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Dataset results
34 results for “Aerial surveys”
Beaver aerial surveys in Ohio
<p>Dataset of aerial surveys for beaver presence and abundance on 54 40x40 km plots in Ohio between 2013 and 2020 and variables used to model relative abundance (with number of beaver lodges as a proxy)</p>
The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions - dataset
<p>This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:</p> <ol> <li>Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where: <ul> <li>[location] is the place of data collection (e.g., Cocora, Vinicucna)</li> <li>[year] is the year of data collection (e.g., 2023)</li> <li>[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade</li> <li>[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)</li> </ul> </li> <li> <p>Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).</p> </li> </ol> <p>Structure-from-motio<span> </span>n processing was performed in Agisoft Metashape (<a href="https://www.agisoft.com/">https://www.agisoft.com/</a>, Agisoft, 2023). Mapping was performed in ArcGIS Pro (<a href="https://www.esri.com/en-us/arcgis/about-arcgis/overview">https://www.esri.com/en-us/arcgis/about-arcgis/overview</a>, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).</p> <p>Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. <em>International Journal of Applied Earth Observations and Geoinformation</em>, 103474. doi:<a href="https://doi.org/10.1016/j.jag.2023.103474"> https://doi.org/10.1016/j.jag.2023.103474</a></p>
Data from: Aerial survey of sea ducks and whales in winter in eastern Canadian Arctic
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2024 Pacific flyway region Caspian tern (Hydroprogne caspia) colony surveys – aerial photos and colony count data
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Historic courthouse 3d aerial survey Pix4d
3d model of the reconstructed 1819 courthouse at Port Tobacco, Maryland. I created this model from 83 aerial photos shot with a DJI Phantom 4 Professional sUAS, or drone. Used Pix4d Model for processing the images into a point cloud and triangle mesh. Source: Objaverse 1.0 / Sketchfab
Hare Krishna Temple_Aerial Survey
Source: Objaverse 1.0 / Sketchfab
Victorian-era train station aerial UAV survey
Point of Rocks, Maryland MARC train station. Created with 131 drone photos. This was one of my first models so the 3d Mesh is not perfect. Needed to get more detail around the sides of the station. Processed with Pix4d software. Source: Objaverse 1.0 / Sketchfab
Figure 4 in Quantitative assessment of species identification in aerial transect surveys for ice-associated seals
Figure 4. Observed species, age class, and confidence level probabilities for four species of ice-associated seals in the Bering Sea. True species and age classes include spotted seal pup (SDP), spotted seal nonpup (SDN), ribbon seal pup (RNP), ribbon seal nonpup (RNN), bearded seal pup (BDP), bearded seal nonpup (BDN), ringed seal pup (RDP), and ringed seal nonpup (RDN). Observed species classifications include spotted seal (red), ribbon seal (green), bearded seal (yellow), ringed seal (blue), and unknown seal (white). Observed age classes include pup, nonpup, and unknown. For observed species classifications, darker shades indicate greater confidence (e.g., light red = spotted seal guess, red = spotted seal likely, and dark red = spotted seal positive). For observed age classes, the relative density of hash lines indicate greater confidence (e.g., low density = guess, medium density = likely, high density = positive). Solid colors with no hashing indicate unknown age classification. Top panel (a) includes results from an analysis with no observer effects on model parameters. Bottom four panels (b) correspond to four different observers from an analysis including observer effects.
Figure 5 in Quantitative assessment of species identification in aerial transect surveys for ice-associated seals
Figure 5. Frequencies of observed characteristics from images identified as pups (a) and nonpups (b) of four ice-associated seal species in the Bering Sea. Bars are stacked according to the frequencies of nine observed species and age class confidence categories. For pups, only those traits with at least one observation are included for each species. For nonpups, only traits with ≥ 5 observations are included. Trait definitions are provided in Table 1.
2024 Oregon and Washington double-crested cormorant (Nannopterum auritum) colony surveys – aerial photos and count data
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Data from: The effect of a multi-target protocol on cetacean detection and abundance estimation in aerial surveys
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Casa Malpais Aerial Survey - Decimated
A digital model of the Village of Casa Malpais. One of the most unique ancient places in the Southwestern Culture Area with strong ancestral times to the Pueblo of Zuni as well as ties to Hopi and Acoma. People probably lived in this place from about ad 1150 to 1350, when the place was left to the ancients who still reside at this sacred place to this day. Casa Malpais is open to guided tours, which start at the Casa Malpais Musuem in the Springerville Heritage Center, 418 E Main St, Springerville, AZ. Be sure to call the museum at 928-333-2123 to confirm tour availability. This model has been squeezed down to 5% of the available data in order to fit on the Skecthfab Server. Source: Objaverse 1.0 / Sketchfab
U.S. Geological Survey Aerial Photography
The U.S. Geological Survey (USGS) Aerial Photography data set includes over 2.5 million film transparencies. Beginning in 1937, photographs were acquired for mapping purposes at different altitudes using various focal lengths and film types. The resultant black-and-white photographs contain less than 5 percent cloud cover and were acquired under rigid quality control and project specifications (e.g., stereo coverage, continuous area coverage of map or administrative units). Prior to the initiation of the National High Altitude Photography (NHAP) program in 1980, the USGS photography collection was one of the major sources of aerial photographs used for mapping the United States. Since 1980, the USGS has acquired photographs over project areas that require photographs at a larger scale than the photographs in the NHAP and National Aerial Photography Program collections.
Aerial drones reveal the dynamic structuring of sea turtle breeding aggregations and minimum survey effort required to capture climatic and sex-specific effects
<p class="MsoNormal">Quantifying how animals use key habitats and resources for their survival allows managers to optimise conservation planning; however, obtaining representative sample sizes of wildlife distributions in both time and space is challenging, particularly in the marine environment. Here, we used unoccupied aircraft systems (UASs) to evaluate temporal and spatial variation in the distribution of loggerhead sea turtles (<em>Caretta caretta</em>) at two high-density breeding aggregations in the Mediterranean, and the effect of varying sample size and survey frequency. In May–June of 2017 to 2019, we conducted 69 surveys, assimilating 10,075 inwater turtle records at the two sites. Optimal survey frequency to capture the dynamics of aggregations over the breeding period was <2-week intervals and >500 turtles (from the combined surveys). This minimum threshold was attributed to the core-area use of female turtles shifting across surveys in relation to wind direction to access warmer nearshore waters and male presence. Males were more widely distributed within aggregations than females, particularly in May when mating encounters were high. Most males were recorded swimming and oriented parallel to shore, likely to enhance encounter rates with females. In contrast, most females were generally stationary (resting on the seabed or basking), likely to conserve energy for reproduction, with orientation appearing to shift in relation to male numbers at the breeding area. Thus, by identifying the main factors regulating the movement and distribution of animals, appropriate survey intervals can be selected for appropriate home range analyses. Our study demonstrates the versatility of UAS to capture the fine-scale dynamics of wildlife aggregations and associated factors, which is important for implementing effective conservation.</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)
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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.