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14 results for “UAV LiDAR”
UAV-based Lidar point clouds of Experimental Station Britz, Brandenburg, 2024
<p><strong>UAV-based Lidar data of a Experimental Forest Station Britz</strong></p> <ul> <li>Date of acquisition: 09.07.2024</li> <li>Location: Experimental Station Britz, Britz, Brandenburg, Germany</li> <li> <div>DEIMS.iD: <a href="https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762">https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762</a></div> </li> <li>UAV: DJI M300 RTK with SAPOS connection</li> <li>Flight altitude above ground level: 40 m</li> <li>Line spacing: 7 m</li> <li>Sensor: Yellowscan Mapper Plus</li> <li>EPSG: 25833; GCG2016</li> <li>Data products: <br>• High-resolution point cloud in .laz format<br>• Yellowscan Cloudstation strip adjustment report<br>• Trimble Applanix POSPAC - diagnostic report</li> </ul> <p><br><strong>Acknowledgment</strong></p> <p>Dr. Tanja Sanders</p> <p>Dr. Marco Natkhin</p> <p>Institute of Forest Ecosystems<br>Johann Heinrich von Thünen Institute<br>Federal Research Institute for Rural Areas, Forestry and Fisheries</p>
Data and scripts for the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology
<p>This repository contains the modified OpenMMS scripts for Linux and Raspberry Pi firmware for LiDAR sensor presented in the paper Building a Low-cost UAV-based LiDAR Sensor for Landscape Archaeology at the CAA 2024 conference in Auckland, New Zealand. Included are the LiDAR and trajectory data collected at the site of Antiochia ad Cragum in 2022 in an area roughly north-east of what is known as the Small Bath Area. Each zip file contains two adjacent flights oriented either principally east-west or north-south. The four flights cover the same area in an overlapping pattern.</p> <p>The LiDAR and trajectory data are released under the Creative Commons Attribution 4.0 International license and the modified OpenMMS firmware and scripts are released under the original GNU GPL v3.0 or later license.</p>
Data from: Flying high: Sampling savanna vegetation with UAV-lidar
<p>The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. <br>To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. <br>Comparing vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs, or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimeters or less, which shows great promise for future comparison studies.<br>We discuss how these results apply in practice, explaining the potential trade-offs of flying at higher altitudes and alternating flight pattern. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV-lidar sampling designs in savannas.</p>
Data from: Flying high: Sampling savanna vegetation with UAV-lidar
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Datasets used for the publication: UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate
<p>Datasets used for the publication: UAV-Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate.</p> <p>The file "Blanchard_et_al_JoE_2023_data_plot_trees.csv" contains individual tree indentification data for the 46 plots used in the study.</p> <p>The file "Blanchard_et_al_JoE_2023_data_plot_aggregated.csv" contains plot-level aggregated metrics used for the analyses:</p> <p>- distance to the forest edge</p> <p>- diversity indices : the 20-sp rarefied species richness "rar_sp_richness_20" and the 20-sp rarefied Beta diversity "Beta_div" which corresponds to the plos coordinates on the PCoA first axis.</p> <p>- functional indices: the community weighted mean trait values for the four traits used in this study : ,"WD","SLA","LA","LDMC"; the fonctional divergence index "FD_trans.FDiv"; and the synthetic community weigthed mean trait wich corresponds to the postion on plots on the principal component analysis of species trait values "Functional_composition_trans". Note that the SLA and LA values were log-transformed before computing "FD_trans.FDiv" and "Functional_composition_trans".</p> <p>- UAV-LiDAR-dervived metrics: canopy height, gap fraction ("gap_fraction2"), slope, curvature</p> <p>- The estimated plot above graound biomass "agb_plot", and the mean value of the vapor pressure deficit during the drisest month "max_monthly_VPD".</p> <p>- coordinates of the plots in UTM 58S (Coordinate reference system)</p> <p>Please read the material and method section of the article for more informations on this dataset.</p>
High resolution LiDAR dataset acquired using UAV (unmanned aerial vehicle) over two vineyards and two years located in 'Tomiño', Pontevedra, Spain.
<p>This dataset features an extensive collection of LiDAR data from vineyards in northern Spain, targeting vineyards to address the growing demand for public UAV LiDAR datasets in Agricultural Sciences. The data was gathered using a DJI M300 multi-rotor platform equipped with a DJI Zenmuse L1 LiDAR sensor, conducting UAV flights at 20, 30, and 50 meters Above Ground Level (AGL) across two vineyards during 2021 and 2022. The dataset comprises ten high-density 3D LiDAR point clouds stored in .laz format with embedded RGB information in each point. This information is essential for studying vineyard morphology and development and plays a key role in refining vineyard management tactics. In addition, the dataset is valuable for agricultural robotics, providing detailed terrain and canopy data crucial for designing efficient flight paths and navigation algorithms. Finally, it serves as a true "ground truth" dataset to verify satellite-derived models, enabling the generation of high-precision digital elevation models (DEMs) and other derivatives.</p>
LAEA: A 2D LiDAR Assisted UAV Exploration Algorithm for Unknown Environment
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Supplementary Material for "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" article.
<p>Data presented in the study "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" </p>
Supplementary material for "Evaluating the Performance of High Spatial Resolution UAV-photogrammetry and UAV-LiDAR for Salt Marshes: the Cádiz Bay Study Case" article
<p>Data presented in the study "Evaluating the Performance of High Spatial Resolution UAV-photogrammetry and UAV-LiDAR for Salt Marshes: the Cádiz Bay Study Case".</p>
Dataset for cattle growth monitoring in PLF using UAV-based LiDAR
<p>This is the dataset for the data article named "Integrated UAV-based LiDAR and RGB Data for Individual-Level Cattle Growth Monitoring in Precision Livestock Farming".</p>
UAV LiDAR and trajectory data from Antiochia ad Cragum
<p>This data set includes LiDAR and trajectory data from four flights of an area located roughly north-east of the Small Bath Area at Antiochia ad Cragum in south-central Turkey in 2022 using a custom LIDAR sensor built with a Livox Mid-40 LiDAR camera and open source hardware and software from the OpenMMS project. Each zip file includes two adjacent flights oriented either principally east-west or north-south. The four flights together offer overlapping coverage of the area. In addition to the LiDAR files trajectory information collected with an AceInna OpenIMU and two Ublox ZED-F9P GNSS modules is included.</p>
UAV-LiDAR point clouds from the Forest and Biodiversity Experiment 2 (2022)
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Dataset of point-clouds generated by UAV LiDAR scanning of barley field-plot trial.
<p>Point clouds from eight scanning flights, scanned by UAV LiDAR system VUX-UAV1 (Ricopter GmBh), provided in forms of separated .las files. Dataset represent raw data that were further processed by software ALFA to extract height of canopies in particular field plots. Software serves for monitoring of plant growth dynamics in agricultural or breeding filed-plot trials.</p>
SnowEx21 Prairie Station Digital Surface Models from UAV-LiDAR V001
This dataset consists of Digital Surface Models (DSMs) derived from UAV-LiDAR acquired during the SnowEx 2021 field campaign at the Central Agricultural Research Center in central Montana (USA). The DSMs are at 0.3 m resolution and consist of one snow-off and seven snow-on flights.
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