EP2135194A2 - Inspection d'arbres - Google Patents

Inspection d'arbres

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Withdrawn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)
English (en)
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
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Treemetrics Ltd filed Critical Treemetrics Ltd
Publication of EP2135194A2 publication Critical patent/EP2135194A2/fr
Withdrawn legal-status Critical Current

Links

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.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Astronomy & Astrophysics (AREA)
  • Remote Sensing (AREA)
  • Length Measuring Devices By Optical Means (AREA)
  • Image Processing (AREA)

Abstract

Un système d'inspection d'arbres comprend un ordinateur et un scanner laser. Il y a un balayage laser pour générer des fichiers de balayage tridimensionnel (2) et le processeur détecte et extrait (3) des contours de cercle. Des fichiers de souches (4) générés à partir du balayage avec différentes origines sont combinés (5) pour fournir des données de souches améliorées. Une courbure de souches d'arbre est automatiquement générée par suivi des points de centre de souche à un certain nombre de niveaux au-dessus du sol tels que définis par un modèle de terrain numérique. Les données de souches améliorées sont combinées avec des données aériennes (6) déterminées par balayage laser aérien, pour fournir des données (7) de souches de niveau forêt, qui sont traitées pour générer des données et des instructions de découpe (10).
EP08719886A 2007-03-27 2008-03-27 Inspection d'arbres Withdrawn EP2135194A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IE20070213 2007-03-27
PCT/IE2008/000028 WO2008117263A2 (fr) 2007-03-27 2008-03-27 Inspection d'arbres

Publications (1)

Publication Number Publication Date
EP2135194A2 true EP2135194A2 (fr) 2009-12-23

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EP08719886A Withdrawn EP2135194A2 (fr) 2007-03-27 2008-03-27 Inspection d'arbres

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Country Link
EP (1) EP2135194A2 (fr)
IE (1) IE20080223A1 (fr)
WO (1) WO2008117263A2 (fr)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2133822A1 (fr) * 2008-06-13 2009-12-16 University College Cork Procédé de prédiction de forme, volume et de débordement du produit
FI20135625L (fi) 2013-06-05 2014-12-22 Ponsse Oyj Menetelmä ja sovitelma puukappaleen mittaamiseksi
SE541287C2 (en) 2017-02-27 2019-06-11 Katam Tech Ab Forest surveying apparatus and method using video sequences to generate 3D models
SE546370C2 (en) * 2017-02-27 2024-10-15 Katam Tech Ab Improved forest surveying
CN107643048B (zh) * 2017-07-28 2020-09-29 北京农学院 基于点云数据的测树因子自动提取方法
CN108009474B (zh) * 2017-11-01 2020-05-19 武汉万集信息技术有限公司 一种基于激光测距的车辆表面图文提取方法及装置

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AUPR301501A0 (en) * 2001-02-09 2001-03-08 Commonwealth Scientific And Industrial Research Organisation Lidar system and method
WO2002071832A1 (fr) * 2001-03-14 2002-09-19 Air Logistics (Nz) Limited Procede d'evaluation d'arbres sur pied
WO2002085586A1 (fr) * 2001-04-20 2002-10-31 Logmaker International Limited Procede de decoupe de billes dans un tronc
FI117490B (fi) * 2004-03-15 2006-10-31 Geodeettinen Laitos Menetelmä puustotunnusten määrittämiseksi laserkeilaimen, kuvainformaation ja yksittäisten puiden tulkinnan avulla

Non-Patent Citations (1)

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Title
See references of WO2008117263A2 *

Also Published As

Publication number Publication date
WO2008117263A3 (fr) 2009-03-26
IE20080223A1 (en) 2008-12-24
WO2008117263A2 (fr) 2008-10-02

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