EP2135194A2 - Tree surveying - Google Patents

Tree surveying

Info

Publication number
EP2135194A2
EP2135194A2 EP08719886A EP08719886A EP2135194A2 EP 2135194 A2 EP2135194 A2 EP 2135194A2 EP 08719886 A EP08719886 A EP 08719886A EP 08719886 A EP08719886 A EP 08719886A EP 2135194 A2 EP2135194 A2 EP 2135194A2
Authority
EP
European Patent Office
Prior art keywords
data
tree
stem
surveying system
scan
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP08719886A
Other languages
German (de)
French (fr)
Inventor
Fergal Mohan
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Treemetrics Ltd
Original Assignee
Treemetrics Ltd
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Filing date
Publication date
Application filed by Treemetrics Ltd filed Critical Treemetrics Ltd
Publication of EP2135194A2 publication Critical patent/EP2135194A2/en
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/13—Satellite images

Definitions

  • the invention relates to surveying trees.
  • the value of a forest is estimated by manually measuring sample diameters of trees at breast height (D.B.H), estimating the tree height and from that using 'mensuration' tables based on forest type and taper equations, to derive a rough estimate how much timber can likely be extracted.
  • These tables are based on average idealised tree shapes, which introduces inaccuracy as tree shape and quality is determined by a range of factors including climate, orientation of the plot, density of planting, and soil conditions.
  • This estimate is used by the purchaser (typically a sawmill) to propose how the trees are to be cross-cut (a procedure called bucking) into logs for their requirements. However, it is only at the saw-mill when the logs are sawn into final product, that the true value is realised.
  • the invention is directed towards providing a system for improved tree surveying.
  • a tree surveying system comprising an optical scanner for scanning a plurality of trees to generate original scan data and a processor for:
  • stem data by: fitting circles to the identified arcs, and using said circles to locate tree stem centre points, and
  • the processor comprises means for using the original scan data and a plurality of circles at different altitudes to extract tree cylinders around the centre points.
  • the scanning system comprises means for scanning through 360°.
  • the processor extracts tree cylinders around and outside a stem by sizing and positioning a circle with best fit to survey points to exclude cylinders of adjacent trees.
  • the processor performs post-processing to smooth circles.
  • the scanning system generates a plurality of scan files, each generated for a different origin.
  • the scanning system comprises means for illuminating an origin for a different scan, so that it is identified in a current scan, and the processor is adapted to determine the offset of scan origins on the basis of detection of the illuminated origin.
  • said illuminating means comprises a luminescent object such as a sphere.
  • system further comprises means for determining from GPS tracking data offset between scan origins.
  • the system comprises a differential GPS system.
  • the processor is adapted to combine stem data generated from different scans with different origins to provide enhanced stem data. In one embodiment, the processor uses scan data from a second scan to add more points to circles defining stems.
  • system further comprises means for importing aerial survey data and for correlating aerial survey data with the stem data to identify trees and provide tree height data derived from the aerial survey data.
  • aerial data includes information indicating heights of frees and stratification of sub-compartments linked with GPS locations of the stems.
  • the processor is adapted to complete free identifications using stem data derived from the scan data even if the stem is occluded in aerial view and so omitted from the aerial data.
  • each free is described in the correlated data by its GPS coordinates.
  • the system further comprises a range camera for providing original scan data.
  • the scanner is hand-held.
  • the hand-held scanner comprises a 3D sensor and a positional sensor such as a GPS or an inertial measurement system.
  • the system comprises means for writing the stem data to a persisted stem file.
  • the processor is adapted to combine forest plot-level stem data to provide forest stand-level data.
  • the processor generates from the stem data harvester cutting pattern instructions specifying how stems should be cross cut.
  • the invention provides a computer program product comprising software code for performing operations of a processor of any system defined above when executing on a digital processor.
