CN110823813A - Forest land ground biomass estimation method - Google Patents
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Abstract
The invention relates to a forest land ground biomass estimation method, which comprises the following steps: collecting multispectral information of the forest area by an unmanned aerial vehicle; a processing step, namely acquiring the tree height and vegetation index of the forest area based on multispectral information; sampling, namely performing field sampling on biomass at a plurality of places in a forest area to obtain biomass of a plurality of sampling points; and an estimation step, wherein the ground biomass in the forest area is estimated based on the biomass of the sampling point, the height of the tree and the vegetation index. According to the technical scheme, multispectral information of the forest area is collected through the unmanned aerial vehicle, biomass investigation of the forest land in a large range can be met, and estimation efficiency can be improved; moreover, the unmanned aerial vehicle can acquire the three-dimensional structure information of the forest area and simultaneously ensure the high precision of the acquired information, so that the precision and the accuracy of biomass estimation are improved; in addition, the biomass of a plurality of sampling points in the forest region is combined for estimation, so that the accuracy of biomass estimation is further enhanced.
Description
Technical Field
The invention belongs to the field of biomass inversion, and particularly relates to a forest land biomass estimation method.
Background
Forest land resources are one of the most important ecological systems in China, play an indispensable role in water circulation and biological diversity, and play an important role in maintaining ecological balance. The ground biomass is an important index of a dynamic ecosystem and is an important basis for reasonable application of forest land resources. Thus, the timeliness and effectiveness of ground biomass is critical to help assess and monitor the ecosystem and to measure the impact of human activity on it.
The biomass survey mode most commonly used at present is field survey. Although the field measurement can obtain more accurate biomass estimation, the method consumes a lot of time and manpower, is easily influenced by the subjectivity of investigators, and has larger destructiveness to the forest land, so the method is only suitable for biomass estimation with small range and low time efficiency requirement, and has poorer effect under the condition of large range and high time efficiency requirement. In addition to field investigations, another biomass investigation method that is popular today is based on satellite remote sensing images. Based on the increasingly developed remote sensing technology, biomass investigation can be carried out on a large range of forest lands through satellite remote sensing images, and the time efficiency of the satellite remote sensing images is far beyond that of the traditional field investigation. However, the method has the problems of low resolution of the remote sensing image and larger weather-affected image. Therefore, for a large-scale forest region, how to improve the investigation accuracy while ensuring the investigation efficiency is a difficult problem for the current forest region biomass investigation.
Disclosure of Invention
The invention mainly aims to provide a forest land biomass estimation method, aiming at realizing biomass estimation of organisms distributed in a forest land with high efficiency and high precision.
In order to achieve the above object, the present invention provides a method for estimating forest land biomass, comprising:
the collection step comprises: collecting multispectral information of a forest area through an unmanned aerial vehicle;
the processing steps are as follows: acquiring tree height and vegetation index of a forest area based on multispectral information;
a sampling step: carrying out biomass field sampling at a plurality of places in a forest area to obtain biomass of a plurality of sampling points; and
an estimation step: estimating the ground biomass in the forest area based on the biomass of the sampling point, the height of the tree and the vegetation index.
Preferably, the processing step comprises:
generating a digital surface model and a digital elevation model of the forest area based on the multispectral information to obtain the height of the tree; and
and generating a digital orthophoto map of the forest region based on the multispectral information to obtain the vegetation index.
Preferably, the difference between the numerical value of the digital surface model and the numerical value of the digital elevation model at the same place is taken as the tree height of the place; and acquiring a near infrared band pattern and a red light band image of the forest area based on the multispectral information digital orthophoto map, and taking the ratio of the near infrared band reflectivity and the red light band reflectivity at the same place as the vegetation index of the ground.
Preferably, the processing step further comprises: and selecting imaging points of two different images corresponding to the same position of the tree height and the vegetation index on the ground to match the geographic coordinates of the two images so as to register the tree height and the vegetation index.
Preferably, the sampling step comprises:
selecting a plurality of sampling points in a forest area; and
the biomass of the site is acquired in the field at each sampling point as the sampling point biomass.
Preferably, the estimating step comprises:
generating a biomass index based on the tree height and the vegetation index;
and performing regression processing on the biomass indexes at the plurality of sampling points and the biomass at the corresponding sampling points to estimate the ground biomass of the forest area.
Preferably, the biomass index of the forest area is generated by multiplying the height of the tree by the vegetation index.
Preferably, the biomass indexes at the plurality of sampling points and the biomass at the corresponding sampling points are subjected to linear regression to estimate the ground biomass of the forest area.
Preferably, the regression function of the linear regression is: AGB 172.31 AGBI-25.14; wherein AGB is the estimated forest land ground biomass, and AGBI is a biomass index.
Preferably, the method further comprises a pre-treatment step prior to the treatment step; image stitching, geometric correction, and radiometric correction are performed on the multispectral information in the preprocessing step.
