CN108896021B - Method for extracting artificial forest stand structure parameters based on aerial photogrammetry point cloud - Google Patents
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Abstract
The invention discloses a method for extracting a structure parameter of an artificial forest stand based on aerial photogrammetry point cloud, which comprises the steps of filtering discrete point cloud data of an airborne laser radar, generating a digital terrain model through interpolation, and carrying out normalization processing on the point cloud data; extracting and matching the characteristic points of the true color image pair, performing space-three encryption to generate aerial photogrammetry point cloud, and performing normalization processing on aerial photogrammetry point cloud data by using the generated digital terrain model; extracting characteristic variables based on the normalized aerial photogrammetry point cloud; and respectively constructing a multiple regression model by combining the ground measured data and the extracted characteristic variables to predict the structural characteristics of each forest stand. High-overlapping-degree image data efficiently acquired by an unmanned aerial vehicle and three-dimensional point cloud extracted from image pairs by means of a stereo photogrammetry method are used, so that three-dimensional structural features of forest canopy layers are acquired, the inversion accuracy of structural parameters of artificial forest stands is improved, and the problem of structural parameter inversion saturation of forest stands with high forest coverage and high biomass is effectively solved.
Description
Technical Field
The invention belongs to the technical field of forest resource detection, and particularly relates to a method for extracting structural parameters of an artificial forest stand based on aerial photogrammetry point cloud.
Background
The method has the advantages that the accurate parameter extraction of the structure of the forest stand of the artificial forest is significant for forest resource monitoring, ecological factor investigation and biodiversity research. Meanwhile, the information can also be used for mastering the forest space structure and the dynamic change rule and providing data support for forest management and management, ecological environment modeling and carbon cycle analysis. The conventional artificial forest stand structure parameter extraction mainly depends on field investigation, aerial photograph or satellite photograph interpretation and the like, the precision is often not high, and the practical popularization on the aspect is difficult.
In recent years, the research of forest stand structure parameter extraction based on aerial photogrammetry data is as follows: in Zhang et al 2016, "surveying the forest from humans," Biological survey of light weight humans as a tool for long-term forest survey ", published in volume 198, the research used to extract forest structure parameters of subtropical forests by extracting crown and terrain variables from true color image data acquired by unmanned aerial vehicles and combining measured ground data based on the evaluation of the importance of these variables. In the research, Wangmei et al published in 2017 on volume 4 of forestry resource management, "automatic extraction of crown parameter information of sub-high mountain conifer forest based on visible light images of unmanned aerial vehicles", wherein the unmanned aerial vehicles are adopted to obtain visible light remote sensing images, crown information is obtained based on an object-oriented method, and forest stand structure parameters of the sub-high mountain conifer forest are estimated according to the information. The study of the evergreen broad-leaved forest window distribution pattern and the cause thereof in subtropical evergreen broad-leaved forest in southern mountains and lakes and the like in 2017 of biodiversity, namely Suidan and the like, adopts images acquired by an unmanned aerial vehicle and estimates forest stand structure parameters by combining a wave band operation and supervision classification method. However, the above methods are all based on the feature extraction of the spliced two-dimensional images, and the three-dimensional structural features of the canopy are not extracted for extracting the structural parameters of the forest stand. Meanwhile, a method for comprehensively and deeply calculating the point cloud characteristics of the aerial photogrammetry of the artificial forest canopy and extracting the structural parameters of the forest stand is not available.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects in the prior art, the invention aims to provide a method for extracting the structural parameters of the forest stand of the artificial forest based on aerial photogrammetry point cloud.
