CN111913185B - TLS (TLS (visual inspection) measuring method for low shrub pattern investigation of severe cold fragile region - Google Patents
TLS (TLS (visual inspection) measuring method for low shrub pattern investigation of severe cold fragile region Download PDFInfo
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Abstract
The invention discloses a TLS measurement method for low shrub sample plot investigation in a severe cold fragile region, and belongs to the field of forest resource and ecological benefit evaluation. The method comprises the steps of scanning different scanning points of a sample plot through a foundation laser radar, extracting parameters of data sets registered by different scanning point combinations, comparing importance of each scanning point by taking the data sets registered by all the scanning points as references, and providing a selection basis for selecting the scanning points of the sample plot. The invention collects vegetation structure data of low bushes in a severe cold fragile area based on TLS (transport layer security) frame station modes in single scanning and multiple scanning modes, and analyzes the influence of different frame station modes on the extraction of three visual vegetation growth parameters of the quantity, the height and the crown width of the low bushes in the severe cold fragile area by taking the gradient and the topography fluctuation factors as environmental conditions. The invention can reduce the risk and cost of vegetation sample investigation in the plateau areas with fragile biology and severe environment, and improve the collection efficiency and the 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 measurement method for investigation of a low bush sample in a severe cold fragile region.
Background
Bushes, grasslands and meadows are the main vegetation types in alpine regions, with short plants but widespread and play an important role in the global carbon circulation, preventing land degradation and biodiversity protection. In the 80 s of the 20 th century, grasslands and meadows are severely degenerated and are accompanied by the characteristic of shrubrication, so that the loss of biodiversity, water-holding capacity and soil nutrients is caused.
For this type of vegetation, biomass and carbon sequestration are typically estimated in combination with field surveys and optical telemetry data. In recent years, the laser radar technology capable of providing three-dimensional information gradually replaces the conventional optical remote sensing technology in terms of 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 specialized operators. TLS can provide vegetation vertical structure information accurate to millimeter level, with unique advantages for vegetation information acquisition on the sample scale, such as trunk detection and mapping, breast diameter, tree height, and biomass estimation. However, laser radars have been used as vegetation survey tools mainly in forestry, and little research has been done to date on low-height vegetation.
In forest investigation, TLS scanning methods are classified into Single-Scan (SS) and Multiple-Scan (MS) modes, and researches on the scanning methods are mostly to compare the difference in accuracy of the two modes. For low-altitude vegetation surveys, li et al (2019) evaluated the effect of terrain on vegetation recovery using the MS mode of TLS, and point cloud data was obtained by registering 18 scan points. Xu et al (2020) estimated grassland biomass using TLS data registered with 5 scan points. These studies all employed MS-mode registered TLS data to obtain more complete pattern information. However, few studies provide criteria for determining the location of scan points and explore factors that affect the integrity of data acquisition. In addition, in some areas with severe environmental conditions (such as Qinghai-Tibet plateau), vegetation surveys are at high risk and cost, and are not efficient and of high data quality. Therefore, the scientific and reasonable arrangement of the scanning points according to the topographic features and vegetation coverage of the area where the sample is located is particularly important.
Disclosure of Invention
The invention aims to: aiming at the defects existing in the prior art, the invention aims to provide a TLS measurement method for investigation of a low shrub sample in a severe cold fragile area, which is used for investigating vegetation sample in a plateau area with fragile environment, reducing risks and costs and improving acquisition efficiency and data quality.
The technical scheme is as follows: in order to achieve the above object, the present invention adopts the following technical scheme:
a TLS measuring method for low-altitude shrub sample plot investigation of a severe cold fragile region comprises the steps of scanning sample plots with foundation laser radar at different scanning points, extracting parameters of data sets combined and registered with different scanning points, comparing importance of all the scanning points by taking the data sets registered with all the scanning points as references, and providing selection suggestions for selecting the scanning points of the sample plot.
Preferably, the scan points include 1 center scan point and N peripheral scan points, and the data sets include a data set formed by the center scan points alone, a reference data set formed by 1+n scan points, and n+1 data sets formed by any N scan point combinations. When sample TLS data are acquired, a central scanning point is firstly determined at the center of the sample, and then N peripheral scanning points are determined to be positioned near four corners of the sample.