According to the technical scheme, multispectral information of the forest area is collected through the unmanned aerial vehicle, biomass investigation of the forest land in a large range can be met, and estimation efficiency can be improved; moreover, the unmanned aerial vehicle can acquire the three-dimensional structure information of the forest area and simultaneously ensure the high precision of the acquired information, so that the precision and the accuracy of biomass estimation are improved; in addition, the biomass of a plurality of sampling points in the forest region is combined for estimation, so that the accuracy of biomass estimation is further enhanced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flow chart of an alternative forest land biomass estimation method according to various embodiments of the present invention;
FIG. 2 is a flow diagram of the processing steps of one embodiment of FIG. 1;
FIG. 3 is a graph of a regression based on biomass index for a specific forest land biomass estimation.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, a flow chart of an alternative forest floor biomass estimation method according to various embodiments of the present invention is shown, the method comprising the steps of:
s100, an acquisition step: collecting multispectral information of a forest area through an unmanned aerial vehicle;
s200, a processing step: acquiring tree height and vegetation index of a forest area based on multispectral information;
s300, sampling: carrying out biomass field sampling at a plurality of places in a forest area to obtain biomass of a plurality of sampling points; and
s400, an estimation step: estimating the surface biomass in the forest area based on the biomass of the sampling point, the height of the tree and the vegetation index.
Those skilled in the art will appreciate that the S300 sampling step may be performed together with the S100 acquisition step and the S200 processing step, and may be performed in a staggered manner, as long as it is ensured that these three steps are completed before the S400 estimation step is performed. Of course, the execution of the S200 processing step depends on the completion of the S100 acquisition step.
In the forest land ground biomass estimation method, multispectral information of the forest land is collected by the unmanned aerial vehicle, so that biomass investigation on the forest land in a large range can be met, and estimation efficiency can be improved; moreover, the unmanned aerial vehicle can acquire the three-dimensional structure information of the forest area and simultaneously ensure the high precision of the acquired information, so that the precision and the accuracy of biomass estimation are improved; in addition, the biomass of a plurality of sampling points in the forest region is combined for estimation, so that the accuracy of biomass estimation is further enhanced.
In the collecting step, a person skilled in the art can adopt the existing and any suitable mode to collect the multispectral information of the forest area through the unmanned aerial vehicle, and the detailed process is not repeated.
In the processing step, a Digital Surface Model (DSM) and a Digital Elevation Model (DEM) of the forest area are generated based on the multispectral information to obtain the height of the tree; and meanwhile, generating a vegetation index based on the multispectral information.
Referring to fig. 2, in an embodiment, the multispectral information may be first encrypted in space-three, and the photo is relatively oriented by matching the same name point of the multispectral image photo of the unmanned aerial vehicle, so as to obtain the relative coordinates. And secondly, acquiring absolute coordinates of photo points by combining the acquired coordinate information of the ground control points through a block adjustment technology and generating a point cloud model. The generated point cloud model comprises ground point cloud, house point cloud, vegetation point cloud and other surface object point clouds. The invention only needs to obtain the height data of the trees, thereby eliminating the point clouds of other ground surface objects except vegetation. And screening point clouds with the same elevation from the point cloud model to serve as equal-height points and generate contour lines, and establishing an irregular triangular network to perform fitting encryption on the point cloud model so as to extract the DSM and the DEM.
The DEM value contains the elevation information of the terrain; the DSM has a value that includes both elevation information of natural features and elevation information of objects on the surface other than the ground, such as buildings, vegetation, etc., so that the DSM can more realistically represent the conditions of relief. The same resolution may be used in generating the DSM and DEM, for example, it may be desirable that both the DSM and DEM have a resolution of 0.5 meters or 0.3 meters, etc. Therefore, in order to obtain the tree height of a certain forest region, the DSM value and the DEM value of the same position can be extracted firstly, the difference value obtained by subtracting the DEM value from the DSM value is tree height grid data, and the height difference between the DSM and the DEM is the height of an earth surface object, so that the tree height of the ground can be obtained.
The multispectral image data of the unmanned aerial vehicle contains a plurality of wave bands, such as a near infrared wave band and a red light wave band. Based on multispectral information acquired by the unmanned aerial vehicle, a near-infrared band pattern and a red-light band image of a forest area can be acquired, and the ratio of the near-infrared band reflectivity and the red-light band reflectivity at the same place is taken as a vegetation index of the ground. This index is used to estimate biomass because of the strong correlation between it and biomass.
In a preferred implementation of the embodiment of fig. 2, the processing step further comprises: the imaging points of two different images corresponding to the same position of the tree height and the vegetation index on the ground are selected to match the geographic coordinates of the two images so as to register the tree height and the vegetation index, thereby further improving the estimation precision.
In the sampling step, firstly, a plurality of sampling points are selected in a forest area to be estimated; and then acquiring the biomass of the site as sampling point biomass by means of field investigation at each sampling point. Specifically, the biomass survey value can be estimated from data such as the height and the chest diameter of the sample at the time of actual sampling. For example, when the collected sample includes shrubs and trees, the biomass survey value estimation of shrubs requires the use of height data of the sample, while for trees, height and breast diameter data of the sample are required.
