Find research datasets worth reusing

opencc-by-4.0Mar 2024View details →

Aerial photographs of Atlantic walruses subspecies Odobenus rosmarus rosmarus on Matveev Island

The dataset represents aerial photographs taken from a UAV on Matveev Island, Russian Federation. There are a total of 197 images with a minimum resolution of 1632x1088 and a maximum of 5472x3648 (WxH). The main directory "walruses" contains three directories ("images", "markup", "masks"). The data is marked for instance segmentation in the form of json files. The minimum number of objects per image is 22, and the maximum is 948.

openMay 2020View details →

Unmanned aerial system data of Lirung Glacier and Langtang Glacier for 2013–2018

This dataset contains the raw data as well as produced image mosaics, digital elevation models (DEMs) and data derivatives of optical (RGB) unmanned aerial vehicle surveys of the debris-covered Lirung Glacier (9 surveys, 2013–2018) and Langtang Glacier (7 surveys, 2014–2018) in the <a href="https://www.google.com/maps/@28.2525566,85.6197635,32922m/data=!3m1!1e3">Langtang Catchment</a>, Nepalese Himalaya. All data in this dataset are stored in tape archive (<code>tar</code>) or gzip-compressed tape archive (<code>tar.gz</code>) formats and require extraction before use. The projected coordinate system used in this dataset is WGS 1984 UTM Zone 45N (EPSG:32645). Survey dates are always provided as yyyymmdd.   File descriptions <ul> <li><code>dems_<glacier>.tar</code> 20 cm resolution DEMs that were derived from the raw UAV images. DEMs of all survey dates are included in the tar archives. File format is GeoTIFF.  </li> <li><code>orthomosaics_<glacier>.tar</code> 10 cm resolution image mosaics of orthorectified source imagery (orthomosaics) that were derived from the raw UAV images. Orthomosaics of all survey dates are included in the tar archives. File format is GeoTIFF.  </li> <li><code>point-clouds_<glacier>.tar.gz</code> Raw dense point clouds that were derived from the raw UAV images. Point clouds of all survey dates are included in the tar archive. File format is ASPRS LAS. Note that additional gzip-compression has been applied to the archives.  </li> <li><code>raw-data_<glacier>_<datestamp>.tar</code> Raw data of each of the surveys that were performed over 2013–2018. The filename includes the glacier name and the survey date. Each tar archive contains directories for each UAV flight associated to that specific survey, indicated by f1, f2, ..., fn. The flight directories have the following contents: <ul> <li><code>img</code> Subdirectory that contains the individual images in JPEG format captured by the UAV camera.</li> <li><code>*flight_path.kml</code> (not present for all surveys) Keyhole Markup Language file that contains the flight path of the UAV recorded by the UAV's internal GPS+GLONASS sensor.</li> <li><code>*image_geoinfo.txt</code> Table with coordinates (x,y,z) and UAV orientation (roll, tilt, yaw) for every image in <code>img</code>, which were recorded by the UAV's internal GPS+GLONASS sensor and gyroscope, respectively.</li> <li><code>*drone_log.bbx</code> or <code>*drone_log.bb3</code> Binary flight log file from the UAV containing detailed flight information. Can be read by the proprietary eMotion software by UAV manufacturer <a href="https://www.sensefly.com/">senseFly</a>.  </li> </ul> </li> <li><code>supplementary-animation_<glacier>.gif</code> Animations of Langtang Glacier (2014–2018) and Lirung Glacier (2013–2017) supplementary to Kraaijenbrink and Immerzeel (2025). The high resolution time lapse animations are constructed from composites of the orthomosaic and hillshaded DEM. Since the animations are in GIF format, they are best viewed in a web browser in which they can be zoomed and panned.</li> <li><code>supplementary-animation_lirung_terminus_retreat.mp4 </code>Three-dimensional fly-by video animation of the terminus retreat of Lirung Glacier (2013–2017), supplementary to Kraaijenbrink and Immerzeel (2025).  </li> <li><code>supplementary-data-to-article.tar</code> Data derivatives as presented in Kraaijenbrink & Immerzeel (2024). The tar archive contains a README file with additional information for each of the datasets present in the archive. The archive contains: <ul> <li>Error measurements of the UAV product</li> <li>Vector outlines of the area of interests of both glaciers</li> <li>Point cloud extracts of supraglacial ice cliff cross profiles</li> <li>Flow and gradient corrected DEMs (1 m resolution)</li> <li>Pixel-wise regression of the uncorrected and flow-corrected DEMs (1 m resolution)</li> <li>Surface velocity between survey pairs (8 m resolution)</li> </ul> </li> </ul>   Reference For further information, e.g. about the UAV systems and cameras used, as well as detailed descriptions of the data and the applied data processing please refer to the accompanying journal article. Kraaijenbrink, P. D. A., & Immerzeel, W. W. (2025). Spatial and temporal variability of the surface mass balance of debris‐covered glacier tongues. Journal of Geophysical Research: Earth Surface, 130, e2024JF007935. <a href="https://doi.org/10.1029/2024JF007935" target="_blank" rel="noopener">https://doi.org/10.1029/2024JF007935</a>   License This dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). (<a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>)   Correspondence Dr Philip Kraaijenbrink (<a href="mailto:p.d.a.kraaijenbrink@uu.nl">p.d.a.kraaijenbrink@uu.nl</a>) Prof Dr Walter Immerzeel (<a href="mailto:w.w.immerzeel@uu.nl">w.w.immerzeel@uu.nl</a>)

