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390 results for “LiDAR data”
DQ-1 Aerosols and Carbon Dioxide Lidar detects orbital data on power plant emissions
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Data for: Single-blind determination of methane detection limits and quantification accuracy using aircraft-based LiDAR
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Data from: Evaluating LiDAR-derived structural metrics for predicting bee assemblages in managed forests
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Data from: Assessing Catharus bicknelli (Bicknell’s Thrush) habitat dynamics: A high-resolution model based on LiDAR metrics
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Data from: Evaluating the use of lidar to discern snag characteristics important for wildlife
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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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2m Digital Terrain Model Combining LIDAR and Stereo-Enhanced Photogrammetric Data, Niwot Ridge LTER Project Area, Colorado
Citation: Manley, W.F., Parrish, E.G., and Lestak, L.R., 2009, High-Resolution Orthorectified Imagery and Digital Elevation Models for Study of Environmental Change at Niwot Ridge and Green Lakes Valley, Colorado: Niwot Ridge LTER, INSTAAR, University of Colorado at Boulder, digital media. This dataset is a Digital Terrain Model (DTM) for the Niwot Ridge Long Term Ecological Research (LTER) project area at 2 m resolution, and is well suited for hydrologic modeling and other analyses of bare-earth terrain. The DTM is derived from the first reflective surface or a Digital Surface Model (DSM) that was created from 12 micron digital stereo aerial photography. Elevation points were both automatically filtered and hand-adjusted to better represent bare earth conditions. Green Lakes Valley LiDAR (Light Detection and Ranging) point data were appended to the filtered and edited data. Breakline information (ie. ridge tops, lake edges, and streams) was added. A final 2 meter gridded DTM and shaded relief model was then generated. The DTM is useful for terrain analysis and derivation of layers such as slope angle, aspect, shaded relief images, and contours. The DTM and shaded relief model covers a total area of 98 km2 and is available in Environmental Systems Research Institute's (ESRI's) GRID format for a total dataset size of 125 MB. They share a UTM zone 13 projection, NAD83 horizontal datum and NAVD88 vertical datum, with FGDC-compliant metadata. The DTM is available through an unrestricted public license, and can be obtained online or on DVD by request (see Distributor contact information below). Imagery available in this series includes orthorectified aerial photography for 1953, 1972, 1985, 1990, 1999, 2000, 2002, 2004, 2006 and 2008. Together, the digital elevation models and imagery will be of interest to land managers, scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospat
2m Digital Terrain Shaded Relief Model Combining LIDAR and Stereo-Enhanced Photogrammetric Data, Niwot Ridge LTER Project Area, Colorado
Citation: Manley, W.F., Parrish, E.G., and Lestak, L.R., 2009, High-Resolution Orthorectified Imagery and Digital Elevation Models for Study of Environmental Change at Niwot Ridge and Green Lakes Valley, Colorado: Niwot Ridge LTER, INSTAAR, University of Colorado at Boulder, digital media. This dataset is a Digital Terrain Shaded Relief Model (DTM) for the Niwot Ridge Long Term Ecological Research (LTER) project area at 2 m resolution, and is well suited for visualization of bare-earth terrain. The DTM is derived from the first reflective surface or a Digital Surface Model (DSM) that was created from 12 micron digital stereo aerial photography. Elevation points were both automatically filtered and hand-adjusted to better represent bare earth conditions. Green Lakes Valley LiDAR (Light Detection and Ranging) point data were appended to the filtered and edited data. Breakline information (ie. ridge tops, lake edges, and streams) was added. A final 2 meter gridded DTM and shaded relief model was then generated. The DTM is useful for terrain analysis and derivation of layers such as slope angle, aspect, shaded relief images, and contours. The DTM and shaded relief model covers a total area of 98 km2 and is available in Environmental Systems Research Institute's (ESRI's) GRID format for a total dataset size of 125 MB. They share a UTM zone 13 projection, NAD83 horizontal datum and NAVD88 vertical datum, with FGDC-compliant metadata. The DTM is available through an unrestricted public license, and can be obtained online or on DVD by request (see Distributor contact information below). Imagery available in this series includes orthorectified aerial photography for 1953, 1972, 1985, 1990, 1999, 2000, 2002, 2004, 2006 and 2008. Together, the digital elevation models and imagery will be of interest to land managers, scientists, and others for observation and analysis of natural features and ecosystems. NOTE: This EML metadata file does not contain important geospatial data pr
High Resolution LiDAR Elevation Data for Hog Island, VA May 2013
High Resolution LiDAR elevation and nearshore bathymetry data for Hog Island, Northampton County, VA, collected on May 26, 2013 on behalf of the USACE Engineer Research and Development Center using the Coastal Zone Mapping and Imaging Lidar (CZMIL) system. Elevations are in meters and coordinates are in degrees.
