CN111797494A - Airborne laser radar design method considering vegetation canopy density - Google Patents

Airborne laser radar design method considering vegetation canopy density Download PDF

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CN111797494A
CN111797494A CN202010403637.0A CN202010403637A CN111797494A CN 111797494 A CN111797494 A CN 111797494A CN 202010403637 A CN202010403637 A CN 202010403637A CN 111797494 A CN111797494 A CN 111797494A
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vegetation
density
point cloud
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canopy
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CN111797494B (en
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曹成度
胡玉雷
李海亮
胡晓斌
汤建凤
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China Railway Siyuan Survey and Design Group Co Ltd
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Abstract

A design method of an airborne laser radar considering vegetation canopy density comprises the following steps: s100, dividing the measuring area according to a rectangular grid, and classifying the grid according to the distribution density degree of the implanted seeds in the grid; s200, generating corresponding digital orthoimages aiming at different types of grids; s300, based on a digital image processing algorithm, carrying out vegetation detection on all the extracted digital orthoimages of the grids, and segmenting out vegetation covered areas; for each category, the sum S of the vegetation coverage area is countediCalculating the gloomy degree index beta of each categoryi(ii) a S400, according to the final earth surface point cloud densityDegree rhoiDegree of occlusion betaiDesign point cloud density rho0Establishing a point effect model considering vegetation canopy density; s500, calculating to obtain the point cloud density rho of the optimal design point of the project according to the point effect model considering the vegetation canopy density0According to the optimum design point cloud density rho0And designing the optimal parameters of the method to achieve the aim that the point cloud density of the subsurface of vegetation meets the technical requirements of projects.

