CN108981616A - A method of by unmanned plane laser radar inverting artificial forest effective leaf area index - Google Patents
A method of by unmanned plane laser radar inverting artificial forest effective leaf area index Download PDFInfo
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- CN108981616A CN108981616A CN201810930500.3A CN201810930500A CN108981616A CN 108981616 A CN108981616 A CN 108981616A CN 201810930500 A CN201810930500 A CN 201810930500A CN 108981616 A CN108981616 A CN 108981616A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/28—Measuring arrangements characterised by the use of optical techniques for measuring areas
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
Abstract
The invention discloses a kind of methods based on unmanned plane laser radar empirical model inverting artificial forest effective leaf area index, belong to forest inventory investigation, Forest Site Quality Evaluation and Forest Productivity estimation research field.Unmanned plane laser radar original point cloud data is normalized the present invention, canopy structure characteristic variable is extracted from normalization point cloud data, the characteristic variable of combined ground measured data and extraction, using statistical model method, to sample in research area the effective leaf area index of scale is estimated on the basis of screening characteristic variable.The present invention is by obtaining unmanned plane laser radar point cloud and extracting canopy characteristic variable, and combined ground measured data, efficiency and the precision for obtaining effective leaf area index continuously distributed on " face " of particular range are relatively high, artificial forest effective leaf area index is extracted through the invention, compared with using other close remote sensing techniques, the coefficient of determination improves 5% or more.
Description
Technical field
The invention belongs to forest inventory investigation, Forest Site Quality Evaluation and Forest Productivities to estimate research field, more
It says to body, is related to a kind of method by unmanned plane laser radar inverting artificial forest effective leaf area index.
Background technique
Accurate artificial forest effective leaf area index extracts, for forest inventory investigation, Forest Site Quality Evaluation and gloomy
The estimation of woods productivity is significant, while these information can be used for grasping Forest Canopy space distribution rule, and to gloomy
Woods Sustainable Operation, eco-environment restoration and reconstruction and maintenance Carbon balance provide data and support.The effective blade face of traditional artificial forest
Product exponent extracting depends on direct method and apparatus measures, and time and effort consuming is surveyed on these ground, and efficiency is very low, some direct methods
(such as fresh weight punch method retouches shape weight method) can cause Forest Canopy centainly to destroy toward contact, and with can only obtaining sample scale
Information, it is more difficult to obtain continuous leaf area index distribution on a large scale.
In recent years, the research of the standing forest effective leaf area index inverting based on airborne laser radar technology are as follows: Lim etc. 2003
" the LiDAR remote sensing year delivered on " Canadian Journal of Remote Sensing " volume 29
Of biophysical properties of tolerant northern hardwood forests ", the research use machine
Small light spot laser radar data, extraction height percentile feature variable are carried, combined ground surveys effective leaf area index data,
Statistical model is constructed, the broad-leaf forest effective leaf area index of Canadian northern territory is estimated.Morsdorf etc. 2006 exists
" the Estimation of LAI and delivered on " Remote Sensing of Environment " volume 104
fractional cover from small footprint airborne laser scanning data based on
Gap fraction ", the discrete point cloud data which obtains by laser radar are returned by with calculating sample internal point sansan layer
The ratio (point sansan layer penetrability) of wave number and ground echo number, and the effective of Switzerland lycopod forest has been estimated in conjunction with Beer law
Leaf area index.However above method all has man-machine laser radar data to go to estimate with single characteristic variable using low-density
Effective leaf area index is surveyed, has no that highdensity unmanned plane laser radar point cloud data is applied to effective leaf area index inverting,
Meanwhile more having no the method for deeply calculating unmanned plane laser radar point cloud feature and effective leaf area index extraction comprehensively.
Summary of the invention
Goal of the invention: in view of the above-mentioned problems existing in the prior art, the purpose of the present invention is to provide one kind to pass through nobody
Machine laser radar point cloud extracts canopy characteristic variable, and the side of combined ground measured data inverting artificial forest effective leaf area index
Method.