The technical scheme is as follows: in order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
a method for extracting artificial forest stand structure parameters based on aerial photogrammetry point cloud comprises the following steps:
(1) setting a sample plot on the ground, and acquiring high-overlapping-degree image data and LiDAR (light detection and ranging) original point cloud data by means of an unmanned aerial vehicle; recording tree species in the sample plot, counting, and measuring the breast diameter and the tree height of each tree;
(2) filtering and interpolating LiDAR original point cloud data to generate a digital terrain model; processing the image data to generate aerial photogrammetry point cloud; normalizing the aerial photogrammetry point cloud data by using the generated digital terrain model to obtain a normalized aerial photogrammetry point cloud;
(3) extracting a structural characteristic variable of the artificial canopy layer based on the normalized aerial photogrammetry point cloud;
(4) screening characteristic variables through correlation analysis;
(5) and (3) taking the ground actual measurement forest stand structure parameters as dependent variables, taking aerial photogrammetry point cloud characteristic variables as independent variables, and establishing a multiple regression model to predict the forest stand structure parameters of each artificial forest stand.
Further, in the step (1), high-overlapping-degree image data are collected by means of a fixed-wing unmanned aerial vehicle, and LiDAR raw point cloud data collection is carried out by means of a LiDAR sensor carried by a multi-rotor unmanned aerial vehicle; and estimating the accumulation amount by combining an unary volume formula with the actually measured breast height, and calculating the aboveground biomass by combining the breast height and the tree height through a different-speed growth equation.
Further, in the step (2), firstly, noise points of LiDAR original point cloud data are removed, non-ground points are removed based on an IDW filtering method, and then a digital terrain model is generated by calculating the average value of the heights of the laser points in each pixel.
Further, in the step (2), the processing of the image data includes feature point extraction and matching, aerial triangulation and three-dimensional point cloud encryption.
Further, shooting digital images of a research area through a remote sensing platform, and recording the attitude parameters of each image in real time through an Inertial Measurement Unit (IMU); then, acquiring characteristic points of an image pair, matching the characteristic points, analyzing the inside and outside orientation elements of the image by a light beam method, and generating aerial photogrammetry point cloud by combining aerial triangulation; on the basis of aerial photogrammetry point cloud encryption, a ground control point is added to correct the spatial position of the aerial photogrammetry point cloud, and the aerial photogrammetry point cloud is normalized through the generated digital terrain model to obtain normalized aerial photogrammetry point cloud data.
Further, the structural characteristic variables of the artificial forest canopy in the step (3) comprise three groups, namely a percentile height variable, a coverage variable of each layer and a canopy volume and profile characteristic variable.
Further, the percentile height variable comprises: canopy height distribution percentile, coverage above canopy point cloud distribution average height, and variation coefficient of canopy point cloud distribution;
the coverage variable of each layer is the percentage of points with the number of point clouds above the height of each percentage in all the point clouds;
the canopy volume and profile characteristic variables are obtained by fitting a canopy height distribution profile by utilizing a Weibull function to obtain 2 profile characteristic quantities Weibull α and Weibull β, the volume percentage of each structure type of the canopy comprises four canopy structure types of an open layer, a light-transmitting layer, a low-light layer and a closed layer, and the volume percentage of each canopy structure type.
Further, in the step (4), firstly, the characteristic variables with the correlation between the characteristic variables lower than 0.6 are screened, and then the characteristic variables with the correlation between the characteristic variables and the forest stand structure parameters higher than 0.6 are further screened.
Further, in the step (5), selecting variables entering the model by using a stepwise regression method, namely performing significance test under a preset F level, and rejecting the variables if the t test does not reach the significance level p > 0.1; if the t test reaches the significant level p less than 0.05, entering; and calculating a correlation matrix through principal component analysis to obtain a control factor k, wherein k is the ratio of the square root of the maximum characteristic root to the minimum characteristic root, and if k is less than 30, the model is further selected.