Data from: Contrasting patterns of population connectivity between regions in a commercially important mollusc Haliotis rubra: integrating population genetics, genomics and marine LiDAR data
Estimating contemporary genetic structure and population connectivity in marine species is challenging, often compromised by genetic markers that lack adequate sensitivity, and unstructured sampling regimes. We show how these limitations can be overcome via the integration of modern genotyping methods and sampling designs guided by LiDAR and SONAR data sets. Here we explore patterns of gene flow and local genetic structure in a commercially harvested abalone species (Haliotis rubra) from southeastern Australia, where the viability of fishing stocks is believed to be dictated by recruitment from local sources. Using a panel of microsatellite and genomewide SNP markers, we compare allele frequencies across a replicated hierarchical sampling area guided by bathymetric LiDAR imagery. Results indicate high levels of gene flow and no significant genetic structure within or between benthic reef habitats across 1400 km of coastline. These findings differ to those reported for other regions of the fishery indicating that larval supply is likely to be spatially variable, with implications for management and long-term recovery from stock depletion. The study highlights the utility of suitably designed genetic markers and spatially informed sampling strategies for gaining insights into recruitment patterns in benthic marine species, assisting in conservation planning and sustainable management of fisheries.
Data from: LiDAR and RGB-image analysis to predict hairy vetch biomass in breeding nurseries
Hairy vetch is a fall seeded annual legume that can be used as a forage and cover crop. As a cover crop, it can provide numerous ecosystem services, such as soil erosion reduction, carbon sequestration, and pollinator habitat, but also agronomic services such as weed suppression and N fixation via soil rhizobium species. To improve cover crop function, traits such as biomass production are especially relevant, making it a first priority trait for cover crop breeders. However, direct phenotypic methods for biomass production are destructive. Breeders have thus relied on subjective, visual scoring methods for biomass, which are generally correlative, but are not quantitative or absolute. In this study, we evaluated two low-cost remote sensing tools, LiDAR and RGB-image analysis, for their effectiveness at predicting biomass in vivo. We evaluated these tools in two common forage breeding scenarios, spaced-plant and sward-plot nurseries, at three Minnesota locations following the winter of 2016/2017. Ground cover, determined from RGB image binarization using the Canopeo application, had a significant and linear relationship with above-ground biomass in spaced-plants (R2=0.93), and sward-plots (R2=0.89). Once the image area became saturated with vegetative pixels, a near-exponential relationship with biomass would occur. Because of the low-growth habit of hairy vetch, RGB image analysis was more appropriate at lower plant densities, such as spaced-plant nurseries. LiDAR measures of sward-plot height were also linearly and strongly related to dry-matter biomass in sward-plots (R2=0.80). The dimensionality of LiDAR sensing gave it greater predictive ability at higher plant densities, where RGB analysis could not detect vertical increases in biomass production. Lastly, we combined RGB and LiDAR data to predict sward-plot biomass in a multiple mixed-effect regression model. By doing so, we were able to explain more biomass variation than with use of either phenotypic tool as a single predictor (R2=0.94).
Data from: LiDAR-derived canopy structure supports the more-individuals hypothesis for arthropod diversity in temperate forests
Despite considerable progress in the ability to measure the complex 3-D structure of forests with the improvement of remote-sensing techniques, our mechanistic understanding of how biodiversity is linked to canopy structure is still limited. Here we tested whether the increase in arthropod abundance and richness in beech forest canopies with increasing canopy complexity supports the more-individuals hypothesis or the habitat-heterogeneity hypothesis. We used fogging to collect arthropod samples from 80 standardized plots from canopies of single- to multi-layered mature montane European beech stands. Tree height and an independent measure of vertical heterogeneity — the vertical distribution ratio — on each arthropod sampling plot were derived from high-resolution full-waveform airborne laser scanning data. Mixed-model path analysis based on almost 20,000 specimens of 762 species from 11 orders provided support for the more-individuals hypothesis, with higher arthropod abundance but not higher species richness in stands with a more equal vertical distribution of plant biomass. By contrast, we found no support for the habitat-heterogeneity hypothesis. The increase in the number of individuals with increasing vertical distribution of biomass might be caused either by increasing leaf area, as indicated by higher space filling and productivity in multi-layered stands, or by higher persistence of arthropod populations owing to better shelter, reduced competition and more refuges under harsh conditions, or by both. High-resolution airborne laser scanning, with its ability to penetrate dense canopies under leaf-on conditions, has proved suitable for measuring vertical structures as a predictor for canopy diversity. Expanding combinations of remote-sensing and canopy-biodiversity data opens many avenues for improving our understanding of the link between diversity and forest structures.
