CN111913185A - TLS measurement method for investigation of short shrub and shrub sample plot in alpine and cold vulnerable area - Google Patents
TLS measurement method for investigation of short shrub and shrub sample plot in alpine and cold vulnerable area Download PDFInfo
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Abstract
The invention discloses a TLS measurement method for investigation of low bush sample plot in a high and cold fragile area, and belongs to the field of forest resource and ecological benefit evaluation. Different scanning points of the sample plot are scanned through the foundation laser radar, parameters of data sets registered in combination with the different scanning points are extracted, the data sets registered with all the scanning points are used as reference, the importance of each scanning point is compared, and a selection basis is provided for selection of the scanning points of the sample plot. The method is based on TLS (TLS) station erecting modes in single scanning and multi-scanning modes, vegetation structure data acquisition is carried out on the low bushes in the alpine and fragile areas, gradient and topographic relief factors are used as environmental conditions, and the influence of different station erecting modes on the extraction of three visual vegetation growth parameters including the number, the height and the crown width of the low bushes in the alpine and fragile areas is analyzed. The method can be used for researching vegetation sample plots in plateau areas with fragile ecology and severe environment, reducing risks and cost and improving acquisition efficiency and data quality.
Description
Technical Field
The invention belongs to the technical field of forest resource and ecological benefit evaluation, and particularly relates to a TLS (survey of short shrub and shrub sample plot) measurement method for high and cold fragile areas.
Background
Brush, grassland and meadow are the main types of vegetation in alpine regions, whose plants are short but widely distributed and play an important role in global carbon circulation, prevention of land degradation and protection of biodiversity. Since the 80 s of the 20 th century, the grassland and the meadow have been seriously degraded and have the characteristics of shrubbling, so that the biodiversity, the water retention capacity and the loss of soil nutrients are caused.
For this type of vegetation, biomass and carbon fixation are typically estimated in conjunction with field surveys and optical remote sensing data. In recent years, laser radar technology capable of providing three-dimensional information has gradually replaced conventional optical remote sensing technology in vegetation investigation. Lidar systems are largely divided into airborne laser scanning systems (ALS) and terrestrial laser scanning systems (TLS). ALS is suitable for regional scale data acquisition, but its low density point cloud is difficult to fully express vegetation information, and ALS-based data acquisition is greatly limited by environmental conditions and professional operators. TLS can provide vegetation vertical structure information accurate to the millimeter scale, with unique advantages for vegetation information collection on the scale of the sample, such as trunk detection and mapping, breast height, tree height, and biomass estimation. However, to date, lidar has been used primarily in forestry as a tool for investigating vegetation, and application to low-height vegetation has been rarely studied.
In forest investigation, TLS scanning methods are divided into Single-Scan (SS) and multi-Scan (MS) modes, and most studies on scanning methods compare the accuracy difference between the two modes. For low-elevation vegetation surveys, Li et al (2019) evaluated the effect of terrain on vegetation recovery using the MS mode of TLS, and obtained point cloud data by registering 18 scan points. Xu et al (2020) estimated grassland aboveground biomass using TLS data registered with 5 scan points. Both of these studies use MS mode registered TLS data to obtain more complete plot information. However, very few studies have provided criteria for determining the position of the scanning spot and for exploring factors that affect the integrity of the data acquisition. In addition, in some regions with severe environmental conditions (such as Tibet plateau regions), the risk and cost of planting to be investigated are high, and the efficiency and data quality are not high. Therefore, it is important to scientifically and reasonably arrange the scanning points according to the topographic features and vegetation coverage of the area where the sample plot is located.
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 TLS measuring method for investigation of vegetation sample plots in ecological fragile plateau areas and harsh environments, which reduces risks and cost and improves acquisition efficiency and data quality.
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 TLS measuring method for investigation of low and short bush sample plots in a high and cold fragile area scans different scanning points of a sample plot, extracts parameters of data sets registered in different scanning point combinations, compares the importance of each scanning point by taking the data sets registered in all the scanning points as reference, and provides a selection suggestion for selection of the sample plot scanning points.
