CN110148116A - A kind of forest biomass evaluation method and its system - Google Patents
A kind of forest biomass evaluation method and its system Download PDFInfo
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Abstract
The invention discloses a kind of forest biomass evaluation method and its systems.The method includes the steps: the forest parameters for obtaining region to be measured calculate the forest biomass of sampling point；LiDAR point cloud data are obtained, and the LiDAR point cloud data are separated, digital elevation model is generated according to the ground point；The LiDAR point cloud data are normalized, normalized LiDAR point cloud data are obtained；The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；The vegetation index distributed data in the region is calculated according to the multispectral data；The forest biomass and trees height grid data and vegetation index distributed data are estimated to the forest biomass in region to be measured using biomass regression formula.The present invention combines the spectral information of unmanned plane multispectral data and the forest three-dimensional structure information of unmanned plane LiDAR data, carries out inverting estimation using the truthful data of eyeball, substantially increases the precision of inverting.
Description
Technical field
The present invention relates to remote-sensing inversion technical field more particularly to a kind of forest biomass evaluation method and its it is
System.
Background technique
Traditional forest inventory investigation mainly obtains related data with field survey, and this method exists takes time and effort,
Subjectivity is strong, looks into time and effort consuming, destructive big, is often confined to small range standing crop biomass and Stand Biomass estimation, no
It is suitble to the forest survey of large area.And the appearance of remote sensing technology, possibility is provided for a wide range of forest inventory investigation.But it is common
Satellite optical remote sensing image is only capable of providing the spectral information of forest, it is difficult to obtain important three-dimensional structure information, and part shadow
Picture resolution ratio is lower, vulnerable to the influence of weather, it is made to there is a problem of that precision is not high in obtaining forest biomass.
Emerging LiDAR technology then while obtaining forest horizontal distribution information, is also capable of providing passive optical remote sensing
The Forest Vertical structural information that cannot reflect has preferable advantage on reflection ground biomass.But since LiDAR can not
The information such as spectrum, the vegetation index of forest are provided, there are still certain limitations on the biomass of inverting forest.
Therefore, the existing technology needs to be improved and developed.
Summary of the invention
The technical problem to be solved in the present invention is that in view of the above drawbacks of the prior art, providing a kind of forest biomass
Evaluation method and its system, it is intended to solve that there is asking for certain limitation to the evaluation method of forest biomass in the prior art
Topic.
It is as follows that the present invention solves technical solution used by above-mentioned technical problem:
A kind of evaluation method of forest biomass, wherein comprising steps of
Forest parameters by obtaining region to be measured calculate the forest biomass of sampling point；
The LiDAR point cloud data in region to be measured are obtained, and the LiDAR point cloud data are separated, obtain ground point；According to
The ground point generates digital elevation model；
The LiDAR point cloud data are normalized using the digital elevation model, obtain normalized LiDAR point
Cloud data；
The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；
The multispectral data for obtaining region to be measured calculates the vegetation index distribution number in the region according to the multispectral data
According to；
The forest biomass and trees height grid data and vegetation index distributed data are used into biomass regression formula
Estimate the forest biomass in region to be measured.
The forest biomass evaluation method, wherein the step obtains the multispectral data in region to be measured, according to described
Multispectral data calculates the vegetation index distributed data in the region, specifically includes:
According to geographical coordinate control point of taking photo by plane, image joint is carried out to the Unmanned Aerial Vehicle Data image in region to be measured；
Radiant correction is carried out to the spliced Unmanned Aerial Vehicle Data image, calculates the normalization vegetation of vegetation in region to be measured
Index.
The forest biomass evaluation method, wherein the step switchs to the normalized LiDAR point cloud data pre-
The raster data for determining resolution ratio obtains trees height grid data, specifically includes:
LiDAR point cloud data are normalized, obtain trees for the absolute altitude on ground；
To the LiDAR point cloud data after normalized, it is arranged according to predetermined layering and carries out horizontal slice；The predetermined layering is set
It sets and refers to since designated position, be layered with certain layering interval；
A cloud is carried out using K-Means clustering method to each layer that layering obtains to cluster, and obtains cluster；
Cluster polygon is set, the cluster polygon is stacked, stacking figure is obtained；
Local maximum is identified in the stacking figure using the window of fixed size, obtains single wooden parameter；
Using single wooden parameter as seed point, cloud list wood is carried out to the seed point using PCS algorithm and is divided, to described point
It cuts result to be edited, obtains trees height grid data.