Data from: Combining ground‐penetrating radar with terrestrial LiDAR scanning to estimate the spatial distribution of liquid water content in seasonal snowpacks
Many communities and ecosystems around the world rely on mountain snowpacks to provide valuable water resources. An important consideration for water resources planning is runoff timing, which can be strongly influenced by the physical process of water storage within and release from seasonal snowpacks. The aim of this study is to present a novel method that combines light detection and ranging with ground‐penetrating radar to nondestructively estimate the spatial distribution of bulk liquid water content in a seasonal snowpack during spring snowmelt. We develop these methods in a manner to be applicable within a short time window, making it possible to spatially observe rapid changes that occur to this property at subdaily timescales. We applied these methods at two experimental plots in Colorado, showing the high variability of liquid water content in snow. Volumetric liquid water contents ranged from near zero to 19%vol within the scale of meters. We also show rapid changes in bulk liquid water content of up to 5%vol that occur over subdaily timescales. The presented methods have an average uncertainty in bulk liquid water content of 1.5%vol, making them applicable for future studies to estimate the complex spatio‐temporal dynamics of liquid water in snow.
Data from: Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010
Forests with high above ground biomass (AGB), including those growing on peat swamps, have historically not been thought suitable for biomass mapping and change detection using Synthetic Aperture Radar (SAR). However, by integrating L-band (λ = 0.23 m) SAR with lidar data from the ALOS and ICESat earth-observing satellites respectively, and 56 forest plots, we were able to create a forest biomass and change map for a 10.7 Mha section of eastern Sumatra that still contains high AGB peat swamp forest. Using a time series of SAR data we estimated changes in both forest area and AGB. We estimate that there were 274 ± 68 Tg AGB remaining in natural forest (≥ 20 m height) in the study area in 2007, with this stock reducing by approximately 11.4% over the subsequent 3 years. A total of 137.4 kha of the study area were deforested between 2007 and 2010; an average rate of 3.8% yr−1. The ability to attribute forest loss to different initial biomass values allows for far more effective monitoring and baseline modelling for avoided deforestation projects than traditional, optical-based remote sensing. Furthermore, given SAR's ability to penetrate the smoke and cloud which normally obscure land cover change in this region, SAR-based forest monitoring can be relied on to provide frequent imagery. This study demonstrates that even at L-band, which typically saturates at medium biomass levels (ca. 150 Mg ha−1), it is possible to make reliable estimates of not just the area but the carbon emissions resulting from land use change.
Data from: Eigenfeature-Enhanced Deep Learning: Advancing Tree Species Classification in Mixed Conifer Forests with Lidar
<p>Data and code used in <i>Lidar-derived eigenfeatures improve species classification using deep-learning in Southwestern mixed conifer forests</i>. Lidar data is provided for UAV-LS and ALS in <i>lidar_data.zip</i>. Las files are named by site and plot (e.g., 3-2). Field data, shapefiles, and code is provided in <i>data.zip</i>. R code is provided for tree segmentation, tree matching, image creation, and neural network cross validation. </p>
The hourly data of the aerosol extinction coefficient of the Mount Qomolangma lidar
<p>珠穆朗玛峰激光雷达垂直平均气溶胶消光系数0.15-2.5 km逐小时数据。</p>
The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar
<p><strong>The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar from the surface to 14.985 km.</strong></p>
Hyperspectral and LiDAR data of the Botanical Garden of Rio de Janeiro
<p>In this repository you can find a hyperspectral image, a LiDAR point cloud and a shapefile of polygons of individual tree crowns from the the Botanical Garden of Rio de Janeiro. For more details refer to <a href="https://doi.org/10.1016/j.ufug.2024.128362">Ferreira et al. (2024)</a>.</p>
Lidar Data for "Kelvin_Helmholtz_Instabilities_Within_a_Semi_diurnal_Tide"
<p>These files contains the lidar observations of the KHI and generated waves observed by the sodium resonance wind temperature lidar and the Rayleigh density temperature lidar</p>
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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.