Further, in the step (5), the effect of regression model fitting and the estimation accuracy are evaluated by using the decision coefficient, the root mean square error and the relative root mean square error:
in the formula: x is the number ofiThe measured value of the structural parameter of a certain forest stand;
the measured average value of the structural parameters of a certain forest stand is obtained;
a model estimation value of a certain forest stand structure parameter; 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 following advantages:
the unmanned aerial vehicle data acquisition is flexible, convenient and low in cost, high-overlapping-degree image data are efficiently acquired by the unmanned aerial vehicle, and the three-dimensional point cloud is extracted from the image pair by means of a stereo photogrammetry method, so that the three-dimensional structural characteristics of the forest canopy are acquired, the inversion accuracy of the structural parameters of the forest stand of the artificial forest can be improved, and the inversion saturation problem of the structural parameters of the forest stand with high forest coverage and high biomass can be effectively inhibited. In the prior art, two-dimensional features are directly extracted based on a spliced image, while in the method, three-dimensional point cloud is extracted from an image pair through stereo photogrammetry, and then the canopy structure features are obtained based on the point cloud. Because the forest stand structure parameters and the forest canopy structure characteristics have good correlation and mechanism relation, the method and the device enhance the inversion capability and accuracy of the forest stand structure parameters. The method comprehensively and deeply extracts the characteristics of the multiple groups of artificial forest canopy aerial photogrammetry point clouds and performs characteristic variable optimization, thereby extracting the structural parameters of the artificial forest stand with high quality. Meanwhile, the invention is not only beneficial to the mechanism explanation of characteristic variables, but also easy to carry out method transplantation (namely, the invention can be applied to natural forests and secondary forests). The verification result shows that the determination coefficient of the method for extracting the structural parameters of the main tree species (i.e. the poplar and the metasequoia) of the plain artificial forest is improved by more than 5 percent compared with the forest stand structural parameters by using other similar remote sensing methods.
Drawings
FIG. 1 is an aerial photogrammetry point cloud banding effect map (a);
FIG. 2 is a diagram (b) of aerial photogrammetry and lidar point cloud overlay effects;
FIG. 3 is a side view of three exemplary plot lidar point clouds (a);
FIG. 4 is a top view of three exemplary plot lidar point clouds (b);
FIG. 5 is a three exemplary plot lidar and aerial photogrammetry point cloud profiles (c);
FIG. 6 is a plot (d) of the vertical distribution profile and Weibull curve fit of three typical plot aerial photogrammetry point clouds.
Detailed Description
The present invention will be further illustrated by the following specific examples, which are carried out on the premise of the technical scheme of the present invention, and it should be understood that these examples are only for illustrating the present invention and are not intended to limit the scope of the present invention.
The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetry point cloud mainly comprises the following steps:
(1) setting a sample plot on the ground, and acquiring high-overlapping-degree image data and LiDAR (light detection and ranging) original point cloud data by means of an unmanned aerial vehicle; recording tree species in the sample plot, counting, and measuring the breast diameter and the tree height of each tree;
(2) filtering and interpolating LiDAR original point cloud data to generate a digital terrain model; processing the image data to generate aerial photogrammetry point cloud;
(3) normalizing the aerial photogrammetry point cloud data by using the generated digital terrain model to obtain a normalized aerial photogrammetry point cloud;
(4) extracting a structural characteristic variable of the artificial canopy layer based on the normalized aerial photogrammetry point cloud;
(5) screening characteristic variables through correlation analysis;
(6) and (3) taking the ground actual measurement forest stand structure parameters as dependent variables, taking aerial photogrammetry point cloud characteristic variables as independent variables, and establishing a multiple regression model to predict the forest stand structure parameters of each artificial forest stand.
Specifically, in the step (1), high-overlapping-degree image data are acquired by means of a fixed-wing unmanned aerial vehicle, and LiDAR original point cloud data are acquired by using a LiDAR sensor carried by a multi-rotor unmanned aerial vehicle; a LiDAR sensor, i.e., a laser radar Ranging sensor (Light Detection And Ranging-LiDAR). Setting a sample plot on the ground, recording tree species in the sample plot, counting, and measuring the breast diameter and the tree height of each tree; and estimating the accumulation amount by combining an unary volume formula with the actually measured breast height, and calculating the aboveground biomass by combining the breast height and the tree height through a different-speed growth equation.
Step (2), filtering and interpolating point cloud data of the LiDAR raw point to generate a digital terrain model; processing the image data to generate aerial photogrammetry point cloud; and normalizing the aerial photogrammetry point cloud data by using the generated digital terrain model to obtain a normalized aerial photogrammetry point cloud.