opencc-zeroFeb 2022View details →

Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping

Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.

opencc-zeroApr 2022View details →

Morphological adaptations linked to flight efficiency and aerial lifestyle determine natal dispersal distance in birds

Natal dispersal—the movement from birthplace to breeding location—is often considered the most significant dispersal event in an animal's lifetime. Natal dispersal distances may be shaped by a variety of intrinsic and extrinsic factors, and remain poorly quantified in most groups, highlighting the need for indices that capture variation in dispersal among species. In birds, it is hypothesized that dispersal distance can be predicted by flight efficiency, which can be estimated using wing morphology. However, the use of morphological indices to predict dispersal remains contentious and the mechanistic links between flight efficiency and natal dispersal are unclear. Here, we use phylogenetic comparative models to test whether hand-wing index (HWI, a morphological proxy for wing aspect ratio) predicts natal dispersal distance across a global sample of 114 bird species. In addition, we assess whether HWI is correlated with flight usage in foraging and daily routines. We find that HWI is a strong predictor of both natal dispersal distance and a more aerial lifestyle. Our results support the use of HWI as a valid proxy for relative natal dispersal distance, and also suggest that evolutionary adaptation to aerial lifestyles is a major factor connecting flight efficiency with patterns of natal dispersal.

opencc-by-4.0Apr 2022View details →

SROADEX: Dataset for binary recognition and semantic segmentation of road surface areas from high resolution Aerial Orthoimages Covering Approximately 8,650 km2 of the Spanish Territory Tagged with Road Information

The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography representing the axes of the different types of roads (urban, interurban and rural). This cartography has been obtained from different Spanish official sources (National Geographic Institute and autonomic cartographic agencies) that we have revised and edited in a meticulous and systematic way to verify that the roads are represented on the cartography according to the orthoimages, available on January 1, 2021 in the download center of the National Center of Geographic Information (CNIG), on 16 rectangular areas (28,5 km * 18,5 km) of the Spanish territory (insular and peninsular). The dataset consists of  777599 images in png format of 256x256 pixels, organized in folders for the different trainings, separating those corresponding to training, testing and validation. The structure of the data is as follows: 1-Road-Ortho and 1-Road-Mask contain the images and ground true for training the semantic segmentation networks. 1-Road-Ortho and 2-NoRoad-Ortho contain aerial images containing or not containing vials, for the training of binary tessellation networks identifying tessellations with vials. Moreover, in each folder the structure is the same: train, test, validation containing 90%, 5% and 5% of the total images and masks of each type. 1-Road-Ortho     |----Train     |----Test     -----Validation 1-Road-Mask     |----Train     |----Test     -----Validation 2-NoRoad-Ortho     |----Train     |----Test     -----Validation

