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45 results for “Google Earth”
Nimble code and dataset for: Estimating true density in large, alpine herbivores using Google Earth imagery
<p>This nimble code will estimate elk density from the count data (DO_data_list.RData) and covariates (DO_constants_list.RData). Data and covariates include: a 'y' matrix of double observer counts with 3315 rows (1 for each 250 m square plot) and 3 columns for the observer 1's exclusive counts, observer 2's exclusive counts, and the count of elk detected by both observers. The double-observe protocol was only employed in 372 random cells. 'n' is a vector of total counts – the total number of uniquely detected elk in each plot. 'obs1tot' is a vector of the total count of elk by just observer 1, who counted elk in all 3315 cells. There are two dummy indicator variables that are all 1's ('obs1constraint' and 'obs2constraint') to constrain unobserved but estimated counts to sum to expected totals. For example, observer 1 was the only observer in 2943 plots. This single count would be the equivalent of the sum of the exclusive observer 1 counts and the joint observer 1 and 2 counts in a double-observer protocol. Additionally, the sum of observer 2s exclusive counts and observer 1's total counts must equal the total count of unique individuals in a cell. The covariates 'meadow' and 'deadfall' are the additive log-ratio transformations of the proportion of open meadow and burned forest, respectively, in each 250-m plot. The 'nonforest' covariate is the number of hectares in each plot that was not conifer forest. 'M' is the total number of plots. 'firstsearch' is an indicator variable, indicating which plots were part of the first search area in the alpine >2750 m above sea level. 'domethod' is an indicator variable, indicating which plots were searched using the double-observer protocol. </p>
Long-term soil erosion monitoring in China using the RUSLE model based on Google Earth Engine
<p>This dataset contains soil erosion maps in China at 500m resolution from 2010 to 2020. The dataset is stored in GeoTif format with the unit of t/(km<sup>2 </sup>· a).</p>
SCALE-WIN22 cruise track, stations, sea-ice edge, and SAR images on Google Earth
<p>This Google Earth KMZ file contains the cruise track of the SCALE-WIN22 expedition, the description of activities on each station, the daily ice-edge location, and the quick-view SAR images. This is an interactive description of the cruise report:</p> <p>Vichi, M. (2023). SCALE-WIN22 Cruise Report. Zenodo. <a href="https://doi.org/10.5281/zenodo.7901529">https://doi.org/10.5281/zenodo.7901529</a></p> <p>The layers are separated in single folders that can be activated or deactivated. All data have a time stamp and a time span. By moving the time slider on the upper left (and adjusting the time interval), it is possible to follow the ship and display the corresponding SAR images and sea-ice edge. All the images are displayed for a time duration of one day. The exact time and name of the image is found in the object description.</p> <p>Legend:</p> <ul> <li>White line: ship track</li> <li>Red pin: station (with the main activities: refer to the report for more informations)</li> <li>Yellow pin: ship location at the time of the COSMO-SkyMed image</li> <li>Green pin: ship location at the time of the Terrasar-X image</li> <li>Red thick line: sea-ice edge from AMSR2 (green shading is the uncertainty)</li> </ul> <p>Quick-view SAR images are courtesy of DLR and ASI. Sea-ice edge location was produced by SAWS.</p> <p>The code to generate the KML file is available on github: <a href="https://github.com/mvichi/SCALE">https://github.com/mvichi/SCALE</a></p>
Nimble code and dataset for: Estimating true density in large, alpine herbivores using Google Earth imagery
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FIGURE 4. Google Earth 2015 in Checklist of Recent thecideoid brachiopods from the Indian Ocean and Red Sea, with a description of a new species of Thecidellina from Europa Island and a re-description of T. blochmanni Dall from Christmas Island
FIGURE 4. Google Earth 2015 Digital Globe map of Christmas Island, showing collecting areas for Thecidellina blochmanni and T. cf. blochmanni at Flying Fish Cove and West White Beach (Bay), respectively. Flying Fish Cove coordinates are lat. 10° 25'S, long. 105° 40'E.
FIGURE 3 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 3. (A) Specimen of the bromeliad Alcantarea imperialis with inflorescence photographed about 1 km from the type locality of Scinax v-signatus in the Municipality of Teresópolis, State of Rio de Janeiro. (B) A granitic outcrop with several individuals of A. imperialis, photographed in the Municipality of Miguel Pereira, State of Rio de Janeiro. (C) The same outcrop as it appears on Google Earth images. Yellow arrows indicate small whitish dots that represent individual bromeliads.
FIGURE 4 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 4. Relief map of the State of Rio de Janeiro (Modified from http://www.agritempo.gov.br/altimetria/RJ.html) with the potential distribution of Alcantarea imperialis produced based on images of Google Earth (yellow circles), occurrence of Scinax v-signatus based on voucher specimens (blue stars), localities for Scinax insperatus (gray triangle), localities we identified A. imperialis on the field but were unable to detect using Google Earth (white square), and localities Based on A. imperialis from the Herbarium on the Jardim Botânico do Rio de Janeiro (Square) – http://www.jbrj.gov.br/.
FIGURE 2 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 2. Map from GAA depicting the distribution of Scinax v-signatus. Note that the type locality (Black circle) was not included in distribution area.
FIGURE 1 in Predictive-like distribution mapping using Google Earth: Reassessment of the distribution of the bromeligenous frog, Scinax v-signatus (Anura: Hylidae)
FIGURE 1. Map of southeastern Brazil highlighting the area of the States of Minas Gerais, Rio de Janeiro, and Espírito Santo. Symbols indicate the type localities for Scinax v-signatus (white circle), the type locality for S. arduous (white square) and S. belloni (black square). The white hexagons indicate three new localities for S. arduous. The star indicates two localities where an undescribed species was collected in the south of the State of the Espírito Santo, and the black circle indicates the locality for another undescribed species collected in the north of the State of Rio de Janeiro.
