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12 results for “Unmanned aerial vehicles (UAVs)”

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zenodo40/100

Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil

Title: Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil   Data description:   The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also Drone) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.   Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.   UAV and camera settings for the acquisition (Specifications Table):   Parameters Specification/value Date (YYYYMMDD): 20170711 Time of day (BRT = -3) 10h a.m. UAV – Drone - Camera Phantom 4. Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky (  ) Low cloud coverage (some clouds) (  ) Completely cloudy Wind condition ( x ) no wind (  ) Low speed (  ) High speed wind Approximate data acquisition duration 16 minutes Total of photographs acquired 143 Across track coverage 80% Cross-track coverage 80% Fly planning software Pix4D Capture   For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a> An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.   Figure 2. The capture of an orthomosaic and processing workflow     References to the main project/publications:   Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>. Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0 Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8   Acknowledgments: This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul  (Grant 23830.388.22048.19092016).   Other considerations   PS. A pdf file is also attached with this description   Declaration of Competing Interest The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.   References associated: Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943 Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, Meteorologische Zeitschrift, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507> Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897548 Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897909 Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil. PANGAEA, https://doi.org/10.1594/PANGAEA.910114

opencc-by-4.0Jul 2017View details →

zenodo40/100

Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil

Title:Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil   Data description:   The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also Drone) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.   Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.   UAV and camera settings for the acquisition (Specifications Table):   Parameters Specification/value Date (YYYYMMDD): 20170707 Time of day (BRT = -3) 14h a.m. UAV – Drone - Camera Phantom 4. Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky (  ) Low cloud coverage (some clouds) (  ) Completely cloudy Wind condition ( x ) no wind (  ) Low speed (  ) High-speed wind Approximate data acquisition duration 16 minutes Total of photographs acquired 143 Across track coverage 80% Cross-track coverage 80% Fly planning software Pix4D Capture   For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a> An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.   Figure 2. The capture of an orthomosaic and processing workflow     References to the main project/publications:   Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>. Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0 Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8   Acknowledgments: This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul  (Grant 23830.388.22048.19092016).   Other considerations   PS. A pdf file is also attached with this description   Declaration of Competing Interest The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.   References associated:   Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, Meteorologische Zeitschrift, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507> Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897548 Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897909 Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil. PANGAEA, https://doi.org/10.1594/PANGAEA.910114 Title:   Unmanned Aerial Vehicle (UAV) data acquired over an subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil   Data description:   The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also Drone) covering an forest area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.   Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files.   UAV and camera settings for the acquisition (Specifications Table):   Parameters Specification/value Date (YYYYMMDD): 20170707 Time of day (BRT = -3) 14h a.m. UAV – Drone - Camera Phantom 4. Fly high (meters above ground) 250 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky (  ) Low cloud coverage (some clouds) (  ) Completely cloudy Wind condition ( x ) no wind (  ) Low speed (  ) High speed wind Approximate data acquisition duration 16 minutes Total of photographs acquired 143 Across track coverage 80% Cross-track coverage 80% Fly planning software Pix4D Capture   For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a> An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.   Figure 2. The capture of an orthomosaic and processing workflow     References to the main project/publications:   Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>. Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0 Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8   Acknowledgments: This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul  (Grant 23830.388.22048.19092016).   Other considerations   PS. A pdf file is also attached with this description   Declaration of Competing Interest The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.   References associated:   Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, Meteorologische Zeitschrift, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507> Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897548 Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897909 Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil. PANGAEA, https://doi.org/10.1594/PANGAEA.910114

opencc-by-4.0Jul 2017View details →

zenodo40/100

Snow Albedo Measurements in Mountainous Regions Using a Dual-sensor Unmanned Aerial Vehicle (UAV)

We used a commercially available UAV (drone) to measure the albedo of the Earth in snowy, mountainous environments. These data represent four initial flights conducted during the spring of 2019 in SW Montana, USA. These UAV-based measurements of albedo allow us to measure a larger and more varied area than do measurements from a stationary tower.

opencc-by-4.0Sep 2019View details →

zenodo40/100

HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection

Add citation file.

opencc-zeroFeb 2023View details →

zenodo36/100

High resolution LiDAR dataset acquired using UAV (unmanned aerial vehicle) over two vineyards and two years located in 'Tomiño', Pontevedra, Spain.

