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183 results for “Laser Scanning”
Data from: Spatiotemporal rank filtering improves image quality compared to frame averaging in 2-photon laser scanning microscopy
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Supplementary Data for article: Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists
<p>This is a collection of scripts and research data to assess the robustness of forest structure characterization from airborne laser scanning (ALS). It replicates the main analysis in the article <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em> and accompanies the main research data set (<a href="https://doi.org/10.5281/zenodo.10878070" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10878070</a>).</p> <p>In this replication study, we assess the derivation of canopy height models (CHMs) from point cloud data, how sensitive CHM algorithms are to pulse density variation and how uncertainties and biases propagate to commonly used forest structure metrics.</p> <p>The main data source for this study are ALS point clouds from nine U.S. sites, acquired by the National Ecological Observatory Network (NEON, 6 sites, 3 km x 3 km) and by the United States Geological Survey's 3DEP program (3 sites, also 3 km x 3 km). The underlying data can be found here: https://data.neonscience.org/data-products/DP3.30024.001 (NEON) and here: https://apps.nationalmap.gov/lidar-explorer (3DEP)</p> <p>The different data layers are:</p> <p><strong>replicate.US.R</strong> </p> <p> is a single R script that contains all the code necessary to reproduce the analyses, including point cloud manipulations and derivation of CHMs from the raw data as well as the overall robustness analysis. To replicate the processing of the raw point clouds step by step, this script should be located in a folder called "rscripts".</p> <p><strong>pointclouds_original.zip</strong></p> <p> is the set of original point clouds (3 km x 3 km in extent) used for the replication test, separated into 9 subfolders/sites. Can be used to reproduce the original workflow by placing them in a folder called "data/original". The script will then automatically produce derived point clouds at pulse densities of 2 and 16 per squaremetre and process them into digital terrain models (DTMs), digital surface models (DSMs) and CHMs. Note that, for convienence, these derived products are also included in a separate .zip file (cf. below).</p> <p><strong>processed_foranalysis.zip</strong></p> <p> is the set of derived products (DTMs, DSMs, CHMs), separated into 9 subfolders/sites, i.e. the result of processing the original point clouds. To use these layers directly with the provided script, they should be put into a folder called "processed_foranalysis".</p> <p><strong>summaries.zip</strong></p> <p><strong> </strong>is a set of summary statistics (as .csv files) that were used to generate the main analysis tables in the replication study. To use these summary statistics directly with the provided script, they should be put into a folder called "summaries".</p> <p><strong>figures.zip</strong></p> <p> is a set of figures displayed in the Supplementary Material of the paper <em>Robust characterization of forest structure from airborne laser scanning – a systematic assessment and sample workflow for ecologists</em>.</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>
The Value of I-Scan and Confocal Laser Endomicroscopy for the Assessment of Chronic Inflammatory Bowel Disease
ClinicalTrials.gov study NCT02481687. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Multiphoton Laser Scanning Microscopy in Cutaneous Optical Pathology
ClinicalTrials.gov study NCT00508781. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Widefield Confocal Scanning Laser Ophthalmoscope Optimized for Pediatric and Neonatal Imaging
ClinicalTrials.gov study NCT06177639. IPD Sharing: YES. Countries: 1. Publications: 0.
Intraoperative Confocal Laser Scanning Microscopy With Use of AI for Optimized Surgical Excision of Basal Cell Carcinoma
ClinicalTrials.gov study NCT06600165. IPD Sharing: NO. Countries: 1. Publications: 0.
In Vivo Confocal Scanning Laser Microscopy of Benign Nevi
ClinicalTrials.gov study NCT00487864. IPD Sharing: Not stated. Countries: 1. Publications: 0.
FIGURE 48 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 48. Eriocheir sinensis, ZVI, maxilla, CLSM images with Drishti processing. (A) whole appendage, applying "large images" option with a scanned area of 3×3 fields for image stitching, 1 seta on exopod circled and arrowed, (B) maxilla rotated to show from reverse angle of image A and the setation of the coxal and basial endites. Objective: 40× oil immersion. Scale bars = 200 µm.
FIGURE 46 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 46. Eriocheir sinensis, ZVI, mandible, CLSM images with Drishti processing. (A) mandible with developing palp, (B) lateral view of mandible with incisor teeth and molar region; both applying "large images" option with a scanned area of 1×2 fields for image stitching. Objective: 40× oil immersion. Scale bars A = 200 µm; B = 100 µm.