Preferably, the method specifically comprises the following steps:
(1) The method comprises the steps that a foundation laser radar scanner collects LiDAR point cloud data in a sample area, and actual measurement data are recorded in the sample area;
(2) Data registration: combining and registering different scanning points to obtain different data sets for coarse splicing and automatic fine splicing;
(3) And (5) filtering and classifying: respectively filtering and classifying the data of different data sets, and interpolating the ground points obtained by filtering to generate a digital elevation model;
(4) Respectively carrying out normalization processing on the data of different data sets according to the digital elevation model to obtain normalized point cloud data, and simultaneously obtaining the topographic features of the 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 by different data sets by taking the registered data sets of all the scanning points as references to obtain important conditions of all the scanning points, and providing 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, three-dimensional information of point cloud, intensity information of point cloud and color attachment information of RGB image; the plot measured data includes a terrain characteristic and vegetation coverage of the plot, the terrain characteristic including a slope and a heave of the terrain.
Preferably, in the step (2), different scanning points are combined and registered to obtain different data sets, coarse stitching is performed by selecting control points, and then automatic fine stitching is performed by iterative closest point algorithm.
Preferably, in the step (3), the specific steps of filtering classification are as follows: firstly, removing noise points and outliers through statistical filtering; then, separating the ground points from the non-ground points by a filtering method based on progressive triangle network encryption; the ground points are then interpolated according to TIN to obtain a Digital Elevation Model (DEM).
Preferably, the specific step of the point cloud normalization in the step (4) is as follows: three-dimensional coordinates (X, Y, Z) of each point are identified, and ground points (X, Y, Z) corresponding to the horizontal position are identified 0 ) Subtracting the corresponding Z from each Z value 0 The values yield new three-dimensional coordinates (X, Y, Z 1 ) And 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 the crown width in the north-south direction and the east-west direction, and finally the average value is obtained.
Preferably, in step (6), the comparison between the data sets comprises:
(a) Comparing the data set formed by the central scanning point and the reference data set, wherein the comparison uses different distances from the central scanning point as influence factors to determine the optimal scanning range of the scanning mode;
(b) And comparing the data set formed by combining any N scanning points with the reference data set, determining the importance of different scanning points, and combining the topographic features to obtain a scientific scanning point setting method.
Preferably, the vegetation parameter values extracted from the different data sets are compared by comparing the ratio obtained by the plant number, and the root mean square error RMSE of the height and the crown amplitude, with reference to the data sets registered by all the scanning points in the step (6).
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
according to the invention, the foundation laser radar is used for carrying out data scanning on different scanning points on the low-height vegetation sample plot, so that the applicability of the SS and MS scanning modes is compared and analyzed. In addition, the influence of the topographic features on the scan point setting is analyzed, and standard suggestions of scan point setting methods of two scan modes are provided. The method is beneficial to improving the efficiency of resource investigation, can quickly grasp the vegetation resource distribution in the friable region of high and cold, reduces the field investigation work, reduces the investigation cost, and provides data and technical support for the sand vegetation recovery and ecological function recovery expressed by multidimensional information.
The invention refers to and analogizes the resource investigation method of TLS in forest sample plot scale in the prior art. By combining the characteristics of low-height vegetation, the TLS-based sample plot investigation scanning method suitable for the vegetation type is obtained, the blank that the standard proposal of scientifically arranging scanning points is lacking in the current sample plot investigation of the vegetation type by using TLS is filled, the error method of blindly adding the scanning points to obtain more complete sample plot information is corrected, the data investigation efficiency is improved, and especially the vegetation investigation of a friable region of high and cold (Qinghai-Tibet plateau region) is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic view of a topography of a sample site and a sample site scan point;
FIG. 3 is a plot of the plot 1;
FIG. 4 is a schematic representation of topographical features;
FIG. 5 is a graph of vegetation parameter extraction based on SS scanning and reference data sets;
FIG. 6 is a plot of terrain characteristics and vegetation distribution for each scan point of plot 2;
figure 7 analysis of importance errors for different scan points of plot 2.
Detailed Description
The invention will be further illustrated with reference to specific examples, which are carried out on the basis of the technical solutions of the invention, it being understood that these examples are only intended to illustrate the invention and are not intended to limit the scope thereof.