The forest biomass evaluation method, wherein the biomass regression formula are as follows:
Wherein, AGB is ground biomass, and H is trees height, and NDVI is normalized differential vegetation index.
The forest biomass evaluation method, wherein the step carries out the spliced Unmanned Aerial Vehicle Data image
Radiant correction calculates the normalized differential vegetation index of vegetation in region to be measured, wherein radiant correction includes: that green wave band corrects, is close red
Wave section correction, the correction of red side wave section, the correction of red wave band.
The forest biomass evaluation method, wherein the calculation formula of the normalized differential vegetation index are as follows:
Wherein,WithRespectively indicate the reflectivity of near infrared band and red spectral band.
The forest biomass evaluation method, wherein the predetermined resolution is 0.3-0.6 meters of resolution ratio.
The forest biomass evaluation method, wherein the position that the designated position is 0.5-0.7 meters is divided into 1 between layering
Rice.
The forest biomass evaluation method, wherein the fixed size is 1.0-2.0 meters.
A kind of ground biomass estimation system, wherein include: processor, and the storage being connected with the processor
Device；
The memory is stored with ground biomass estimation program, and the ground biomass estimation program is executed by the processor
When perform the steps of
Obtain the forest biomass of region sampling point to be measured；
The LiDAR point cloud data in region to be measured are obtained, and the LiDAR point cloud data are separated, obtain ground point；
Digital elevation model is generated according to the ground point；
The LiDAR point cloud data are normalized using the digital elevation model, obtain normalized LiDAR point
Cloud data；
The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；
The multispectral data for obtaining region to be measured calculates the vegetation index distribution number in the region according to the multispectral data
According to；
The forest biomass and trees height grid data and vegetation index distributed data are used into biomass regression formula
Estimate the forest biomass in region to be measured.
The utility model has the advantages that the present invention passes through the multispectral ground biomass estimation method with LiDAR data of fusion unmanned plane,
The spectral information of unmanned plane multispectral data and the forest three-dimensional structure information of unmanned plane LiDAR data are combined, actual measurement is utilized
The truthful data of point carries out inverting estimation, substantially increases the precision of inverting.
Detailed description of the invention
Fig. 1 is a kind of flow chart of forest biomass evaluation method preferred embodiment in the present invention.
Fig. 2 is the functional schematic block diagram of forest biomass estimating system preferred embodiment in the present invention.
Fig. 3 is the multispectral ground biomass estimation system structure diagram with lidar data of unmanned plane.
Fig. 4 is the reflectance map of trees in different wavelength range in Fig. 3.
Fig. 5 is that region to be measured is taken photo by plane geographical coordinate control point identification schematic diagram in embodiment.
Fig. 6 is the image joint figure in region to be measured in embodiment.
Fig. 7 is the green wave band radiant correction figure of region Unmanned Aerial Vehicle Data image to be measured in embodiment.
Fig. 8 is region Unmanned Aerial Vehicle Data image near infrared band radiant correction figure to be measured in embodiment.
Fig. 9 is the red side wave section radiant correction figure of region Unmanned Aerial Vehicle Data image to be measured in embodiment.
Figure 10 is the red wave band radiant correction figure of region Unmanned Aerial Vehicle Data image to be measured in embodiment.
Figure 11 is NDVI distribution map in region to be measured in embodiment.
Figure 12 is denoising preceding LiDAR point cloud atlas in region to be measured in embodiment.
Figure 13 is LiDAR point cloud atlas after the denoising of region to be measured in embodiment.
Figure 14 is the filtered bottom surface point diagram in region to be measured in embodiment.
Figure 15 is the filtered non-bottom surface point display diagram in region to be measured in embodiment.
Figure 16 is the 0.5 Miho Dockyard EM figure of Area generation to be measured in embodiment.
