Estimating forest stand structure attributes from terrestrial laser scans

Pascu, Ionuț-Silviu; Dobre, Alexandru-Claudiu; Badea, Ovidiu; Tanase, Mihai A.

doi:10.1016/j.scitotenv.2019.06.536

Cited by 13 publications

(16 citation statements)

References 46 publications

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“…0.02-0.04 meters-medium-strength coregistration; >0.04 meters-weak coregistration) [35], averaging between 0.004 and 0.018 m. Any error exceeding the accepted tolerance for biophysical parameters (0.02 m) required supervised coregistration (Table 2). 5 4 0.0073 0.0071 0.0039 SoY 2 5 4 0.0078 0.0077 0.0055 SoY 3 5 4 0.0072 0.0071 0.0016 SoO 1 6 4 0.0184 0.0175 0.0058 SoO 2 5 4 0.0082 0.0080 0.0030 SoO 3 5 4 0.0101 0.0100 0.0040 SY 1 7 4 0.0069 0.0069 0.0058 SY 2 7 4 0.0136 0.0075 0.0113 SY 3 7 4 0.0068 0.0067 0.0030 SO 1 8 4 0.0051 0.0051 0.0038 SO 2 8 4 0.0053 0.0037 0.0052 SO 3 8 4 0.0038 0.0031 0.0023 mean 4 0.0084 0.0075 0.0046 1 Minimum number of connections for each scan; 2 Maximum error for distances computed two ways; 3 Maximum error for horizontally normalized distances; 4 Maximum error for vertically normalized distances; 5 Young sessile oak stands; 6 Old sessile oak stands; 7 Young spruce stands; 8 Old spruce stands.…”

Section: Terrestrial Laser Scans Pre-processingmentioning

confidence: 99%

“…Consequently, both the field and the multiple scan data sets were subsampled to only display those individuals identified through single scanning since it provided the lowest number of stems (Table 3). 4 Percentage of extraction from multiple scans in relation to field measurements; 5 Percentage of extraction from single scan in relation to field measurements; 6 Percentage of increase/decrease between multiple and single scans.…”

Section: Individual Tree Segmentationmentioning

confidence: 99%

“…Similarly, we can observe an increase in data processing solutions capable of segmenting and reconstructing accurate individual tree models [1][2][3]. This facilitated the use of such close range remote sensing technologies for describing forest stands structural parameters [4][5][6] and biophysical parameters [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Forest Structural Parameters From Terrestrial Laser Scanning: A Romanian Case Study

Pascu

Dobre

Badea

et al. 2020

Forests

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Research Highlights: The present study case investigates the differences occurring when tree’s biophysical parameters are extracted through single and multiple scans. Scan sessions covered mountainous and hill regions of the Carpathian forests. Background and Objectives: We focused on analyzing stems, as a function of diameter at breast height (DBH) and the total height (H), at sample plot level for natural forests, with the purpose of assessing the potential for transitioning available methodology to field work in Romania. Materials and Methods: We performed single and multiple scans using a FARO Focus 3D X130 phase shift terrestrial laser scanner at 122kpts and 0.3:0.15 mm noise compression ratio, resulting in an average point density of 6pts at 10m. The point cloud we obtained underpinned the DBH and heights analysis. In order to reach values similar to those measured in the field, we used both the original and the segmented point clouds, postprocessed in subsamples of different radii. Results: Pearson’s correlation coefficient above 0.8 for diameters showed high correlation with the field measurements. Diameter averages displayed differences within tolerances (0.02m) for 10 out of 12 plots. Height analysis led to poorer results. For both acquisition methods, the values of the correlation coefficient peaked at 0.6. The initial hypothesis that trees positioned at a distance equivalent to their height can be measured more precise, was not valid; no increase in correlation strength was visible for either heights or diameters as the distance from scanner varied (r = 0.52). Conclusions: With regard to tree biophysical parameters extraction, the acquisition method has no major influence upon visible trees. We emphasize the term “visible”, as an increase in the number of acquisitions led to an increased number of detected trees (16% in old stands and 29% in young stands).

