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15 results for “terrestrial laser scanner”
LAUT - Terrestrial and Personal laser scanner data from Austrian forest Inventory plots
<p>In forest inventory, trees are usually measured by handheld instruments; among the most relevant are calipers, inclinometers, ultrasonic devices, and laser range finders. Traditional forest inventory is nowadays redesigned, since modern laser scanner technology became available. Laser scanner generate massive data in the form of 3D point clouds. Novel methodology is currently developed to provide estimates of the tree positions, stem diameters, and tree heights from these 3D point clouds. This dataset was made publicly accessible to test new software routines for the automatic measurement of forest trees using laser scanner data. Benchmark studies with performance tests of different algorithms are welcome. The dataset contains co-registered raw 3D point-cloud data collected on 20 forest inventory sample plots in Austria. The data was collected by two different laser scanning systems: (i) a mobile personal laser scanner (PLS) (ZEB Horizon, GeoSLAM Ltd., Nottingham, UK), and (ii) a static terrestrial laser scanner (TLS) (Focus3D X330, Faro Technologies Inc., Lake Mary, FL, USA). The data also contains digital terrain models (DTM), field measurements as reference data (“ground-truth”), and the output of recent software routines for the automatic tree detection and the automatic stem diameter measurement.</p>
Terrestrial Laser Scanner observations of snow depth distribution at Col du Lautaret and Col du Lac Blanc mountain sites
<p>This dataset contains snow depth distribution observations obtained in two high mountain experimental sites, Col du Lac Blanc and Col du Lautaret, both located in French Alps. The snow depth distribution maps were generated using a Terrestrial Laser Scanner (TLS) for 10 acquisition dates. Observations obtained in Col du Lac Blanc were acquired in the 2014-15 snow season while Col du Lautaret observations were acquired in 2017-18 snow season. The snow depth maps have a grid cell size of 1x1m. The two study sites have extensions comprised between 17 and 31 ha with elevations ranging from 2000-2100 m a.s.l. (Col du Lautaret)and 2600-2800 m a.s.l. (Col du Lac Blanc)and show a patchy distribution of bare soil and alpine grass. The dataset allows a better understanding of snow related processes in mountain areas.</p>
Surveyed forest plots in Nagano and Gifu prefecture, Japan, using a terrestrial laser scanner
<p>The data was collected using a Leica RTC360 TLS and includes a total of 20 forest plots from different locations in the prefectures Nagano and Gifu, Japan. </p> <p>The data also contains a folder "Quercus_Serrata_Positions" including subfolders for each plot. Inside these folders are .txt files containing all positions of Quercus serrata trees inside the respective forest plot visible in the point cloud data.</p>
Terrestrial laser scanner (TLS) and destructive measurements in short-rotation woody crops (SRWCs) in NE of Romania
<p>The data are obtained from hybrid poplar crops installed in NE Romania, managed in short rotation (SRWCs) between 5, 6, and 7 growing seasons. Planted every spring, outside the growing season, at a depth of 0.6 m in the ground with two clones: AF8 and Pannonia. Rods (2 m long cuttings) were used as planting material for a density of 1667 trees/ha (3 x 2 m). For the estimation of volume and biomass, scans and gravimetric measurements were performed on tree component parts (trunk/stem and branches). Scanning of the sample areas (3 x 10 trees for each variant) was carried out using the Z+F Imager 5010. The scanning period was outside the growing seasons. The destructive (gravimetric) method of estimating biomass involves weighing the trees (184 in total) by component parts: trunk and branches (10 g accuracy). The moisture content of the components was determined by drying the samples (wood discs and branches) at a temperature of 105 °C to a constant mass. The QSM Model in MatLab was used for tree volume reconstruction. Where d is the diameter (cm) - measured at the base and at 1 m height, h (m) is the total height - measured after harvesting by roulette and by QSM reconstruction (after TLS scan). B (kg) is the dry mass of the whole tree (tot = branches + stem) and stem, and V (litres/ dm3) is the tree volume (tot) for the whole tree and stem. Branch weight results by difference for both variables.</p>
Data from: Can terrestrial laser scanners (TLSs) and hemispherical photographs predict tropical dry forest succession with liana abundance?