  • Fig. 1 is a flow diagram illustrating a tree surveying method of the invention
  • Fig. 2 is a sample scan file generated by a laser scanner in a 360° scan for a 3D scan file
  • Fig. 3 is a diagram illustrating two scans with different origins
  • Fig. 4 is a diagram showing tree cylinder survey points from two origins.
  • Fig. 5 is a diagram showing the stacked diameters and the centre point information for a stem
  • Fig. 6 is a diagram showing the meshed structure of the stem data
  • Fig. 7 is an illustration of a stem file, at the level of a forest plot
  • Fig. 8 is a plot view of the stem locations
  • Fig. 9 is a graphic of terrestrial and Aerial Lidar information combined based on the stem's GPS location. Description of the Embodiments
  • a tree surveying method 1 of the invention is shown.
  • the method 1 is implemented by a system comprising: - Laser scanner, of the phase-based type (Faro LS880TM) - Computer , of conventional PC type
  • the method 1 comprises steps 2 to 10, which in summary are: 2, laser scanning to generate 3D scan files, a portion of which is shown in Fig. 2; 3, detecting and extracting circle fits, as shown in Figs. 3 and 4;
  • step 8 processing the data 7 and timber product demand data 9 to provide cross-cut data and instructions; and 10, cutting pattern generation according to the output of step 8.
  • the system converts laser point cloud data, captured by a laser scanner performing a 360° scan and representing 3D data in millions of "laser points" to diameter and 3D centre point stem data.
  • This 3D stem data is arranged in stem files which are used by programs and cross-cutting harvesting simulations and subsequent recommendations .
  • a typical cluster of point cloud data is a horseshoe of survey points that have landed on the trunks of trees.
  • Fig. 2 is a representation of part of a 360° scan produced by a laser scanner.
  • the first step in detecting the trees is to determine a Digital Terrain Model (DTM). This is done using an algorithm that calculates high densities of survey points landed on the ground and builds up a grid that models the slope of the ground.
  • the system cuts a slice (2-dimensional plane) through the scan data and segments the data, searching for circular objects. The slice is cut a specified height above the calculated DTM. The objects found are then classified on the likelihood of whether they are trees.
  • DTM Digital Terrain Model
  • the list of detected trees is used to extract tree cylinders from the original point cloud data.
  • the diameter at breast height of the detected trees is calculated based on sizing and positioning a circle with best fit to the survey points. These measurements are calculated at set heights (e.g. every 10cm) up along the stem.
  • the scanner can provide up to 320 degrees in the vertical which forms a hemispherical cloud of survey points for everything in the plot except the tripod.
  • the tree cylinder effectively extracts the tree survey points, allowing for considerable lean or sweep of the trunk but removing much of the survey point data corresponding to the branches and leaves.
  • the diameter of the tree cylinder is 2 m, this radius omitting data from neighbouring trees and leaving enough space for curvature of the stem up along its height.
  • the processing of the laser scan data involves extracting the stem measurements based on a single scan position (plot origin). Further post-processing is performed after the circle fitting. These steps allow the diameters to be smoothed using filtering based on circularity (or ovality), using linear interpolation, smoothing splines and artificial intelligence techniques.
  • Stems are scanned from more than one perspective so that automatic circle-fitting is more accurate.
  • An ultra-luminescent sphere is placed in the vacant scanner origin location. After the scanning is complete the scan data is processed in multiple passes.
  • the first pass processes the individual point clouds to find the centre points of all of the stems which can be achieved using a tree-detection algorithm.
  • a small cylinder around the tree is extracted from the overall scan data file that contains the related survey points.
  • the ultra-luminescent sphere will be detected and its centre point determined so that the offset between scans can be calculated.
  • the tree cylinders that are detected in the first pass are supplemented with tree cylinders from the other scan with each of the survey points offset appropriately. The result as illustrated in Fig.
