CN102393180A - Method for automatically extracting forest stand upper layer tree parameters from LiDAR point cloud data - Google Patents
Method for automatically extracting forest stand upper layer tree parameters from LiDAR point cloud data Download PDFInfo
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- 239000002023 wood Substances 0.000 claims description 17
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Abstract
The invention aims to prevent the dependence on prior knowledge when forest stand upper layer tree parameters are extracted from LiDAR point cloud data and provides a method for automatically extracting forest stand upper layer tree parameters from LiDAR point cloud data. The method is realized through the following steps: detecting and acquiring the tree top of each single tree to be detected in a region to be detected based on the partial maximal value; acquiring parameters of the single tree to be detected by combining a region increasing method and a polynomial fitting method on the basis of the tree top of the single tree to be detected; and collecting the parameters of each single tree to be detected in the region to be detected, thus the forest stand upper layer tree parameters in the region to be detected are obtained. By utilizing the method provided by the invention, the segmentation of each single tree and the automatic extraction of the forest stand upper layer tree parameters are realized by combining the detection of the partial maximal value, a region increasing method and a polynomial fitting method without the prior knowledge, thus the extraction process of the forest stand upper layer tree parameters is simplified.
Description
Technical field
The present invention relates to laser radar data and handle and information extraction technology, relate in particular to a kind of method of from the LiDAR cloud data, extracting standing forest upper strata trees parameter automatically.
Background technology
Laser radar (Light Detection And Ranging, LiDAR) be one through come the active remote sensing technology of asking distance of determination sensor and object by the laser pulse that sensor sent.In the forestry applications field, laser radar can be used for inverting forest vertical stratification and estimate the height of crop.For example, airborne small light spot laser radar system successfully is used for the estimation of single wooden parameter, and the wooden parameter of described list comprises the height of tree, crown size of trees, tree positions etc.
Development in recent years the Dan Mu of a large amount of standing forest upper strata trees based on the LiDAR cloud data cut apart and single wooden parameter extraction algorithm.These algorithms all are from laser point cloud data, generate CHM (Canopy Height Model, canopy height model), and the position of decision tree is through cutting apart the border that obtains tree crown.But these algorithms mostly need some prioris of test site as the starting condition of cutting apart.Some algorithms can be used the test site height of tree usually and confirm the size of search window, the size of matching template, the interior mark or the external standard of watershed segmentation with the relation of the hat width of cloth as known conditions.Also have some algorithms to avoid requirement to known conditions through multiple dimensioned detection method, but maybe yardstick confirm, each height of tree grade corresponding threshold is isoparametric still need be according to some prioris when definite.
Summary of the invention
The purpose of this invention is to provide and a kind ofly from the LiDAR cloud data, automatically extract the method for standing forest upper strata trees parameter, thereby avoid when extracting standing forest upper strata trees parameter dependence priori.
The object of the invention is realized through following technical scheme.
A kind of method of from the LiDAR cloud data, extracting standing forest upper strata trees parameter automatically comprises:
Step 1, generate CHM based on the LiDAR cloud data in zone to be detected;
Step 2, the CHM that generates is carried out local maximum detection, obtain the treetop of each strain Dan Mu to be detected in the said zone to be detected;
Step 3, begin, utilize the method for region growing, upwards extract the height of tree crown section respectively many other side from the treetop of every strain Dan Mu to be detected;
Step 4, to every strain Dan Mu to be detected, respectively each height of tree crown section is carried out fitting of a polynomial, and obtains the polynomial flex point of match, the flex point that makes progress according to each the other side obtains the hat width of cloth of the wooden all directions of individual plant list to be detected;
Step 5, to every strain Dan Mu to be detected, each hat width of cloth to direction averages to it, obtains the hat width of cloth of individual plant Dan Mu to be detected;
Step 6, to every strain Dan Mu to be detected, extract in its hat width of cloth maximum CHM value highly as single wood;
Step 7, to every strain Dan Mu to be detected, extract the treetop in CHM corresponding position as single wooden coordinate;
Step 8, preserve and show the parameter of detected Dan Mu, the parameter of said Dan Mu comprises: the hat width of cloth, single wood height, single wooden coordinate.