Preferably, the scanning points include 1 central scanning point and N peripheral scanning points, and the data sets include a data set formed by the central scanning point alone, a reference data set formed by 1+ N scanning points, and N +1 data sets formed by any combination of N scanning points. When sample plot TLS data is acquired, a central scanning point is determined at the center of the sample plot, and then N scanning points at the periphery are determined to be located near the four corners of the sample plot.
Preferably, the method specifically comprises the following steps:
(1) the method comprises the following steps that a ground-based laser radar scanner collects LiDAR point cloud data in a sample area, and records actually measured data in a sample plot;
(2) data registration: combining and registering different scanning points to obtain different data sets for rough splicing and automatic fine splicing;
(3) and (3) filtering classification: respectively carrying out filtering classification on data of different data sets, and carrying out interpolation on ground points obtained by filtering to generate a digital elevation model;
(4) respectively normalizing the data of different data sets according to a digital elevation model to obtain normalized point cloud data, and simultaneously acquiring topographic features of positions of different scanning points through the digital elevation model;
(5) respectively carrying out visual interpretation on the normalized point clouds on the data of different data sets, and extracting required vegetation parameters;
(6) and comparing vegetation parameter values extracted from different data sets by taking the registered data sets of all the scanning points as reference to obtain the important condition of each scanning point, and giving a scanning point selection suggestion by combining the topographic features of the scanning points.
Preferably, in the step (1), the LiDAR point cloud data comprises echo information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image coloring information; the plot measured data includes topographical features of the plot including slope and undulation of the terrain and vegetation coverage.
Preferably, in the step (2), different scanning points are combined and registered to obtain different data sets, rough splicing is performed by selecting control points, and then automatic fine splicing is performed by an iterative closest point algorithm.
Preferably, in the step (3), the specific steps of filtering and classifying are as follows: 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.
Preferably, 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.
Preferably, the vegetation parameters in the step (5) specifically include three parameters of plant number, height and crown width, wherein the crown width includes crown widths in north-south and east-west directions, and finally, an average value is taken.
Preferably, in step (6), the comparison between the data sets comprises:
(a) comparing a data set formed by the central scanning point independently with a reference data set, wherein the comparison takes different distances from the central scanning point as influence factors to determine the optimal scanning range of a scanning mode;
(b) and comparing a data set formed by combining any N scanning points with a reference data set, determining the importance of different scanning points by the comparison, and obtaining a scientific scanning point setting method by combining topographic features.
Preferably, the comparison of the values of the vegetation parameters extracted from the different data sets with reference to the data sets registered at all the scanning points in step (6) is a ratio obtained by comparing the number of plants and the root mean square error RMSE of the height and the crown width.
Has the advantages that: compared with the prior art, the invention has the following advantages:
the invention carries out data scanning of different scanning points on the low-altitude vegetation sample plot through the foundation laser radar, and contrasts and analyzes the applicability of the SS scanning mode and the MS scanning mode. In addition, the influence of topographic features on the scanning point setting is analyzed, and standard suggestions of scanning point setting methods of two scanning modes are provided. The method is beneficial to improving the efficiency of resource investigation, can quickly grasp the vegetation resource distribution in the alpine and fragile area, reduces the field investigation work, reduces the investigation cost, and provides data and technical support for the sandy vegetation recovery and ecological function recovery expressed by multi-dimensional information.
The invention relates to a resource investigation method for TLS in a forest land scale in the prior art by reference and analogy. The TLS-based sample plot investigation and scanning method suitable for the vegetation type is obtained by combining the characteristics of low-height vegetation, the blank that standard suggestions for scientifically laying scanning points are lacked in the sample plot investigation of the vegetation type by using TLS at present is filled, the error method that the scanning points are increased blindly to obtain more complete sample plot information is corrected, the efficiency of data investigation is improved, and particularly the vegetation investigation of a cold fragile area (Qinghai-Tibet plateau area) is realized.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic view of a topographical map of a sample site and a sample scanning spot;
FIG. 3 is a sample distribution plot for sample plot 1;
FIG. 4 is a schematic view of a topographical feature;
FIG. 5 shows the vegetation parameter extraction based on the SS scanning mode and the reference data set;
2, the topographic features and vegetation distribution of each scanning point in the figure 6 same way;
figure 7 also 2 important error analysis of different scanning points.