  • each of the tree cylinders is supplemented with points from the other scan allowing the circle fitting to be more accurate with more points from around the stem or even an oval to be fitted to non-circular tree.
  • An advantageous aspect of the combination of processed stem data is that this data does not have inaccuracies which are present in the original scan data due to factors such as trees swaying in wind.
  • the ultra-luminescent sphere is an advantageous mechanism for offset mapping.
  • GPS may alternatively be used.
  • the DTM from the first scan can be used or a composite of both scans can be generated.
  • the processing also accommodates the identification of tree stems which are visible in only one of the scans (due to occlusion) in which case the circle-fitting algorithm will use the points from that one scan. This technique accommodates the better measurement of trees which were partially hidden from the other scanner position.
  • the data derived from this process is expressed digitally (and can be stored in a variety of forms) and is further processed to accurately estimate the volume of wood in a given stem, the shape of the stem, and to some extent the qualities of that wood (e.g. density of wood varies between the two sides of a leaning tree), and in a given plot, the distribution of the tree stems.
  • Figs 5 and 6 illustrate stem data. This is generated by recording the X and Y coordinates of the centre point and storing that information along with the corresponding diameter at 10cm intervals into a 3D stem file.
  • the 3D stem files that are generated allow many of the inaccuracies that currently exist due to non-straight trees to be eliminated.
  • This real-world information can be also used instead of traditional inventory information when statistically scaling from the forest plot-level to the forest stand-level.
  • the truly objective nature of this information can also be used to "train" Lidar Data.
  • 3D Stem data for all forest stands allows the timber resources to be crosscut based on harvester cutting patterns that are generated in advance. This allows the forest owner or buyer to determine the optimal cutting instruction prior to harvesting and also determine the forest value. The system further allows for groups of forest stands to be optimally harvested to ensure the maximum value of each forest is achieved whilst satisfying the sawmill demands.
  • the aerial data is combined in step 5 by identifying the same trees based on GPS locations and integrating the information. Diameter versus Height curves can be applied from the plot-level to the stand-level for different diameter classes. But in addition stem straightness can also be extrapolated for different strata within the forest.
  • this information can be used to calculate a range of commercially useful information, such as:
  • the system determines curvature and shape of standing tree stems contained in a given 360 degree laser scan file by: a. Determining the DTM (Digital Terrain Model) b. Examining slices of data at given intervals above the DTM searching for circular patterns c. Best-fitting circles to the identified patterns d. Calculating centre points of each circle and identifying offsets of each centre point e. Expressing the calculated stem circles and curvature path digitally f. Combining multiple stems into a 3D Stem File g. Integrating forest information (such as GPS positions and heights of trees, or stratification of sub-compartments) from aerial based (Lidar) scanner or other sources. Stem counts can be scaled up to forest stand-level.
  • DTM Digital Terrain Model
  • the system further allows for groups of forest stands to be optimally harvested to ensure the maximum value of each forest is achieved whilst satisfying the sawmill demands h. Instructions specifying how stems should be cross cut (Cutting Patterns) can be generated as an output of the system.
  • a luminescent circular object may be placed in each of two scans representing the origin point of the other scan, in order to accurately calculate the offset between the two scans.
  • Multiple luminescent circular objects may be placed in each of multiple scans each representing the origin point of one of the other scans, in order to accurately calculate the offset between each of the scans.
  • the measurements from identified circular patterns (after steps a through e above) in the common area of two or more scans may be amalgamated, resulting in a more accurate and complete mapping of each of the stems in that area.
  • Results from two or more scans may be used to identify otherwise-occluded tree stems in overlapping areas
  • the original single-pass method may be used for the non-overlapping areas.

