CN108981616B - Method for inverting effective leaf area index of artificial forest by unmanned aerial vehicle laser radar - Google Patents
Method for inverting effective leaf area index of artificial forest by unmanned aerial vehicle laser radar Download PDFInfo
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Abstract
The invention discloses a method for inverting an effective leaf area index of an artificial forest based on an unmanned aerial vehicle laser radar empirical model, and belongs to the field of forest resource investigation, forest land quality evaluation and forest productivity estimation research. According to the method, the original point cloud data of the laser radar of the unmanned aerial vehicle is subjected to normalization processing, canopy structure characteristic variables are extracted from the normalized point cloud data, and the effective leaf area index of the sample-area scale in the research area is estimated by using a statistical model method on the basis of screening the characteristic variables by combining ground actual measurement data and the extracted characteristic variables. According to the method, the efficiency and the precision of obtaining the effective leaf area indexes continuously distributed on the 'surface' in a specific range are higher by obtaining the unmanned aerial vehicle laser radar point cloud and extracting the canopy characteristic variable and combining ground actual measurement data, and compared with other similar remote sensing methods, the method for extracting the effective leaf area indexes of the artificial forest improves the decision coefficient by more than 5%.
Description
Technical Field
The invention belongs to the field of forest resource investigation, forest land quality evaluation and forest productivity estimation research, and particularly relates to a method for inverting an effective leaf area index of an artificial forest by using an unmanned aerial vehicle laser radar.
Background
The accurate extraction of the effective leaf area index of the artificial forest has important significance for forest resource investigation, forest land quality evaluation and forest productivity estimation, and meanwhile, the information can also be used for mastering the forest canopy space distribution rule and providing data support for forest sustainable management, ecological environment recovery and reconstruction and carbon balance maintenance. The traditional artificial forest effective leaf area index extraction mainly depends on direct methods and instrument measurement, the ground actual measurement is time-consuming and labor-consuming, the efficiency is low, some direct methods (such as fresh weight punching methods and shape drawing weighing methods) can often cause certain damage to forest canopy, only information of sample plot scale can be obtained, and large-scale continuous leaf area index distribution is difficult to obtain.
In recent years, the research of forest stand effective leaf area index inversion based on an airborne laser radar technology is as follows: LiDAR Remote Sensing of biophysical properties of tall northern soil equations, published by Lim et al in 2003 on volume 29 of Canadian Journal of Remote Sensing, which uses airborne small-spot LiDAR data to extract height percentile characteristic variables, combines with ground actual measurement effective leaf area index data to construct a statistical model, and estimates the effective leaf area index of broad-leaved forest in northern Canada. The research of the Estimation of LAI and fractional coverage from small laser scanning data base on the volume 104 published by Morsdorf et al in 2006 estimates the effective leaf area index of the Swiss Stone pine forest by calculating the ratio of the number of crown layers of point clouds in a sample plot to the number of ground returns (point cloud crown layer penetrability) by means of the discrete point cloud data acquired by a laser radar and combining with the beer law. However, the above methods all use low-density manned laser radar data and use a single characteristic variable to estimate the effective leaf area index, and no high-density unmanned aerial vehicle laser radar point cloud data is applied to effective leaf area index inversion, and meanwhile, no method for comprehensively and deeply calculating the unmanned aerial vehicle laser radar point cloud characteristics and extracting the effective leaf area index is available.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention aims to provide a method for extracting canopy characteristic variables through unmanned aerial vehicle laser radar point cloud and inverting effective leaf area indexes of an artificial forest by combining ground actual measurement data.
The technical scheme is as follows: in order to solve the problems, the technical scheme adopted by the invention is as follows:
a method for inverting effective leaf area indexes of artificial forests by using an unmanned aerial vehicle laser radar comprises the steps of carrying out normalization processing on original point cloud data of the unmanned aerial vehicle laser radar, extracting canopy structure characteristic variables from the normalized point cloud data, and estimating the effective leaf area indexes of sample land scales in a research area by using a statistical model method on the basis of screening the characteristic variables by combining ground actual measurement data and the extracted characteristic variables.