Technical solution: to solve the above-mentioned problems, the technical solution adopted in the present invention is as follows:
A method of it is by unmanned plane laser radar inverting artificial forest effective leaf area index, unmanned plane laser radar is former
Beginning point cloud data is normalized, and canopy structure characteristic variable, combined ground actual measurement are extracted from normalization point cloud data
The characteristic variable of data and extraction utilizes statistical model method to sample in research area scale screen characteristic variable on the basis of
Effective leaf area index is estimated.
The following steps are included:
(1) laser radar data acquisition is carried out by the laser radar sensor that multi-rotor unmanned aerial vehicle is carried, is set on ground
With setting sample, and in sample ground tree species are recorded and are counted, while measuring the diameter of a cross-section of a tree trunk 1.3 meters above the ground and tree height of every wood, and measure Efficient leaf area and refer to
Number；
(2) filtering of laser radar original point cloud data, interpolation are generated into digital elevation model, passes through the digital elevation of generation
Point cloud data is normalized in model；
(3) percentile height variable, each layer coverage, canopy volume and profile features are extracted from normalization point cloud data
Variable；
(4) characteristic variable is screened by correlation analysis；
(5) using ground actual measurement effective leaf area index as dependent variable, unmanned plane laser radar point cloud characteristic variable conduct
Independent variable establishes multivariate regression models, and the variable of model is entered with method of gradual regression selection, is reduced certainly by controlling elements k
Correlation between variable, less than 30 models of k are further selected；
(6) multivariate regression models obtained using step 5 estimates artificial forest effective leaf area index.
Wherein:
The method for measuring effective leaf area index are as follows: select two 30m surveys line on the direction perpendicular to solar irradiation, two
Away from center of circle 7.5m, the position that Canopy Analyzer is placed in from earth's surface 1m high is measured respectively for survey line midpoint, first will under woods window
Two feeler levers are matched, and 90 ° of visual angle lids are used, and direction is consistent, wherein a feeler lever will be placed under woods window and be adopted every 10s
Sample is primary, obtains A value, and another feeler lever is brought into sample primary, acquisition B value every 4m along survey line in test sample ground；When passing through
Between A value and B value are matched, combined calculation effective leaf area index.
The Canopy Analyzer is LAI-2200.
The method that unmanned plane laser radar original point cloud data is normalized are as follows: removal unmanned plane laser first
The noise point of radar original point cloud data removes non-ground points based on IDW filtering algorithm, is then swashed by calculating in each pixel
The average value of luminous point height generates digital elevation model, a cloud is normalized by the digital elevation model of generation,
Unmanned plane laser radar point cloud data after being normalized.
The percentile height variable includes canopy height distribution percentile, and canopy point cloud is distributed average height or more
The coefficient of variation of coverage and the distribution of canopy point cloud；Each layer coverage is point of the point cloud quantity more than each percentage height
Account for the percentage of all the points cloud；The canopy volume and profile features variable are Weibull function to canopy height profile
It is fitted to obtain 2 profile features amounts α, β and four open tier, photic zone, low photosphere and confining bed canopy structure classifications
Volume percentage.
The method of the screening characteristic variable are as follows: correlation is lower than 0.6 characteristic variable first between screening characteristic variable,
Then the characteristic variable that characteristic variable is higher than 0.6 with effective leaf area index correlation is further screened.
Enter the method for the variable of model with method of gradual regression selection are as follows: carry out in the case where previously given F is horizontal significant
Property examine, if t inspection the level of signifiance, i.e. p > 0.1 is not achieved, then rejected, if t inspection reach the level of signifiance, i.e. p
< 0.05, then entered.
The method for obtaining controlling elements k are as follows: k is the square root of Maximum characteristic root and the ratio of smallest real eigenvalue, passes through master
Constituent analysis calculates correlativity matrix and obtains controlling elements k.
Using the coefficient of determination, root-mean-square error, the effect and estimation precision of opposite root-mean-square error evaluation model fitting:
Wherein: R2For the coefficient of determination；RMSE is root-mean-square error；RRMSE is opposite root-mean-square error；xiIt is effective for standing forest
Leaf area index measured value；Average value is surveyed for standing forest effective leaf area index；For the model of standing forest effective leaf area index
Estimated value；N for sample ground quantity；I is for some sample.