Abstract

A tree surveying system comprises a computer and a laser scanner. There is laser scanning to generate 3D scan files (2) and the processor detects and extracts (3) circle fits. Stem files (4) generated from scanning with different origins are combined (5) to provide enhanced stem data. Tree stem curvature is automatically generated by tracking the stem centre points at a number of levels above ground as defined by a digital terrain model. The enhanced stem data is combined with aerial data (6) determined by aerial laser scanning, to provide forest-level stem data (7), which is processed to generate cross-cut data and instructions (10).

Description

"Tree Surveying"
INTRODUCTION
Field of the Invention
The invention relates to surveying trees.
At present, the value of a forest is estimated by manually measuring sample diameters of trees at breast height (D.B.H), estimating the tree height and from that using 'mensuration' tables based on forest type and taper equations, to derive a rough estimate how much timber can likely be extracted. These tables are based on average idealised tree shapes, which introduces inaccuracy as tree shape and quality is determined by a range of factors including climate, orientation of the plot, density of planting, and soil conditions. This estimate is used by the purchaser (typically a sawmill) to propose how the trees are to be cross-cut (a procedure called bucking) into logs for their requirements. However, it is only at the saw-mill when the logs are sawn into final product, that the true value is realised. If the forest has been estimated poorly, then the match between what is expected and what is delivered is considerably different. This means that less than optimal cross-cutting patterns have been employed, which in turn means less valuable timber is recovered. Poor quality arises through trees not having a perfect conical shape. Many factors affect timber recovery such as straightness, excessive branching and trees with double stems etc. If this quality of measurement could be improved, then more realistic cutting patterns can be set for the saw-mill and more valuable timber can be recovered.
Current forest measurement techniques are limited to measuring trees in two dimensions since the process of non-destructively measuring the curvature of the tree (its three dimensional shape) has proved to be very labour intensive. In fact to accurately measure curvature of a tree presently the whole tree needs to be cut and scanned off-site. This same information is used by forest planners and managers for silvicultural decisions such as thinning and selection of plots for sale, and in valuing forests for land sale purposes, so the economic impact of the inherent inaccuracy of these methods is large.
The paper "Analysis of the information content of terrestrial laserscanner point clouds for the automatic determination of forest inventory parameters" by Bienert A. et al Workshop on 3D remote Sensing in Forestry, 14th- 15th Feb. 2006, Vienna describes analysis of point cloud data generated by a laser scanner.
The invention is directed towards providing a system for improved tree surveying.
SUMMARY OF THE INVENTION
According to the invention, there is provided a tree surveying system comprising an optical scanner for scanning a plurality of trees to generate original scan data and a processor for:
generating a digital terrain model from the original scan data, the model defining ground level;
determining a two-dimensional plane above the ground as defined by the digital terrain model and segmenting the scan data in the two-dimensional plane to locate arcs indicative of circular objects;
generating stem data by: fitting circles to the identified arcs, and using said circles to locate tree stem centre points, and
identifying offsets of the centre points relative to centre points of the same stem at different height intervals; and
generating tree curvature data from said centre point offsets. In one embodiment, the processor comprises means for using the original scan data and a plurality of circles at different altitudes to extract tree cylinders around the centre points.
hi one embodiment, the scanning system comprises means for scanning through 360°.
hi one embodiment, the processor extracts tree cylinders around and outside a stem by sizing and positioning a circle with best fit to survey points to exclude cylinders of adjacent trees.
hi one embodiment, the processor performs post-processing to smooth circles.
hi one embodiment, the scanning system generates a plurality of scan files, each generated for a different origin.
hi one embodiment, the scanning system comprises means for illuminating an origin for a different scan, so that it is identified in a current scan, and the processor is adapted to determine the offset of scan origins on the basis of detection of the illuminated origin.
hi one embodiment, said illuminating means comprises a luminescent object such as a sphere.
In one embodiment, the system further comprises means for determining from GPS tracking data offset between scan origins.
hi one embodiment, the system comprises a differential GPS system.
In one embodiment, the processor is adapted to combine stem data generated from different scans with different origins to provide enhanced stem data. In one embodiment, the processor uses scan data from a second scan to add more points to circles defining stems.
In one embodiment, the system further comprises means for importing aerial survey data and for correlating aerial survey data with the stem data to identify trees and provide tree height data derived from the aerial survey data.
In one embodiment, aerial data includes information indicating heights of frees and stratification of sub-compartments linked with GPS locations of the stems.
In one embodiment, the processor is adapted to complete free identifications using stem data derived from the scan data even if the stem is occluded in aerial view and so omitted from the aerial data.
In one embodiment, each free is described in the correlated data by its GPS coordinates.
In one embodiment, the system further comprises a range camera for providing original scan data.
In one embodiment, the scanner is hand-held.
hi one embodiment, the hand-held scanner comprises a 3D sensor and a positional sensor such as a GPS or an inertial measurement system.