Figure 17 is the point cloud chart in embodiment before the normalization of region to be measured.
Figure 18 is the point cloud chart in embodiment after the normalization of region to be measured.
Figure 19 is the seed point display diagram that algorithm generation is folded in region to be measured based on layer heap in embodiment.
Figure 20 is point Yun Danmu segmentation result display diagram of the region based on seed point to be measured in embodiment.
Figure 21 is to the point Yun Danmu segmentation result display diagram after Figure 20 optimization.
Figure 22 is region trees height grid figure to be measured in embodiment.
Figure 23 is region NDVI Grids match control point to be measured display diagram in embodiment.
Figure 24 is biomass index map in region to be measured in embodiment.
Figure 25 is by the calculated regression result figure of regression formula.
Figure 26 is area estimation biomass distribution figure to be measured in embodiment.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention clearer and more explicit, right as follows in conjunction with drawings and embodiments
The present invention is further described.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and do not have to
It is of the invention in limiting.
Referring to Fig. 1, the present invention provides a kind of some embodiments of forest biomass evaluation method.
As shown in Figure 1, forest biomass evaluation method of the invention, comprising the following steps:
S100, the forest biomass that sampling point is calculated by obtaining the forest parameters in region to be measured.
Typical region is selected in region to be measured, on the spot to progress such as arbor, dungarunga, meadow, buildings in sample area
Investigation on biomass records the biomass in the sample area.
S200, the LiDAR point cloud data for obtaining region to be measured, and the LiDAR point cloud data are separated, obtain ground
Millet cake；Digital elevation model is generated according to the ground point.
Specifically, 1 laser radar scanner is carried using UAV flight's platform, region to be measured is scanned,
The LiDAR point cloud data in the region are obtained, due to the precision of laser radar scanning equipment, the characteristic of testee and surrounding
Environmental factor can all make the appearance of noise.Noise in LiDAR is broadly divided into high-altitude noise and low latitude noise.Wherein, high-altitude
Noise mostlys come from big particle and aerial flying object such as birds in air etc..Low noise point is mainly due in measurement
Multipath effect bring.It, must be right before being handled since noise can impact subsequent data processing
Original LiDAR point cloud data are denoised.
LiDAR point cloud data after denoising are filtered, from the point cloud data of magnanimity with non-by ground point
Millet cake is separated.Ground point is calculated using irregular triangle network method and generates digital elevation model (DEM).Using irregular
Triangle network method can effectively remove mostly that millet cake is high-efficient, the preferable advantage of robustness.
S300, the LiDAR point cloud data are normalized using the digital elevation model, are normalized
LiDAR point cloud data.
Specifically, in order to eliminate influence of the landform to Point Cloud Processing process, the point cloud after denoising and filtering is needed
It is normalized, puts the normalization of cloud, be the height by the way that the point cloud data after denoising to be subtracted to the DEM of ground point generation
A kind of processing spent and carried out.
S400, the raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtain trees height
Raster data.
Specifically, the step S400 includes:
S410, LiDAR point cloud data are normalized, obtain trees for the absolute altitude on ground；
S420, to the LiDAR point cloud data after normalized, be arranged according to predetermined layering and carry out horizontal slice；It is described predetermined
Layering setting refers to since designated position, is layered with certain layering interval；
S430, a cloud cluster is carried out using K-Means clustering method to each layer that layering obtains, obtains cluster；
S440, setting cluster polygon, the cluster polygon is stacked, stacking figure is obtained；
S450, local maximum is identified in the stacking figure using the window of fixed size, obtains single wooden parameter；
S460, it regard single wooden parameter as seed point, a cloud list wood segmentation is carried out to the seed point using PCS algorithm, it is right
The segmentation result is edited, and trees height grid data are obtained.
Seed point is folded based on layer heap in the present invention to be split normalized LiDAR point cloud data, identifies part most
Big value is used as seed point, is then based on seed point using PCS (Point Cloud Segmentation) algorithm and carries out a cloud minute
It cuts, finally obtains independent every one tree.