The utility model has the advantages that compared with the prior art, the invention has the benefit that
(1) present invention is by obtaining unmanned plane laser radar point cloud and extracting canopy characteristic variable, and combined ground is surveyed
Data, efficiency and the precision for obtaining effective leaf area index continuously distributed on " face " of particular range are relatively high, verifying knot
Fruit shows through the invention to extract artificial forest effective leaf area index, compared with using other close remote sensing techniques,
The coefficient of determination improves 5% or more.
(2) method of the prior art has been all based on man-machine laser radar data, and point cloud data density is low, and of the invention
Method is then to be then based on highdensity cloud using unmanned plane laser radar data and obtained canopy structure feature.
(3) method of the invention is in depth extracted the artificial storey unmanned plane laser radar point cloud feature of multiple groups comprehensively,
And it is preferred to have carried out characteristic variable, to be extracted plantation stand effective leaf area index in high quality.
(4) present invention not only conducive to characteristic variable mechanism explain, be also easy to the transplanting of carry out method (i.e. wildwood and time
Raw Lin Zhongye may be employed).
Detailed description of the invention
Fig. 1 is effective leaf area index ground measurement method schematic diagram of the invention；
Fig. 2 is the orthography on three groups of typical sample ground of the invention；
Fig. 3 is the spherical mirror image on three groups of typical sample ground of the invention；
Fig. 4 is the laser radar point cloud atlas on three groups of typical sample ground of the invention；
Fig. 5 is the laser radar point cloud sectional view on three groups of typical sample ground of the invention；
In Fig. 2~5: a is first group；B is second group；C is third group；
Fig. 6 is that eLAI is surveyed on ground and statistical model method predicts eLAI cross validation results figure；
In Fig. 6: a is based on altitude feature variable modeling；B is to be modeled based on height+coverage characteristic variable；C be based on
Highly+canopy volumetric features variable modeling.
Specific embodiment
The present invention is further described below combined with specific embodiments below.
Embodiment 1
The enforcement place of the present embodiment is located at Jiangsu Province northern territory Pizhou City town Tie Fu, and 34 ° 33 ' of geographical location north latitude
49 " -34 ° 34 ' 23 ", belong to subhumid and temperate zones monsoon climate, annual rainfall is about by 118 ° 05 ' 1 of east longitude " -118 ° 06 ' 06 "
903mm, maximum rainfall concentrate on 7, August part plum rain season, and year-round average temperature is about 13.9 DEG C, and frost-free period 211 days, main soil
Earth type is smolmitza earth, in acidity.This area's ginkgo large-scale plantation starts from 1993, the gross area about 5400hm2。
The method by unmanned plane laser radar inverting artificial forest effective leaf area index of the present embodiment, including following step
It is rapid:
(1) data acquire: carrying out laser radar data acquisition by the laser radar sensor that multi-rotor unmanned aerial vehicle is carried.
The satellite remote-sensing image data obtained according to history forest resource survey data and early period are in ginkgo artificial forest Core distribution area
The square big plot of 5 pieces of 1 × 1km is had chosen, 9 pieces of radiuses then, which are arranged, according to the method for typical sampling in 5 sample ground is
The round sample of 15m, the center on sample ground by the positioning of Trimble GeoXH6000Handhelds handhold GPS (in conjunction with
JSCROS WAAS-Wide Area Augmentation System) it is positioned, precision is better than 0.5m.Tree species are recorded in sample ground and are counted, while measuring every wood
The diameter of a cross-section of a tree trunk 1.3 meters above the ground and tree it is high, and according to the calculated ginkgo artificial forest Cover characteristic quantity of laser radar (i.e. for the first time in echo higher than 1m's
Laser reentry point accounts for the ratio of all reentry points) size be divided into 3 groups, every group of 15 pieces of sample ground (first group of Cover:0.08-
0.19；Second group of Cover:0.21-0.31；Third group Cover:0.33-0.83).Using LAI-2200 Canopy Analyzer to sample
Ground carries out the measurement of effective leaf area index, and measurement method is following (see Fig. 1): two are selected on the direction perpendicular to solar irradiation
30m survey line, two survey line midpoints are respectively away from center of circle 7.5m.In order to exclude the influence of increased surface covering, by LAI-2200 be placed in from
The position of the high 1m of earth's surface measures.First two feeler levers are matched under spacious woods window, use 90 ° of visual angle lids, and
Direction is consistent；Will wherein one be placed under woods window primary (obtaining A value) every 10s sampling, another is brought into in test sample ground, edge
Survey line it is primary (obtaining B value) every 4m sampling；Finally, A value and B value are matched by the time, the effective leaf of combined calculation
Area index, gained sample standing forest parameter summarize and be shown in Table 1.
