Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

Lovell, Jenny; Jupp, David L.B.; Newnham, Glenn; Culvenor, Darius

doi:10.1016/j.isprsjprs.2010.08.006

Cited by 149 publications

(111 citation statements)

References 25 publications

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“…The main challenge in using a single scan setup is to handle the tree shadows that will occur in the TLS data. This is acknowledged in previous studies [12,27]. Depending on the tree density and factors such as the presence of undergrowth and low branches, a number of trees will be occluded from the view of the scanner and hence cannot be detected.…”

Section: Introductionmentioning

confidence: 92%

“…The use of TLS for the acquisition of specific biophysical tree parameters has been investigated in several studies, including estimation on diameter at breast height (stem diameter at 1.3 m above ground level; DBH) and tree stem volume [10][11][12][13][14][15]. A full or partial reconstruction of individual trees has also been investigated [5,[16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner

et al. 2017

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Airborne laser scanning is now widely used for forest inventories. An essential part of inventory is a collection of field reference data including measurements of tree stem diameter at breast height (DBH). Traditionally this is acquired through manual measurements. The recent development of terrestrial laser scanning (TLS) systems in terms of capacity and weight have made these systems attractive tools for extracting DBH. Multiple TLS scans are often merged into a single point cloud before the information extraction. This technique requires good position and orientation accuracy for each scan location. In this study, we propose a novel method that can operate under a relatively coarse positioning and orientation solution. The method divides the laser measurements into limited time intervals determined by the laser scan rotation. Tree positions and DBH are then automatically extracted from each laser scan rotation. To improve tree identification, the estimated center points are subsequently processed by an iterative closest point algorithm. In a small reference data set from a single field plot consisting of 18 trees, it was found that 14 were automatically identified by this method. The estimated DBH had a mean differences of 0.9 cm and a root mean squared error of 1.5 cm. The proposed method enables fast and efficient data acquisition and a 250 m 2 field plot was measured within 30 s.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner

et al. 2017

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“…This effect can be observed in single TLS-scans in stands. Several studies show that a half reconstructed tree can still make reliable diameter estimations possible [Bienert et al, 2007, Liang et al, 2008, Lovell et al, 2011.…”

Section: Flight Planningmentioning

confidence: 99%

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Fritz

Kattenborn

Koch

2013

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

139

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ABSTRACT:In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground-and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75[m 2 ] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45°were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55[m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14 − 42[mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9[ha] test area, 102 individual trees above 7[cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under-or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50[cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

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“…The use of TLS in forestry has not become operational to the same degree as the use of ALS, but several applications, such as the estimation of tree position and diameter at breast height (DBH), have been investigated [13][14][15][16][17]. There are also studies presenting procedures to estimate, e.g., total above-ground biomass from TLS data [17,18].…”

Section: Introductionmentioning

confidence: 99%

Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

Hauglin

Gobakken

Astrup³

et al. 2014

Forests

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Several methods to conduct single-tree inventories using airborne laser scanning (ALS) have been proposed, and even terrestrial laser scanning (TLS) has recently emerged as a possible tool for the collection of forest inventory data. In the present study, a novel methodological framework for a combined use of ALS and TLS in an inventory was tested and compared to a method without the use of TLS. Single-tree Norway spruce crown biomass was predicted using an ALS-model with training data obtained by TLS. ALS and TLS data were collected for sets of sample trees, including 68 trees with both ALS and TLS data. In total, 29 destructively sampled trees were used to fit a TLS crown biomass model, which then was used to predict crown biomass in a separate set of 68 trees. This dataset was subsequently used to fit an ALS crown biomass model. When validating the model, using a separate dataset with accurately measured crown biomass obtained through destructive sampling, the mean error was 32% of the observed mean biomass. Corresponding crown biomass predictions derived with ALS-predicted diameters and the use of conventional and existing allometric models resulted in a mean error of 35%. Thus, in the present study, a slight improvement, in terms of prediction accuracy, was found when using training data with ground reference values obtained by TLS. OPEN ACCESSForests 2014, 5 385

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Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint

Cited by 149 publications

References 25 publications

Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner

Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Estimating Single-Tree Crown Biomass of Norway Spruce by Airborne Laser Scanning: A Comparison of Methods with and without the Use of Terrestrial Laser Scanning to Obtain the Ground Reference Data

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