The method comprises the following steps:
(1) collecting laser radar data by a laser radar sensor carried by a multi-rotor unmanned aerial vehicle, setting a sample plot on the ground, recording tree species in the sample plot, counting, measuring the breast height and the height of each tree, and measuring the effective leaf area index;
(2) filtering and interpolating original point cloud data of the laser radar to generate a digital elevation model, and performing normalization processing on the point cloud data through the generated digital elevation model;
(3) extracting a percentile height variable, coverage of each layer, volume of a canopy and a profile characteristic variable from the normalized point cloud data;
(4) screening characteristic variables through correlation analysis;
(5) taking an actually measured effective leaf area index on the ground as a dependent variable, taking a point cloud characteristic variable of the unmanned aerial vehicle laser radar as an independent variable, establishing a multiple regression model, selecting variables entering the model by using a stepwise regression method, reducing the correlation among the independent variables by using a control factor k, and further selecting the model if k is less than 30;
(6) and (5) estimating the effective leaf area index of the artificial forest by using the multiple regression model obtained in the step (5).
Wherein:
the method for measuring the effective leaf area index comprises the following steps: selecting two 30m measuring lines in the direction perpendicular to the sunlight, wherein the middle points of the two measuring lines are 7.5m away from the circle center, respectively, placing a canopy analyzer at a position 1m high from the ground surface for measurement, firstly, matching two probe rods under a forest window, using 90-degree visual angle covers, enabling the directions to be consistent, placing one probe rod under the forest window for sampling every 10s to obtain an A value, taking the other probe rod into a sample plot to be measured, and sampling every 4m along the measuring lines to obtain a B value; and matching the A value and the B value through time, and jointly calculating the effective leaf area index.
The canopy analyzer is LAI-2200.
The method for carrying out normalization processing on the original point cloud data of the unmanned aerial vehicle laser radar comprises the following steps: the method comprises the steps of firstly removing noise points of original point cloud data of the unmanned aerial vehicle laser radar, removing non-ground points based on an IDW filtering algorithm, then generating a digital elevation model by calculating the average value of the heights of laser points in each pixel, and carrying out normalization processing on the point cloud through the generated digital elevation model to obtain the normalized point cloud data of the unmanned aerial vehicle laser radar.
The percentile height variable comprises a canopy height distribution percentile, coverage above the average height of canopy point cloud distribution and a variation coefficient of canopy point cloud distribution, the coverage of each layer is the percentage of points with the number above each percentage height of the point cloud to all the point clouds, and the canopy volume and profile characteristic variable are Weibull functions to fit the canopy height distribution profile to obtain 2 profile characteristic quantities α and β and the volume percentage of four canopy structure categories of an open layer, a light-transmitting layer, a low-light layer and a closed layer.
The method for screening the characteristic variables comprises the following steps: firstly, screening characteristic variables with the correlation between the characteristic variables lower than 0.6, and then further screening the characteristic variables with the correlation between the characteristic variables and the effective leaf area index higher than 0.6.
The method for selecting the variables entering the model by using the stepwise regression method comprises the following steps: the significance test is carried out at a predetermined F level, and if the t test does not reach the significance level, i.e. p is greater than 0.1, the T test is rejected, and if the t test reaches the significance level, i.e. p is less than 0.05, the T test is entered.
The method for acquiring the control factor k comprises the following steps: k is the ratio of the square root of the maximum characteristic root to the minimum characteristic root, and a correlation matrix is calculated through principal component analysis to obtain a control factor k.
Evaluating the fitting effect and estimation precision of the model by using the decision coefficient, the root mean square error and the relative root mean square error:
wherein: r2To determine the coefficients; RMSE is root mean square error; rmse is relative root mean square error; x is the number ofiThe forest stand effective leaf area index measured value is obtained;
the area index of the effective leaves of the forest stand is the actually measured average value;
the method comprises the following steps of (1) obtaining a model estimation value of a forest stand effective leaf area index; n is the number of the same plots; i is a certain pattern.
Has the advantages that: compared with the prior art, the invention has the beneficial effects that:
(1) according to the method, the efficiency and the precision of obtaining the effective leaf area indexes continuously distributed on the surface in a specific range are high by obtaining the unmanned aerial vehicle laser radar point cloud and extracting the canopy characteristic variable and combining ground actual measurement data, and the verification result shows that the determination coefficient is improved by more than 5% by extracting the effective leaf area indexes of the artificial forest compared with other similar remote sensing methods.
(2) The method in the prior art is based on the unmanned aerial vehicle laser radar data, and the density of the data point cloud is low, but the method adopts the unmanned aerial vehicle laser radar data, and then the canopy structure characteristics are obtained based on the high-density point cloud.
(3) The method comprehensively and deeply extracts a plurality of groups of unmanned aerial vehicle laser radar point cloud characteristics of the artificial forest canopy, and performs characteristic variable optimization, thereby extracting the effective leaf area index of the artificial forest stand with high quality.
(4) The invention not only facilitates the mechanism explanation of characteristic variables, but also facilitates the method transplantation (namely, the method can be applied to natural forests and secondary forests).