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Section: Terrestrial Laser Scans Pre-processingmentioning

confidence: 99%

Section: Individual Tree Segmentationmentioning

confidence: 99%

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Retrieval of Forest Structural Parameters From Terrestrial Laser Scanning: A Romanian Case Study

Pascu

Dobre

Badea

et al. 2020

Forests

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“…The proposed Hyun method outperformed the classical RANSAC method in a scene with high clutter. Similar to the previous works is a parametrization of the forest stand described in reference [24]. The researchers propose to use, instead of leaf area index, other space characteristics such as the stand denseness, canopy density, crown porosity, and others.…”

Section: Related Workmentioning

confidence: 99%

Advanced Methods for Point Cloud Processing and Simplification

et al. 2020

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Nowadays, mobile robot exploration needs a rangefinder to obtain a large number of measurement points to achieve a detailed and precise description of a surrounding area and objects, which is called the point cloud. However, a single point cloud scan does not cover the whole area, so multiple point cloud scans must be acquired and compared together to find the right matching between them in a process called registration method. This method requires further processing and places high demands on memory consumption, especially for small embedded devices in mobile robots. This paper describes a novel method to reduce the burden of processing for multiple point cloud scans. We introduce our approach to preprocess an input point cloud in order to detect planar surfaces, simplify space description, fill gaps in point clouds, and get important space features. All of these processes are achieved by applying advanced image processing methods in combination with the quantization of physical space points. The results show the reliability of our approach to detect close parallel walls with suitable parameter settings. More importantly, planar surface detection shows a 99% decrease in necessary descriptive points almost in all cases. This proposed approach is verified on the real indoor point clouds.

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“…Terrestrial LiDAR scanning (TLS) has proven to be a suitable method for evaluating tree morphology objectively [8] and non-destructively [9]. TLS is an efficient instrument used to establish the woody tree structure [5,10,11]. More recently, TLS has been shown to be able to identify high quality tree characteristics which are not directly measurable in traditional forest inventories such as trunk volume and biomass components (total trunk, and branches).…”

Section: Introductionmentioning

confidence: 99%

Tree Shape Variability in a Mixed Oak Forest Using Terrestrial Laser Technology: Implications for Mating System Analysis

Tomșa

Curtu

Niţă

2021

Forests

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The accuracy of the description regarding tree architecture is crucial for data processing. LiDAR technology is an efficient solution for capturing the characteristics of individual trees. The aim of the present study was to analyze tree shape variability in a mixed oak forest consisting of four European white oak species: Quercus petraea, Q. frainetto, Q. pubescens, and Q. robur. Moreover, we tested for association between tree shape and individual heterozygosity and whether oak trees identified as pollen donors in a previous genetic study have a larger size in terms of crown and trunk characteristics than non-donors. The woody structure of a tree was defined by the quantitative structure model (QSM) providing information about topology (branching structure), geometry, and volume. For extracting the 3D point clouds a high-speed 3D scanner (FARO FocusS 70) was used. The crown variables were strongly correlated to each other, the branch volume being influenced by branch length, maximum branch order, and the number of branches but not influenced by diameter at breast height (DBH), trunk length, trunk volume, or tree height. There was no relationship between the individual heterozygosity based on nuclear microsatellite genetic markers and crown and trunk characteristics, respectively. Branch volume, total area, DBH, trunk volume, and the total volume of tree were significantly larger in pollen donors compared to non-donor Q. petraea trees. Thus, the mean branch volume was more than three times higher. Pollen donors had nearly two and half times larger total area in comparison to non-donor individuals. Our results suggest that a thorough characterization of tree phenotype using terrestrial laser scanning may contribute to a better understanding of mating system patterns in oak forests.

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Estimating forest stand structure attributes from terrestrial laser scans

Cited by 13 publications

References 46 publications

Retrieval of Forest Structural Parameters From Terrestrial Laser Scanning: A Romanian Case Study

Retrieval of Forest Structural Parameters From Terrestrial Laser Scanning: A Romanian Case Study

Advanced Methods for Point Cloud Processing and Simplification

Tree Shape Variability in a Mixed Oak Forest Using Terrestrial Laser Technology: Implications for Mating System Analysis

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