In this embodiment, a standard setting method of a scanning mode is provided for a sample side investigation of low vegetation by using a TLS technology by taking a friable ecological region of Qinghai-Tibet plateau as a sample, so as to solve the problem of data loss caused by unreasonable setting of scanning points. Through the SS and MS scanning modes and the influence of the terrain features (namely gradient and terrain fluctuation) on the setting of the scanning points, the parameters are compared through the vegetation quantity, height and crown width in the sample party, so that reasonable suggestion is provided for the selection of the scanning points of the sample land.
The implementation plot 1 of this example is located on the north side (91.324 °e,29.181 °n) of the highway from zerumen to Sang Ye town in the southern area of jacob Jiang Zhongyou, and the implementation plot 2 is located near the rassa airport (90.532 °e,29.202 °n) of the third-order land of Gong Jiaxian jacob Jiang Di, at an altitude of about 3560-3730m, belonging to the highland temperate semiarid monsoon climate zone. The climate is characterized by warm semiarid, the annual precipitation is 300-450 mm, the annual average temperature is 6.3-8.7 ℃, the winter and spring are cold and more wind, the summer is warm and moist, and the rain and heat are the same as the season. The size of the sample plot 1 is 100m multiplied by 100m, the vegetation coverage is about 60 percent, the gradient is slower by 3.8 degrees, the topography is flat, and the topography fluctuation is avoided; the size of the sample plot 2 is 50m multiplied by 50m, the vegetation coverage is 35%, the gradient is larger and is 56.2 degrees, and the terrain change is larger.
The method specifically comprises the following steps:
(1) The method comprises the steps of obtaining LiDAR point cloud data of a sampling place by adopting a foundation laser radar scanner, and recording actual measurement data of the sampling place in the sampling place by adopting a Riegl VZ-400i LiDAR sensor, wherein the actual measurement data mainly comprise the topographic features (including the gradient and the fluctuation of the topography) and the vegetation coverage (figure 5) of the sampling place. Data acquisition is carried out on sample plot 1 at 30 days 6 and 6 of 2017; data acquisition was performed on sample No. 2 on day 1, 7 in 2017. First, 1 station scanning point is erected in the center of the sample plot, and then 4 station peripheral scanning points are erected at four corners respectively (fig. 2). The sensor records complete laser pulse information, mainly comprising laser pulse return information, point cloud three-dimensional information, point cloud intensity information and RGB image color information, and the conclusion of the invention is that the Riegl VZ-400iLiDAR sensor is taken as an example, and the results obtained by different scanners may be different, but the experimental conclusion is not influenced.
(2) Data registration: and combining and registering different scanning points to obtain different data sets, wherein the point cloud registration software is Riscan Pro. Coarse splicing is performed by selecting control points, automatic fine splicing is performed by iterating a nearest point algorithm (Itetative Closest Point, ICP), and the method is used for searching the corresponding relation between a target point set and a reference point, further calculating the optimal translation T and rotation R transformation parameters between the two point sets, converting the point cloud models under different coordinate systems into the same coordinate system, and enabling registration errors between the two point cloud models to be minimum, so that three-dimensional point cloud data of the whole sample area is obtained. Further, the different data sets include a data set (1) in which the center scanning point is formed separately, a reference data set (1) in which all 5 scanning points are formed, and a data set (5) in which any four scanning points are combined. For convenience of writing, reference data sets of 2 plots are represented by DS1- (r) and DS2- (r), respectively, DS1- (n) and DS2- (n) representing data sets consisting of the nth scan point alone within 2 plots, and DS1- (n-) and DS2- (n-) representing data sets consisting of four scan points other than the nth scan point within 2 plots.
