitable for sandy shrub vegetation by using and analogy various LiDAR point cloud data processing methods in the prior art. Meanwhile, the multidimensional measurement method for the single-plant vegetation structure can meet all requirement indexes required by traditional investigation, reduces the requirement of traditional ground measurement, and improves the extraction efficiency while meeting the measurement requirement.
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 (10)
1. A TLS-based method for measuring the dominant single plant structural parameters of vegetation in alpine and fragile regions is characterized by comprising the following steps: and acquiring and processing point cloud data by using a foundation laser radar, performing single plant segmentation on the normalized point cloud data by using a semi-automatic segmentation method and a full-automatic segmentation method, and respectively extracting and comparing the number of dominant single plants, the height of dominant single plants and the crown width of the dominant single plants in the single plant scale.
2. The TLS-based method for measuring the dominant individual plant structural parameters of vegetation in the alpine and fragile regions according to claim 1, which comprises the following steps:
(1) acquiring LiDAR point cloud data in a region to be detected by adopting a ground-based laser radar scanner, and recording measured data in a sample plot: the method comprises the steps of measuring the number of dominant single-plant shrubs, the height of the dominant single-plant shrubs and the crown width of the dominant single-plant shrubs;
(2) data preprocessing: registering scanned multi-station point cloud data, performing rough splicing by selecting control points, and performing automatic fine splicing by an iterative closest point algorithm;
(3) and (3) filtering classification: classifying, namely receiving intensity information and waveform data of echoes by using laser, and acquiring corresponding high-resolution optical images by combining equipment to perform filtering classification; interpolating the ground points obtained by filtering to generate a digital elevation model;
(4) normalizing the data according to a digital elevation model, and performing individual plant segmentation on the normalized point cloud data by adopting a semi-automatic segmentation method and a full-automatic segmentation method;
(5) and extracting dominant single plant structure parameters from the point cloud data obtained after the single plant segmentation, wherein the dominant single plant structure parameters comprise dominant single plant quantity, dominant single plant height and dominant single plant canopy width.
3. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: the foundation laser radar scanner in the step (1) is a Riegl VZ-400iLiDAR sensor, and the sensor records complete laser pulse information, and mainly comprises echo information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image coloring information.
4. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: and (3) the iterative closest point algorithm in the step (2) calculates the optimal translation T and rotation R transformation parameters between the two point sets by searching the corresponding relation between the target point set and the reference point, and converts the point cloud models under different coordinate systems into the same coordinate system to minimize the registration error between the point cloud models and obtain the three-dimensional point cloud data of the whole sample plot.
5. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: the filtering classification in the step (3) comprises the following specific steps: firstly, removing noise points and outliers through statistical filtering; then, separating the ground point from the non-ground point based on a filtering method of progressive triangulation network encryption; and then, obtaining a digital elevation model DEM by the ground points according to a TIN interpolation method.
6. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: the point cloud normalization in the step (4) comprises the following specific steps: identifying the three-dimensional coordinates (X, Y, Z) of each point, identifying the ground points (X, Y, Z) corresponding to horizontal positions0) Subtracting each Z value from the corresponding Z0The values are given to new three-dimensional coordinates (X, Y, Z)1) Namely, the normalized point cloud is obtained.
7. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: in the step (4), the semi-automatic segmentation method and the full-automatic segmentation method are used for segmenting by calculating the shortest path distance from each point to the adjacent trunk; and (3) evaluating the effects of the semi-automatic segmentation method and the full-automatic segmentation method by combining a visual interpretation method and through two indexes of the recognition rate and the accuracy.
8. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: and (5) extracting the dominant single plants, namely the number of the single plants with the heights exceeding the height threshold, reasonably setting the height threshold H of the dominant brush according to the heights of all vegetation points in the sample plot, adding the height threshold H into the parameter setting of the single plant segmentation, regarding the segmented brush with the height above the height threshold H as the dominant brush, and counting the number of the dominant single plants.
9. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: step (5)) The highest point Z of the normalized dominant single plant is the dominant single plant1The value is obtained.
10. The TLS-based method for determining the dominant individual plant structural parameters of vegetation in the alpine and vulnerable regions according to claim 2, wherein the method comprises the following steps: the crown width of the dominant single plant extracted in the step (5) is the average value of the crown widths of the dominant single plant after the single plant is divided in the north-south direction and the east-west direction.
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