Drawings
FIG. 1 is a schematic diagram of the ground actual measurement method for effective leaf area index of the present invention;
FIG. 2 is an orthographic view of three exemplary aspects of the present invention;
FIG. 3 is three exemplary spherical mirror images of the present invention;
FIG. 4 is a cloud plot of three exemplary plots of lidar of the present invention;
FIG. 5 is a cross-sectional view of three exemplary sample laser radar point clouds of the present invention;
in FIGS. 2 to 5: a is a first group; b is a second group; c is a third group;
FIG. 6 is a cross-validation result graph of eLAI measured on the ground and eLAI predicted by statistical model method;
in fig. 6: a is based on height characteristic variable modeling; b is modeling based on height + coverage characteristic variables; and c, modeling based on the height + canopy volume characteristic variable.
Detailed Description
The invention is further described with reference to specific examples.
Example 1
The implementation place of the embodiment is located in the iron-rich town of the Tsunzhou city in the northern region of Jiangsu province, the geographic positions of the Feizhou city are 34 degrees 33 '49 < -34 degrees 34' 23 < -and 118 degrees 05 '1 < -118 degrees 06' 06 < -in the east longitude, the Feizhou climate belongs to the semi-humid and warm-zone monsoon climate, the annual rainfall capacity is about 903mm, the maximum rainfall capacity is concentrated in the plum rain season in 7 and 8 months, the annual average temperature is about 13.9 ℃, the frost-free period is 211 days, and the main soil type is black sticky soil and is acidic. The large-scale planting of gingko in the region begins in 1993, and the total area is about 5400hm2。
The method for inverting the effective leaf area index of the forest by the unmanned aerial vehicle laser radar comprises the following steps:
(1) the method comprises the steps of collecting laser radar data by means of a laser radar sensor carried by a multi-rotor unmanned aerial vehicle, selecting 5 square large sample plots with the radius of 1km in a core distribution area of the ginkgo artificial forest according to historical forest resource survey data and satellite remote sensing image data obtained in the early stage, setting 9 circular small sample plots with the radius of 15m in the 5 sample plots according to a typical sample selection method, locating the center positions of the sample plots through Trimble GeoXH6000Handhelds handheld GPS (combined with a JSCROS wide area difference system), recording tree species in the sample plots with the precision better than 0.5m, counting, measuring the chest diameter and the tree height of each tree, dividing the sample plots into 3 groups according to the size of characteristic quantity of the artificial ginkgo forest Cover calculated by the laser radar (namely the proportion of laser return points higher than 1m in a first echo to all return points), dividing each group of 15 sample plots into two groups (the first group of sample plots: 0.08-0.19; the second group of sample plots: 0.21-0.31; the third group of laser return points account for all return points in the first return), carrying out the ratio of all return points in the return points), carrying out the sample plots, and carrying out effective area analysis by using a light sampling window, and carrying out effective area analysis (combining the following two sample-10) in the sample-10 sample-1-10 sample-10-measuring method, and obtaining two effective area values of the effective area measurement by using the effective peak area measurement method for the effective peak area measurement of the sample plots, wherein the effective peak value of the effective peak area measurement of the effective peak of the sample plots under the sample plots, and the sample plots under the sample-1-10-1-10-1-10.
TABLE 1 summary table of measured forest stand characteristic information in sample plot
(2) Data preprocessing: firstly, removing noise points of original point cloud data of the unmanned aerial vehicle laser radar, removing non-ground points based on an IDW filtering algorithm, then generating a Digital Elevation Model (DEM) (the spatial resolution is 0.5m) by calculating the average value of the laser point heights in each pixel, and carrying out normalization processing on the point cloud through the generated digital terrain model to obtain the normalized point cloud data of the unmanned aerial vehicle laser radar.
(3) And (3) extracting characteristic variables, namely extracting three groups of characteristic variables from the normalized point cloud data, namely percentile height, coverage of each layer, canopy volume and section characteristic variables, wherein the percentile height variables comprise a canopy height distribution percentile (H25, H50, H75 and H95), coverage (CCmean) above the average height of canopy point cloud distribution, and a variation coefficient (Hcv) of canopy point cloud distribution, coverage of each layer, namely, points above the percentage height (30th, 50th, 70th and 90th, namely D3, D5, D7 and D9) of the number of point clouds account for all the point clouds, volume and section characteristic variables of the canopy, namely, a Weibull function is used for fitting the canopy height distribution section to obtain 2 section characteristic quantities α (namely, Weibull α and Weibull β), volume ratio of each structure class of the canopy comprises an open layer, a light transmission layer, four canopy structure classes of a low-light layer and a closed layer, and volume percentage of each canopy structure class (Gaopenlip, Ongphop, Gaphop, and Euphop).
