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46 results for “Airborne Lidar”
Data from paper: "Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates"
<p>Data from the paper:</p> <p>Dalagnol, R. <em>et al.</em> Large-scale variations in the dynamics of Amazon forest canopy gaps from airborne lidar data and opportunities for tree mortality estimates. <em>Sci Rep</em> <strong>11, </strong>1388 (2021). https://doi.org/10.1038/s41598-020-80809-w</p> <p>Link: https://www.nature.com/articles/s41598-020-80809-w</p> <p> </p> <p>This repository contains:</p> <p>1) Data frame with data from static and dynamic gaps used in Figure 2 (Dalagnol_2020_Data_Multitemporal_gaps.csv). Each row is the aggregated measurement at 5-km resolution. The site component referes to the five site studied with multitemporal data. Site order from 1 to 5 is DUC, TAP, FN1, BON and TAL.</p> <p>2) Data frame with data from static gaps and environmental factors used in Table 1, Figure 3, 4, 5 (Dalagnol_2020_Data_Singledate_gaps_Modeling.csv). Each row is the aggregated measurement of one site observed by airborne lidar data.</p> <p>3) Raster file at 5-km resolution with dynamic gap fraction estimates presented in Figure 5 (dynamic_gap_fraction_amazon.tif).</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p>
Hong Kong Annotated Airborne LiDAR Point Clouds
<p>The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds <sup>[1]</sup>. The dataset covers 16 tiles of airborne LiDAR data in an area of 7.2 km2 in Shatin, Hong Kong, China. 11 tiles were used for training, while 5 tiles were used for validation. There are multiple types of construction in the dataset including high-rise residential buildings, low-rise village houses, and large public buildings. Green spaces are mainly composed of wood areas in open spaces (e.g., in parks and hills) and planted trees in residential gardens and nearby roads. Point clouds are classified in ground, buildings, and trees.</p> <p>The LiDAR data is owned by the Hong Kong government. Please visit the Spatial Data Portal, Survey Division, CEDD (https://sdportal.cedd.gov.hk/#/en/) for more details.</p> <p> </p>
Large-scale 3D building and tree datasets constructed from airborne LiDAR point clouds in Glasgow, UK
<p>This is the updated version of building 3D model data. The revision includes appending attributes to the lod1 and lod2 shapefile and creating cityjson file for each 3D building model. All 3D building models are available in mesh (.obj), multipath shapefile, and cityjson (.json) now.</p> <p><strong>IMPORTANT NOTE: We suggest using the building footprint, lod1, and lod2 data of this version (Version v4).</strong></p> <p>Urban Big Data Centre of the University of Glasgow generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council. It is a large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR point clouds. </p> <p>The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.</p> <p>The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [1]-[2]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided in 5 km by 5 km tiles.</p> <p>Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[3]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles. The RMSE of Euclidean distances between each point in the point cloud to the reconstructed model was calculated to evaluate the LoD2 model construction. A table of RMSE and a note for a few problematic models are provided.</p>
Dataset from paper "Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning"
<p><strong>Data and code from the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p> <p><strong>Link:</strong> <a href="https://doi.org/10.1002/rse2.264">https://doi.org/10.1002/rse2.264</a></p> <p> </p> <p><strong>This repository contains:</strong></p> <p><strong>1) model_train.R:</strong> This is the code to run the U-Net model in R language.</p> <p><strong>2) input.rar:</strong> Dataset of lidar canopy height model (CHM) images and masks (labels) patches of canopy palms obtained from four sites in the Brazilian Amazon. The images/masks have 128 x 128 pixels, where each pixel represents 0.5 m in the terrain. The dataset contains 2,269 images and masks, with close to 7,000 palms manually labelled.</p> <p><strong>3) unet_weights_best.h5:</strong> These are the best weights for the U-Net architecture achieved in the paper.</p> <p><strong>4) palm_stats.RData:</strong> Data frame with the lat/lon coordinates and palm metrics extracted for the 610 lidar sites in the Brazilian Amazon. (i) n_total is the number of palms, (ii) n_ha is the density of palms per hectare, (iii) crown_ metrics are based on the area of palm segments (in square meters), (iv) cover_total is the total area occupied by palms in the forest canopy (in square meters), (v) cover_rel is the relative cover of palms in the forest canopy (in percentage), (vi) height_ metrics are based on the height of palm segments (in meters), (vii) palm_height_dif_mean is the mean difference between palm height and local canopy height, and (viii) palm_height_dif_pvalue is the p-value assessing the statistical difference between the palm and canopy heights where 0 means no difference and -1/+1 means a negative/positive difference.</p> <p> </p> <p>If you need anything else, please contact the corresponding author: Ricardo Dalagnol (ricds@hotmail.com).</p> <p> </p> <p><strong>If you use these data, please cite the paper:</strong></p> <p>Dalagnol, R., Wagner, F. H., Emilio, T., Streher, A. S., Galvão, L. S., Ometto, J. P. H. B., & Aragão, L. E. O. C. (2022). Canopy palm cover across the Brazilian Amazon forests mapped with airborne LiDAR data and deep learning. Remote Sensing in Ecology and Conservation, 1–14. https://doi.org/10.1002/rse2.264</p>