Method provided by the invention realizes the cutting apart and standing forest upper strata trees Parameter Extraction of Dan Mu do not needed priori in conjunction with the detection of local maximum, region growing method and polynomial fitting method.Thereby simplified standing forest upper strata trees Parameter Extraction process, and improved automaticity.
Description of drawings
The method flow diagram that Fig. 1 embodiment of the invention provides;
The direction synoptic diagram that Fig. 2 provides for the embodiment of the invention.
Embodiment
The invention provides a kind of method of from the LiDAR cloud data, extracting standing forest upper strata trees parameter automatically.This method realizes based on local maximum, region growing and fitting of a polynomial, does not need priori just can access standing forest upper strata trees parameter.This method is as shown in Figure 1, mainly realizes through following steps:
Step 1, generate CHM based on the LiDAR cloud data in zone to be detected;
Step 2, the CHM that generates is carried out local maximum detection, obtain the treetop of each strain Dan Mu to be detected in the said zone to be detected;
In the embodiment of the invention, the local maximum that detection is obtained is as the treetop of Dan Mu to be detected.
Step 3, begin, utilize the method for region growing, upwards extract the height of tree crown section respectively many other side from the treetop of every strain Dan Mu to be detected;
Wherein, a pair of direction is made up of opposite both direction.With " east, south, west, north, northeast, the southeast, northwest, southwest " 8 directions is example, and " east-west ", " north and south ", " northeast-southwest ", " southeast-northwest " are 4 pairs of directions.Accordingly, can begin, upwards extract the tree crown section respectively, obtain 4 height of tree crown sections this 4 the other side from the treetop of Dan Mu to be detected.Direction can also be represented with other modes.For example, " front, rear, left and right ", so, " anterior-posterior ", " L-R " constitute two pairs of directions; Can also represent by clockwise that for example " 0 direction " constitutes a pair of reverse direction or the like with " 6 directions ".
Step 4, to every strain Dan Mu to be detected, respectively each height of tree crown section is carried out fitting of a polynomial, and obtains the polynomial flex point of match, the flex point that makes progress according to each the other side obtains individual plant single each hat width of cloth to direction of wood to be detected;
Wherein, can carry out the polynomial of degree n match, preferred, adopt least square method to carry out 4 order polynomial matches.
Step 5, to every strain Dan Mu to be detected, each hat width of cloth to direction averages to it, obtains the hat width of cloth of individual plant Dan Mu to be detected;
Step 6, to every strain Dan Mu to be detected, extract in its hat width of cloth maximum CHM value highly as single wood;
Step 7, to every strain Dan Mu to be detected, extract the treetop in CHM corresponding position as single wooden coordinate;
Step 8, preserve and show the parameter of detected Dan Mu to be detected, the parameter of said Dan Mu to be detected comprises: the hat width of cloth, single wood height, single wooden coordinate.
Wherein, the summation of the wooden parameter of detected list in the zone to be detected is called the standing forest upper strata trees parameter in this zone to be detected.
Method provided by the invention realizes the cutting apart and standing forest upper strata trees Parameter Extraction of Dan Mu do not needed priori in conjunction with the detection of local maximum, region growing method and polynomial fitting method.Thereby simplified standing forest upper strata trees Parameter Extraction process.Method provided by the invention can but be not only applicable to detection and the application of temperate zone coniferous forest, broad-leaf forest, theropencedrymion, subtropical forest and hylaea, have very high reliability and stability.
With a concrete application implementation example, method provided by the invention is elaborated below.In this application implementation example, treat surveyed area through small light spot airborne laser radar system and survey, obtain the LiDAR cloud data in this zone to be detected.