Wherein for sample plot 1, the data set formed by the central scanning point alone represents an SS scanning mode and is compared with a reference data set, and because of the large vegetation quantity of sample plot 1, the invention divides 3 types of small sample plots according to different distances from the central scanning point, namely 20m (sample plot 9-12), 35m (sample plot 5-8) and 50m (sample plot 1-4) as shown in figure 3
(3) And (5) filtering and classifying: the point cloud filter analysis processing software is LiDAR 360. Firstly, removing noise points and outliers through statistical filtering; then, separating the ground points from the non-ground points by a filtering method based on progressive triangle network encryption; then, the ground points were interpolated by TIN to obtain a digital elevation model DEM (resolution 0.02 m). (separate data sets/small sample squares)
The principle of statistical filtering (SOR) is to perform a statistical analysis on all points, calculate the average distance between each point and its neighbors, and if the distance is not within a certain range, it is regarded as a noise point and removed. The principle of a filtering method (PTD) of progressive triangle network encryption is that initial ground seed points are obtained through morphological open operation, then seed points with larger residual values are removed through plane fitting, a triangle network is built through the residual ground seed points, and final ground points are obtained through encryption. The principle of irregular Triangulated Interpolation (TIN) is to extract the cell values of a grid on a surface from a plurality of triangles consisting of nearest neighbors together using the Delaunay triangulation algorithm.
(4) And carrying out normalization processing on the data according to the digital elevation model to obtain normalized point cloud data (divided into different data sets/small sample sides), and simultaneously obtaining the topographic features of the positions of different scanning points through the digital elevation model.
The specific steps of the point cloud normalization are as follows: three-dimensional coordinates (X, Y, Z) of each point are identified, and ground points (X, Y, Z) corresponding to the horizontal position are identified 0 ) Subtracting the corresponding Z from each Z value 0 The values yield new three-dimensional coordinates (X, Y, Z 1 ) And the normalized point cloud is obtained.
(5) And carrying out visual interpretation on the normalized point cloud, and extracting required vegetation parameters (according to different data sets/small sample parties). The vegetation parameters specifically comprise three parameters of vegetation quantity, vegetation height and vegetation crown. Wherein the crown webs comprise the crown webs in the north-south direction and the east-west direction, and finally, the average value is taken.
(6) And comparing vegetation parameter values extracted by different data sets by taking the registered data sets of all the scanning points as references to obtain important conditions of all the scanning points, and giving a standard suggestion by combining the topographic features of the scanning points.
The vegetation parameter values extracted from the different data sets are compared against the data sets registered at all scan points, the ratio obtained by comparing the vegetation numbers, and the RMSE (root mean square error) of the height and crown amplitude.
The comparison of the data sets includes:
(a) And (3) comparing the SS data set formed by the single central scanning point with the reference data set, wherein the comparison uses different distances from the central scanning point as influence factors (comparing according to 3 classes of small sample squares), and exploring the optimal scanning range of the SS scanning mode.
(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, and combining the topographic features to obtain a scientific scanning point setting method.
In this example, the results of vegetation quantity (N), height average (H-mean) and crown average (CW-mean) extracted from DS1- (5), DS2- (5), DS1- (r) and DS2- (r) were obtained by performing a vegetation parameter survey on three types of swatches of swatch 1 and comparing with a reference dataset (FIG. 5). In plot 1, the ratio of the number of vegetation extracted (Ns/Nr) is sequentially equal to plot 9-12 (97.9%) > plot 5-8 (94.8%) > plot 1-4 (40.6%), while the RMSE of H-mean and CW-mean are the same as the order of the number of vegetation, so that the error in the parameters of vegetation extracted from plot 9-12 is minimal, which means that the data integrity of SS mode acquisition based on 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 scan blind area (circular area with a radius of about 2m, which is related to the height of the support when measuring is used) should be considered when setting up the sample side. The vegetation H-mean and CW-mean of the sample parties 9-12 are 0.95m (rmse=0.186 m) and 1.23m (rmse=0.208 m), respectively, so that the accuracy of the vegetation parameters extracted based on data within 20m of the center scan point acquired by the SS is about 80%. The sampling area of the bush is usually 5m×5m to 20m×20m (China environmental protection department, 2014), so that the data within 20m from the central scanning point can meet the area requirement of the sample side. Whereas in plot 2, the H-mean and CW-mean of all vegetation are 2.86m (rmse=0.4 m) and 4.24m (rmse=0.854 m), respectively, demonstrating that the vegetation parameters extracted from the SS scan pattern in the steep slope region can also provide an accuracy of about 80%. Therefore, the gradient is not a main factor influencing vegetation data acquisition, and the shielding (shielding between vegetation and shielding caused by topography fluctuation) effect is only a main influencing factor.