opencc-zeroMay 2022View details →

Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest

Edge effects - abiotic and biotic changes associated with habitat boundaries - are key drivers of community change in fragmented landscapes. Their influence is heavily modulated by matrix composition. With over half of the world's tropical forests predicted to become forest edge by the end of the century, it is paramount that conservationists gain a better understanding of how tropical biota is impacted by edge gradients. Bats comprise a large fraction of tropical mammalian fauna and are demonstrably sensitive to habitat modification. Yet, knowledge about how bat assemblages are affected by edge effects remains scarce. Capitalizing on a whole-ecosystem manipulation in the Central Amazon, the aims of this study were to i) assess the consequences of edge effects for twelve aerial insectivorous bat species across the interface of primary and secondary forest and ii) investigate if the activity levels of these species differed between the understory and canopy and if they were modulated by distance from the edge. Acoustic surveys were conducted along four 2-km transects each traversing equal parts of primary and ca. 30-year-old secondary forest. Five models were used to assess the changes in the relative activity of forest specialists (three species), flexible forest foragers (three species), and edge foragers (six species). Modelling results revealed no evidence of edge effects, except for forest specialists in the understory. No significant differences in activity were found between the secondary or primary forest but most species exhibited pronounced vertical stratification. Our study highlights that forest specialist bats are more edge-sensitive than both flexible forest and edge foraging bats and suggests that the influence of edge effects on aerial insectivorous bats may exceed 2 km. The absence of pronounced edge effects and the comparable activity levels between primary and old secondary forests indicates that old secondary forest can help ameliorate the consequences of fragmentation on tropical aerial insectivorous bats.

opencc-zeroSep 2022View details →

Using Unoccupied Aerial Vehicles (UAVs) to map and monitor changes in emergent kelp canopy after an ecological regime shift

Kelp forests are complex underwater habitats that form the foundation of many nearshore marine environments and provide valuable services for coastal communities. Despite their ecological and economic importance, increasingly severe stressors have resulted in declines in kelp abundance in many regions over the past few decades, including the North Coast of California, USA. Given the significant and sustained loss of kelp in this region, management intervention is likely a necessary tool to reset the ecosystem and geospatial data on kelp dynamics are needed to strategically implement restoration projects. Because canopy-forming kelp forests are distinguishable in aerial imagery, remote sensing is an important tool for documenting changes in canopy area and abundance to meet these data needs. We used small unoccupied aerial vehicles (UAVs) to survey emergent kelp canopy in priority sites along the North Coast in 2019 and 2020 to fill a key data gap for kelp restoration practitioners working at local scales. With over 4,300 hectares surveyed between 2019 and 2020, these surveys represent the two largest marine resource-focused UAV surveys conducted in California to our knowledge. We present remote sensing methods using UAVs and a repeatable workflow for conducting consistent surveys, creating orthomosaics, georeferencing data, classifying emergent kelp, and creating kelp canopy maps that can be used to assess trends in kelp canopy dynamics over space and time. We illustrate the impacts of spatial resolution on emergent kelp canopy classification between different sensors to help practitioners decide which data stream to select when asking restoration and management questions at varying spatial scales. Our results suggest that high spatial resolution data of emergent kelp canopy from UAVs have the potential to advance strategic kelp restoration and adaptive management.

opencc-by-4.0May 2022View details →

Data repository for "3D coseismic surface displacements from historical aerial photographs of the 1987 Edgecumbe earthquake, New Zealand"