Description

Airborne laser radar design method considering vegetation canopy density
Technical Field
The invention relates to an airborne laser radar data acquisition method, in particular to an airborne laser radar design method considering implantation canopy closure degree.
Background
An airborne laser radar is a measuring means which actively transmits laser beams to the ground and receives echo signals to acquire three-dimensional information of ground targets by carrying a laser sensor, an IMU (inertial measurement unit) and a GNSS (global navigation satellite system) unit on an airplane, and is gradually applied to various fields of national infrastructure, such as traffic survey, national survey, resource exploration and the like.
The vegetation coverage problem is always the problem that digital aerial photography cannot solve, and the airborne laser radar has the obvious advantages of real time, high efficiency, high precision and the like, and multiple echoes of the airborne laser radar can acquire the elevation of the earth surface and has certain penetrability on the vegetation. The existing engineering practice is summarized, and after the aerial photography flight is finished, the actual point cloud density of the ground surface under the vegetation is related to the designed point cloud density and the vegetation sparsity degree. Therefore, it is necessary to quantitatively research the relationship between the actual point cloud density of the subsurface of vegetation and the designed point cloud density and vegetation sparsity.
The vegetation canopy density is the ratio of the total projected area of the crown to the total area of the forest land, and is an important index for dividing dense forest land, medium forest land, sparse forest land and non-forest land.
Disclosure of Invention
In view of the above-mentioned problem of the influence of vegetation coverage on aerial photography, a design method of an airborne lidar that takes account of vegetation canopy has been proposed that overcomes or at least partially addresses the above-mentioned problem.
The technical scheme provided by the invention is as follows:
a design method of an airborne laser radar considering vegetation canopy density comprises the following steps:
s100, dividing the test area according to a rectangular grid, and dividing the grid into four types of areas according to the distribution density degree of the planted grids;
s200, respectively extracting P from the four types of regionsiAnd (i is 1, 2, 3 and 4 and represents the ith type area) each grid, carrying out unmanned aerial vehicle aerial photography on each grid, setting different image ground resolutions according to different types of grids, acquiring images of each grid, carrying out space-time three-processing on the images of each grid by utilizing photogrammetric data processing software, generating a digital elevation model, and then producing a digital orthographic image.
S300, based on a digital image processing algorithm, carrying out vegetation detection on all the extracted digital orthoimages of the grids, and segmenting out vegetation covered areas; for each category, the sum S of the vegetation coverage area is countediCalculating the gloomy degree index beta of each categoryiWherein i is 1, 2, 3, 4;
s400, establishing a point effect model considering vegetation canopy density, and obtaining the final earth surface point cloud density rhoiDegree of occlusion betaiDesign point cloud density rho0The mathematical relationship is established between:
ρi＝aβiρ0+b
wherein, a and b are model coefficients, i is 1, 2, 3 and 4;
counting the final earth surface point cloud density rho of each category of a plurality of developed airborne laser radarsiAnd the canopy density index betaiObtaining model coefficients a and b to obtain a point cloud penetration effect model considering vegetation canopy closure;
s500, aiming at a new airborne laser radar design project, calculating the canopy density beta of each type of area in the measurement area by adopting an S100-S300 methodi(i represents the i-th area, 1, 2, 3, 4), and the final surface point cloud density ρ based on the itemiCalculating to obtain the point cloud density rho of the optimal design point of the project according to the point cloud penetrating effect model considering the vegetation canopy density0According to the optimum design point cloud density rho0The optimal parameters are designed to meet the technical requirements that the point cloud density of the subsurface of vegetation meets the projectThe purpose of (1).
Further, in S100, the specific method of mesh division is as follows: and dividing the grid into 2km multiplied by 2km for the measurement area.
Further, in S100, according to the distribution density of the implanted grid, the grid is divided into four regions, and a "sampling point method" is adopted, specifically: evenly set up m sampling points in every graticule mesh, judge whether the sampling point is that the crown covers based on the satellite image, count and is covered the number n of sampling points, calculate the vegetation cover factor a of this graticule mesh and be n/m x 100%, divide into four kinds of regions through the vegetation cover factor a size of graticule mesh.
Further, in S100, four types of regions into which the grid is divided are: non-forest land, sparse land, medium-forest land and dense-forest land.
Further, the grids extracted for the non-forest land, the sparse land, the medium-forest land and the dense-forest land are 1, 2, 4 and 8 respectively.
Furthermore, the ground resolution of the image for the non-forest land and the sparse land is 0.2m, and the ground resolution of the image for the medium-forest land and the dense land is 0.1 m.
Further, the canopy density index βiThe formula is as follows:
Figure BDA0002490432800000041
wherein S isiIs the sum of the vegetation coverage areas, PiAnd i is 1, 2, 3 and 4 for the number of the current type grids.
Further, the optimal parameters include: laser pulse frequency, scan angle, relative altitude, flight speed, side lap.
Compared with the prior art, the invention at least has the following beneficial effects:
the method quantitatively researches the relationship between the actual point cloud density of the subsurface of vegetation and the point cloud density and vegetation sparsity degree of design. By measuring the vegetation canopy density in the forest region, the relationship between the actual point cloud density of the ground surface under the vegetation and the designed point cloud density and vegetation canopy density is quantitatively described, and guidance is provided for designing and flying the aerial photography technology. According to the method, the influence of different vegetation coverage conditions on the penetration effect of the laser point cloud is quantitatively researched by measuring the canopy closure degree parameters of the forest lands covered by different vegetation, and the problem that the density of the point cloud on the ground surface under the intensive vegetation in the conventional aerial photography task does not reach the standard can be predicted in advance.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