(4) Screening characteristic variables: and screening the characteristic variables through correlation analysis, namely firstly screening the characteristic variables of which the correlation between the characteristic variables is lower than 0.6, and then further screening the characteristic variables of which the correlation with the effective leaf area index of each forest stand is higher than 0.6.
(5) Establishing a model: taking the actually measured effective leaf area index on the ground as a dependent variable, taking the point cloud characteristic variable of the unmanned aerial vehicle laser radar as an independent variable, establishing a multiple regression model, selecting the variable entering the model by using a stepwise regression method, namely performing significance test at a preset F level, and rejecting the variable if the t test does not reach the significance level (p is more than 0.1); the t-test was entered if significant levels were reached (p < 0.05). In order to reduce the correlation between the independent variables, the embodiment calculates the correlation matrix through principal component analysis to obtain a control factor k (i.e. the ratio of the square root of the maximum characteristic root to the minimum characteristic root), and if k is smaller than 30, the model is further selected.
(6) And (5) estimating the effective leaf area index of the artificial forest by using the multiple regression model obtained in the step (5).
The cross validation result of the model for predicting the effective leaf area index by the statistical model method is shown in figure 6. As can be seen from the figure, the accuracy of establishing a model only by the height characteristic variable and the ground actual measurement eLAI is R2＝0.38(rRMSE 54%) (see fig. a); establishing a model (R) by combining the height characteristic variable and the coverage characteristic variable with the ground actual measurement eLAI20.64, rmse 26%) (fig. b); establishing a model (R) by combining a height characteristic variable and a canopy volume characteristic variable with a ground actual measurement eLAI20.61, rmse 28%) (see fig. c).
The present embodiment employs a coefficient of determination (R)2) Root Mean Square Error (RMSE) and relative Root Mean Square Error (RMSE) the effect of regression model fitting and the accuracy of the estimation were evaluated:
in the formula, xiThe forest stand effective leaf area index measured value is obtained;
the area index of the effective leaves of the forest stand is the actually measured average value;
the method comprises the following steps of (1) obtaining a model estimation value of a forest stand effective leaf area index; n is the number of the same plots; i is a certain pattern.
The effective leaf area index estimation model and model prediction accuracy based on different point cloud characteristic variables are shown in table 2, and it can be seen from table 2 that the result of estimating the effective leaf area index by combining the characteristic variables of 'height' with 'coverage' is superior to the estimation result of combining the characteristic of 'height' with 'canopy volume', and the prediction accuracy is the lowest by using the characteristic variables of 'height'. Fig. 2 is an orthographic image of three exemplary panels of the present invention, fig. 3 is a spherical mirror image of three exemplary panels of the present invention, fig. 4 is a laser radar point cloud image of three exemplary panels of the present invention, fig. 5 is a cross-sectional view of a laser radar point cloud of three exemplary panels of the present invention, in fig. 2-5, a is a first panel, b is a second panel, and c is a third panel. As can be seen from FIGS. 2 to 5, the ginkgo artificial forest under the same growth and operation conditions has different results on the orthoscopic image, the spherical mirror image, the three-dimensional point cloud and the point cloud profile. Meanwhile, the quantiles of 50th, 75th and 95th in the point cloud vertical distribution are also distributed in the ginkgo artificial forest under different growth conditions, and the general trend of the quantiles is towards the upper layer of the canopy.
TABLE 2 estimation model of effective leaf area index based on different point cloud characteristic variables and model prediction precision
Note: h25, H50, H75 and H95 are height distribution percentiles of 25%, 50%, 75% and 95% of the canopy; d5, D7 is the percentage of the number of point clouds with points at the percentage heights 50th and 70th to all point clouds.