This data repository includes supplementary files used in the accompanying manuscript:  Delano, J. E, Howell, A., Stahl, T. A., Clark, K. (submitted 2022). 3D coseismic surface displacements from historical aerial photographs of the 1987 Edgecumbe earthquake, New Zealand. Journal of Geophysical Research: Solid Earth. Contents: <ol> <li>Supplementary Text S1, containing additional methods and discussion</li> <li>Supplementary Figures S1-S9</li> <li>Supplementary Tables S1-S6 </li> <li>Raster files (TIFF) of SfM results and differenced DSM</li> <li>Raster files of orthophoto mosaics (pre- and post-earthquake)</li> <li>Shapefiles containing fault trace mapping and displacement locations</li> </ol> See README for individual file descriptions.

openother-openOct 2022View details →

5G Aerial RF Radiation Data

Data is contained in zipped files. Each zipped file represents a measurement campaign. Data includes measurement (.mat, .csv, .xlsx) files, plot (.jpg, .fig) files, and a readme (.txt) file.

opencc-by-4.0Oct 2022View details →

Aerial and Terrestrial Thermal Images of German Multi-Family Buildings

This dataset consists of 968 thermal images including 693 captured by hand-held camera on the ground and 275 via UAV. Of the aerial images, 139 were recorded manually and 136 in automatic flight mode. The images depict four multi-family buildings of 18 m height in the German city of Karlsruhe belonging to the local municipal housing association Volkswohnung Karlsruhe GmbH. Table 1 gives an overview of the buildings in question, all of which were fully rented out on the days of image acquisition. Table 1: Building information <table> <tbody> <tr> <td>                      Building Details </td> <td> Sophienstr. 201-203 </td> <td> Volzstr. 2 </td> <td> Wichernstr. 4 </td> <td> Wichernstr. 10-18 </td> </tr> <tr> <td> construction year </td> <td> 1957 </td> <td> 1954 </td> <td> 1953 </td> <td> 1953 </td> </tr> <tr> <td> apartments </td> <td> 30 </td> <td> 12 </td> <td> 25 </td> <td> 30 </td> </tr> </tbody> </table> The aerial images were acquired using DJI’s “Matrice 600” UAV (DJI, 2022) equipped with the “Zenmuse XT2”, a combination of FLIR’s “Duo Pro R” thermal and RGB camera technology and DJI’s gimbal (FLIR, 2021a). All thermal images were recorded in FLIR’s proprietary image format RJPEG. The terrestrial thermographic images were captured with FLIR’s “T200” hand-held camera (FLIR, 2021b) in the standard JPEG format. The emissivity was set to 0.95 throughout the acquisition of both aerial and terrestrial images. Thermographic image processing and analysis was realized using the "FLIR Thermal Studio" software (FLIR Systems Inc., 2022). The temperature scale was set to -8 °C to +13 °C and color distribution function "signal linear" selected. The thermal images of this dataset were recorded on February 28th and March 1st, 2022, between 8 p.m. and 1 a.m. On February 28th the outside air temperature registered at between 1 °C and 3 °C. Wind speeds reached a maximum of 17 km/h. The sky was cloudless both during the flights and in the preceding 24 hours. A maximum temperature of 11 °C was recorded by local weather stations in that time period. Very similar weather conditions were present on March 1st. The outside air temperature was recorded at between -1 °C and 5 °C during acquisition, with wind speeds of max. 11 km/h. Again, the sky was entirely clear both during the flights and in the preceding 24 hours, with a maximum temperature of 9 °C present in that time period. The sun set at around 6:10 p.m. on both days (timeanddate, 2022). The images were recorded using ten different flight settings of varying speed, flight height, and camera angle. Details are summarized in Table 2. Table 2: Flight settings <table align="center"> <tbody> <tr> <td> Flight </td> <td> Building </td> <td> Automatically/ manually performed flight route </td> <td> Camera angle [°] </td> <td> Height above ground [m] </td> <td> Height above building [m] </td> <td> Distance to façade [m] </td> <td> Flight speed [m/s] </td> </tr> <tr> <td> 1 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 1 </td> </tr> <tr> <td> 2 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 3 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 5 </td> </tr> <tr> <td> 4 </td> <td> Full area </td> <td> Automatically </td> <td> 45 (oblique) </td> <td> 60 </td> <td> 42 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 5 </td> <td> Full area </td> <td> Automatically </td> <td> 90 (nadir) </td> <td> 40 </td> <td> 22 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 6 </td> <td> Full area </td> <td> Automatically </td> <td> 90 (nadir) </td> <td> 60 </td> <td> 42 </td> <td> - </td> <td> 3 </td> </tr> <tr> <td> 7 </td> <td> Wichernstr. 4 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 4 </td> <td> - </td> </tr> <tr> <td> 8 </td> <td> Wichernstr. 4 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 8 </td> <td> - </td> </tr> <tr> <td> 9 </td> <td> Wichernstr. 4 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 15 </td> <td> - </td> </tr> <tr> <td> 10 </td> <td> Wichernstr. 10-18 </td> <td> Manually </td> <td> 0 (manual) </td> <td> 4 to 12 </td> <td> - </td> <td> 15 </td> <td> - </td> </tr> </tbody> </table>   Acknowledgments: The authors appreciate the support of Marinus Vogl (Air Bavarian GmbH) in acquiring the thermal images via UAV. Moreover, they thank Harald Schneider (Karlsruhe Institute of Technology) for his advice and assistance. Lastly, they gratefully acknowledge the consent and support of Karlsruher Volkswohnung GmbH within this research project.   References: DJI (2022).  Matrice 600 - DJI. URL: https://www.dji.com/de/matrice600 (accessed 10th January 2022) FLIR (2021a).  FLIR XT2 product information (Wilsonville, USA). URL: https://www.flir.de/products/xt2/ (accessed 10th January 2022) FLIR (2021b).  FLIR T-series (Wilsonville, USA). URL: https://www.flir.com/instruments/t-series/  (accessed 10th January 2022) FLIR Systems Inc. (2022). User’s manual Flir Thermal Studio. URL: https://www.sahkonumerot.fi/6708162/doc/operatinginstructions/ (accessed 12th August 2022) Timeanddate (2022). Wetter im Februar 2022 in Karlsruhe, Baden-Württemberg, Deutschland. URL: https://www.timeanddate.de/wetter/deutschland/karlsruhe/rueckblick?month=2&year=2022  (accessed 12th March 2022)