fig. 1 is a flowchart of a design method of an airborne laser radar considering vegetation canopy density in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of dividing a measurement area according to a rectangular grid in embodiment 1 of the present invention.
Detailed Description
Example 1
The embodiment discloses a design method of an airborne laser radar considering vegetation canopy density, as shown in fig. 1, comprising the following steps:
s100, dividing the measuring area according to a rectangular grid, and dividing the grid into four types of areas according to the distribution density degree of the implanted quilt in the grid.
Specifically, as shown in fig. 2, in some preferred embodiments, the survey area is gridded by 2km × 2 km; according to the density degree of the implanted grid, the grid is divided into four regions, and a sampling point method is adopted, specifically comprising the following steps: evenly set up m sampling points in every graticule mesh, judge whether the sampling point is that the crown covers based on the satellite image, count and is covered the number n of sampling points, calculate the vegetation cover factor a of this graticule mesh and be n/m x 100%, divide into four kinds of regions through the vegetation cover factor a size of graticule mesh.
In some preferred embodiments, the four types of regions into which the grid is divided may be non-wooded land, sparse land, medium-wooded land, and dense-wooded land. Wherein, the size range of the vegetation coverage factor a is as shown in table one for the grid type. To ensure the accuracy of the method, the number of sampling points per grid is at least 3 × 3, and at most 10 × 10.
TABLE 1
Range of vegetation coverage factor Categories
[0,0.20) Class I (non-forest land)
[0.20,0.40) Class II (sparse land)
[0.40,0.70) Class III (moderate forest land)
[0.70,1] IV class (Mi Lin Di)
S200, respectively extracting P from the four types of regionsiAnd (i is 1, 2, 3 and 4 and represents the ith type area) each grid, carrying out unmanned aerial vehicle aerial photography on each grid, setting different image ground resolutions according to different types of grids, acquiring images of each grid, carrying out space-time three-processing on the images of each grid by utilizing photogrammetric data processing software, generating a digital elevation model, and then producing a digital orthographic image.
The number of the extracted grids is different for different types of regional grids, in some preferred embodiments, the number of the grids extracted for the non-forest land, the sparse land, the medium forest land and the dense forest land is 1, 2, 4 and 8, a multi-rotor unmanned aerial vehicle is adopted, unmanned aerial vehicle aerial photography is carried out on each grid, high-definition digital images are collected, the image ground resolution is as shown in table 2, the image ground resolution for the non-forest land and the sparse land is 0.2m, and the image ground resolution for the medium forest land and the dense forest land is 0.1 m.
TABLE 2
Figure BDA0002490432800000061
S300, based on a digital image processing algorithm, carrying out vegetation detection on all the extracted digital orthoimages of the grids, and segmenting out vegetation covered areas; for each category, the sum S of the vegetation coverage area is countediCalculating the gloomy degree index beta of each categoryiWherein, i is 1, 2, 3, 4, which represents the i-th area;
the vegetation canopy density is the ratio of the total projected area of the crown to the total area of the forest land, and is an important index for dividing dense forest land, medium forest land, sparse forest land and non-forest land.
In some preferred examples, the canopy density indicator β is determined when the survey area is gridded at 2km × 2kmiThe formula is as follows:
Figure BDA0002490432800000071
wherein S isiIs the sum of the vegetation coverage areas, PiFor the number of the current type grids, i is 1, 2, 3, 4, and represents the ith type area.
S400, establishing a point effect model considering vegetation canopy density, and obtaining the final earth surface point cloud density rhoiDegree of occlusion betaiDesign point cloud density rho0The mathematical relationship is established between:
ρi＝aβiρ0+b
wherein, a and b are model coefficients, i is 1, 2, 3 and 4.
Counting the final earth surface point cloud density rho of each category of a plurality of developed airborne laser radarsiAnd the canopy density index betai，And (5) obtaining model coefficients a and b to obtain a point cloud penetration effect model considering vegetation canopy closure degree.
S500, aiming at a new airborne laser radar design project, adopting a method S100-S300 to countCalculating the canopy density beta of each type of region in the measurement areai(i represents the i-th type region, 1, 2, 3, 4). Final surface point cloud density ρ based on the projectiCalculating to obtain the point cloud density rho of the optimal design point of the project according to the point cloud penetrating effect model considering the vegetation canopy density0According to the optimum design point cloud density rho0And designing the optimal parameters of the method to achieve the aim that the point cloud density of the subsurface of vegetation meets the technical requirements of projects. In some preferred embodiments, the optimal parameters include: laser pulse frequency, scan angle, relative altitude, flight speed, side lap.
In the embodiment, the relationship between the actual point cloud density of the ground surface under the vegetation and the designed point cloud density and vegetation canopy density is quantitatively described by measuring the vegetation canopy density of the forest region, so that guidance is provided for designing and flying an aerial photography technology. According to the method, the influence of different vegetation coverage conditions on the penetration effect of the laser point cloud is quantitatively researched by measuring the canopy closure degree parameters of the forest lands covered by different vegetation, and the problem that the density of the point cloud on the ground surface under the intensive vegetation in the conventional aerial photography task does not reach the standard can be predicted in advance.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a non-exclusive "or".