Use method provided by the invention, the implementation of from the LiDAR cloud data, extracting standing forest upper strata trees parameter comprises following operation:
Step 1, the LiDAR cloud data is carried out the Filtering Processing of ground point and vegetation point, utilize ground point to generate the ground elevation model, the ground elevation model value that utilizes the elevation of vegetation point to deduct the relevant position obtains the data set after the elevation normalization;
Among the present invention, the ground elevation model is meant DEM (Digital Elevation Model, digital elevation model).
Step 2, the data set after the above-mentioned elevation normalization is carried out assignment handle; Obtain the maximal value in each grid of data centralization; Concentrating the pixel that does not obtain effective LiDAR echo data to carry out interpolation to data fills processing, fills processing through assignment and interpolation and obtaining the tree crown elevation model;
Among the present invention, the tree crown elevation model is meant CHM (Canopy Height Model).
Step 3, CHM is carried out Gauss's The disposal of gentle filter, so that further reduce the influence of empty pixel (being the above-mentioned pixel that does not obtain effective LiDAR echo data) and noise figure;
Step 4, the CHM after the The disposal of gentle filter is carried out local maximum detection, obtain the treetop of each strain Dan Mu to be detected in the zone to be detected;
In addition; In step 4; Can also the local maximum of 4 quadrants be averaged,, the ratio of single wood height to be detected and dominant tree mean height is set as the dominant tree mean height in zone to be detected; Utilize the threshold value of the product of this ratio and dominant tree mean height, thereby control quantity and the ratio of upper strata trees finally to be detected as local maximum.Described ratio can have the user flexibility definition.
Step 5, to individual plant Dan Mu to be detected, from the treetop of Dan Mu to be detected, utilize the method for region growing upwards to extract the height of tree crown section respectively 4 the other side, obtain 4 height of tree crown sections;
As shown in Figure 2, these 4 pairs of directions are respectively " east-west ", " south-north ", " northeast-southwest " and " southeast-northwest ".
Step 6, to individual plant Dan Mu to be detected, respectively each height of tree crown section is carried out fitting of a polynomial, and obtains the polynomial flex point of match, the flex point that makes progress according to each the other side obtains individual plant single each hat width of cloth to direction of wood to be detected;
Wherein, can but be not limited only to carry out 4 order polynomial matches, fitting algorithm can but be not limited only to adopt least square method.
Step 7, to individual plant Dan Mu to be detected, each hat width of cloth to direction averages to it, obtains the hat width of cloth of individual plant Dan Mu to be detected;
Step 8, to individual plant Dan Mu to be detected, extract maximum CHM value in its hat width of cloth (maximum CHM value is meant CHM is carried out before the The disposal of gentle filter) as single highly wooden;
Step 9, to individual plant Dan Mu to be detected, extract the position of treetop in CHM as single wooden coordinate;
Through step 5～step 9, obtain the parameter of each strain Dan Mu to be detected in the zone to be detected.
If confirmed the threshold value of local maximum, then before step 5, also the threshold value with detected each local maximum and local maximum compares, and only carries out above-mentioned steps 5～step 9 to the to be detected single wood greater than threshold value.
Step 10, the distance of the wooden coordinate of detected list is judged, if the distance of two wooden coordinates of list less than the coronule width of cloth of arbitrary Dan Mu wherein, then the parameter with two Dan Mu merges;
The operation of repeating step 10, stable up to single wood sum.
Wherein, The coronule width of cloth of Dan Mu be meant Dan Mu each to the minimum value in the hat width of cloth of direction; Said merging is meant: the highly big value of list wood of getting two strain Dan Muzhong is got the hat width of cloth of the big value of the hat width of cloth of two Dan Muzhong as the Dan Mu after merging as the single wood height after merging;
Step 11, the parameter of the Dan Mu to be detected in the zone to be detected that obtains through step 10 back is preserved and exported.