Layer heap folds algorithm basic thought: (1) before being split, needing for original point cloud to be normalized, eliminate landform
Influence to segmentation obtains trees for the absolute altitude on ground.(2) cloud horizontal slice is put, with 1 meter Wei Fen since 0.5 meter
Highest point is arrived in interlayer blocking to layering.(3) clustering algorithm is applied to each layer, it is therefore an objective to remove some be not required to by point cloud cluster
The short vegetation wanted.In addition, nethermost 3 layers need advanced line density scanning, pass through density and user-defined each cluster
Minimum points, by aggregation in groups.Using the CHM grid of the smooth 1 meter of resolution ratio of 3*3 pixel window, 3 meters of fixed windows are used
Radius identifies that local maximum is high as tree.Using local maximum as seed point, each layer all uses the cluster side K-Means
Method recalculates the center of cluster as new seed point, then cluster all the points in cloud cluster to the seed point nearest from it
Cloud is iteratively repeated always this process to the seed point nearest from it, until seed point location no longer changes.(4) cluster polygon,
One 0.5 meter of polygon buffer area is set around each cluster.Main there are two purposes: firstly, 0.5 meter away from main cluster
Point in addition may be separated by wrong minute in the group with another cluster.Secondly, as tie point and vector quantization cluster
A kind of means, the size of buffer area can qualitatively determine the optimum size of tree crown by repetition test and visual assessment, and
And optimum size may be slightly different because of impulse density and Forest Types.(5) it is overlapped Vean diagram, stacks every layer of polygon, it is raw
At the rasterizing non-overlapping polygon of a large amount of 0.5 meter of resolution ratio.Overlay chart has determined the region of tree crown middle-high density, multiple polygons
Overlapping shows that there are one trees.(6) smooth to stack figure, each layer of progress heap poststack is carried out smoothly with 1.5 meters of window sizes.
(7) using 1.5 meters of fixed window sizes, recognition detection local maximum, these local maximums just represent trees in stacking figure
Center, and the highest point of degree of overlapping in entire canopy.
Further, which needs to input several important parameters, the first resolution ratio of CHM and pulse spacing, point cloud
Density is related.Point cloud data density is 10 pts/m2 or so in text, and then the present invention uses 0.5 meter of resolution ratio CHM.It is minimum
Tree spacing is also extremely important, it according to the average headway set under Different Forest situation with being configured.In addition Gaussian smoothing degree meeting
The trees being partitioned into are influenced, smoothing factor is bigger, and smoothness is higher, otherwise the Gaussian smoothing factor is smaller, and smoothness is smaller.
If smoothness is excessively high less divided easily occurs, smoothness is too low easily to there is over-segmentation, therefore the size of the Gaussian smoothing factor
It is critically important.The window size that smooth radius, that is, Gaussian smoothing uses, should be with average canopy sizableness.It can be obtained after segmentation
Obtain the parameters such as single wooden position, tree height, crown diameter, tree crown area and Tree Crown Volume.It will remove dryness, separate ground point and normalizing
Point cloud after change switchs to the raster data of 0.5m resolution ratio, as trees height grid data.
S500, the multispectral data for obtaining region to be measured, refer to according to the vegetation that the multispectral data calculates the region
Number distributed data；
Specifically, since LiDAR data only provides the three-dimensional structure information on ground, and there is the plant of strong correlation with biomass
It is then difficult to provide by growing state information.Therefore, the normalized differential vegetation index that present invention combination unmanned plane multispectral data calculates
To describe the upgrowth situation information of vegetation.
The calculation formula of the normalized differential vegetation index (NDVI) are as follows:
Wherein,WithRespectively indicate the reflectivity of near infrared band and red spectral band.
Normalized differential vegetation index (NDVI) is reflection one of Grain Growth Situation and the important parameter of nutritional information, and normalization is planted
There is stronger correlation between index and biomass, be commonly used for the ground biomass estimation of large area.
S600, the forest biomass and trees height grid data and vegetation index distributed data are used into biomass
Regression formula estimates the forest biomass in region to be measured.