During data preprocessing, noise points of LiDAR original point cloud data are removed, non-ground points are removed through filtering, and then a Digital Terrain Model (DTM) (the spatial resolution is 0.5m) is generated through calculating the average value of the heights of laser points in each pixel.
Further processing of the image data includes feature point extraction and matching, aerial triangulation, and three-dimensional point cloud encryption. Firstly, shooting digital images of a research area through a remote sensing platform (namely an unmanned aerial vehicle provided with a remote sensor in the invention), and recording the attitude parameters of each image in real time through an Inertial Measurement Unit (IMU); then, acquiring characteristic points of an image pair, matching the characteristic points, analyzing the inside and outside orientation elements of the image by a light beam method, and generating aerial photogrammetry point cloud by combining aerial triangulation; on the basis of aerial photogrammetry point cloud encryption, a ground control point is added to correct the spatial position of the aerial photogrammetry point cloud, and the aerial photogrammetry point cloud is normalized through the generated digital terrain model to obtain normalized aerial photogrammetry point cloud data.
Step (3), extracting a plurality of groups of artificial canopy structure characteristic variables based on the normalized aerial photogrammetry point cloud;
the structural characteristic variables of the artificial forest canopy comprise three groups, namely a percentile height variable, a coverage variable of each layer and a canopy volume and profile characteristic variable.
The percentile height variables include: canopy height distribution percentiles (H25, H50, H75, H95), coverage (CCmean) above the canopy point cloud distribution average height, and coefficient of variation (Hcv) of canopy point cloud distribution;
each layer coverage variable: the point with the point cloud number above each percentage height (30th, 50th, 70th, 90th, i.e. D3, D5, D7, D9) accounts for the percentage of all the point clouds;
crown volume and profile characteristic variables Weibull function fitting the crown height distribution profile to obtain 2 profile characteristic quantities α (namely Weibull α and Weibull β), wherein the volume ratio of each structural class of the crown comprises four crown structural classes of an open layer, a light-transmitting layer, a low-light layer and a closed layer, and the volume of each crown structural class is percentage (namely OpenGap, Oligophoric, European and ClosedGap).
Step (4), screening characteristic variables through correlation analysis;
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 forest stand structure parameters higher than 0.6.
And (5) taking the ground actual measurement forest stand structure parameters as dependent variables, taking aerial photogrammetry point cloud characteristic variables as independent variables, and establishing a multivariate regression model.
Selecting variables entering the model by using a stepwise regression method, namely performing significance test under a preset F level, and rejecting the variables if the t test does not reach the significance level p which is more than 0.1; if the t test reaches the significant level p less than 0.05, entering; and calculating a correlation matrix through principal component analysis to obtain a control factor k, wherein k is the ratio of the square root of the maximum characteristic root to the minimum characteristic root, and if k is less than 30, the model is further selected. Wherein F represents the F test, i.e., the homogeneity test of variance. It is a test where the statistical values obey the F-distribution under the null hypothesis. t represents t test, which means to deduce the probability of occurrence of difference by t distribution theory, and thus to compare whether the difference of two averages is significant or not.
Using 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: x is the number ofiThe measured value of the structural parameter of a certain forest stand;
the measured average value of the structural parameters of a certain forest stand is obtained;
a model estimation value of a certain forest stand structure parameter; n is the number of the same plots; i is a certain pattern.
The application is further explained by taking the yellow sea national forest park in the salt city of Jiangsu province as a research area:
the research area is located in the yellow sea national forest park in the salt city of Jiangsu province, and the geographic position of the research area is 32 degrees 33 'to 32 degrees 57' in the north latitude and 120 degrees 07 'to 120 degrees 53' in the east longitude. The terrain in east China is smooth, the ground elevation is 1.4-5.1 m, and most areas are 2.6-4.6 m. The area belongs to a typical northern subtropical monsoon climate area, has obvious transitional, oceanic and monsoon climates, and is full of sunshine. The average annual air temperature is 14.5 ℃, the average annual illumination total number is 2169.6h, the sunshine rate is 51%, the frost-free period is 225d, the rainfall is 1051mm, and the soil texture is sandy soil. The forest coverage rate of the forest farm is about 85%, the forest type is artificial forest, and the main tree species are Metasequoia and poplar (Populus).