In the estimating step, a biomass index may be generated based on the tree height and vegetation index obtained in the processing step; and carrying out regression processing on the biomass indexes at the plurality of sampling points and the biomass at the corresponding sampling points to estimate the ground biomass of the forest area.
In one embodiment, the biomass index of the forest area may be determined as the product of the tree height multiplied by the vegetation index.
In the regression processing, the biomass indexes at a plurality of sampling points and the biomass of the corresponding sampling points are subjected to multiple regression calculation, and an optimal regression formula is fitted. For example, in one estimation, the biomass indexes at a plurality of sampling points and the biomass at corresponding sampling points are linearly regressed to estimate the ground biomass of a forest area. The method is characterized in that a regression formula is obtained through linear regression, and the quantitative relation between biomass of a biological sampling point and a biomass index value is determined based on the linear regression formula.
In the estimation of a specific forest region, 8 sampling points are selected, see table 1, after multispectral information is obtained through an unmanned aerial vehicle, a vegetation index and tree height are generated, and the product of the vegetation index and the tree height is a biological index; and obtaining biomass of sampling points at each sampling point by a field sampling mode, and taking the mass of the biomass in each square meter as the biomass (kg/m) of the sampling points2). Referring to fig. 3, the biomass indexes at a plurality of sampling points and the biomass at the corresponding sampling points are regressed to obtain the estimation as linear regression, and the regression function of the linear regression is: AGB 172.31 AGBI-25.14; wherein AGB is the estimated forest land ground biomass, and AGBI is a biomass index. A biomass estimation profile is generated based on the regression function.
TABLE 1 forest zone estimation information List
In order to further improve the estimation accuracy, in another embodiment of the present invention, a preprocessing step may be further performed before the processing step; image stitching, geometric correction, and radiometric correction are performed on the multispectral information in the preprocessing step. I.e. the multispectral information is pre-processed before the processing steps are performed.
In image splicing, original data obtained after aerial photography by an unmanned aerial vehicle consists of hundreds of frame-type small-area images. The purpose of image splicing is to seamlessly inlay the images into a whole complete image of a target area, and the spliced images are used for more conveniently carrying out subsequent data processing work.
In geometric correction, the original image usually has data distortion. The phenomenon is mainly caused by reasons such as unstable attitude of the unmanned aerial vehicle, various earth surface changes and the like. The purpose of the geometric correction is to eliminate such image distortion.
In radiation correction, the images of the unmanned aerial vehicle may be subjected to radiation distortion due to atmospheric scattering and the like, and the distortion needs to be eliminated by means of radiation correction.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for estimating land biomass in a forest area, comprising:
the collection step comprises: collecting multispectral information of a forest area through an unmanned aerial vehicle;
the processing steps are as follows: acquiring the tree height and vegetation index of the forest area based on the multispectral information;
a sampling step: carrying out biomass field sampling at a plurality of places of the forest district to obtain biomass of a plurality of sampling points; and
an estimation step: estimating a ground biomass within the forest area based on the sample point biomass, the tree height, and the vegetation index.
2. The forest land ground biomass estimation method of claim 1, wherein the processing step comprises:
generating a digital surface model and a digital elevation model of the forest area based on the multispectral information to obtain the height of the tree; and
generating the vegetation index based on the multispectral information.
3. The forest land ground biomass estimation method of claim 2, wherein,
taking the difference between the numerical value of the digital surface model and the numerical value of the digital elevation model at the same place as the tree height of the place;
and acquiring a near infrared band pattern and a red light band image of the forest area based on the multispectral information, and taking the ratio of the near infrared band reflectivity and the red light band reflectivity at the same place as the vegetation index of the ground.
4. The forest land ground biomass estimation method of claim 2, wherein the processing step further comprises: and selecting imaging points of two different images corresponding to the same positions of the tree height and the vegetation index on the ground to match the geographic coordinates of the two images so as to register the tree height and the vegetation index.
5. The forest land ground biomass estimation method of claim 1, wherein the sampling step comprises:
selecting a plurality of sampling points in the forest zone; and
and acquiring the biomass of the ground as the biomass of the sampling point in the field at each sampling point.
6. The forest land ground biomass estimation method of claim 1, wherein the estimating step comprises:
generating a biomass index based on the tree height and the vegetation index;
and performing regression processing on the biomass indexes at a plurality of sampling points and the biomass at the corresponding sampling points to estimate the ground biomass of the forest area.
7. The forest floor biomass estimation method of claim 6, wherein multiplying the tree height by the vegetation index generates the biomass index for the forest floor.
8. The forest floor biomass estimation method of claim 7, wherein the biomass indexes at a plurality of the sampling points and the biomass at the corresponding sampling points are linearly regressed to estimate the floor biomass of the forest floor.
9. The forest land ground biomass estimation method of claim 8, wherein the regression function of the linear regression is: AGB 172.31 AGBI-25.14; wherein AGB is the estimated forest land ground biomass, and AGBI is a biomass index.
10. The forest land ground biomass estimation method of claim 1, further comprising a pre-processing step prior to the processing step; and performing image splicing, geometric correction and radiation correction on the multispectral information in the preprocessing step.
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