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.

opencc-by-4.0Dec 2022View details →

zenodo36/100

The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions - dataset

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: <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> 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). </li> </ol> Structure-from-motio 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). 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. International Journal of Applied Earth Observations and Geoinformation, 103474. doi:<a href="https://doi.org/10.1016/j.jag.2023.103474"> https://doi.org/10.1016/j.jag.2023.103474</a>

opencc-by-4.0Feb 2023View details →

zenodo32/100

Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil

Fábio Marcelo Breunig¹ (author) ¹ Universidade Federal de Santa Maria, Departamento de Engenharia Florestal, Frederico Westphalen, Rio Grande do Sul, Brasil. E-mail: breunig@ufsm.br Title:   Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil Data description:   The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also Drone) covering an experimental area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.   Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table):   Parameters Specification/value Date (YYYYMMDD): 20201020 Time of day (BRT = -3) 11 h a.m. UAV – Drone - Camera Matrice 100 X3 Fly high (meters above ground) 80 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky (  ) Low cloud coverage (some clouds) (  ) Completely cloudy Wind condition ( x ) no wind (  ) Low speed (  ) High speed wind Approximate data acquisition duration 16 minutes Total of photographs acquired 224 Across track coverage 80% Cross-track coverage 80% Fly planning software Pix4D Capture   For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a> An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.   Figure 2. The capture of an orthomosaic and processing workflow     References to the main project/publications:   Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.   Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0   Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8   Acknowledgments:   This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul  (Grant 23830.388.22048.19092016).   Other considerations   PS. A pdf file is also attached with this description   Declaration of Competing Interest   The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.   References associated:   Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943 Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, Meteorologische Zeitschrift, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507> Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897548 Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897909 Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil. PANGAEA, https://doi.org/10.1594/PANGAEA.910114 Breunig, Fábio Marcelo  (2017, July 11). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4328340

opencc-by-4.0Oct 2020View details →

dryad32/100

Data from: A view from above: A view from above: unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies

1. Tropical forests store and sequester large quantities of carbon, mitigating climate change. Lianas (woody vines) are important tropical forest components, most conspicuous in the canopy. Lianas reduce forest carbon uptake and their recent increase may, therefore, limit forest carbon storage with global consequences for climate change. Liana infestation of tree crowns is traditionally assessed from the ground, which is labour-intensive and difficult, particularly for upper canopy layers. 2. We used a light-weight unmanned aerial vehicle (UAV) to assess liana infestation of tree canopies from above. We used a commercially available quadcopter UAV with an integrated, standard three-waveband camera to collect aerial image data for 150ha of tropical forest canopy. By visually interpreting the images, we assessed the degree of liana infestation for 14.15ha of forest for which ground-based estimates were collected simultaneously. We compared the UAV liana infestation estimates with those from the ground to determine the validity, strengths and weaknesses of using UAVs as a new method for assessing liana infestation of tree canopies. 3. Estimates of liana infestation from UAV correlated strongly with ground-based surveys at individual tree and plot level, and across multiple forest types and spatial resolutions, improving liana infestation assessment for upper canopy layers. Importantly, UAV-based surveys, including the image collection, processing and visual interpretation, were considerably faster and more cost-efficient than ground-based surveys. 4. Synthesis and applications: UAV image data of tree canopies can be easily captured and used to assess liana infestation at least as accurately as traditional ground data. This novel method promotes reproducibility of results and quality control, and enables additional variables to be derived from the image data. It is more cost-effective, time-efficient and covers larger geographical extents than traditional ground surveys, enabling more comprehensive monitoring of changes in liana infestation over space and time. This is important for assessing liana impacts on the global carbon balance, and particularly useful for forest management where knowledge of the location and change in liana infestation can be used for tailored, targeted and effective management of tropical forests for enhanced carbon sequestration (e.g. REDD+ projects), timber concessions and forest restoration.14-Nov-2018

opencc-zeroDec 2018View details →

zenodo32/100

Unmanned Aerial Vehicle (UAV) image dataset.

The  dataset contains 2,919 images and  separated into five classes of car, taxi, truck, bus and motorcycle.

opencc-by-4.0Aug 2022View details →

dryad32/100

Data from: A view from above: A view from above: unmanned aerial vehicles (UAVs) provide a new tool for assessing liana infestation in tropical forest canopies

Open the record for dataset details and reuse information.

publicJan 2019View details →

dryad24/100

Pictures of unmanned aerial vehicle (UAV) experiments on testing paper

Open the record for dataset details and reuse information.

publicApr 2021View details →

nasa8/100

Aviation Safety Reporting System: Unmanned Aerial Vehicle (UAV) Reports

A sampling of reports involving Unmanned Aerial Vehicle (UAV) events.

restrictednotspecifiedApr 2025View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record