Survey stand characteristics information summary sheet to 1 sample of table
(2) data prediction: removing the noise point of unmanned plane laser radar original point cloud data first, is filtered based on IDW
Algorithm removes non-ground points, then by calculating the average value of laser point height in each pixel, generates digital elevation model
(DEM) (spatial resolution 0.5m), and a cloud is normalized in the digital terrain model by generating, and obtains normalizing
Unmanned plane laser radar point cloud data after change.
(3) characteristic variable is extracted: extracting three groups of characteristic variables, i.e. percentile height, each layer from normalization point cloud data
Coverage and canopy volume and profile features variable.Percentile height variable include: canopy height distribution percentile (H25,
H50, H75, H95), canopy point cloud is distributed the coverage (CCmean) of average height or more, the coefficient of variation of canopy point cloud distribution
(Hcv)；Each layer coverage: point cloud quantity each percentage height (30th, 50th, 70th, 90th, i.e. D3, D5, D7, D9) with
On point account for the percentage of all the points cloud；Canopy volume and profile features variable: Weibull function is to canopy height profile
It is fitted to obtain 2 profile features amounts α, β (i.e. Weibull α and Weibull β)；Each structured sort volume accounting of canopy, packet
Include open tier, photic zone, four canopy structure classifications of low photosphere and confining bed, percentage shared by the volume of each canopy structure classification
Than (i.e. OpenGap, Oligophotic, Euphotic, ClosedGap).
(4) it screens characteristic variable: characteristic variable is screened by correlation analysis, i.e., screen first related between characteristic variable
Property be lower than 0.6 characteristic variable, then further screening characteristic variable and each standing forest effective leaf area index correlation higher than 0.6
Characteristic variable.
(5) model is established: using ground actual measurement effective leaf area index as dependent variable, unmanned plane laser radar point cloud feature
Variable establishes multivariate regression models as independent variable, the variable of model is entered with method of gradual regression selection, i.e., previously given
F level under carry out significance test, if t inspection the level of signifiance (p > 0.1) is not achieved, rejected；T inspection reaches
The level of signifiance (p < 0.05) is then entered.In order to reduce the correlation between independent variable, the present embodiment passes through principal component analysis
It calculates correlativity matrix and obtains controlling elements k (i.e. the ratio of the square root of Maximum characteristic root and smallest real eigenvalue), k is less than 30
Then model is further selected.
(6) multivariate regression models obtained using step 5 estimates artificial forest effective leaf area index.
Statistical model method prediction effective leaf area index model cross validation results are shown in Fig. 6.As can be seen from Figure, only lead to
It crosses height characteristic variable and ground actual measurement eLAI establishes the precision of model as R2=0.38 (rRMSE=54%) (such as figure a)；Pass through
Altitude feature variable is combined with coverage characteristic variable establishes model (R with ground actual measurement eLAI2=0.64, rRMSE=26%)
(such as figure b)；It is combined by altitude feature variable with canopy volumetric features variable and establishes model (R with ground actual measurement eLAI2=
0.61, rRMSE=28%) (such as figure c).
The present embodiment uses the coefficient of determination (R2), root-mean-square error (RMSE) and opposite root-mean-square error (rRMSE) evaluate
The effect and estimation precision of Regression Model Simulator:
In formula, xiFor standing forest effective leaf area index measured value；Average value is surveyed for standing forest effective leaf area index；
For the model estimated value of standing forest effective leaf area index；N for sample ground quantity；I is for some sample.