In one embodiment, the system comprises means for writing the stem data to a persisted stem file.
In one embodiment, the processor is adapted to combine forest plot-level stem data to provide forest stand-level data.
In one embodiment, the processor generates from the stem data harvester cutting pattern instructions specifying how stems should be cross cut. In another aspect, the invention provides a computer program product comprising software code for performing operations of a processor of any system defined above when executing on a digital processor.
DETAILED DESCRIPTION OF THE INVENTION
Brief Description of the Drawings
The invention will be more clearly understood from the following description of some embodiments thereof, given by way of example only with reference to the accompanying drawings in which:-
Fig. 1 is a flow diagram illustrating a tree surveying method of the invention;
Fig. 2 is a sample scan file generated by a laser scanner in a 360° scan for a 3D scan file;
Fig. 3 is a diagram illustrating two scans with different origins;
Fig. 4 is a diagram showing tree cylinder survey points from two origins.
Fig. 5 is a diagram showing the stacked diameters and the centre point information for a stem;
Fig. 6 is a diagram showing the meshed structure of the stem data;
Fig. 7 is an illustration of a stem file, at the level of a forest plot;
Fig. 8 is a plot view of the stem locations; and
Fig. 9 is a graphic of terrestrial and Aerial Lidar information combined based on the stem's GPS location. Description of the Embodiments
Referring to Fig. 1 a tree surveying method 1 of the invention is shown. The method 1 is implemented by a system comprising: - Laser scanner, of the phase-based type (Faro LS880™) - Computer , of conventional PC type
The method 1 comprises steps 2 to 10, which in summary are: 2, laser scanning to generate 3D scan files, a portion of which is shown in Fig. 2; 3, detecting and extracting circle fits, as shown in Figs. 3 and 4;
4, generating 3D stem files, as shown in Figs. 5 to 7, in which stem files generated from scanning with different origins are combined to provide enhanced stem data;
5, combining the enhanced stem data with aerial data 6 determined by aerial laser scanning, to provide forest-level stem data 7;
8, processing the data 7 and timber product demand data 9 to provide cross-cut data and instructions; and 10, cutting pattern generation according to the output of step 8.
In more detail, the system converts laser point cloud data, captured by a laser scanner performing a 360° scan and representing 3D data in millions of "laser points" to diameter and 3D centre point stem data. This 3D stem data is arranged in stem files which are used by programs and cross-cutting harvesting simulations and subsequent recommendations .
A typical cluster of point cloud data is a horseshoe of survey points that have landed on the trunks of trees. Fig. 2 is a representation of part of a 360° scan produced by a laser scanner. The first step in detecting the trees is to determine a Digital Terrain Model (DTM). This is done using an algorithm that calculates high densities of survey points landed on the ground and builds up a grid that models the slope of the ground. The system cuts a slice (2-dimensional plane) through the scan data and segments the data, searching for circular objects. The slice is cut a specified height above the calculated DTM. The objects found are then classified on the likelihood of whether they are trees.
The list of detected trees is used to extract tree cylinders from the original point cloud data. The diameter at breast height of the detected trees is calculated based on sizing and positioning a circle with best fit to the survey points. These measurements are calculated at set heights (e.g. every 10cm) up along the stem. The scanner can provide up to 320 degrees in the vertical which forms a hemispherical cloud of survey points for everything in the plot except the tripod. The tree cylinder effectively extracts the tree survey points, allowing for considerable lean or sweep of the trunk but removing much of the survey point data corresponding to the branches and leaves. Typically, the diameter of the tree cylinder is 2 m, this radius omitting data from neighbouring trees and leaving enough space for curvature of the stem up along its height.
By fitting circles up along the stem of the tree the centre point of each disc is calculated. By tracking the positions of these centre points the curvature of the stem is calculated. The calculation of the centre point and the position of that centre point along that stem is very important in objectively determining sweep in a systematic fashion.
The processing of the laser scan data involves extracting the stem measurements based on a single scan position (plot origin). Further post-processing is performed after the circle fitting. These steps allow the diameters to be smoothed using filtering based on circularity (or ovality), using linear interpolation, smoothing splines and artificial intelligence techniques.
Stems are scanned from more than one perspective so that automatic circle-fitting is more accurate.
A technique is described as follows for two passes but any multiple of passes can be used to build up a more comprehensive forest plot description. In the case for two passes there are two scanner origins for the plot which should be clearly visible to each other, as shown in Fig. 3.
An ultra-luminescent sphere is placed in the vacant scanner origin location. After the scanning is complete the scan data is processed in multiple passes. The first pass processes the individual point clouds to find the centre points of all of the stems which can be achieved using a tree-detection algorithm. A small cylinder around the tree is extracted from the overall scan data file that contains the related survey points. The ultra-luminescent sphere will be detected and its centre point determined so that the offset between scans can be calculated. The tree cylinders that are detected in the first pass are supplemented with tree cylinders from the other scan with each of the survey points offset appropriately. The result as illustrated in Fig. 4 is that each of the tree cylinders is supplemented with points from the other scan allowing the circle fitting to be more accurate with more points from around the stem or even an oval to be fitted to non-circular tree. An advantageous aspect of the combination of processed stem data is that this data does not have inaccuracies which are present in the original scan data due to factors such as trees swaying in wind.