Data from : Classifying wetland‐related land cover types and habitats using fine‐scale lidar metrics derived from country‐wide Airborne Laser Scanning
<p>This data repository contains the processed lidar metrics for characterizing the habitat structure for classifying main land cover and habitat types in the Lauwersmeer area in the northern part of the Netherlands in the province of Groningen (5754 ha). The lidar metrics were derived from Airborne Laser Scanning (ALS) data using the Actueel Hoogtebestand Nederland 2 (AHN2) openly available dataset from https://www.pdok.nl/. </p> <p>The derived lidar metrics saved in *.grd file format and contain 32 bands. Each band represents a lidar metric and the water surface was masked out in the dataset. The *l1* in the file name indicates that the file was used for level 1 (wetland) classification and *l23* used for level 2 (land cover types within wetland) and level 3 (reedbed habitats) classification. The lidar metrics were calculated using lidR (<a href="https://github.com/Jean-Romain/lidR">https://github.com/Jean-Romain/lidR</a>) software package. Further details related to the lidar metrics extraction can be found at <a href="https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats">https://github.com/eEcoLiDAR/PhDPaper1_Classifying_wetland_habitats</a> Github repository.</p> <p> </p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
LiDAR metrics generated from Airborne Laser Scanning (ALS) data across the Netherlands
<p>This data repository contains the LiDAR metrics generated from country-wide Airborne Laser Scanning (ALS) data from the Netherlands. The LiDAR metrics (10-meter resolution) are derived from AHN3 using <a href="https://laserfarm.readthedocs.io/en/latest/">Laserfarm</a> workflow. Raw point cloud data can be downloaded <a href="https://app.pdok.nl/ahn3-downloadpage/">here</a>. </p>
Robust retrieval of forest canopy structural attributes using multi-platform airborne LiDAR
<p><strong>Data and R code to replicate the analyses presented in</strong>:<br>Zhang et al. (2024) Robust retrieval of forest canopy structural attributes using multi-platform airborne LiDAR. Remote Sensing in Ecology and Conservation, <a href="https://doi.org/10.1002/rse2.398">https://doi.org/10.1002/rse2.398</a></p> <p>If using these data and/or R code in your work please cite the original publication listed above, as well as this repository using the corresponding DOI.</p>
Extracting Ridge and Valley Lines in Mountainous Areas from Airborne Lidar Data by Utilizing Line Feature Strength
<p><strong><span>Background</span></strong><strong><span>:</span></strong><span> </span><span>DEMs (digital elevation models) are very important in many fields, such as in Geomatics and in water conservation of mountainous areas etc. Geomorphic feature lines are necessary data for the topography interpolation and computation from DEMs.</span></p> <p><strong><span>Methods</span></strong><strong><span>:</span></strong><span> </span><span>Instead of the parameter space, we propose a novel automatic extraction of Geomorphic feature lines in the feature space from discrete airborne LiDAR (Light detection and ranging) data by TVM (tensor voting method) developed originally for image data in this article. A tensor field for discrete airborne LiDAR points is first established and then utilizing the TVM, a new geometric feature metric of data, the line feature strength, was captured. A practical line growing method based on the local maximum line feature strength is proposed in the article.</span></p> <p><strong><span>Results</span></strong><strong><span>:</span></strong><span> </span><span>Compared with the general line growing that is based on a certain threshold, our line growing method is quite effective, in particular for the extraction of primary and minor ridge and valley lines in mountainous areas.</span></p> <p><strong><span>Conclusions</span><span>:</span></strong><span> </span><span>The method presented in this paper is fast and automated and can furnish operators with a wealth of detailed information about minor line features. This will enable the extraction of ridge and valley lines tailored to specific requirements. It is no doubt that the method developed here can be generalized to a large amount of Lidar data.</span></p>
Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds
