CN114265036B - Vegetation aggregation index estimation method based on foundation laser radar point cloud - Google Patents
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Abstract
The invention belongs to the technical field of laser radar remote sensing data processing, and particularly relates to a vegetation aggregation index estimation method based on a foundation laser radar point cloud. According to the method, after the point cloud data of the foundation laser radar are preprocessed, the point cloud data are converted into a coordinate system and then projected to a hemispherical surface for calculating the clearance rate, the vertical direction information of the three-dimensional point cloud is expressed in a calculation result after hemispherical projection through laser point area reconstruction, and the concept that laser points with different projection radiuses are endowed with different areas is introduced, so that three-dimensional elevation information is not ignored and converted into two-dimensional available information, and the defect that the third-dimensional information cannot be effectively used in the traditional method is effectively avoided; and finally, counting the gap rate value by dividing the regions, and calculating to obtain the aggregation index result of each region by using a finite length averaging method. The method can fully consider the vertical structure information of vegetation when estimating the aggregation index, and overcomes the problem of low precision of the traditional method.
Description
Technical Field
The invention belongs to the technical field of laser radar remote sensing data processing, and particularly relates to a ground-based laser radar point cloud-based plant aggregation index estimation method.
Background
Forest is an important influencing factor of the earth ecological change, is the main body of the land ecological system, is the ecological system with the largest land area, the widest distribution, the most complex composition structure and the most abundant substance resources, is the most perfect resource library in nature, exchanges carbon, water and energy with the atmosphere, and plays an important role in water circulation, carbon circulation and climate regulation (the week of the Camel society, 2012). Today, the development speed of human society is increasing rapidly, and global environmental problems such as climate warming, biodiversity decline, ecological system function weakening and the like threaten the sustainable development capability of the earth. Thus, accurate monitoring of parameters of the forest ecosystem has great reference and research value for assessing the impact of human activity on the environment (Guo Qinghua et al, 2014). Most of traditional measurement methods monitor an ecosystem at the sample side level, and can obtain the most accurate data. However, with the continuous development and progress of the detection technology in recent years, the traditional field measurement has shown the limitation that the traditional field measurement cannot adapt to large-scale dynamic monitoring, and is time-consuming and labor-consuming. The laser radar is an emerging active remote sensing technology, can well make up for a plurality of defects of the traditional measurement mode, and can play an important role in the detection and simulation of a forest ecosystem.
Laser is one of the most important inventions in the history of human development in the 20 th century, and the laser radar technology is developed very rapidly from the birth of the middle and later period of the 20 th century; liDAR is first named LASER RADAR (radio detection and ranging) and is now commonly referred to simply as LiDAR, i.e., laser detection and ranging. The laser radar has extremely wide application in the field of earth science, such as mapping, three-dimensional city, city planning, forestry, agriculture and the like. As an active detection means, the laser radar has the advantages of high accuracy, high sampling speed, high resolution, no need of contacting with a detection target, high anti-interference capability and the like. As a non-contact active remote sensing technology. The lidar technology can obtain spatial three-dimensional information of a target object at high speed and high efficiency without contacting the target object (f.m. danson et al, 2007). The laser radars can be classified into foundation, vehicle-mounted, airborne, satellite-mounted and the like according to different platforms, wherein the foundation laser radars refer to laser radars erected on a ground platform and can be called foundation laser scanning (TLS).