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.
In this embodiment, a standard setup method of a scanning manner is provided for a survey of samples of low and short vegetation by using TLS technology, using a severe cold and vulnerable ecological region such as Qinghai-Tibet plateau as a sample, so as to solve the problem of data loss caused by unreasonable scan point setting. Through SS and MS two kinds of scanning mode to the influence that topographic features (namely slope and topography fluctuation) set up the scanning point, carry out the parameter comparison through the implantation quantity of sample prescription, height, crown width, thus propose reasonable suggestion to the selection of sample prescription scanning point.
The embodiment 1 is located on the north side of the highway from zedang to jueyi town in the south area of yaluzan bujiang midstream (91.324 ° E, 29.181 ° N), and the embodiment 2 is located near the rasa airport (90.532 ° E, 29.202 ° N) in the third place of yaluzan bujiang, gonga, with an altitude of about 3560 + 3730m, and belongs to a semiarid monsoon climate zone in high altitude temperature 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 a sample plot 1 is 100m multiplied by 100m, the vegetation coverage is about 60 percent, the gradient is 3.8 degrees, the terrain is flat, and no terrain fluctuation exists; the plot 2 is 50m x 50m, vegetation coverage is 35%, slope is 56.2 ° with large terrain variation.
The method specifically comprises the following steps:
(1) LiDAR point cloud data of a sample plot is acquired by using a ground-based laser radar scanner, the ground-based laser radar scanner uses a Riegl VZ-400i LiDAR sensor, and measured data of the sample plot is recorded in the sample plot, wherein the measured data mainly comprises topographic features (including gradient and fluctuation of terrain) and vegetation coverage of the sample plot (figure 5). Collecting data of the No. 1 sample plot in 2017, 6 months and 30 days; data was collected on day 1, 7 months in 2017 for sample No. 2. First, 1 scan point was set up at the center of the sample plot, and 4 peripheral scan points were set up at the four corners (fig. 2). The sensor records complete laser pulse information, mainly comprising echo information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image coloring information, and the conclusion of the invention is that the results obtained by different scanners may have differences but do not influence the experimental conclusion by taking a Riegl VZ-400iLiDAR sensor as an example.
(2) Data registration: and combining and registering different scanning points to obtain different data sets, wherein the point cloud registration software is Riscan Pro. The method comprises the steps of selecting control points to carry out rough splicing, then carrying out automatic fine splicing through an iterative Closest Point algorithm (ICP), and calculating optimal translation T and rotation R transformation parameters between two Point sets by searching the corresponding relation between a target Point set and a reference Point, and converting Point cloud models under different coordinate systems to the same coordinate system to minimize the registration error between the Point cloud models and the same coordinate system to obtain the three-dimensional Point cloud data of the whole sample plot. In addition, the different data sets include a data set (1) formed by the central scanning point alone, a reference data set (1) formed by all 5 scanning points, and a data set (5) formed by any four scanning points in combination. For convenience of writing, reference data sets of 2 plots are represented by DS1- (r) and DS2- (r), respectively, DS1- (n) and DS2- (n) represent data sets consisting of the nth scan point alone in the 2 plots, and DS1- (n-) and DS2- (n-) represent data sets consisting of the remaining four scan points except the nth scan point in the 2 plots.
Wherein for sample plot 1, the data set formed by the central scanning point alone represents SS scanning mode, compared with the reference data set, because the vegetation amount of the sample plot 1 is large, the invention divides 3 types of small sample squares into 20m (sample squares 9-12), 35m (sample squares 5-8) and 50m (sample squares 1-4) according to different distances from the central scanning point, and the sample squares are respectively shown in the figure 3
(3) And (3) filtering classification: the point cloud filtering analysis processing software is LiDAR 360. 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; then, the ground points are subjected to a TIN interpolation method to obtain a digital elevation model DEM (the resolution is 0.02 m). (different data sets/small sample)
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 the digital elevation model to obtain normalized point cloud data (divided into different data sets/small sample parties), and simultaneously acquiring the topographic characteristics of the positions of different scanning points through the digital elevation model.