The biomass regression formula are as follows:
Wherein, AGB is ground biomass, and H is trees height, and NDVI is normalized differential vegetation index.One can therefrom have been drawn newly
Index, biological volume index (AGBI), with ground biomass have higher correlation, formula specific as follows:
So ground biomass and the fitting formula such as formula of biological volume index are as follows:
The present invention also provides a kind of preferred embodiments of forest biomass estimating system:
As shown in Fig. 2, the forest biomass estimating system of the embodiment of the present invention, comprising: processor 10, and with the processor
The memory 20 of 10 connections,
The memory 20 is stored with forest biomass estimation program, and the forest biomass estimation program is by the processor 10
It is performed the steps of when execution
The memory is stored with ground biomass estimation program, and the ground biomass estimation program is executed by the processor
When perform the steps of
Obtain the forest biomass of region sampling point to be measured；
The LiDAR point cloud data in region to be measured are obtained, and the LiDAR point cloud data are separated, obtain ground point；
Digital elevation model is generated according to the ground point；
The LiDAR point cloud data are normalized using the digital elevation model, obtain normalized LiDAR point
Cloud data；
The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；
The multispectral data for obtaining region to be measured calculates the vegetation index distribution number in the region according to the multispectral data
According to；
The forest biomass and trees height grid data and vegetation index distributed data are used into biomass regression formula
Estimate the forest biomass in region to be measured.
Below by a specific embodiment, a kind of forest biomass evaluation method provided by the present invention and system are done
Further explanation:
The system architecture of the embodiment includes 1 UAV flight's platform, 1 laser radar scanner, 1 multispectral image biography
Sensor, 1 GPS receiver, as shown in Figure 3.By UAV system laser radar scanner multispectral image sensor, obtain
LiDAR point cloud data and multispectral image data.Wherein, it when multispectral image sensor obtains multispectral image data, plants
The light of different-waveband can be reflected, reflectivity is as shown in Figure 4.
1. unmanned plane multispectral image flow chart of data processing is as follows:
(1) Unmanned Aerial Vehicle Data image joint geometric correction, according to geographical coordinate control point of taking photo by plane as shown in figure 5, to unmanned plane number
Spliced according to image, image result is as shown in Figure 6.
(2) Unmanned Aerial Vehicle Data image radiant correction, including the correction of green wave band radiant correction, near infrared band, red side wave section
Correction, the correction of red wave band, correction result is as is seen in figs 7-10.
(3) unmanned plane image data calculates normalized differential vegetation index (NDVI), as a result as shown in figure 11.
2. unmanned plane laser point cloud data process flow is as follows:
(1) aerial noise is removed, the quality of data is improved；It as shown in figure 12, is the LiDAR point cloud atlas before denoising, Figure 13 is
LiDAR point cloud atlas after making an uproar.
(2) ground point is separated from point cloud data, it is filtered ground point diagram as shown in figure 14, and Figure 15 is after filtering
Non-ground points figure.
(3) DEM is generated based on ground point, Figure 16 is the 0.5 Miho Dockyard EM figure generated
(4) cloud is normalized using DEM, remove the influence of topography, such as Figure 17, for denoising after point cloud data not into
Row normalized.Figure 18 is the point cloud data after left figure is normalized, wherein the Z value of each point is point and ground
True vertical distance, when point is at treetop, its Z value be set it is high.
(5) layer heap is storied at seed point, folds algorithm using layer heap and extracts single wooden position (kind from normalized point cloud data
Sub- dot file is the csv file that comma separates, wherein comprising four column, successively are as follows: tree ID, X, Y, Z coordinate), with these information work
For seed point, single wood segmentation is carried out to cloud.As shown in figure 19.
(6) single wood segmentation is carried out based on the point-to-point cloud of seed, obtain single wooden position, tree height, crown diameter, tree crown area and
Tree Crown Volume.Single wood segmentation result is as shown in figure 20.
(7) it is checked by result of the ALS edit tool to single wood segmentation, meanwhile, seed point is increased, is deleted
Equal man-machine interactivelies editor, and a cloud is split again based on edited seed point, as a result as shown in figure 21.
(8) raster data that the point cloud after removing dryness, separating ground point and normalize is switched to 0.5m resolution ratio, as sets
The wooden height grid figure.As shown in figure 22.
(9) accuracy registration is carried out to NDVI grid map using trees height grid figure.As shown in figure 23.