The ultra-luminescent sphere is an advantageous mechanism for offset mapping. However, GPS may alternatively be used. The DTM from the first scan can be used or a composite of both scans can be generated.
The same compositing of scan data is performed for each stem in the plot and diameters are calculated using the circle- fitting algorithm.
The processing also accommodates the identification of tree stems which are visible in only one of the scans (due to occlusion) in which case the circle-fitting algorithm will use the points from that one scan. This technique accommodates the better measurement of trees which were partially hidden from the other scanner position.
The data derived from this process is expressed digitally (and can be stored in a variety of forms) and is further processed to accurately estimate the volume of wood in a given stem, the shape of the stem, and to some extent the qualities of that wood (e.g. density of wood varies between the two sides of a leaning tree), and in a given plot, the distribution of the tree stems. Figs 5 and 6 illustrate stem data. This is generated by recording the X and Y coordinates of the centre point and storing that information along with the corresponding diameter at 10cm intervals into a 3D stem file.
The 3D stem files that are generated allow many of the inaccuracies that currently exist due to non-straight trees to be eliminated. This real-world information can be also used instead of traditional inventory information when statistically scaling from the forest plot-level to the forest stand-level. In addition the truly objective nature of this information can also be used to "train" Lidar Data.
Typically aerial data tends to have good height and stem count information as well as a good overview of the compartmentalization of different strata within the forest. However a certain amount of "ground truthing" (traditional inventory data) still needs to be collected on a number of plots within the forest. With 3D Stem files as generated by this terrestrial tree surveying system not only can the exact GPS locations of the trees allow for one-to-one matching, but the curvature information ensures that the same trees can be identified even if there is considerable offset between their centre point at ground level versus canopy level. Also any smaller trees that are hidden underneath the canopy can be accounted for even though they are invisible from above. This allows the volume underestimations that are inherent in Lidar-only systems to be systematically eliminated.
Using the 3D Stem data for all forest stands allows the timber resources to be crosscut based on harvester cutting patterns that are generated in advance. This allows the forest owner or buyer to determine the optimal cutting instruction prior to harvesting and also determine the forest value. The system further allows for groups of forest stands to be optimally harvested to ensure the maximum value of each forest is achieved whilst satisfying the sawmill demands.
The aerial data is combined in step 5 by identifying the same trees based on GPS locations and integrating the information. Diameter versus Height curves can be applied from the plot-level to the stand-level for different diameter classes. But in addition stem straightness can also be extrapolated for different strata within the forest.
In turn, this information can be used to calculate a range of commercially useful information, such as:
- The potential uses of the timber and its suitability to stated requirements of sawmills
- More accurate calculation of growth models for carbon-trading purposes - Better decision-making in regard to silvicultural decisions such as thinning
- Better selection (or non selection) of crops for harvesting
- Better cross-cutting recommendations in harvesting
- More accurate valuation of forest inventory
- Better estimation of biomass for energy purposes
In summary, the system determines curvature and shape of standing tree stems contained in a given 360 degree laser scan file by: a. Determining the DTM (Digital Terrain Model) b. Examining slices of data at given intervals above the DTM searching for circular patterns c. Best-fitting circles to the identified patterns d. Calculating centre points of each circle and identifying offsets of each centre point e. Expressing the calculated stem circles and curvature path digitally f. Combining multiple stems into a 3D Stem File g. Integrating forest information (such as GPS positions and heights of trees, or stratification of sub-compartments) from aerial based (Lidar) scanner or other sources. Stem counts can be scaled up to forest stand-level. This allows the forest owner or buyer to determine the optimal cutting instruction prior to harvesting and also determine the forest value. The system further allows for groups of forest stands to be optimally harvested to ensure the maximum value of each forest is achieved whilst satisfying the sawmill demands h. Instructions specifying how stems should be cross cut (Cutting Patterns) can be generated as an output of the system.
A luminescent circular object may be placed in each of two scans representing the origin point of the other scan, in order to accurately calculate the offset between the two scans.
Multiple luminescent circular objects may be placed in each of multiple scans each representing the origin point of one of the other scans, in order to accurately calculate the offset between each of the scans.
The measurements from identified circular patterns (after steps a through e above) in the common area of two or more scans may be amalgamated, resulting in a more accurate and complete mapping of each of the stems in that area.
Results from two or more scans may be used to identify otherwise-occluded tree stems in overlapping areas
hi addition to the 2-pass algorithm applied to the common area(s) of two or more scans, the original single-pass method may be used for the non-overlapping areas.
The invention is not limited to the embodiments described but may be varied in construction and detail.