With the development of the laser radar technology, the number of research on the reverse modeling of vegetation structural parameters by using the ground-based laser radar data is increasing, and many inversion cases are successful in researching the tree structural parameters by using the ground-based laser radar data. The Gap Fraction (Gap Fraction) and the aggregation Index (CI) are two important canopy structure characteristic parameters for describing a vegetation interception light process and a canopy radiation transmission process. The aggregation index was set forth by Nilson (1971) during the study of radiation propagation theory through the canopy of plants; he obtained a mathematical expression between the clearance rate and the leaf area index during the study and introduced a correction parameter lambda based on Markov model 0 The aggregation of the canopy is described in this way, and is used up to now. Hereinafter Chen (1991) defines the aggregation level index as the effective leaf area index divided by the real leaf area index; and then, a gap size distribution method is provided, so that the assumption of a spatial distribution mode of the blade and the canopy is eliminated, the aggregation effect in the canopy is quantitatively measured, and the large gap between crowns is eliminated to a great extent. Lang and Xiang (1986) proposed using a finite length averaging method to calculate the aggregation index, assuming that the internal leaves of the subsampled line are randomly distributed over a finite length; the method is simple and practical, and is widely accepted. The two methods are the main methods for calculating the blade aggregation index at present under the condition of taking the measurement of the laser radar and the optical instrument on the Shan Muhuo forest stand and the advantages and disadvantages of each method into consideration, namely (1) the logarithmic average method CI based on the canopy gap rate LX (2) CI based on gap size distribution method CC 。
Danson et al (2007) calculated the gap rate by converting slice projections of the xy axis of TLS data into two-dimensional images, while comparing with the gap rate calculated for digital hemispherical photographs, and the results indicate that the gap rate obtained with high resolution point cloud data and digital hemispherical images is similar, some of the differences being likely due to sun glare seen in the photographs or errors associated with manual thresholds of the digital images. Garcinia et al (2015) respectively calculate the aggregation index of different vegetation types based on the foundation and the airborne laser radar technology, and meanwhile, the inversion result is compared with the aggregation index result obtained based on the hemispherical photography technology, so that the results show that different methods have stronger correlation when inverting the aggregation index. Bao (2016) inverts the gap rate with ground-based laser radar (TLS) data and Digital Hemispherical Photography (DHP), and calculates the aggregation index by using the formula proposed by Lang (1986), wherein R2 (determining coefficient) of the inversion result of the two reaches 0.863, which shows that the aggregation indexes of TLS and DHP have good correlation.
At present, in the inversion process of the clearance rate, the method is mainly divided into a voxel method and a projection method. The voxel method mainly divides the space where the point cloud is located by three-dimensional voxels with a certain size, judges whether the point cloud exists in the voxels and counts the quantity of all voxels containing the point cloud, so that the gap rate is calculated. The principle of the projection method is mainly similar to the digital hemispherical photographing method, three-dimensional information is projected to a two-dimensional plane, and then a gap rate is calculated. The two methods, especially the voxel method, use three-dimensional coordinates when using three-dimensional point cloud data to quantitatively describe the canopy, usually project each voxel into a hemispherical image after calibrating its attribute, compress three-dimensional information directly to two dimensions, and do not really use three-dimensional information when extracting the final parameters, so the above method has great information loss.
Disclosure of Invention
Aiming at the problems or the shortcomings, the invention provides a vegetation aggregation index estimation method based on a ground-based laser radar point cloud, which aims at solving the problems of poor final result precision caused by characteristic selection and insufficient data utilization of the existing vegetation aggregation index estimation method.
A vegetation aggregation index estimation method based on a foundation laser radar point cloud comprises the following steps:
registering and denoising the foundation laser radar point cloud data of the target sample site by using software, exporting the foundation laser radar point cloud data into a text format, and then eliminating points lower than the height of the foundation laser radar (such as a scanner).
Because the point cloud data collected by the foundation laser scanner is large in general data volume and high in density and has optical characteristic information of the scanned object, pretreatment such as multi-measuring site cloud data registration and point cloud denoising is needed before the study of the canopy structure parameters is carried out. Only under the condition of accurately completing preprocessing work, accurate three-dimensional radar point cloud data can be obtained, so that the canopy structure parameters can be inverted more accurately.
Setting a sampling party in a research area, selecting a station measuring position according to the condition of a sampling place, and erecting a foundation laser radar, wherein the condition that a complete forest canopy can be scanned is ensured. And at least 3 targets are set in the scanning range of the laser radar and serve as the basis of post multi-station registration.
Step 2, converting point cloud coordinates and dividing areas:
the point cloud data acquired by the foundation laser radar is a space rectangular coordinate system, and because the path of the incident solar canopy is generally described by zenith angles, the point cloud coordinate acquired in the step 1 is converted into a spherical coordinate, namely, the point cloud coordinate is converted from P (x, y, z) to P
Wherein θ is zenith angle>
And r is the distance from the canopy point cloud to the origin point. The formula is as follows:
in order to increase the running speed of the program, the point cloud data needs to be divided into a plurality of areas uniformly within the range of 0-90 degrees of zenith angle and 0-360 degrees of azimuth angle for data blocking.