(10) according to the X, Y coordinates of Investigation on biomass point, corresponding seed dot file, csv format, such as one institute of following table are configured
Show.
Table one
| TreeID | TreeLocationX | TreeLocationY | TreeLocationZ |
| 1 | 225397.939 | 2521767.749 | 5 |
| 2 | 225404.607 | 2521766.955 | 6 |
| 3 | 225420.72 | 2521735.919 | 7 |
| 4 | 225486.767 | 2521680.314 | 8 |
| 5 | 225618.834 | 2521571.57 | 10 |
| 6 | 225759.509 | 2521099.765 | 12 |
| 7 | 225687.277 | 2521068.015 | 13 |
| 8 | 225284.078 | 2521420.229 | 6 |
(11) this seed dot file is opened in lidar360, according to these seed points and has carried out the point after single wood segmentation
Cloud (can obtain single wooden position, tree high), read the adjacent nearest single ebon of these seed points it is high (or most suitable tree height,
It needs to manually check).As shown in Table 2.
Table two
| TreeID | TreeLocationX | TreeLocationY | NDVI | Bio | TreeHeight |
| 1 | 225397.939 | 2521767.749 | 0.185 | 1 | 0.377 |
| 2 | 225404.607 | 2521766.955 | -0.207 | 0 | 0 |
| 3 | 225420.72 | 2521735.919 | 0.729 | 600 | 7.204 |
| 4 | 225486.767 | 2521680.314 | 0.548 | 30 | 4.297 |
| 5 | 225618.834 | 2521571.57 | 0.762 | 300 | 6.283 |
| 6 | 225759.509 | 2521099.765 | 0.788 | 1000 | 6.122 |
| 7 | 225687.277 | 2521068.015 | 0.689 | 200 | 4.758 |
| 8 | 225284.078 | 2521420.229 | 0.467 | 10 | 0.061 |
2, biomass calculates calculation process
(1) trees height grid figure is multiplied with the NDVI figure being registrated and obtains biomass index map.As shown in figure 24.
(2) biomass samples on the spot
More than 10 sampling points of acquisition on the spot, the sample of biomass sampling on the spot are as follows altogether in research area:
Weeds；Geographical coordinate: 22.782071,114.334434；Biomass: 0.50kg；Eucalyptus, 12 meters of height, the diameter of a cross-section of a tree trunk 1.3 meters above the ground 30 are public
Point；Geographical coordinate: 22.774872,114.328818；Biomass: about 680kg；Dungarunga, 2 meters, 8 centimeters of the diameter of a cross-section of a tree trunk 1.3 meters above the ground of height；It is geographical
Coordinate: 22.783026,114.326606；Biomass: about 30kg.
(3) it is returned according to Investigation on biomass value on the spot with biomass index value, calculates regression formula.Regression result
As shown in figure 25.
(4) according to regression formula estimation biomass and at figure, as shown in figure 26.
In conclusion a kind of forest biomass evaluation method provided by the present invention and its system, the method includes steps
Rapid: the forest parameters by obtaining region to be measured calculate the forest biomass of sampling point；Obtain the LiDAR point cloud number in region to be measured
According to, and the LiDAR point cloud data are separated, obtain ground point；Digital elevation model is generated according to the ground point；It adopts
The LiDAR point cloud data are normalized with the digital elevation model, obtain normalized LiDAR point cloud number
According to；The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；
The multispectral data for obtaining region to be measured calculates the vegetation index distributed data in the region according to the multispectral data；It will
The forest biomass is estimated with trees height grid data and vegetation index distributed data using biomass regression formula
The forest biomass in region to be measured.The present invention passes through the multispectral ground biomass estimation side with LiDAR data of fusion unmanned plane
Method combines the spectral information of unmanned plane multispectral data and the forest three-dimensional structure information of unmanned plane LiDAR data, utilizes
The truthful data of eyeball carries out inverting estimation, substantially increases the precision of inverting.
It should be understood that the application of the present invention is not limited to the above for those of ordinary skills can
With improvement or transformation based on the above description, all these modifications and variations all should belong to the guarantor of appended claims of the present invention
Protect range.