<p>Our proposed dataset comprises 199 cylindrical plots of 10 m radius corresponding to typical pasture land parcels in South-Eastern France. Each plot contains between 3000 and 17000 3D points, and each point is attributed with a total of 10 features: (i) absolute 3D coordinates, (ii) RGB and Near-InfraRed reflectance obtained with aerial cameras, (iii) uncalibrated laser intensity, return number and number of returns provided by the aerial LiDAR.</p> <p>This dataset can be used as training data for our model deep learning model "Vegetation Stratum Occupancy Prediction from Airborne LiDAR 3D Point Clouds" available on <a href="https://github.com/ekalinicheva/plot_vegetation_coverage">https://github.com/ekalinicheva/plot_vegetation_coverage</a></p> <p> </p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Mato Grosso, Amazonas e Pará)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Mato Grosso (1 zip file), Amazonas (2 zip files) and Pará (8 zip files).</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Roraima e Amapá)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Amapá (1 zip file) and Roraima (1 zip file).</p>
L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Acre e Rondônia)
<p>In two campaigns (2016/2017 and 2017/2018), we collected LiDAR transects across the Brazilian Amazon. Some transects were randomly distributed over the forest and secondary forest, some were randomly distributed over the deforestation arch, and others overlapped field plots to allow for model calibration. Each transect covered a minimum of 375 hectares (12.5 km x 300 m) and was surveyed by emitting full-waveform laser pulses from a Trimble Harrier 68i airborne sensor (Trimble; Sunnyvale, CA) aboard a Cessna aircraft (model 206). The average point density was set at four returns per m², the field of view was 30°, the flying altitude was 600 m, and the transect width on the ground was approximately 494 m. Global Navigation Satellite System (GNSS) data were collected on a dual-frequency receiver (L1/L2). The pulse footprint was below 30 cm, based on a divergence angle between 0.1 and 0.3 milliradians. Horizontal and vertical accuracy were controlled to be under 1 m and 0.5 m, respectively.</p> <p>We used the PRODES forest mask (2015) and secondary vegetation (forest regrown after complete forest clearing) from TerraClass (2014) to distribute the transects. To calibrate and validate the airborne LiDAR predictions of biomass, we intentionally overlapped some transects with field plots from 15 research partners. In 2017/2018, we complemented the expanded the transects survey improving the representation of secondary forest based on TerraClass (INPE, 2014). To calibrate and validate the airborne LiDAR predictions of biomass. The metadata about each transect is included in the shapefile hosted at Zenodo repository (<a href="https://doi.org/10.5281/zenodo.4968706">https://doi.org/10.5281/zenodo.4968706</a>).</p> <p>To position the transects, we randomly generated center points with X, Y coordinates and assigned a random alpha slope angle to each point. We visually inspected the start points to ensure they were within the forest or secondary vegetation mask. If the start point was not entirely within a forest, as seen by satellite image, we discarded the seed point and selected another one. For each point, we created a shapefile with a 12.5 km x 300 m polygon. For both campaigns, if there were any conflicts with the flight plan (e.g., proximity to an airport or military restrictions), the company making the flights requested repositioning it to the closest allowed area.</p> <p>This deposit delivers data from Acre (1 zip file) and Rondônia (1 zip file).</p>
Canopy top height models at 10m GSD from airborne LIDAR (derived from LVIS and small-footprint ALS)
<p>Rasterized canopy top height models (CTHM) at 10m ground sampling distance (GSD) derived from airborne LIDAR.</p><p>The CTHMs were created to be comparable to GEDI canopy top heights (within 25m footprints) using two sources:</p><p>1) NASA's LVIS airborne LIDAR campaigns (here we rasterized the RH98).<br>2) High-resolution canopy height models derived from small-footprint airborne laser scanning campaigns in Europe (max pooled with a circular 25m footprint corresponding to the GEDI footprint).</p><p>The original LVIS LIDAR data is available here: <a href="https://lvis.gsfc.nasa.gov">https://lvis.gsfc.nasa.gov</a></p><p>Links to the original ALS data are available here: <a href="https://publications.jrc.ec.europa.eu/repository/bitstream/JRC126223/jrc126223_jrc126223_lidaropensourcedata.pdf">https://publications.jrc.ec.europa.eu/repository/bitstream/JRC126223/jrc126223_jrc126223_lidaropensourcedata.pdf</a></p><p>Code to create GEDI-like canopy top heights from high-resolution ALS data is available here: https://github.com/langnico/global-canopy-height-model</p><p>More information is available in the Lang et al. (2022). Please cite our paper if you use these derived data in your own work.</p><p><strong>Reference:</strong></p><p>Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023). A high-resolution canopy height model of the Earth. Nature Ecology & Evolution, 1-12, <a href="https://doi.org/10.1038/s41559-023-02206-6">https://doi.org/10.1038/s41559-023-02206-6</a></p>
Geodetic displacement data from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study.
