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24 results for “Terrestrial Laser Scanning”
Plot-level wood-leaf separation for terrestrial laser scanning point clouds
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Terrestrial Laser Scans of surface deformation associated with the 11/11/2019 Mw 4.7 Le Teil earthquake (SE France)
<p>Terrestrial Laser Scans of surface deformation produced by the 11/11/2019 Mw 4.7 Le Teil earthquake in SE France.</p> <p>All scans were produced with a Faro X330 equipment at 1/2 resolution, low laser power, with in-field filters (lost points). Processing includes import and registration with Faro Scene software, export as LAS files, manual editing of noise, vegetation and scattered points with CloudCompare software and rasterization with universal kriging with Golden Software Surfer software.</p>
Field-Acquired DBH Measurements of Trees in Durango, Mexico, using Terrestrial Laser Scanning (TLS) and Tree Caliper
<p><strong>Introduction<br></strong>The data generated in this study provide a valuable resource for developing and testing algorithms focused on circumference fitting and diameter estimation of tree stems. By combining field-measured diameters and high-resolution terrestrial laser scanning (TLS) data, the dataset includes precise point clouds representing individual trees, with diameter at breast height (DBH) measured at 1.30 meters above ground level. This standardized height ensures consistency in diameter measurements, a crucial metric in forestry and ecological studies. The dataset supports advancements in remote sensing applications, offering researchers an opportunity to refine methods for accurately determining DBH and enhancing the reliability of biomass and carbon estimation models.<strong><br><br>Study Area<br></strong>The research was conducted in Mexico within a permanent forest research plot, established based on the methodology outlined by Corral-Rivas et al. The plot covers a quadrangular area of 625 m² and is situated in the Sierra Madre Occidental region, specifically within the "La Victoria" management unit in the municipality of Pueblo Nuevo, Durango, Mexico. This area experiences a temperate climate, with average annual temperatures between 20 and 22 °C and rainfall ranging from 800 to 1200 mm per year. The dominant vegetation type is a coniferous forest, primarily consisting of <em>Pinus cooperi</em>, with an estimated tree density of 960 trees per hectare.<strong><br><br>Field Data</strong><br>In this study, we focused on 50 trees with diameters exceeding 10 cm, a commonly used threshold in forest inventory protocols for estimating biomass and carbon, chosen to enhance accuracy and consistency. According to Hoover and Smith, excluding smaller trees generally has a negligible effect on biomass estimates across most forest types, which supports the practicality of this 10 cm cutoff. This threshold also helps eliminate saplings, which can add variability due to their inconsistent growth patterns, and aligns with standard inventory practices. We conducted a conventional inventory to measure DBH for each tree, using a Häglof caliper and taking two measurements at perpendicular angles to achieve a reliable average (<strong>data_field.xlsx</strong>).<br><br><strong>Data Capture and Acquisition via Terrestrial Laser Scanning</strong><br>The terrestrial laser scanning data were collected using a FARO Focus M70 scanner, capable of measuring distances up to 70 meters with an accuracy of ±3 mm. The scanner was set to an "exterior" profile with a resolution of 1/4 and quality level of 4x, achieving an average point density of 234,679 points per square meter and a resolution of 10,310 x 4,268 points. Before starting the scans, ten targets were strategically positioned on-site to assist with alignment during post-processing. Four scans were then performed, with a 180-degree vertical angle and a 360-degree horizontal angle, capturing a complete view of the surrounding environment. The scans were merged using FARO Scene software to ensure accurate alignment. All details regarding the scanner and tree positions can be found in Appendix A.<br><br></p> <p><strong>Extraction of 2D Planes from Individual Tree Stem Point Clouds</strong><br>The point cloud was initially loaded into CloudCompare, where selected trees were manually segmented, and ground points were removed to minimize slope effects. Each tree was represented as a series of n points in three-dimensional space: <br><br>(x_1, y_1, z_1), (x_2, y_2, z_2), ..., (x_i, y_i, z_i), ..., (x_n, y_n, z_n)</p> <p>For each tree stem, a specific cylindrical section was isolated by selecting points with z-coordinates between 1.25 m and 1.35 m, filtering out points outside this range. This method enabled the extraction of a segment corresponding to the diameter at breast height (DBH).</p> <p>Following this step, the z-coordinates within the selected cylindrical sections were excluded, and duplicate points were removed, resulting in a two-dimensional dataset. This reduction in dimensionality produced a new collection of n points:</p> <p>(x_1,y_1),(x_2,y_2),…,(x_i,y_i),…,(x_n,y_n).<br><br>The information about the planes and their representation can be found in <strong>tls_tree_discs.zip</strong> and <strong>tls_tree_imgs.zip</strong>.</p>
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
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