And (1) acquiring high-overlapping-degree image data by means of a fixed-wing unmanned aerial vehicle, and acquiring LiDAR data by using a LiDAR sensor carried by a multi-rotor unmanned aerial vehicle. 44 circular plots (26 poplar, 18 fir) each with a diameter of 30m were set in the area of the study. The coordinates of the center of the sample are measured by using a GPS (TrimbleGeoXH6000), and the GPS is positioned by receiving a wide area differential signal, and the precision is better than 0.5 m. And the tree species were recorded and counted in the plot while the breast diameter and tree height were measured for each tree. The accumulation amount is estimated by combining a unitary volume formula with the actually measured breast diameter, and the aboveground biomass is calculated by combining the breast diameter and the tree height through a different-speed growth equation. According to the survey data of the single trees, summarizing forest stand structure parameters of sample plot scale, including forest stand density, average breast diameter, breast height cross-sectional area, average tree height, accumulation amount and aboveground biomass, and referring to table 1:
TABLE 1 summary table of measured forest stand characteristic information in sample plot
The estimation model of the structural parameters of each forest stand and the prediction precision of the model are obtained by calculation in the steps (2) to (5) of the application, and the concrete results are shown in table 2. Comparison of the effects of lidar point clouds and aerial photogrammetry point clouds, as well as aerial photogrammetry point cloud vertical distribution profiles and Weibull curve fitting effects for three representative plots (1, 2 and 3) are shown in figures 3-6.
TABLE 2 estimation model of forest stand structure parameters and model prediction accuracy
| Stand characteristics | Intercept of a beam | H25 | H50 | H75 | H95 | D5 | D7 | α | β | R2 | rRMSE(％) |
| Density of forest stand | 7239.59 | -168.72 | -1879.87 | -6902.28 | 0.48 | 53.97 | |||||
| Mean chest diameter | -7.64 | 1.25 | 2.71 | 19.95 | 0.72 | 22.42 | |||||
| Chest height cross-sectional area | 23.89 | 0.37 | -5.18 | -1.68 | 0.52 | 41.57 | |||||
| Mean tree height | -4.41 | 1.18 | 15.71 | 0.26 | 0.83 | 25.28 | |||||
| Accumulated amount of | 81.75 | -3.02 | 5.99 | 62.97 | 0.66 | 47.66 | |||||
| Aboveground biomass | 70.73 | 0.74 | 13.55 | -3.53 | 0.63 | 40.73 |
Note that H25, H50, H75 and H95 are height distribution percentiles of 25%, 50%, 75% and 95% of the canopy, D5 and D7 are percentages of points of the number of point clouds on the percentage heights of 50th and 70th in all the point clouds, α is 2 section characteristic quantities obtained by fitting the canopy height distribution section through a Weibull function, R2 is a determination coefficient, and rRMSE is a relative root mean square error.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (7)
1. A method for extracting forest stand structure parameters based on aerial photogrammetry point cloud is characterized by comprising the following steps:
(1) setting a sample plot on the ground, and acquiring high-overlapping-degree image data and LiDAR (light detection and ranging) original point cloud data by means of an unmanned aerial vehicle; recording tree species in the sample plot, counting, and measuring the breast diameter and the tree height of each tree; estimating the accumulation amount by combining an unary volume formula with the actually measured breast diameter, calculating the aboveground biomass by combining a different-speed growth equation with the breast diameter and the tree height, and summarizing forest stand structure parameters of the sample plot scale according to the single tree survey data, wherein the forest stand structure parameters comprise forest stand density, average breast diameter, breast height cross-sectional area, average tree height, accumulation amount and aboveground biomass;
(2) filtering and interpolating LiDAR original point cloud data to generate a digital terrain model; processing the image data to generate aerial photogrammetry point cloud; normalizing the aerial photogrammetry point cloud data by using the generated digital terrain model to obtain a normalized aerial photogrammetry point cloud;
(3) the method comprises the steps of extracting artificial canopy structure characteristic variables based on normalized aerial photogrammetry point clouds, wherein the artificial canopy structure characteristic variables comprise three groups which are respectively a percentile height variable, a coverage degree variable of each layer, a canopy volume and a section characteristic variable, fitting a canopy height distribution section by using a Weibull function to obtain 2 section characteristic quantities α and β, the volume ratio of each structure type of the canopy comprises four canopy structure types of an open layer, a light-transmitting layer, a low-light layer and a closed layer, and the volume ratio of each canopy structure type.