<p>This repository the results produced by Hurtado-Pulido, Amer, Ebinger, and Holcomb “Variations in subsidence patterns in the Gulf of Mexico passive margin from Airborne-LiDAR data and Time Series InSAR: Baton Rouge Case Study”.</p> <p>This repository presents data sets for figures 4, 5, 6, 7 and 8. Processing methods are described in the paper. The READme file contains details about each file. Please address any questions about this dataset to Hurtado-Pulido.</p> <ul> <li>LiDAR data from 1999 is stored and distributed by the Atlas: The Louisiana Statewide GIS (<a href="https://maps.ga.lsu.edu/lidar2000/">https://maps.ga.lsu.edu/lidar2000/</a>). LiDAR data from 2018 is stored and distributed by the USGS Server through The National Map Download Manager (<a href="https://apps.nationalmap.gov/downloader/">https://apps.nationalmap.gov/downloader/</a>).</li> <li>EnviSAT SAR images were retrieved from the Earth Observation Catalogue (<a href="https://eocat.esa.int/sec/#data-services-area">https://eocat.esa.int/sec/#data-services-area</a>). Sentinel-1 SAR images from the Copernicus Open Access Hub (<a href="https://scihub.copernicus.eu/dhus/#/home">https://scihub.copernicus.eu/dhus/#/home</a>). Both property of the European Space Agency.</li> <li>GNSS information was processed by the Nevada Geodetic Laboratory (Blewitt et al., 2018; <a href="http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html">http://geodesy.unr.edu/NGLStationPages/gpsnetmap/GPSNetMap.html</a>).</li> <li>Data from water, injection, and extraction wells is stored in the Strategic Online Natural Resources Information System property of the Louisiana Department of Natural Resources (<a href="http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181">http://sonris-www.dnr.state.la.us/gis/agsweb/IE/JSViewer/index.html?TemplateID=181</a>).</li> </ul>
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Data from dissertation: 'Landscape and Aboveground Biomass Dynamics of Brazilian Savanna using airborne LIDAR and MapBiomas datasets : case study of Rio Vermelho Watershed, Brazil'
<p>This dissertation was submitted to University of Manchester as part of MSc GIS program</p> <p>This repository contains:</p> <p>1) Contains the R language code used in the dissertation (CHM_&_LiDAR_metric.R; Landscape_metric.R; Generalized_Linear_Model.R; Random_Forest_Model.R).</p> <p><br> 2) Canopy Height Model (CHM) and 56 LiDAR metric raster files with a resolution of 1m (CHM_&_LiDAR_metric_2014.zip; CHM_&_LiDAR_metric_2018.zip), the original LiDAR data come from Brazil project supported by the Brazilian Agricultural Research Corporation (EMBRAPA), the US Forest Service, USAID, and the US Department of State.</p> <p><br> 3) AGB raster files with a resolution of 10m (AGB_2014.tif; AGB_2018.tif; AGB_dynamic.tif), field plots used for AGB estimation come from Sabrinado Couto de Miranda from University of Goiás State (UEG), Brazil, and her team.</p> <p><br> 4) Landscape metric interpolation raster file (SHDI.tif; SHEI.tif; AREA_CV.tif; CIRCLE_MN.tif; SHAPE_MN.tif) with a resolution of 10m, land cover map Map come from Biomas team for landscape metric calculation.</p>
Data for: Characterizing individual tree-level snags using airborne lidar-derived forest canopy gaps within closed-canopy conifer forests
<p><span>1. Airborne lidar is often used to calculate forest metrics about trees but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting, and foraging.</span></p> <p><span>2. We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n=270) and live trees (n=2186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, USA. We paired airborne lidar with ground reference data collected at fixed-radius plots (n=53) to evaluate local gap structure. The R package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above ground), and (b) horizontally across small (16 m<sup>2</sup>), medium (36 m<sup>2</sup>), and large (64 m<sup>2</sup>) footprint sizes.</span></p> <p><span>3. Our results suggest this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid-canopy heights (~20 m above ground) and at the smallest footprint size (16 m<sup>2</sup>).</span></p> <p><span>4. These results show potential to improve understanding of gap dynamics in closed-canopy conifer forests, and we suggest snag modeling could be improved by incorporating lidar-derived canopy gap analyses alongside existing methodologies.</span></p>
Data for: Characterizing individual tree-level snags using airborne lidar-derived forest canopy gaps within closed-canopy conifer forests
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Data from: Tropical tree size-frequency distributions from airborne lidar
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.