FIGURE 44 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 44. Eriocheir sinensis, ZVI, antennule, CLSM images with Drishti processing. (A) antennule showing small proximal seta, applying "large images" option with a scanned area of 1×2 fields for image stitching, (B) antennule rotated to show reverse angle of image A and the developing accessory flagellum bud, image merged using Adobe Photoshop. Objective: A = 20× dry; B = 40× oil immersion. Scale bars = 200 µm.
FIGURE 40 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 40. Eriocheir sinensis, ZV, second maxilliped, CLSM images with Drishti processing. (A) basis and endopod, applying "large images" option with a scanned area of 1×2 fields for image stitching (reverse angle of endopod arrowed), (B) endopod with 0,1,7 setae, (C) exopod with 12 natatory setae, (D) exopod with 13 natatory setae. Objective: 20× dry. Scale bars A, C, D = 500 µm; B = 200 µm.
FIGURE 37 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 37. Eriocheir sinensis, ZV, maxillule, CLSM images with Drishti processing. (A) maxillule, applying "large images" option with a scanned area of 2×3 fields for image stitching, (B) coxal endite, (C) basial endite. Objective: 40× oil immersion. Scale bars = 200 µm.
FIGURE 36 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 36. Eriocheir sinensis, ZV, mandible, CLSM images with Drishti processing. (A) mandible with palp bud present, (B) lateral view of mandible with incisor teeth and molar region; both applying "large images" option with a scanned area of 1×2 fields for image stitching. Objective: 40× oil immersion. Scale bars A = 200 µm; B = 100 µm.
FIGURE 35 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 35. Eriocheir sinensis, ZV, CLSM images with Drishti processing. (A) antennule with a pair of proximal setae and a developing accessory flagellum bud, (B) antennule, primary flagellum showing two rows of subterminal aesthetascs, (C) antenna with a more developed endopod and with one exopodal seta arrowed; all applying "large images" option with a scanned area of 2×3 fields for image stitching, (D) exopod of antenna with two setae. Objective: A-C = 40×; D = 60× oil immersion. Scale bars A, C = 200 µm; B = 100 µm; D = 10 µm.
FIGURE 51 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 51. Eriocheir sinensis, ZVI, CLSM images with Drishti processing. (A) maxilliped three, applying "large images" option with a scanned area of 1×2 fields for image stitching, (B) pereiopods with bilobed chela, (C) ventral view of developing fifth pleopods with endopods and uropods without endopods; both applying "large images" option with a scanned area of 2×2 fields for image stitching. Objective: 20× dry. Scale bars A = 200 µm; B-C = 500 µm.
FIGURE 32 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 32. Eriocheir sinensis, ZIV, pleon and telson, CLSM images with Drishti processing. (A) dorsal view of pleon and telson, (B) lateral view of pleon and telson; both applying "large images" option with a scanned area of 1×2 fields for image stitching, (C) pleomere 1 with 5 medial setae, (D) pleomere 1 with 2 medial setae, (E) dorsal view of telson. Objective: A-B = 10× dry; C-E =20× dry. Scale bars = 500 µm.
FIGURE 31 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 31. Eriocheir sinensis, ZIV, CLSM images with Drishti processing. (A) third maxilliped, (B) pereiopods with bilobed chela. Objective: 40× oil immersion. Scale bars = 100 µm.
FIGURE 39 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 39. Eriocheir sinensis, ZV, first maxilliped, CLSM images with Drishti processing. (A) coxa with 3 setae and basis with 10 setae, (B) coxa with 3 setae and 1 smaller seta, basis with 12 setae, (C) endopod with 1,3,2,2,6 setae, (D) endopod with 2,3,2,2,6 setae, (E) exopod with 12 natatory setae, (F) exopod with 13 natatory setae; all applying "large images" option with a scanned area of 1×2 fields for image stitching. Objective: 20× dry. Scale bars = 500 µm.
FIGURE 28 in Re-description of Chinese mitten crab Eriocheir sinensis H. Milne Edwards, 1853 (Crustacea: Brachyura: Grapsoidea: Varunidae) zoeal development using confocal laser scanning microscopy
FIGURE 28. Eriocheir sinensis, ZIV, maxilla, CLSM images with Drishti processing. (A) whole appendage, applying "large images" option with a scanned area of 2×3 fields for image stitching, (B) maxilla rotated to show reverse angle of image A and the setation of the coxal and basial endites. Objective: 40× oil immersion. Scale bars = 200 µm.
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