Claims (8)

1. A design method of an airborne laser radar considering vegetation canopy density is characterized by comprising the following steps:
s100, dividing the test area according to a rectangular grid, and dividing the grid into four types of areas according to the distribution density degree of the planted grids;
s200, respectively extracting P from the four types of regionsi(i is 1, 2, 3 and 4, and represents the i-th type area), carrying out unmanned aerial vehicle aerial photography on each grid, setting different image ground resolutions according to different types of grids, acquiring images of each grid, carrying out space-time three-processing on the images of each grid by using photogrammetric data processing software, generating a digital elevation model, and then producing a digital orthographic image;
s300, based on a digital image processing algorithm, carrying out vegetation detection on all the extracted digital orthoimages of the grids, and segmenting out vegetation covered areas; for each category, the sum S of the vegetation coverage area is countediCalculating a canopy density index β i of each category, wherein i is 1, 2, 3, 4;
s400, establishing a point effect model considering vegetation canopy density, and obtaining the final earth surface point cloud density rhoiDegree of occlusion betaiDesign point cloud density rho0The mathematical relationship is established between:
ρi＝aβiρ0+b
wherein, a and b are model coefficients, i is 1, 2, 3 and 4;
counting the final earth surface point cloud density rho of each category of a plurality of developed airborne laser radarsiAnd the canopy density index betaiObtaining model coefficients a and b to obtain the point cloud penetration effect considering vegetation canopy densityA model;
s500, aiming at a new airborne laser radar design project, calculating the canopy density beta of each type of area in the measurement area by adopting an S100-S300 methodi(i 1, 2, 3, 4), the final surface point cloud density ρ based on the termiCalculating to obtain the point cloud density rho of the optimal design point of the project according to the point cloud penetrating effect model considering the vegetation canopy density0According to the optimum design point cloud density rho0And designing the optimal parameters of the method to achieve the aim that the point cloud density of the subsurface of vegetation meets the technical requirements of projects.
2. The method for designing the airborne laser radar considering vegetation canopy density of claim 1, wherein in S100, the specific method of meshing is as follows: and dividing the grid into 2km multiplied by 2km for the measurement area.
3. The method for designing the airborne laser radar considering vegetation canopy density of claim 1, wherein in S100, the grid is divided into four types of regions according to the distribution density of the planted vegetation in the grid, and a "sampling point method" is adopted, specifically: evenly set up m sampling points in every graticule mesh, judge whether the sampling point is that the crown covers based on public satellite image, count and is covered the number n of sampling points, calculate the vegetation cover factor a of this graticule mesh n/m x 100%, divide into four kinds of regions through the vegetation cover factor a size of graticule mesh with the net.
4. The design method of the airborne laser radar considering vegetation canopy density of claim 1, wherein in S100, four types of areas into which grids are divided are: non-forest land, sparse land, medium-forest land and dense-forest land.
5. The design method of the vegetation canopy-aware airborne lidar of claim 4, wherein the number of grids extracted for the non-forest land, the sparse land, the medium-forest land, and the dense-forest land is 1, 2, 4, and 8, respectively.
6. The method for designing an airborne lidar according to claim 4, wherein the ground resolution of the images of the non-forest land and the sparse land is 0.2m, and the ground resolution of the images of the moderate forest land and the dense land is 0.1 m.
7. The method of claim 2, wherein the canopy density index β is a measure of the canopy densityiThe formula is as follows:
Figure FDA0002490432790000031
wherein S isiIs the sum of the vegetation coverage areas, PiAnd i is 1, 2, 3 and 4 for the number of the current type grids.
8. The method of claim 1, wherein the optimal parameters include: laser pulse frequency, scan angle, relative altitude, flight speed, side lap.
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