opencc-by-4.0Aug 2022View details →

supplementary data about Extraction, Isolation and Structure elucidation of Two Phenolic acids from Aerial parts of Celery and Coriander.

supplementary  data about Extraction, Isolation and Structure elucidation of Two Phenolic acids from Aerial parts of Celery and Coriander. caffiec acid nmr 2.pdf                supplementary data.docx <a href="https://zenodo.org/api/files/52908924-99c6-4a2c-8047-ef7131714205/p%20coumaric%20acid%20nmr%202.pdf">p coumaric acid nmr 2.pdf</a>

FIGURE 8. Molfetta dinosaur tracks, photogrammetry derived 3D in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 8. Molfetta dinosaur tracks, photogrammetry derived 3D models and interpretations; 1-2, DEM and contour line map of a theropod footprint (contour lines have an interval of 0.2 cm); 4-5, DEM and contour line map of an ornitischian footprint; 3 and 6, interpretative outline drawings of the studied tracks.

FIGURE 7 in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 7. Orthophotomosaic of the Molfetta tracksite produced by the aerial survey performed with the hexacopter.

FIGURE 6 in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 6. Comparison of the products generated for the sector including the "L-shaped trace". 1, orthophoto raster map; 2, hillshade raster map; 3, slope raster map; 4, contour lines vector map.

FIGURE 4 in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 4. Comparison of the results obtained on a sample area. 1, orthophoto; 2, DEM; 3, slope raster map produced by the quadcopter at flight height of 30 m; 4, orthophoto; 5, DEM; 6, slope raster map produced by the quadcopter at a flight height of 10 m; 7, orthophoto; 8, DEM; 9, slope raster map produced by the hexacopter at a flight height of 15 m.