The point cloud 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.
(5) And visually interpreting the normalized point cloud, and extracting the required vegetation parameters (different data sets/small sample parties). The vegetation parameters specifically comprise three parameters of vegetation quantity, height and crown width. Wherein the crown frames comprise crown frames in south-north and east-west directions, and finally, an average value is taken.
(6) And comparing the vegetation parameter values extracted from different data sets by taking the registered data sets of all the scanning points as reference to obtain the important condition of each scanning point, and giving a standard suggestion by combining the topographic features of the scanning points.
The values of vegetation parameters extracted from different datasets are compared with reference to the dataset registered for all scan points, the proportions obtained by comparing the number of vegetation, and the RMSE (root mean square error) of the height and crown.
The comparison of the data sets includes:
(a) and (4) comparing the SS data set formed by the central scanning point alone with the reference data set, wherein the comparison takes different distances from the central scanning point as influence factors (the comparison is carried out by 3 classes of small samples), and the optimal scanning range of the SS scanning mode is researched.
(b) And comparing the MS data set formed by combining any four scanning points with the reference data set, determining the importance of different scanning points by the comparison, and obtaining a scientific scanning point setting method by combining topographic features.
In this example, the vegetation parameters of three types of the sample plot 1 were investigated and compared with the reference data set, that is, the results of the number of vegetation (N), the height average (H-mean), and the crown width average (CW-mean) extracted from DS1- (5), DS2- (5), DS1- (r), and DS2- (r) (fig. 5). In plot 1, the extracted vegetation number ratio (Ns/Nr) is in turn 9-12 squares (97.9%) > 5-8 squares (94.8%) > 1-4 squares (40.6%), and the RMSE of H-mean and CW-mean is in the same order as the vegetation number, so the 9-12 plots extract the least error in vegetation parameters, which indicates that the integrity of data collected based on the SS mode of a single scan point is affected by the distance from the center scan point. The closer the distance, the more complete the data acquisition, but the dead zone of the scan (circular area with radius of about 2m, which is related to the height of the stand when the measurement is in use) should be taken into account when setting up the prescription. Vegetation H-mean and CW-mean for squares 9-12 are 0.95m (RMSE 0.186m) and 1.23m (RMSE 0.208m), respectively, and therefore the accuracy of the vegetation parameters extracted based on data within 20m of the SS acquired central scan point is approximately 80%. The sampling area of the bush is usually 5m × 5m to 20m × 20m (environmental protection department of china, 2014), so that the data within 20m from the central scanning point can meet the area requirement set by the sample. In the same manner as 2, the H-mean and CW-mean of all vegetation are 2.86m (RMSE ═ 0.4m) and 4.24m (RMSE ═ 0.854m), respectively, which proves that the vegetation parameters extracted by the SS scan mode in the steep slope region can also provide an accuracy of about 80%. Therefore, the slope is not the main factor affecting the vegetation data acquisition, and the occlusion (occlusion between vegetation and occlusion caused by topographic relief) effect is the main factor.