Claims (10)
1. a kind of forest biomass evaluation method, which is characterized in that comprising steps of
Forest parameters by obtaining region to be measured calculate the forest biomass of sampling point；
The LiDAR point cloud data in region to be measured are obtained, and the LiDAR point cloud data are separated, obtain ground point；According to
The ground point generates digital elevation model；
The LiDAR point cloud data are normalized using the digital elevation model, obtain normalized LiDAR point
Cloud data；
The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；
The multispectral data for obtaining region to be measured calculates the vegetation index distribution number in the region according to the multispectral data
According to；
The forest biomass and trees height grid data and vegetation index distributed data are used into biomass regression formula
Estimate the forest biomass in region to be measured.
2. forest biomass evaluation method according to claim 1, which is characterized in that the step obtains the more of region to be measured
Spectroscopic data calculates the vegetation index distributed data in the region according to the multispectral data, specifically includes:
According to geographical coordinate control point of taking photo by plane, image joint is carried out to the Unmanned Aerial Vehicle Data image in region to be measured；
Radiant correction is carried out to the spliced Unmanned Aerial Vehicle Data image, calculates the normalization vegetation of vegetation in region to be measured
Index.
3. forest biomass evaluation method according to claim 1, which is characterized in that the step will be described normalized
LiDAR point cloud data switch to the raster data of predetermined resolution, obtain trees height grid data, specifically include:
LiDAR point cloud data are normalized, obtain trees for the absolute altitude on ground；
To the LiDAR point cloud data after normalized, it is arranged according to predetermined layering and carries out horizontal slice；The predetermined layering is set
It sets and refers to since designated position, be layered with certain layering interval；
A cloud is carried out using K-Means clustering method to each layer that layering obtains to cluster, and obtains cluster；
Cluster polygon is set, the cluster polygon is stacked, stacking figure is obtained；
Local maximum is identified in the stacking figure using the window of fixed size, obtains single wooden parameter；
Using single wooden parameter as seed point, cloud list wood is carried out to the seed point using PCS algorithm and is divided, to described point
It cuts result to be edited, obtains trees height grid data.
4. forest biomass evaluation method according to claim 1, which is characterized in that the biomass regression formula are as follows:
Wherein, AGB is ground biomass, and H is trees height, and NDVI is normalized differential vegetation index.
5. forest biomass evaluation method according to claim 2, which is characterized in that the step is to the spliced nothing
Man-machine image data carries out radiant correction, calculates the normalized differential vegetation index of vegetation in region to be measured, wherein radiant correction packet
It includes: green wave band correction, near infrared band correction, the correction of red side wave section, the correction of red wave band.
6. forest biomass evaluation method according to claim 2, which is characterized in that the calculating of the normalized differential vegetation index
Formula are as follows:
Wherein,WithRespectively indicate the reflectivity of near infrared band and red spectral band.
7. forest biomass evaluation method according to claim 1, which is characterized in that the predetermined resolution is 0.3-0.6
The resolution ratio of rice.
8. forest biomass evaluation method according to claim 3, which is characterized in that the designated position is 0.5-0.7 meters
Position, 1 meter is divided between layering.
9. forest biomass evaluation method according to claim 1, which is characterized in that the fixed size is 1.0-2.0 meters.