(4) Screening characteristic variables through correlation analysis, 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 each forest stand structure parameter is higher than 0.6.
(5) Taking the ground actual measurement forest stand structure parameters as dependent variables, taking aerial photogrammetry point cloud characteristic variables as independent variables, and establishing a multiple regression model to predict the forest stand structure parameters of each artificial forest stand;
selecting variables entering the model by using a stepwise regression method, performing significance test at a preset F level, and rejecting the variables if the t test does not reach the significance level p which is more than 0.1; if the t test reaches the significant level p less than 0.05, entering; and calculating a correlation matrix through principal component analysis to obtain a control factor k, wherein k is the ratio of the square root of the maximum characteristic root to the minimum characteristic root, and if k is less than 30, the model is further selected.
2. The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetric point cloud according to claim 1, wherein the structural parameters comprise: in the step (1), high-overlapping-degree image data are collected by means of a fixed-wing unmanned aerial vehicle, and LiDAR primary point cloud data collection is carried out by using a LiDAR sensor carried by a multi-rotor unmanned aerial vehicle; and estimating the accumulation amount by combining an unary volume formula with the actually measured breast height, and calculating the aboveground biomass by combining the breast height and the tree height through a different-speed growth equation.
3. The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetric point cloud according to claim 1, wherein the structural parameters comprise: in the step (2), noise points of LiDAR original point cloud data are removed, non-ground points are removed based on a filtering method, and then a digital terrain model is generated by calculating the average value of the heights of laser points in each pixel.
4. The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetric point cloud according to claim 1, wherein the structural parameters comprise: in the step (2), the image data processing comprises feature point extraction and matching, aerial triangulation and three-dimensional point cloud encryption.
5. The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetry point cloud as claimed in claim 4, wherein: firstly, shooting digital images of a research area through a remote sensing platform, and recording the attitude parameters of each image in real time through an inertial measurement unit; then, acquiring characteristic points of an image pair, matching the characteristic points, analyzing the inside and outside orientation elements of the image by a light beam method, and generating aerial photogrammetry point cloud by combining aerial triangulation; on the basis of aerial photogrammetry point cloud encryption, a ground control point is added to correct the spatial position of the aerial photogrammetry point cloud, and the aerial photogrammetry point cloud is normalized through the generated digital terrain model to obtain normalized aerial photogrammetry point cloud data.
6. The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetric point cloud according to claim 5, wherein the structural parameters comprise: 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 the canopy point cloud distribution;
and the coverage variable of each layer is the percentage of points with the number of the point clouds above the height of each percentage in all the point clouds.
7. The method for extracting the structural parameters of the forest stand of the artificial forest based on the aerial photogrammetric point cloud according to claim 1, wherein the structural parameters comprise: in the step (5), the effect of fitting the regression model and the estimation precision are evaluated by adopting a decision coefficient R2, a root mean square error RMSE and a relative root mean square error rRMSE:
in the formula: x is the number ofiThe measured value of the structural parameter of a certain forest stand;
the measured average value of the structural parameters of a certain forest stand is obtained;
a model estimation value of a certain forest stand structure parameter; n is the number of the same plots; i is a certain pattern.
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| RU2773144C1 (en) * | 2021-06-29 | 2022-05-31 | Федеральное государственное бюджетное образовательное учреждение высшего образования "Московский государственный университет имени М.В. Ломоносова" (МГУ) | A method for determining the stocks of stem wood using aerial unmanned survey data |
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