The topographic features of 5 scanning points in the sample plot 2 are extracted according to DEM data (figure 6a), the topographic features of the scanning points 1, 2 and 5 are windward slopes, the scanning point 3 is a leeward slope and the scanning point 4 is a slope peak. It can be seen from the vegetation map that the number of vegetation on windward slopes and hills is significantly greater than on leeward slopes (fig. 6 b). By comparing the vegetation parameters extracted from DS2- (r) and DS2- (n-) (FIG. 7), the data set for all four scan point combinations (DS2- (n-)) can identify all vegetation in plot 2. The maximum RMSE for CW-mean (0.191m) occurs in DS2- (5-), but the RMSE for H-mean is relatively small (0.026m), indicating that a data set consisting of 4 surrounding scan points lacks vegetation information, particularly crown information, at the center of the plot 2. For the other data sets, the RMSE of H-mean is arranged in the order of DS2- (1-) (0.009m) < DS2- (2-) (0.087m) < DS2- (3-) (0.127m) < DS2- (4-) (0.152m), and the RMSE of CW-mean is arranged in the order of DS2- (1-) (0.064m) < DS2- (2-) (0.083m) < DS2- (4-) (0.140m) < DS2- (3-) (0.148m), so that the importance of data collected by 4 scan points in the periphery except for the central scan point 5 is scan point # 4 > #3 > #2 > #1 in this order. Thus, for plot 2, which has a large slope and fluctuates, each scan point contributes to data acquisition first. But a part of the scanning points (such as scanning point 1) has smaller contribution and can be replaced by other scanning points (such as scanning point 2) because the positions of the scanning points 1 and 2 belong to the windward slope and the vegetation is more around the scanning point 2 (fig. 6 b). The scanning point 4 is located at the top of the slope (fig. 6a), has a good scanning visual field, is a connection scanning point combining a windward slope and a leeward slope, and makes up for information loss caused by topographic relief. The scanning point 3 mainly provides vegetation information of a leeward slope, although the scanning point 4 is located at a slope peak, the distance from the vegetation on the leeward slope is far, and complete vegetation information cannot be acquired through the single scanning point 4, so that data acquisition of the scanning point 3 is very necessary. And the central scanning point 5 has a strong capability of collecting the whole vegetation information due to the special position in the plot 2. Therefore, for low-height vegetation such as shrubs, the high identification rate (95%) of vegetation by the central scanning point and the high-precision three-dimensional coordinate information of the point cloud enable most of the vegetation in the plot to be identified.
Through the tests, the invention provides the following scanning point setting method for low-height vegetation according to different data acquisition requirements and different conditions of sample plots:
(1) the SS scanning mode is suitable for rapidly acquiring vegetation information in the sample plot, and the vegetation parameter extraction precision requirement is low. In addition, the position of the central scanning point should take the relief of the terrain into consideration, rather than the slope, and the position of the hill at the center of the terrain is preferably selected so as to reduce the occlusion effect of the terrain.
(2) When the collected data needs high-precision vegetation parameters, a scanning mode of MS can be adopted. Firstly, before setting a scanning point in a sample plot, the invention needs to investigate the terrain and vegetation condition, and preferentially determines a central scanning point according to the mode in (1). Secondly, many factors need to be considered in the determination of the peripheral scanning points, mainly including the occlusion effect caused by the terrain and the vegetation in the scanning direction. The results show that scan points #3 and #4 have a greater impact on data acquisition than #1 and #2, indicating that the occlusion effect caused by terrain has a greater impact. Therefore, the peripheral scanning points need to be located in areas with different topographic features (including windward slopes, leeward slopes, hills) and evenly distributed around the same plot.
The invention provides a TLS improved station erecting mode based on the combination of Single-Scan (SS) and multi-Scan (MS) modes, which is used for carrying out vegetation structure data acquisition on low bushes in an alpine and fragile area, and analyzing the advantages of the station erecting mode in three vegetation parameters of the quantity, the height and the crown width of the low bushes in the alpine and fragile area by taking a slope and a terrain fluctuation factor as environmental conditions. The method can be used for researching vegetation sample plots in plateau areas with fragile ecology and severe environment, reducing risks and cost and improving acquisition efficiency and data quality.
(3) Taking the device used in this study as an example (RIEGLVZ-400i), the present invention needs to note the size of the plot or the distance between the scanning points. For SS scan mode, the size of the plots (squares) should not exceed 50m, otherwise the information at the plot edges is unreliable. In addition, in the MS scanning mode, the distance between adjacent scanning points cannot exceed 50m, so as to ensure the repeatability and complementarity of the point cloud.