Furthermore, the zenith angle is 5 degrees, the azimuth angle is 45 degrees, and the zenith angle is used as an equally divided basic parameter, so that compatibility and operation efficiency of later-stage software and data are easy.
Step 3, hemispherical projection:
and (3) projecting all the point cloud data obtained in the step (2) onto the hemispherical surface so as to facilitate the subsequent calculation of the clearance rate. The hemispherical projection surface radius L is a range of 1m above and below the maximum tree height of the target pattern, and each laser point in the point cloud data obtains a projection radius D related to the distance of the laser point relative to the projection surface.
Step 4, calculating the area of the laser point:
considering the sampling interval when the foundation laser radar scans the blades and the large difference of the areas of the blades of different tree species, the growth conditions of different directions in the same day top ring are also different, so that the factors are required to be simultaneously considered when the areas of the laser points are set.
Let the laser spot radius be e, the distance of the laser spot from the origin of spherical coordinates be r, and the radius of the hemispherical projection surface be L, the laser spot projection radius D may be expressed as (hereinafter referred to as projection radius):
D＝|L-r| (2)
for the used foundation laser radar, two laser points with the distance of F meters can be identified at the position of F meters, and then the relation between the laser point radius e and the projection radius D is satisfied:
e＝k*D*f/F (3)
wherein k is an adjusting coefficient, and the value range is 2-5.
Finally, the area s of a single laser spot projected on a hemisphere can be expressed as:
s＝pi*e 2 (4)
wherein pi is the circumference ratio
the total laser spot areas projected in all directions of different zenith rings are counted, and then the ratio of the sum Se of the areas of the unoccupied parts on the hemispherical surface to the total level St of the projected hemispherical surface is calculated, so that the gap rate GF of all directions of each zenith ring can be obtained, and the gap rate GF can be expressed as:
GF＝Se/St (5)
the gap rate data of different directions of each zenith ring is calculated by a finite length averaging method, and an aggregation index result of the canopy can be obtained and can be expressed as:
wherein ,
represents a zenith angle of θ and an azimuth angle of +.>
The gap ratio at the time of the measurement is omega (theta) which represents the aggregation index at the zenith angle of theta, the numerator is the logarithm of the average gap ratio of the canopy, and the denominator is the logarithmic average of the gap ratio.
In the invention, for the selection of the projection surface radius L, a plurality of groups of radius values are generally required to be compared to find a more suitable value. Multiple experiments prove that the radius of the optimal projection surface is 1 meter up and down of the maximum tree height in the sample area. The adjustment coefficient in the laser spot area formula is usually selected as a constant between 2 and 5, and the size of the adjustment coefficient can be adjusted according to different tree types in the applied sample. Finally, the method for estimating the aggregation level index is obtained by calculating the gap rate data through a finite length averaging method. The method assumes that each area where the processed data is located has gaps, and when the gap rate of a certain area is 0, a gap element needs to be added into the area; in addition, the method also assumes that the data collected from the canopy is randomly distributed within the finite length units, which is also the name of the finite length averaging method. The logarithmic average method based on the canopy gap ratio was proposed by Lang and Xiang in 1986, i.e. the aggregation index was calculated by using the ratio of the average number of gap ratios to the average number of gap ratio pairs, and the formula is shown as formula (6).
According to the method, three-dimensional laser point cloud data of a forest vegetation canopy are obtained through a foundation laser radar, and the method for inverting the aggregation index from the forest canopy based on the TLS point cloud data is established by combining three-dimensional structural characteristics of the tree canopy and blade distribution forms, performing preprocessing processes such as registration, point cloud normalization, denoising, elevation filtering and the like, hemispherical projection, reconstructing laser point areas and estimating gap rate so as to calculate the aggregation index by using a finite length averaging method.
The method comprises the steps of firstly carrying out registration, denoising and filtering data preprocessing on the point cloud data of the foundation laser radar in sequence; then converting the processed point cloud data into a coordinate system and projecting the coordinate system to a hemispherical surface so as to calculate the clearance rate, expressing the vertical direction information of the three-dimensional point cloud in a calculation result after hemispherical projection by using laser point area reconstruction, and introducing the concept that laser points with different projection radiuses are endowed with different areas, so that the three-dimensional elevation information is not ignored and converted into two-dimensional available information, and the defect that the third-dimensional information cannot be effectively used by the traditional method is effectively avoided; finally, counting the gap rate value in each region, and calculating to obtain the aggregation index result of each region by using a finite length averaging method; the flow is shown in figure 1.
