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239 results for “remote sensing data”
Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
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Spatio-temporal analysis of remotely sensed forest loss data in the Cordillera Administrative Region, Philippines
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Wildland-urban interface in California using remote sensing data
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Restore Centre of Excellence: High-resolution mapping of Louisina vegetation remote sensing data for surge modelling
<p>Satellite derived Leaf Area Index map of the Louisiana coast, translated into plant dimensions using field data from CMRS stations and dedicated project sampling. Plant dimensions have been used to prescribe hydraulic roughness fields for a hydrodynamic model (Delft3D) used to asses the effect of wetlands on storm surge levels.</p>
Data from: Bioclimatic variables derived from remote sensing: assessment and application for species distribution modeling
Remote sensing techniques offer an opportunity to improve biodiversity modeling and prediction worldwide. Yet, to date, the weather-station based WorldClim dataset has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those datasets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado, and Patagonia. We compare the bioclimatic datasets derived from MERRA and AMSR-E information with in situ station data, and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these datasets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia, and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental datasets with higher spatial and temporal fidelity may improve upon these results.
Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images"
<p>Dataset for "Deep Learning with remote sensing data for image segmentation: example of rice crop mapping using Sentinel-2 images". </p> <p> </p> <p>image_prediction_pt1 and _pt2 have the same content as image_prediction.zip but split in two parts for faster downloading with Google Colab (to avoid time out)</p> <p> </p> <p>Contact</p> <p>Ricardo Dalagnol</p> <p>ricds@hotmail.com</p>
Data from: Towards a framework for agent-based image analysis of remote-sensing data
Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).
Figure 6 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 6 - Species photographs. A Lycodryas sanctijohannis, male, ZSM 38/2010, Anjouan B Lycodryas sanctijohannis, female, ZSM 40/2010, Anjouan C Furcifer cephalolepis, male, Grand Comoro D Furcifer polleni, male, Anjouan E Furcifer cephalolepis, female, Grand Comoro F Furcifer polleni, female, Mayotte G Trachylepis comorensis, Mohéli H Trachylepis striata, ZSM 70/2010, Anjouan.
Figure 3 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 3 - Forest areas on the Comoros. For each level of altitude (in intervals of 100 m), the area occupied by forest is given as percentage of the total area occupied by all habitat classes in this level.
Figure 19 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 19 - Distribution maps, and distribution over habitat and altitude classes, for Trachylepis striata, Typhlops comorensis and Typhlops sp.
Figure 8 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 8 - Species photographs. A. Phelsuma nigristriata, Mayotte B Phelsuma comorensis, Grand Comoro C Phelsuma pasteuri, Mayotte D Phelsuma v-nigra anjouanensis, Anjouan E Phelsuma v-nigra comoraegrandensis, Grand Comoro F Phelsuma v-nigra v-nigra, Mohéli G Ebenavia inunguis, ZSM 68/2010, Anjouan H Paroedura sanctijohannis, ZSM 98/2010, Mayotte.
Figure 16 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 16 - Distribution maps, and distribution over habitat and altitude classes, for Phelsuma comorensis, Phelsuma dubia and Phelsuma laticauda.
Figure 15 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 15 - Distribution maps, and distribution over habitat and altitude classes, for Lycodryas sanctijohannis, Oplurus cuvieri comorensis and Paroedura sanctijohannis.
Figure 5 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 5 - Species photographs. A Blommersia sp., ZSM 1706/2008, Mayotte B Boophis sp., ZSM 1711/2008, Mayotte C Ramphotyphlops braminus, ZSM 163/2010, Anjouan D Typhlops comorensis, MNHN 1895/126, Grand Comoro E Typhlops sp., ZSM 361/2002, Grand Comoro F drawings of heads of Ramphotyphlops braminus, Typhlops comorensis and Typhlops sp. (left to right) G Leioheterodon madagascariensis, Nosy Boraha, Madagascar H Liophidium mayottensis, ZSM 1693/2008, Mayotte.
Figure 14 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 14 - Distribution maps, and distribution over habitat and altitude classes, for Hemidactylus platycephalus, Leioheterodon madagascariensis and Liophidium mayottensis.
Figure 9 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 9 - Species photographs. A Hemidactylus frenatus, ZSM 116/2010, Mohéli B Hemidactylus parvimaculatus, ZSM 370/2002, Mohéli C Hemidactylus mercatorius, ZSM 121/2010, Anjouan D Hemidactylus platycephalus, Anjouan E Geckolepis maculata, ZSM 83/2010, Anjouan F Oplurus cuvieri comorensis, Grand Comoro G Agama agama, male, Grand Comoro H Agama agama, juvenile, Grand Comoro.
Figure 12 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 12 - Distribution maps, and distribution over habitat and altitude classes, for Furcifer cephalolepis, Furcifer polleni and Geckolepis maculata.
Figure 11 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 11 - Distribution maps, and distribution over habitat and altitude classes, for Boophis sp., Cryptoblepharus boutonii and Ebenavia inunguis.
Figure 10 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 10 - Distribution maps, and distribution over habitat and altitude classes, for Agama agama, Amphiglossus johannae and Blommersia sp.
Figure 1 from: Hawlitschek O, Brückmann B, Berger J, Green K, Glaw F (2011) Integrating field surveys and remote sensing data to study distribution, habitat use and conservation status of the herpetofauna of the Comoro Islands. ZooKeys 144: 21-79. https://doi.org/10.3897/zookeys.144.1648
Figure 1 - Habitats on the Comoro islands. Maps of habitat classes and relative land cover are given for each island.
ScienceDex guides
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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)
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.