FIGURE 3 in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 3. The UAVs (Unmanned Aerial Vehicles) used for aerial survey of the tracksite. 1, quadcopter SZ DJI Phantom 4; 2, hexacopter Tarot FY680 Pro.

FIGURE 5 in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 5. Comparison of the products generated for one of the most trampled sector. 1, orthophoto raster map; 2, hillshade raster map; 3, slope raster map; 4, contour lines vector map.

opencc-by-4.0Feb 2021View details →

Рис. 3. РаспреΑеΛение гнезΑ ΑаΛьневосточного аиста на воΑно-боΛотных угоΑьях оз. БоΛонь по материаΛам авиаучетов: 1999 г. — треугоΛьник (по: Δарман, АнΑронов, Хигучи и Αр. 2000); 2004 г. — звезΑочка; 2005 г. — кружок Fig. 3. Distribution of nests of the Oriental White Stork in the wetlands of Lake Bolon based on aerial surveys: 1999 — triangle (based on: Darman et al. 2000a); 2004 — asterisk; 2005 — circle in The number and distribution of the Oriental White Stork Ciconia boyciana Swinhoe, 1873 in the Khabarovskiy Region

Рис. 3. РаспреΑеΛение гнезΑ ΑаΛьневосточного аиста на воΑно-боΛотных угоΑьях оз. БоΛонь по материаΛам авиаучетов: 1999 г. — треугоΛьник (по: Δарман, АнΑронов, Хигучи и Αр. 2000); 2004 г. — звезΑочка; 2005 г. — кружок Fig. 3. Distribution of nests of the Oriental White Stork in the wetlands of Lake Bolon based on aerial surveys: 1999 — triangle (based on: Darman et al. 2000a); 2004 — asterisk; 2005 — circle

opencc-by-4.0Dec 2023View details →

Maldivian seagrass aerial extent raster layers 2021 - 2000

Contemporary Seagrass Map (2021) The contemporary product was derived from Sentinel-2 satellite imagery, operated by the European Space Agency (ESA). The imagery, with a spatial resolution of 10 meters was pre-processed in Google Earth Engine (GEE) following established methods for retrieval of benthic signals. A support vector machine (SVM) classifier was used for classification. Training data encompassed three classes: seagrass, non-seagrass (including coral reefs, mangroves, sand/rubble, and macroalgal beds), and optical-deep water (ODW), totaling 25,463 training pixels. Important: the classification output is a binary (seagrass/non-seagrass) class. Validation of the map was conducted independently using 1,019 in-situ field survey points collected from 2017-2023. Mapping accuracy was assessed through an error matrix. Overall accuracy = 82% Historical Seagrass Maps (2000-2021) The historical mapping product is derived from Landsat data spanning 2000 to 2021. The Landsat missions, operated by the United States Geological Survey (USGS) in collaboration with NASA, provide satellite data with a spatial resolution of 30 meters. There are no suitable data for 2010-2011. Each composite, representing a two-year period, underwent radiometric normalisation relative to a reference image from 2020-2021. Training and validation data were designated using an identical methodology as the contemporary maps, with 823 validation points utilised for accuracy assessment from 2017-2023. A fixed pixel approach was adopted to assess accuracy across the entire time series, involving the manual delineation of seagrass and non-seagrass areas. Overall accuracy was >89% in all cases.   These data represent GeoTIFF files of seagrass habitat extent (binary classification). Contemporary data come from habitat classification of Sentinel-2 imagery (10 m pixel size). Historical maps come from habitat classification of Landsat data (30 m pixel size). For further details of workflow and data specifications please see the original publication DOI: 10.1038/s41598-024-61088-1

FIGURE 1 in The use of aerial and close-range photogrammetry in the study of dinosaur tracksites: Lower Cretaceous (upper Aptian/lower Albian) Molfetta ichnosite (Apulia, southern Italy)

FIGURE 1. Locality map showing the San Leonardo quarry tracksite, Apulia, southern Italy. Image obtained using Google Earth Pro.