Claims (7)
1. A method for inverting effective leaf area indexes of an artificial forest by using an unmanned aerial vehicle laser radar is characterized in that the original point cloud data of the unmanned aerial vehicle laser radar is subjected to normalization processing, canopy structure characteristic variables are extracted from the normalized point cloud data, and the effective leaf area indexes of sample land scales in a research area are estimated by using a statistical model method on the basis of screening the characteristic variables by combining ground actual measurement data and the extracted characteristic variables; the method comprises the following steps:
1. collecting laser radar data by a laser radar sensor carried by a multi-rotor unmanned aerial vehicle, setting a sample plot on the ground, recording tree species in the sample plot, counting, measuring the breast height and the height of each tree, and measuring the effective leaf area index;
2. filtering and interpolating original point cloud data of the laser radar to generate a digital elevation model, and performing normalization processing on the point cloud data through the generated digital elevation model;
3. extracting a percentile height variable, coverage of each layer, volume of a canopy and a profile characteristic variable from the normalized point cloud data;
4. screening characteristic variables through correlation analysis;
5. taking an actually measured effective leaf area index on the ground as a dependent variable, taking a point cloud characteristic variable of the unmanned aerial vehicle laser radar as an independent variable, establishing a multiple regression model, selecting variables entering the model by using a stepwise regression method, reducing the correlation among the independent variables by using a control factor k, and further selecting the model if k is less than 30;
6. estimating the effective leaf area index of the artificial forest by using the multiple regression model obtained in the step 5;
the method for measuring the effective leaf area index comprises the following steps: selecting two 30m measuring lines in the direction perpendicular to the sunlight, wherein the sample plot is circular, the middle point of each measuring line is 7.5m away from the center of the sample plot, placing a canopy analyzer at a position 1m away from the ground surface for measurement, wherein the canopy analyzer is LAI-2200, matching two probe rods under a forest window, using 90-degree visual angle covers, and enabling the directions to be consistent, placing one probe rod under the forest window for sampling once every 10s to obtain a value A, placing the other probe rod into the sample plot to be measured, and sampling once every 4m along the measuring lines to obtain a value B; and matching the A value and the B value through time, and jointly calculating the effective leaf area index.
2. The method for inverting the effective leaf area index of the forest by the unmanned aerial vehicle laser radar according to claim 1, wherein the method for normalizing the original point cloud data of the unmanned aerial vehicle laser radar comprises the following steps: the method comprises the steps of firstly removing noise points of original point cloud data of the unmanned aerial vehicle laser radar, removing non-ground points based on an IDW filtering algorithm, then generating a digital elevation model by calculating the average value of the heights of laser points in each pixel, and carrying out normalization processing on the point cloud through the generated digital elevation model to obtain the normalized point cloud data of the unmanned aerial vehicle laser radar.
3. The method for inverting effective leaf area index of artificial forest by unmanned aerial vehicle laser radar as claimed in claim 1, wherein the percentile height variable comprises a canopy height distribution percentile, a coverage above the average height of canopy point cloud distribution and a variation coefficient of canopy point cloud distribution, the coverage of each layer is the percentage of points with the number above each percentage height of the point clouds to all the point clouds, and the canopy volume and profile characteristic variable is Weibull function to fit the canopy height distribution profile to obtain 2 profile characteristic quantities α and β and the volume percentage of four canopy structure categories of open layer, euphotic layer, low-light layer and closed layer.
4. The method for inverting effective leaf area index of forest by unmanned aerial vehicle lidar according to claim 1, wherein the method of screening the characteristic variables is: firstly, screening characteristic variables with the correlation between the characteristic variables lower than 0.6, and then further screening the characteristic variables with the correlation between the characteristic variables and the effective leaf area index higher than 0.6.
5. The method for inverting effective leaf area index of forest by unmanned aerial vehicle lidar according to claim 1, wherein the method for selecting the variables entering the model using stepwise regression method is: the significance test is carried out at a predetermined F level, and if the t test does not reach the significance level, i.e. p is greater than 0.1, the T test is rejected, and if the t test reaches the significance level, i.e. p is less than 0.05, the T test is entered.
6. The method for inverting the effective leaf area index of the forest by the unmanned aerial vehicle laser radar according to claim 1, wherein the method for obtaining the control factor k is as follows: k is the ratio of the square root of the maximum characteristic root to the minimum characteristic root, and a correlation matrix is calculated through principal component analysis to obtain a control factor k.
7. The method for inverting effective leaf area index of artificial forest by unmanned aerial vehicle laser radar according to claim 1, wherein the effect of model fitting and the estimation accuracy are evaluated by using decision coefficient, root mean square error, relative root mean square error:
wherein: r2To determine the coefficients; RMSE is root mean square error; rmse is relative root mean square error; x is the number ofiThe forest stand effective leaf area index measured value is obtained;
the area index of the effective leaves of the forest stand is the actually measured average value;
the method comprises the following steps of (1) obtaining a model estimation value of a forest stand effective leaf area index; n is the number of the same plots; i is a certain pattern.
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| CN111950336B (en) * | 2020-04-14 | 2021-07-20 | 成都理工大学 | Vegetation canopy ecological water estimation method based on backpack type laser radar |
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