The topographical features of 5 scan points in the plot 2 are extracted from DEM data (fig. 6 a), the topographical features of scan points 1, 2, 5 are windward slopes, scan point 3 is a leeward slope, and scan point 4 is a hillside. It can be seen from the vegetation profile that the number of vegetation at the windward slope and peak is significantly greater than the leeward slope (fig. 6 b). By comparing the vegetation parameters extracted for DS2- (r) and DS2- (n-) (FIG. 7), all four scan point combined datasets (DS 2- (n-)) are able to identify all vegetation within plot 2. The maximum RMSE for CW-mean (0.191 m) occurs in DS2- (5-) but the RMSE for H-mean is relatively small (0.026 m), indicating that the dataset consisting of 4 peripheral scan points lacks vegetation information, especially coronal information, at the center of plot 2. For other data sets, H-mean RMSE is arranged in order DS2- (1-) (0.009 m) < DS2- (2-) (0.087 m) < DS2- (3-) (0.127 m) < DS2- (4-) (0.152 m), CW-mean RMSE is arranged in order DS2- (1-) (0.064 m) < DS2- (2-) (0.083 m) < DS2- (4-) (0.140 m) < DS2- (3-) (0.148 m), so the importance of collecting data at the peripheral 4 scan points is scan point #4 > #3 > #2 > #1 in order except for the center scan point 5. Thus, for a pattern 2 with a large gradient and a fluctuation, each scan point first contributes to the data acquisition. But some scan points (e.g. scan point 1) contribute less, and can be replaced by other scan points (e.g. scan point 2) because both scan points 1 and 2 are located on a windward slope and the vegetation surrounding scan point 2 is more (fig. 6 b). The scanning point 4 is located on a slope peak (fig. 6 a), has a good scanning visual field, is a connecting scanning point combining a windward slope and a leeward slope, and compensates for information loss caused by topography fluctuation. The scanning point 3 mainly provides vegetation information of the leeward slope, and although the scanning point 4 is located at a peak of the slope, the scanning point is far away from vegetation on the leeward slope, and complete vegetation information cannot be obtained through the single scanning point 4, so that data acquisition of the scanning point 3 is necessary. And the central scanning point 5 has stronger capability of collecting the whole vegetation information due to the special position in the sample plot 2. Therefore, for low-height vegetation such as shrubs and the like, the high recognition rate (95%) of the 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 sample land to be recognized.
Through the experiment, the invention provides the following scanning point setting method for low-height vegetation according to different data acquisition requirements and different situations of sample areas:
(1) The SS scanning mode is suitable for rapidly acquiring vegetation information in the sample land, and has low requirements on vegetation parameter extraction precision. Furthermore, the position of the center scan point should take into account the relief of the terrain, rather than the slope, and it is preferable to select the position of the slope peak at the center of the plot so that the shadowing effect of the terrain can be reduced.
(2) When high-precision vegetation parameters are needed for data acquisition, an MS scanning mode can be adopted. First, the present invention requires investigation of terrain and vegetation conditions and a preferential determination of the center scan point according to the approach of (1) before the scan point is set in the plot. Second, the determination of the peripheral scan points requires consideration of many factors, including mainly the effect of shadowing by terrain and vegetation in the scan direction. The results show that scan points #3 and #4 have a greater impact on data acquisition than #1 and #2, indicating that the shadowing effect caused by terrain has a greater impact. Thus, the perimeter scan points need to be located in areas with different topographical features (including windward slopes, leeward slopes, hills) and evenly distributed around the sample.
The invention provides a TLS improved standing method based on a combination of Single-Scan (SS) and Multiple-Scan (MS) modes, which is used for collecting vegetation structure data of low bushes in a severe cold fragile area, analyzing the advantages of the standing method in three vegetation parameters of the quantity, the height and the crown width of the low bushes in the severe cold fragile area by taking gradient and a topography fluctuation factor as environmental conditions. The invention can reduce the risk and cost of vegetation sample investigation in the plateau areas with fragile biology and severe environment, and improve the collection efficiency and the data quality.
(3) Taking the equipment used in this study as an example (RIEGLVZ-400 i), the present invention requires attention to the size of the swatch or the distance between the scan points. For the scanning mode of SS, the size of the pattern (square) should not exceed 50m, otherwise the information at the edges of the pattern is unreliable. In addition, in the MS scanning mode, the distance between adjacent scanning points cannot exceed 50m, so that the repeatability and complementarity of the point cloud are ensured.