The method that the invention described above application implementation example provides, the user can control quantity and the ratio of upper strata trees to be detected flexibly.Can fully automatically obtain the parameter of standing forest upper strata trees; For forest parameters quantitative estimation, forest quality evaluation, orest management decision-making provide good remote sensing estimation means; Along with airborne Lidar data are obtained increasingly extensively, the present invention has very strong practical value.
The above; Be merely the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technician who is familiar with the present technique field is in the technical scope that the present invention discloses; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claims.
Claims (6)
1. a method of from the LiDAR cloud data, extracting standing forest upper strata trees parameter automatically is characterized in that, comprising:
Step 1, generate canopy height MODEL C HM based on the LiDAR cloud data in zone to be detected;
Step 2, the canopy height model that generates is carried out local maximum detection, obtain the treetop of each strain Dan Mu to be detected in the said zone to be detected;
Step 3, begin from the treetop of every strain Dan Mu to be detected, utilize the method for region growing, upwards extract the height of tree crown section respectively many other side, wherein, a pair of direction is made up of opposite both direction;
Step 4, to every strain Dan Mu to be detected, respectively each height of tree crown section is carried out fitting of a polynomial, and obtains the polynomial flex point of match, obtain individual plant single each hat width of cloth of wood to be detected according to the flex point on all directions to direction;
Step 5, to every strain Dan Mu to be detected, each hat width of cloth to direction averages to it, obtains the hat width of cloth of individual plant Dan Mu to be detected;
Step 6, to every strain Dan Mu to be detected, extract in its hat width of cloth maximum CHM value highly as single wood;
Step 7, to every strain Dan Mu to be detected, extract the treetop in CHM corresponding position as single wooden coordinate;
Step 8, preserve and show the parameter of detected Dan Mu, the parameter of said Dan Mu to be detected comprises: the hat width of cloth, single wood height, single wooden coordinate.
2. method according to claim 1 is characterized in that step 1 specifically comprises:
Step 11, said LiDAR cloud data is carried out the Filtering Processing of ground point and vegetation point, utilize ground point to generate ground elevation model DEM, the DEM value that the elevation of vegetation point is deducted the relevant position obtains the data set after the elevation normalization;
Step 12, the data set after the above-mentioned elevation normalization is carried out assignment handle, obtain the maximal value in each grid, the pixel that does not obtain effective LiDAR echo data is carried out interpolation fill and handle, fill to handle through assignment and interpolation and obtaining CHM.
3. method according to claim 1 is characterized in that, between step 7 and step 8, also comprises single wooden Parameter Optimization processing:
Distance to the wooden coordinate of detected list is judged; If the distance of two wooden coordinates of list is less than the coronule width of cloth of arbitrary Dan Mu wherein; Then the parameter with two Dan Mu merges; The coronule width of cloth of said Dan Mu be meant Dan Mu each to the minimum value in the hat width of cloth of direction, said merging is meant: get the height of the highly big value of list wood of two Dan Muzhong as the Dan Mu after merging, get the hat width of cloth of the Dan Mu after the big value conduct of the hat width of cloth merging of two Dan Muzhong;
Repeat aforesaid operations, stable up to single wood sum.
4. method according to claim 3 is characterized in that, said step 8 specifically comprises:
Preservation also shows through the wooden parameter of list after single wooden Parameter Optimization processing.
5. according to any described method of claim 1～4, it is characterized in that step 2 further comprises:
Local maximum to 4 quadrants averages, as the dominant tree mean height in zone to be detected;
The ratio of single wood height to be detected and dominant tree mean height is set;
With the product of said ratio and dominant tree mean height threshold value as local maximum.
6. method according to claim 5 is characterized in that, between step 2 and step 3, this method also comprises: the threshold value of detected each local maximum and said local maximum is compared;
To the Dan Mu of local maximum greater than said threshold value, execution in step 3～step 7.
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