Claims

Claims
1. A tree surveying system comprising an optical scanner for scanning a plurality of trees to generate original scan data and a processor for:
generating a digital terrain model from the original scan data, the model defining ground level;
determining a two-dimensional plane above the ground as defined by the digital terrain model and segmenting the scan data in the two-dimensional plane to locate arcs indicative of circular objects;
generating stem data by: fitting circles to the identified arcs, and using said circles to locate tree stem centre points, and
identifying offsets of the centre points relative to centre points of the same stem at different height intervals; and
generating tree curvature data from said centre point offsets.
2. A tree surveying system as claimed in claim 1, wherein the processor comprises means for using the original scan data and a plurality of circles at different altitudes to extract tree cylinders around the centre points.
3. A tree surveying system as claimed in claims 1 or 2, wherein the scanning system comprises means for scanning through 360°.
4. A tree surveying system as claimed in claims 1, 2, or 3, wherein the processor extracts tree cylinders around and outside a stem by sizing and positioning a circle with best fit to survey points to exclude cylinders of adjacent trees.
5. A tree surveying system as claimed in any preceding claim, wherein the processor performs post-processing to smooth circles.
6. A tree surveying system as claimed in any preceding claim, wherein the scanning system generates a plurality of scan files, each generated for a different origin.
7. A tree surveying system as claimed in claim 6, wherein the scanning system comprises means for illuminating an origin for a different scan, so that it is identified in a current scan, and the processor is adapted to determine the offset of scan origins on the basis of detection of the illuminated origin.
8. A tree surveying system as claimed in claim 7, wherein said illuminating means comprises a luminescent object such as a sphere.
9. A tree surveying system as claimed in claims 7 or 8, further comprising means for determining from GPS tracking data offset between scan origins.
10. A tree surveying system as claimed in claim 9, wherein the system comprises a differential GPS system.
11. A tree surveying system as claimed in any of claims 6 to 10, wherein the processor is adapted to combine stem data generated from different scans with different origins to provide enhanced stem data.
12. A tree surveying system as claimed in claim 11, in which the processor uses scan data from a second scan to add more points to circles defining stems.
13. A tree surveying system as claimed in any preceding claim, wherein the system further comprises means for importing aerial survey data and for correlating aerial survey data with the stem data to identify trees and provide tree height data derived from the aerial survey data.
14. A tree surveying system as claimed in claim 13, wherein the aerial data includes information indicating heights of trees and stratification of sub- compartments linked with GPS locations of the stems.
15. A tree surveying system as claimed in either of claims 13 or 14, wherein the processor is adapted to complete tree identifications using stem data derived from the scan data even if the stem is occluded in aerial view and so omitted from the aerial data.
16. A tree surveying system as claimed in any of claims 13 to 15, wherein each tree is described in the correlated data by its GPS coordinates.
17. A tree surveying system as claimed in any preceding claim, further comprising a range camera for providing original scan data.
18. A tree surveying system as claimed in any preceding claim, wherein the scanner is hand-held.
19. A tree surveying system as claimed in claim 18, wherein the hand-held scanner comprises a 3D sensor and a positional sensor such as a GPS or an inertial measurement system.
20. A tree surveying system as claimed in any preceding claim, wherein the system comprises means for writing the stem data to a persisted stem file.
21. A tree surveying system as claimed in any preceding claim, wherein the processor is adapted to combine forest plot-level stem data to provide forest stand-level data.
22. A tree surveying system as claimed in any preceding claim, wherein the processor generates from the stem data harvester cutting pattern instructions specifying how stems should be cross cut.
23. A computer program product comprising software code for performing operations of a processor of a system of any preceding claim when executing on a digital processor.
EP08719886A 2007-03-27 2008-03-27 Tree surveying Withdrawn EP2135194A2 (en)

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EP2133822A1 (en) * 2008-06-13 2009-12-16 University College Cork A method of stem taper, volume and product breakout prediction
FI20135625L (en) * 2013-06-05 2014-12-22 Ponsse Oyj Method and arrangement for measuring a piece of wood
SE541287C2 (en) 2017-02-27 2019-06-11 Katam Tech Ab Forest surveying apparatus and method using video sequences to generate 3D models
CN107643048B (en) * 2017-07-28 2020-09-29 北京农学院 Automatic extraction method of tree measuring factor based on point cloud data
CN108009474B (en) * 2017-11-01 2020-05-19 武汉万集信息技术有限公司 Vehicle surface image-text extraction method and device based on laser ranging

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WO2002085586A1 (en) * 2001-04-20 2002-10-31 Logmaker International Limited Method of cutting logs from a stem
FI117490B (en) * 2004-03-15 2006-10-31 Geodeettinen Laitos Procedure for defining attributes for tree stocks using a laser scanner, image information and interpretation of individual trees

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