Effective leaf area index estimation models and model prediction accuracy based on difference cloud characteristic variable are shown in Table 2, by table 2
As can be seen that the result of " height " combination " coverage " characteristic variable estimation effective leaf area index is better than " height " combination " hat
The estimation of layer volume " feature is as a result, that " altitude feature variable " precision for being predicted is used only is minimum.Fig. 2 is of the invention three
The orthography on group typical sample ground, Fig. 3 are the spherical mirror image on three groups of typical sample ground of the invention, and Fig. 4 is of the invention three groups
The laser radar point cloud atlas on typical sample ground, Fig. 5 be three groups of typical sample ground of the invention laser radar point cloud sectional view, Fig. 2~
In Fig. 5, a is first group, and b is second group, and c is third group.With the ginkgo under growth and management state it can be seen from Fig. 2~5
The result that artificial forest is presented on orthography, spherical mirror image, three-dimensional point cloud and point cloud section is different.Meanwhile it putting cloud and hanging down
Directly 50th, 75th and the 95th quantile in distribution are distributed also not identical in the ginkgo artificial forest under different upgrowth situations, always
Body is in the trend toward the offset of canopy upper layer.
Effective leaf area index estimation models and model prediction accuracy of the table 2 based on difference cloud characteristic variable
Note: H25, H50, H75, H95 are canopy 25%, and 50%, 75%, 95% height is distributed percentile；D5, D7 are
Point of the point cloud quantity on percentage height 50th and 70th accounts for the percentage of all the points cloud.
Claims (10)
1. a kind of method by unmanned plane laser radar inverting artificial forest effective leaf area index, which is characterized in that by unmanned plane
Laser radar original point cloud data is normalized, and canopy structure characteristic variable, knot are extracted from normalization point cloud data
The characteristic variable for closing ground measured data and extraction, using statistical model method in research area on the basis of screening characteristic variable
Sample the effective leaf area index of scale estimated.
2. the method according to claim 1 by unmanned plane laser radar inverting artificial forest effective leaf area index, special
Sign is, comprising the following steps:
(1) laser radar data acquisition is carried out by the laser radar sensor that multi-rotor unmanned aerial vehicle is carried, sample is set on ground
Ground, and record tree species in sample ground and count, while measuring the diameter of a cross-section of a tree trunk 1.3 meters above the ground and tree height of every wood, and measure effective leaf area index；
(2) filtering of laser radar original point cloud data, interpolation are generated into digital elevation model, passes through the digital elevation model of generation
Point cloud data is normalized；
(3) percentile height variable, each layer coverage, canopy volume and profile features are extracted from normalization point cloud data to become
Amount；
(4) characteristic variable is screened by correlation analysis；
(5) using ground actual measurement effective leaf area index as dependent variable, unmanned plane laser radar point cloud characteristic variable is used as from change
Amount, establishes multivariate regression models, and the variable of model is entered with method of gradual regression selection, reduces independent variable by controlling elements k
Between correlation, less than 30 models of k are further selected；
(6) multivariate regression models obtained using step 5 estimates artificial forest effective leaf area index.
3. the method according to claim 2 by unmanned plane laser radar inverting artificial forest effective leaf area index, special
Sign is, the method for measuring effective leaf area index are as follows: select two 30m surveys line on the direction perpendicular to solar irradiation, two
Away from center of circle 7.5m, the position that Canopy Analyzer is placed in from earth's surface 1m high is measured respectively for survey line midpoint, first will under woods window
Two feeler levers are matched, and 90 ° of visual angle lids are used, and direction is consistent, wherein a feeler lever will be placed under woods window and be adopted every 10s
Sample is primary, obtains A value, and another feeler lever is brought into sample primary, acquisition B value every 4m along survey line in test sample ground；When passing through
Between A value and B value are matched, combined calculation effective leaf area index.
4. the method according to claim 3 by unmanned plane laser radar inverting artificial forest effective leaf area index, special
Sign is that the Canopy Analyzer is LAI-2200.