10. a kind of ground biomass estimation system characterized by comprising processor, and be connected with the processor
Memory；
The memory is stored with ground biomass estimation program, and the ground biomass estimation program is executed by the processor
When perform the steps of
Obtain the forest biomass of region sampling point to be measured；
The LiDAR point cloud data in region to be measured are obtained, and the LiDAR point cloud data are separated, obtain ground point；
Digital elevation model is generated according to the ground point；
The LiDAR point cloud data are normalized using the digital elevation model, obtain normalized LiDAR point
Cloud data；
The raster data that the normalized LiDAR point cloud data are switched to predetermined resolution, obtains trees height grid data；
The multispectral data for obtaining region to be measured calculates the vegetation index distribution number in the region according to the multispectral data
According to；
The forest biomass and trees height grid data and vegetation index distributed data are used into biomass regression formula
Estimate the forest biomass in region to be measured.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110781862A (en) * | 2019-11-07 | 2020-02-11 | 航天信德智图（北京）科技有限公司 | Multispectral image-based forest arbor and shrub identification method |
| CN110823813A (en) * | 2019-11-19 | 2020-02-21 | 深圳市智绘科技有限公司 | Forest land ground biomass estimation method |
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| CN114463512A (en) * | 2021-12-24 | 2022-05-10 | 广州极飞科技股份有限公司 | Point cloud data processing method, vectorization method and device |
| CN115629393A (en) * | 2022-11-30 | 2023-01-20 | 北京市农林科学院智能装备技术研究中心 | Biomass estimation method, device, system, electronic device and storage medium |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393180A (en) * | 2011-10-19 | 2012-03-28 | 中国林业科学研究院资源信息研究所 | Method for automatically extracting forest stand upper layer tree parameters from LiDAR point cloud data |
| CN104778451A (en) * | 2015-03-31 | 2015-07-15 | 中国科学院上海技术物理研究所 | Grassland biomass remote sensing inversion method considering grassland height factor |
| CN107085710A (en) * | 2017-04-26 | 2017-08-22 | 长江空间信息技术工程有限公司(武汉) | A kind of single wooden extraction method based on multispectral LiDAR data |
| CN108020211A (en) * | 2017-12-01 | 2018-05-11 | 云南大学 | A kind of method of unmanned plane aeroplane photography estimation instruction plant biomass |
| US20190034725A1 (en) * | 2016-01-29 | 2019-01-31 | Global Surface Intelligence Limited | System and method for earth observation and analysis |
-
2019
- 2019-04-12 CN CN201910295160.6A patent/CN110148116A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393180A (en) * | 2011-10-19 | 2012-03-28 | 中国林业科学研究院资源信息研究所 | Method for automatically extracting forest stand upper layer tree parameters from LiDAR point cloud data |
| CN104778451A (en) * | 2015-03-31 | 2015-07-15 | 中国科学院上海技术物理研究所 | Grassland biomass remote sensing inversion method considering grassland height factor |
| US20190034725A1 (en) * | 2016-01-29 | 2019-01-31 | Global Surface Intelligence Limited | System and method for earth observation and analysis |
| CN107085710A (en) * | 2017-04-26 | 2017-08-22 | 长江空间信息技术工程有限公司(武汉) | A kind of single wooden extraction method based on multispectral LiDAR data |
| CN108020211A (en) * | 2017-12-01 | 2018-05-11 | 云南大学 | A kind of method of unmanned plane aeroplane photography estimation instruction plant biomass |
Non-Patent Citations (1)
| Title |
|---|
| 杨秋丽: "机载LiDAR单木分割方法对比及其影响因素分析", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110781862A (en) * | 2019-11-07 | 2020-02-11 | 航天信德智图（北京）科技有限公司 | Multispectral image-based forest arbor and shrub identification method |
| CN110823813A (en) * | 2019-11-19 | 2020-02-21 | 深圳市智绘科技有限公司 | Forest land ground biomass estimation method |
| CN111754515B (en) * | 2019-12-17 | 2024-03-01 | 北京京东乾石科技有限公司 | Sequential gripping method and device for stacked articles |
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| CN112434617A (en) * | 2020-11-26 | 2021-03-02 | 南京观微空间科技有限公司 | Forest biomass change monitoring method and system based on multi-source remote sensing data |
| CN112434617B (en) * | 2020-11-26 | 2021-08-13 | 南京观微空间科技有限公司 | Forest biomass change monitoring method and system based on multi-source remote sensing data |
| 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 |
| CN112730306A (en) * | 2020-12-21 | 2021-04-30 | 合肥工业大学智能制造技术研究院 | Cloud cluster three-dimensional space distribution measuring method, device and system |
| CN114120158A (en) * | 2021-11-29 | 2022-03-01 | 四川省自然资源科学研究院 | Leaf area statistical method and device based on ultra-low altitude remote sensing and storage medium |
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| CN116127327B (en) * | 2023-04-07 | 2023-07-11 | 广东省科学院广州地理研究所 | Forest ground biomass inversion method, device, equipment and storage medium |
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