In summary, the invention can fully consider the vertical structural information of vegetation when estimating the aggregation index, overcomes the limitation of using time, weather and other factors when using the traditional digital hemispherical photography method, and can not collect data all weather; and the gap rate results are underestimated in the sparse regions of the canopy due to the influence of exposure parameter setting. According to the invention, under the condition of considering the difference of different crown growth distribution conditions at different heights, different area values are given to the laser points with different projection radiuses, and the aggregation index of various broadleaf canopy is effectively estimated.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of specific data of an embodiment;
FIG. 3 is a schematic diagram of the relationship between sampling spacing and point cloud to origin distance;
FIG. 4 is a schematic view of a hemispherical laser spot projection;
FIG. 5 is a comparison plot of the gap rate results estimated by the present method and the digital hemisphere projection method;
FIG. 6 is a comparison line graph of the results of the aggregation index estimated by the present method and the digital hemisphere projection method;
Detailed Description
The invention is further illustrated by the following examples of embodiments in a sample and with reference to the accompanying drawings:
the development environment is Microsoft Visual Studio 2013 and the programming language is C.
Sample sides of 25 x 30 meters are arranged in the nanmu forest beside the main building of the clear water river school area of the electronic technology university, foundation laser radar data are acquired by Leica Scanstation C at 4 measuring stations, one measuring station is arranged in the center of the sample, and the other 3 measuring stations are arranged at the edge positions. 3 targets are laid in the forest as common points when the data of multiple measuring stations are aligned. In combination with consideration of the sample situation and the total number of point clouds, a high resolution scan (distance 100 meters, horizontal, vertical spacing 0.05 meters) was set on Leica C10, the resolution should not be too high to avoid data redundancy, and the remaining parameters are listed in the following table. And (3) registering, denoising and normalizing the multi-station TLS data by using the Cyclone software, and removing the point cloud with the z axis smaller than 0 after the data is exported.
Table 1 three-dimensional laser scanner Leica Scanstation C parameters
And step 1, preprocessing the acquired foundation laser radar point cloud data, registering and denoising the foundation laser radar point cloud data by using software, exporting the foundation laser radar point cloud data into a text format, and performing elevation filtering by taking the height of a laser radar scanner as a threshold value. Wherein fig. 2 (a) is canopy point cloud data acquired by a ground-based laser radar, and (b) is digital hemispherical photograph data of a sample plot.
And 2, establishing a space rectangular coordinate system by taking a laser radar scanner station as a coordinate origin for the preprocessed point cloud data, marking the sitting of each point as P (X, Y, Z), wherein the X axis is the forward east direction, the Y axis is the north direction, and the Z axis is the vertical direction. After a coordinate system is established, the laser point coordinates are converted into spherical coordinates from space rectangular coordinates, and data after the coordinates are converted are partitioned according to the interval of 5-degree zenith angle and 45-degree azimuth angle, so that the cost is saved. According to the relation between the tree height and the radius of the sample plot, the effective zenith angle range is 0-70 degrees, so that the range of the zenith angle of 70 degrees is divided into 14 circular rings, the azimuth angle is divided into 8 areas by taking 0 degree, 45 degree, 90 degree, 135 degree, 180 degree, 225 degree, 270 degree and 315 degree as central scales, and the data is divided into 112 areas.
And 3, selecting 15 meters according to the size of the sample as the radius of the hemispherical surface, and projecting the point cloud onto the hemispherical surface.
And 4, converting the resolution of the laser radar scanner according to different relative distances between the laser points in the point cloud data and the projection plane, so as to obtain the radius of the laser point, and further calculating the area of the laser point.
And 5, adding the areas of the laser points in each area to calculate the total area, and subtracting the total area of the laser points from the projected hemispherical area to obtain the area of the gap area, thereby obtaining the gap rate. And (5) taking the logarithmic average of the gap rate data in each direction, and calculating the aggregation index of each zenith ring.
Comparative experiments were performed on the target patterns of the examples using the method of the present invention and the digital hemispherical projection method, respectively. Specific experimental curves are shown in fig. 5 and 6. FIG. 5 is a graph of gap rate results versus a fold line estimated by the present method and the digital hemisphere projection method; fig. 6 is a comparison line graph of the results of the aggregation index estimated by the present method and the digital hemisphere projection method.