Tropical dry forests (TDFs) are ecosystems with long drought periods, a mean temperature of 25 °C, a mean annual precipitation that ranges from 900 to 2000 mm, and that possess a high abundance of deciduous species (trees and lianas). What remains of the original extent of TDFs in the Americas remains highly fragmented and at different levels of ecological succession. It is estimated that one of the main fingerprints left by global environmental and climate change in tropical environments is an increase in liana coverage. Lianas are non-structural elements of the forest canopy that eventually kill their host trees. In this paper we evaluate the use of a terrestrial laser scanner (TLS) in combination with hemispherical photographs (HPs) to characterize changes in forest structure as a function of ecological succession and liana abundance. We deployed a TLS and HP system in 28 plots throughout secondary forests of different ages and with different levels of liana abundance. Using a canonical correlation analysis (CCA), we addressed how the VEGNET, a terrestrial laser scanner, and HPs could predict TDF structure. Likewise, using univariate analyses of correlations, we show how the liana abundance could affect the prediction of the forest structure. Our results suggest that TLSs and HPs can predict the differences in the forest structure at different successional stages but that these differences disappear as liana abundance increases. Therefore, in well known ecosystems such as the tropical dry forest of Costa Rica, these biases of prediction could be considered as structural effects of liana presence. This research contributes to the understanding of the potential effects of lianas in secondary dry forests and highlights the role of TLSs combined with HPs in monitoring structural changes in secondary TDFs.
Multiway Registration Capability Study in Increasing the Accuracy of Registration Results for Infrastructure and Mining Pits Terrestrial Laser Scanner (TLS) Data Point Cloud
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Data for: UK redwoods terrestrial laser scanner point clouds
<p class="MsoNormal">Giant redwoods (<em>Sequoiadendron giganteum) </em>are some of the UK's largest trees, despite only being introduced in the mid-19th century. Given recent interest in planting redwoods in the UK, partly due to their carbon sequestration potential and also their undoubted public appeal, an understanding of their viability is important. However, little or no research has been conducted to quantitatively estimate their carbon uptake in UK conditions. We used 3D terrestrial laser scanning (TLS) to make detailed structure measurements of individual <em>S. giganteum</em> trees at three sites, to estimate aboveground biomass (AGB) and annual biomass accumulation rates. We show that UK-grown <em>S. giganteum</em> can sequester carbon at a rate of 80 - 100 kg C year<sup>-1</sup>, varying with climate, management and age. This accumulation rate is 2.5 and 20 times faster than commonly-grown UK plantation tree species. We develop new UK-specific allometric models for <em>S. giganteum</em>which fit observed AGB with r<sup>2</sup> > 0.93 and bias < 2% and can be used to estimate <em>S. giganteum</em> AGB more generally. <em>S. giganteum</em> appears to represent a small but potentially important addition to the UK's carbon sequestration efforts and this work provides a baseline for estimating their longer term AGB and carbon sequestration capacity.</p>
Data from: Can terrestrial laser scanners (TLSs) and hemispherical photographs predict tropical dry forest succession with liana abundance?
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Data for: UK redwoods terrestrial laser scanner point clouds
Open the record for dataset details and reuse information.
SnowEx20 Boise State University Terrestrial Laser Scanner (TLS) Point Cloud V001
This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.
SnowEx20 CRREL Terrestrial Laser Scanner (TLS) Point Cloud V001
This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2020 SnowEx campaign. Data were collected in fall 2019 (September) and winter 2020 (January and February). Each data file contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available.
SnowEx17 CRREL Terrestrial Laser Scanner (TLS) Point Cloud V001
This data set contains terrestrial LIDAR survey (TLS) point cloud data collected at Grand Mesa, Colorado as part of the 2017 SnowEx campaign. Data were collected in the fall (September and October) and winter (February) seasons. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), along with ancillary information, such as intensity (i) and color (R,G,B), where available. This is unprocessed data which has not been classified by land use (e.g. bare earth, low vegetation, trees).
SnowEx20 Boise State University Raw Terrestrial Laser Scanner (TLS) Point Cloud V001
This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2020 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2019) and snow-on (February 2020) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). A processed version of this data set can be found here: https://doi.org/10.5067/F0M99WK7JW4X.
SnowEx17 Boise State University Raw Terrestrial Laser Scanner (TLS) Point Cloud V001
This data set contains raw, unprocessed terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected in the fall (September 2016) and winter (February 2017). Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i).
SnowEx17 Boise State University Terrestrial Laser Scanner (TLS) Point Cloud V001
This data set contains terrestrial laser scanner (TLS) point cloud data collected as part of the 2017 SnowEx campaign in Grand Mesa, Colorado. Data were collected under both snow-off (September 2016) and snow-on (February 2017) conditions, at both open and forested locations. Multiple scans were conducted at each site and registered together using common targets. Each point contains X, Y, and Z coordinates (Easting, Northing, and Elevation), as well as intensity (i). These TLS data can be used to determine snow depth and explore the interactions between snow and vegetation.
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OpenNeuro
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