According to the method, the comparison between the single-station data set of the central scanning point and the reference data set proves that the SS mode has strong applicability in the field investigation of low-height vegetation. Meanwhile, vegetation parameters (quantity, height and crown width) under different data sets are extracted and compared, the influence of topographic features on the setting of scanning points is analyzed, and the fact that the MS mode has wider applicability in the aspects of data integrity and precision is proved, but the precision is influenced by the size of a sample plot and the shielding effect caused by the topography and the vegetation. Therefore, the invention provides a relatively complete scanning point setting method for TLS-based sample plot survey of low-height vegetation, greatly reduces the problem of cost waste caused by the arrangement of a large number of scanning points during data acquisition, and greatly improves the acquisition efficiency and the data quality.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should also be considered as the protection scope of the present invention.
Claims (10)
1. A TLS measurement method for investigation of short shrub and shrub sample plot in a high and cold vulnerable area is characterized by comprising the following steps: different scanning points of the sample plot are scanned through the foundation laser radar, parameters of data sets registered in combination with the different scanning points are extracted, the data sets registered with all the scanning points are used as reference, the importance of each scanning point is compared, and a selection basis is provided for selection of the scanning points of the sample plot.
2. The method for TLS measurement for tall and cold vulnerable area short bush plot survey according to claim 1, wherein: the scanning points comprise 1 central scanning point and N peripheral scanning points, and the data set comprises a data set formed by the central scanning points independently, a reference data set formed by 1+ N scanning points and N +1 data sets formed by any N scanning points in a combined mode.
3. The method for TLS measurement for tall and short bush plot survey in alpine and vulnerable areas according to claim 2, comprising the steps of:
(1) the method comprises the following steps that a ground-based laser radar scanner collects LiDAR point cloud data in a sample area, and records actually measured data in a sample plot;
(2) data registration: combining and registering different scanning points to obtain different data sets for rough splicing and automatic fine splicing;
(3) and (3) filtering classification: respectively carrying out filtering classification on data of different data sets, and carrying out interpolation on ground points obtained by filtering to generate a digital elevation model;
(4) respectively normalizing the data of different data sets according to a digital elevation model to obtain normalized point cloud data, and simultaneously acquiring topographic features of positions of different scanning points through the digital elevation model;
(5) respectively carrying out visual interpretation on the normalized point clouds on the data of different data sets, and extracting required vegetation parameters;
(6) and comparing vegetation parameter values extracted from different data sets by taking the registered data sets of all the scanning points as reference to obtain the important condition of each scanning point, and giving a scanning point selection suggestion by combining the topographic features of the scanning points.
4. The method for TLS measurement for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: in the step (1), LiDAR point cloud data comprises echo information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image coloring information; the plot measured data includes topographical features of the plot including slope and undulation of the terrain and vegetation coverage.
5. The method for TLS measurement for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: in the step (2), different scanning points are combined and registered to obtain different data sets, rough splicing is carried out by selecting control points, and then automatic fine splicing is carried out by an iterative closest point algorithm.
6. The method for TLS measurement for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: in the step (3), the specific steps of filtering and classifying are as follows: 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 by the ground points according to a TIN interpolation method.
7. The method for TLS measurement for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: 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.
8. The TLS measurement method for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: and (5) specifically, the vegetation parameters comprise three parameters of the number of plants, the height and the crown width, wherein the crown width comprises crown widths in the north-south direction and the east-west direction, and finally, an average value is obtained.
9. The TLS measurement method for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: in step (6), the comparison between the data sets comprises:
(a) comparing a data set formed by the central scanning point independently with a reference data set, wherein the comparison takes different distances from the central scanning point as influence factors to determine the optimal scanning range of a scanning mode;
(b) and comparing a data set formed by combining any N scanning points with a reference data set, determining the importance of different scanning points by comparing, and obtaining a scanning point setting method by combining topographic features.
10. The TLS measurement method for investigation of tall and cold vulnerable area short shrub and shrub plot as claimed in claim 3, wherein: and (4) comparing the vegetation parameter values extracted from different data sets by taking the data sets registered by all the scanning points as reference in the step (6), wherein the vegetation parameter values are the proportion obtained by comparing the number of plants and the Root Mean Square Error (RMSE) of the height and the crown.
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