5. the method according to claim 1 or 2 by unmanned plane laser radar inverting artificial forest effective leaf area index,
It is characterized in that, the method that unmanned plane laser radar original point cloud data is normalized are as follows: removal unmanned plane first swashs
The noise point of optical radar original point cloud data removes non-ground points based on IDW filtering algorithm, then by calculating in each pixel
The average value of laser point height generates digital elevation model, place is normalized to a cloud by the digital elevation model of generation
Reason, the unmanned plane laser radar point cloud data after being normalized.
6. the method according to claim 2 by unmanned plane laser radar inverting artificial forest effective leaf area index, special
Sign is that the percentile height variable includes canopy height distribution percentile, and canopy point cloud is distributed average height or more
The coefficient of variation of coverage and the distribution of canopy point cloud；Each layer coverage is point of the point cloud quantity more than each percentage height
Account for the percentage of all the points cloud；The canopy volume and profile features variable are Weibull function to canopy height profile
It is fitted to obtain 2 profile features amounts α, β and four open tier, photic zone, low photosphere and confining bed canopy structure classifications
Volume percentage.
7. the method according to claim 1 or 2 by unmanned plane laser radar inverting artificial forest effective leaf area index,
It is characterized in that, the method for the screening characteristic variable are as follows: feature of the correlation lower than 0.6 becomes between screening characteristic variable first
Then amount further screens the characteristic variable that characteristic variable is higher than 0.6 with effective leaf area index correlation.
8. the method according to claim 2 by unmanned plane laser radar inverting artificial forest effective leaf area index, special
Sign is, the method for the variable of model is entered with method of gradual regression selection are as follows: carries out conspicuousness in the case where previously given F is horizontal
It examines, if the level of signifiance, i.e. p > 0.1 is not achieved in t inspection, is then rejected, if t inspection reaches the level of signifiance, i.e. p <
0.05, then entered.
9. the method according to claim 2 by unmanned plane laser radar inverting artificial forest effective leaf area index, special
Sign is, the method for obtaining controlling elements k are as follows: k is the square root of Maximum characteristic root and the ratio of smallest real eigenvalue, by it is main at
Point analytical calculation correlativity matrix obtains controlling elements k.
10. the method according to claim 1 or 2 by unmanned plane laser radar inverting artificial forest effective leaf area index,
It is characterized in that, using the coefficient of determination, root-mean-square error, the effect and estimation essence of opposite root-mean-square error evaluation model fitting
Degree:
Wherein: R2For the coefficient of determination；RMSE is root-mean-square error；RRMSE is opposite root-mean-square error；xiFor the effective blade face of standing forest
Product index measured value；Average value is surveyed for standing forest effective leaf area index；Estimate for the model of standing forest effective leaf area index
Value；N for sample ground quantity；I is for some sample.
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| CN110544277A (en) * | 2019-08-12 | 2019-12-06 | 蔡建楠 | Method for inverting subtropical vegetation leaf area index by unmanned aerial vehicle-mounted hyperspectral imager |
| CN110544277B (en) * | 2019-08-12 | 2023-01-10 | 蔡建楠 | Method for inverting subtropical vegetation leaf area index by unmanned aerial vehicle-mounted hyperspectral imager |
| CN111950336A (en) * | 2020-04-14 | 2020-11-17 | 成都理工大学 | Vegetation canopy ecological water estimation method based on backpack type laser radar |
| 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 |
| CN112560661A (en) * | 2020-12-10 | 2021-03-26 | 首都师范大学 | Leaf area index calculation method and device, electronic equipment and readable storage medium |
| CN115372919A (en) * | 2022-08-30 | 2022-11-22 | 中国船舶集团有限公司第七二三研究所 | Radar target echo simulation performance evaluation method based on t test |
| CN117607063A (en) * | 2024-01-24 | 2024-02-27 | 中国科学院地理科学与资源研究所 | Forest vertical structure parameter measurement system and method based on unmanned aerial vehicle |
| CN117607063B (en) * | 2024-01-24 | 2024-04-19 | 中国科学院地理科学与资源研究所 | Forest vertical structure parameter measurement system and method based on unmanned aerial vehicle |
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