Hemispherical projection and laser spot area reconstruction are carried out on data in each direction after the point cloud area is divided, then the gap rate of each area is calculated, and it is not difficult to find out that the same laser spot can be endowed with different areas under different projection radiuses or different adjustment coefficients, and further different gap rate and aggregation index calculation results can be obtained, so that the influence of the projection radiuses and the adjustment coefficients on the gap rate estimation can be explored.
Through verification, when the projection radius is slightly higher than the upper limit of the tree height in the sample side, the obtained gap rate and aggregation index result most accords with the expectation, and the projection radius selected in the sample side is 16; and the adjustment coefficient is more expected when the size is 2.5. The gap rate obtained by the method and the digital hemisphere photographing method are compared with each other, the gap rate obtained by the two methods is relatively high in correlation, the determination coefficient is 0.8104, but the gap rate value obtained by the two methods in the area with relatively low zenith angle is different, and the result obtained by the laser point area projection method is relatively large, because the projection direction of the method in the laser point projection is different from the photographing direction of the fisheye lens to the surrounding scenery in the digital hemisphere photographing, the gap between the two is relatively large in the area with low zenith angle, and the problem that the gap rate is often underestimated by the digital hemisphere photographing technology when the blade density is relatively large is overcome due to the fact that the blade distribution of the crown layer in the vertical direction is fully considered in the area with high zenith angle.
As can be seen from the above experimental data, the aggregation index results obtained by the method and the digital hemispherical photography method according to the present invention are still high, and the decision coefficient is 0.7865, which indicates that it is feasible and effective to calculate the aggregation index of the vegetation canopy by the method.
According to the method, through the area reconstruction of the laser points, the vertical direction information of the three-dimensional point cloud is expressed in the calculated result after hemispherical projection, and the concept that laser points with different projection radiuses are endowed with different areas is introduced, so that the three-dimensional elevation information is not ignored and is converted into two-dimensional usable information, and the defect that the third-dimensional information cannot be effectively used by the traditional method is effectively avoided; and finally, counting the gap rate value by dividing the regions, and calculating to obtain the aggregation index result of each region by using a finite length averaging method. The method can fully consider the vertical structure information of vegetation when estimating the aggregation index, and overcomes the problem of low precision of the traditional method.
Claims (2)
1. A vegetation aggregation index estimation method based on a foundation laser radar point cloud is characterized by comprising the following steps:
step 1, acquiring and preprocessing point cloud data:
registering and denoising the foundation laser radar point cloud data of the target sample area by using software, exporting the foundation laser radar point cloud data into a text format, and removing points lower than the height of the foundation laser radar;
step 2, converting point cloud coordinates and dividing areas:
converting the point cloud coordinate obtained in the step 1 into a spherical coordinate, namely converting the point cloud coordinate from P (x, y, z) into
Wherein θ is zenith angle>
For azimuth, r is the distance from the canopy point cloud to the origin, and the formula is as follows:
equally dividing point cloud data into a plurality of areas within the range of zenith angles of 0-90 degrees and azimuth angles of 0-360 degrees, and performing data blocking to improve the running speed of a program;
step 3, hemispherical projection:
projecting all the point cloud data obtained in the step 2 onto a hemispherical surface, wherein the radius L of the hemispherical projection surface is the range of 1m above and below the maximum tree height of the target sample, and each laser point in the point cloud data can obtain a projection radius D related to the distance between the laser point and the projection surface;
step 4, calculating the area of the laser point:
let the laser spot radius be e, the distance of the laser spot from the spherical origin be r, and the radius of the hemispherical projection surface be L, then the laser spot projection radius D can be expressed as:
D＝|L-r| (2)
for the used foundation laser radar, two laser points with the distance of F meters can be identified at the position of F meters, and then the relation between the laser point radius e and the projection radius D is satisfied:
e＝k*D*f/F (3)
wherein k is an adjusting coefficient, and the value range is 2-5;
finally, the area s of a single laser spot projected on a hemisphere can be expressed as:
s＝pi*e 2 (4)
wherein pi is the circumference ratio;
step 5, estimating the gap rate and calculating the aggregation index:
the total laser spot areas projected in all directions of different zenith rings are counted, and then the ratio of the sum Se of the unopened spare part areas on the hemispherical surface to the total level St of the projected hemispherical surface is calculated, so that the gap rate GF of all directions of each zenith ring can be obtained, and the gap rate GF can be expressed as:
GF＝Se/St (5)
the gap rate data of different directions of each zenith ring is calculated by a finite length averaging method, and an aggregation index result of the canopy can be obtained and can be expressed as:
wherein ,
represents a zenith angle of θ and an azimuth angle of +.>
The gap ratio at the time of the measurement is omega (theta) which represents the aggregation index at the zenith angle of theta, the numerator is the logarithm of the average gap ratio of the canopy, and the denominator is the logarithmic average of the gap ratio.