The comparison between the single-station data set and the reference data set of the central scanning point proves that the SS mode has strong applicability in the low-height vegetation field investigation. And meanwhile, vegetation parameters (quantity, height and crown width) under different data sets are extracted and compared, the influence of the topographic features on the setting of scanning points is analyzed, the MS mode is proved to have wider applicability in the aspects of data integrity and accuracy, but the accuracy is influenced by the size of a sample land block and the shielding effect caused by topography and vegetation. Therefore, the invention provides a complete scanning point setting method for the sample plot investigation of the low-height vegetation based on TLS, thereby greatly reducing the cost waste problem caused by the arrangement of a large number of scanning points when collecting data and greatly improving the collection efficiency and the data quality.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (4)
1. A TLS measurement method for a low shrub-like plot survey of a severe cold fragile region, characterized by: scanning the sample plot by using a foundation laser radar to divide different scanning points, extracting parameters of data sets registered by different scanning point combinations, comparing the importance of each scanning point by taking the data sets registered by all the scanning points as references, and providing a selection basis for selecting the scanning points of the sample plot; the scanning points comprise 1 central scanning point and N peripheral scanning points, and the data sets comprise data sets formed by the central scanning points alone, reference data sets formed by 1+N scanning points and N+1 data sets formed by any combination of N scanning points;
the method specifically comprises the following steps:
(1) The method comprises the steps that a foundation laser radar scanner collects LiDAR point cloud data in a sample area, and actual measurement data are recorded in the sample area;
the LiDAR point cloud data comprises the return information of laser pulses, point cloud three-dimensional information, point cloud intensity information and RGB image color attachment information; the sample plot measured data comprise the topographic features and vegetation coverage of the sample plot, wherein the topographic features comprise the gradient and the fluctuation of the topography;
(2) Data registration: combining and registering different scanning points to obtain different data sets for coarse splicing and automatic fine splicing;
(3) And (5) filtering and classifying: respectively filtering and classifying the data of different data sets, and interpolating the ground points obtained by filtering to generate a digital elevation model;
(4) Respectively carrying out normalization processing on the data of different data sets according to the digital elevation model to obtain normalized point cloud data, and simultaneously obtaining the topographic features of the 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;
the vegetation parameters comprise plant number, height and crown width, wherein the crown width comprises the crown width in the north-south direction and the east-west direction, and finally, the average value is taken
(6) Comparing vegetation parameter values extracted by different data sets by taking the registered data sets of all the scanning points as references to obtain important conditions of all the scanning points, and providing a scanning point selection suggestion by combining the topographic features of the scanning points; the comparison between the data sets includes:
(a) Comparing the data set formed by the central scanning point and the reference data set, wherein the comparison uses different distances from the central scanning point as influence factors to determine the optimal scanning range of the scanning mode;
(b) Comparing the data set formed by combining any N scanning points with a reference data set, determining the importance of different scanning points, and combining the topographic features to obtain a scanning point setting method;
the vegetation parameter values extracted from the different data sets are compared by taking the registered data sets of all the scanning points as references, and the ratio obtained by comparing the number of plants, and the root mean square error RMSE of the height and the crown width are compared.
2. The TLS measurement method for a low-stump-like survey of a severe cold fragile zone according to claim 1, wherein: in the step (2), different scanning points are combined and registered to obtain different data sets, coarse splicing is performed by selecting control points, and then automatic fine splicing is performed by an iterative nearest point algorithm.
3. The TLS measurement method for a low-stump-like survey of a severe cold fragile zone according to claim 1, wherein: in the step (3), the specific steps of filtering classification are as follows: firstly, removing noise points and outliers through statistical filtering; then, separating the ground points from the non-ground points by a filtering method based on progressive triangle network encryption; and then, obtaining the digital elevation model from the ground points according to a TIN interpolation method.
4. The TLS measurement method for a low-stump-like survey of a severe cold fragile zone according to claim 1, wherein: the specific steps of the point cloud normalization in the step (4) are as follows: three-dimensional coordinates (X, Y, Z) of each point are identified, and ground points (X, Y, Z) corresponding to the horizontal position are identified 0 ) Subtracting the corresponding Z from each Z value 0 The values yield new three-dimensional coordinates (X, Y, Z 1 ) And the normalized point cloud is obtained.
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