Global Landside Clustering of Aquaculture Ponds Distribution Acquired from Dense Time-Series Sentinel-2 Images by Google Earth Engine
<p>This dataset reveals the global distribution pattern of landside clustering aquaculture ponds (LCAP) from a spatial perspective for the first time. It was derived from 4,015,054 tiles of the 10-m Sentinel-2 time-series images collected throughout 2020. The total area of global LCAP was estimated at 55,337.03 km2. Accuracy verification revealed that the Omission Error and Commission Error of the data is 7.51% and 16.69% respectively. We provide this dataset in <em>ESRI</em> <em>shapefile </em>format (.zip), which can be opened by <em>ArcGIS. </em>We invite you to download and utilize this dataset and recommend citing the following two references.</p>
FIGURE. Geographical location of the Augusto Ruschi Biological Reserve (ARBR), Santa Teresa, Espírito Santo, Brazil. a the state of Espírito Santo, highlighting the location of the ARBR. b delimitation of the ARBR. c–e ARBR vegetation. (a: prepared by Henrique Lauand Ribeiro, b: adapted from Google Earth Pro, c–e: photos of Gabriel Mendes Marcusso). in Augusto Ruschi Biological Reserve vascular epiphytes: a hotspot in the mountains of the Atlantic Forest of Southeastern Brazil
FIGURE. Geographical location of the Augusto Ruschi Biological Reserve (ARBR), Santa Teresa, Espírito Santo, Brazil. a the state of Espírito Santo, highlighting the location of the ARBR. b delimitation of the ARBR. c–e ARBR vegetation. (a: prepared by Henrique Lauand Ribeiro, b: adapted from Google Earth Pro, c–e: photos of Gabriel Mendes Marcusso).
West Heslerton Anglo-Saxon Settlement -Primary Excavation Archive for interactive viewing in Google Earth Pro
<p>The Landscape Research Centre pioneered digital recording in field archaeology using hand-held computers in the field from the mid 1980s onwards. The excavation of an Early Anglo-Saxon settlement covering nearlly 25Ha, funded by English Heritage from the rescue archaeology commissions budget was one of the largest excavations in Europe conducted between 1986 and 1996 with an analytical program that contuniued into th 2020s. The digital plans provide an interactive interface to the primary excavation archives when this file id loaded inot Google Earth Pro.</p>
Changes Monitoring in Hongjiannao Lake from 1987-2023 using Google Earth Engine and Analysis of Climatic and Anthropogenic Forces
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Data from: Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine
<p><strong>Paper Abstract:</strong></p> <p>The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.</p> <p> </p> <p><strong>Data details:</strong></p> <p>See paper: <a href="https://www.mdpi.com/2072-4292/12/23/3942">Remote Sensing | Free Full-Text | Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine (mdpi.com)</a></p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/KangarooIslandFireHistory_1988to2020">ZZMitch/KangarooIslandFireHistory_1988to2020: Code from "Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine" (RS, 2020) (github.com)</a></p> <p> </p> <p><strong>If you use these data, please reference: </strong></p> <p>Bonney, M.T., He, Y., Myint, S.W., 2020. Contextualizing the 2019–2020 Kangaroo Island bushfires: Quantifying landscape-level influences on past severity and recovery with Landsat and Google Earth Engine. Remote Sensing 12(23), <a href="https://doi.org/10.3390/rs12233942" rel="nofollow">https://doi.org/10.3390/rs12233942</a>.</p>
Flood Hazard Maps using Google Earth Engine: Thrace and Thessaly River Basin Districts (Greece)
<p>This dataset contains three raster files with a spatial resolution of 10 m, derived by the Google Earth Engine:</p> <p> </p> <p>1) DynamicWorld_Floods_2015_2023.tif: Number of days flooded for the River Basin District of Thrace (Greece) starting from 2015 until 2023</p> <p>2) Thessaly_2015_August2023.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until August 2023</p> <p>3) Thessaly_2015_now.tiff: Number of days flooded for the River Basin District of Thessaly (Greece) starting from 2015 until January 2024</p> <p> </p>
Google Earth trace and GPS coordinates of Mongolian Great Wall
<p>Google Earth trace and GPS coordinates of Mongolian Great Wall.</p>
GISD30: global 30-m impervious surface dynamic dataset from 1985 to 2020 using time-series Landsat imagery on the Google Earth Engine platform
<p>A novel and accurate global 30 m impervious surface dynamic dataset (GISD30) for 1985 to 2020 was produced using the spectral generalization method and time-series Landsat imagery, on the Google Earth Engine cloud-computing platform.</p>
A global data set of realized treelines sampled from Google Earth aerial images
<div> <span>We </span><span>sampled</span><span> Google Earth aerial images</span><span> to get a representative and globally distributed dataset of treeline locations</span><span>. </span><span>Google Earth images</span><span> are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.</span> </div> <div> </div> <div> <span>Doing so, we scaled Google Earth's GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth's surface. Within this buffer zone, we took coordinates and elevation of the highest </span><span>realized </span><span>treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline</span><span>. We did not ap</span><span>p</span><span>ly an automated image processing approach. </span><span>We calculated mass elevation effect as the distance to the nearest mountain chain limits. Continentality was assessed by the distance to the nearest coastline. Isolation was calculated by the nearest distance of a mountain chain to another mountain chain within a comparable elevational band. </span> </div>
A global data set of realized treelines sampled from Google Earth aerial images
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Monitoring of Inundation Extent during 2010–2022 in the Inner Niger Delta using Landsat Imagery and Google Earth Engine
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.