2. The method for estimating vegetation concentration index based on the point cloud of the ground-based lidar of claim 1, wherein: in the step 2, the zenith angle is 5 degrees, the azimuth angle is 45 degrees, and the zenith angle is used as an equally divided basic parameter, so that compatibility and operation efficiency of later-stage software and data are easy.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1982004377A1 (en) * | 1981-06-09 | 1982-12-23 | Johansson Rune | Apparatus for bringing together plants in a small heated area of a hothouse |
| CN103871065A (en) * | 2014-03-26 | 2014-06-18 | 福州大学 | Vegetation canopy layer aggregation effect quantitative evaluation method based on hemispherical videos |
| CN106248003A (en) * | 2016-08-24 | 2016-12-21 | 电子科技大学 | A kind of three-dimensional laser point cloud extracts the method for Vegetation canopy concentration class index |
| CN107274417A (en) * | 2017-07-05 | 2017-10-20 | 电子科技大学 | A kind of single wooden dividing method based on airborne laser point cloud aggregation |
| CN107831497A (en) * | 2017-09-26 | 2018-03-23 | 南京大学 | A kind of method that forest building-up effect is quantitatively portrayed using three dimensional point cloud |
| CN108195736A (en) * | 2017-12-19 | 2018-06-22 | 电子科技大学 | A kind of method of three-dimensional laser point cloud extraction Vegetation canopy clearance rate |
| CN112068153A (en) * | 2020-08-24 | 2020-12-11 | 电子科技大学 | Crown clearance rate estimation method based on foundation laser radar point cloud |
-
2021
- 2021-12-21 CN CN202111569528.7A patent/CN114265036B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1982004377A1 (en) * | 1981-06-09 | 1982-12-23 | Johansson Rune | Apparatus for bringing together plants in a small heated area of a hothouse |
| CN103871065A (en) * | 2014-03-26 | 2014-06-18 | 福州大学 | Vegetation canopy layer aggregation effect quantitative evaluation method based on hemispherical videos |
| CN106248003A (en) * | 2016-08-24 | 2016-12-21 | 电子科技大学 | A kind of three-dimensional laser point cloud extracts the method for Vegetation canopy concentration class index |
| CN107274417A (en) * | 2017-07-05 | 2017-10-20 | 电子科技大学 | A kind of single wooden dividing method based on airborne laser point cloud aggregation |
| CN107831497A (en) * | 2017-09-26 | 2018-03-23 | 南京大学 | A kind of method that forest building-up effect is quantitatively portrayed using three dimensional point cloud |
| CN108195736A (en) * | 2017-12-19 | 2018-06-22 | 电子科技大学 | A kind of method of three-dimensional laser point cloud extraction Vegetation canopy clearance rate |
| CN112068153A (en) * | 2020-08-24 | 2020-12-11 | 电子科技大学 | Crown clearance rate estimation method based on foundation laser radar point cloud |
Non-Patent Citations (4)
| Title |
|---|
| Measuring gap raction, element clumping index and LAI in Sierra Forest stands sing a full-waveform ground-based LiDAR;ZHAO F 等;Remote Sensing of Environment;第125卷(第4期);73-79 * |
| 基于激光雷达的森林冠层聚集度指数反演;林森;《中国优秀硕士/博士学位论文全文数据库》;全文 * |
| 祁连山中段植被群落的时空格局变化;葛丽娟;党虹;戎战磊;高云飞;赵传燕;王红义;;干旱区研究(第02期);101-108 * |
| 遥感反演植被叶面积指数的不确定性来源综述;刘远;周买春;;江苏农业科学(第12期);20-27 * |
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