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Dataset results
239 results for “remote sensing data”
NOAA Coastwatch Satellite Course (Set up an Application Model of Digital Satellite Data Simulation by Video Graphic Technology of Oceanic data Remotely Sensed of algerian coast)
<p>The goal of the course is to familiarize NOAA/university researchers, Sea Grant professionals and agency/org. partners with different types of ocean satellite data, different tools, and teach participants how to use satellite data in their own research/outreach using their choice of software (NOAA ,2023)</p>
Estimating carbon stock in unmanaged forests using field data and remote sensing
<p><span>The data used for the study "<strong>Estimating carbon stock in unmanaged forests using field data and remote sensing</strong>" by Thomas Leditznig et al. is published here.</span></p> <p><span><strong>Abbstract:</strong> </span><span>Unmanaged forest ecosystems play a critical role in addressing the ongoing climate and biodiversity crises. As there is no commercial interest in monitoring the health and development of such inaccessible habitats, low-cost assessment approaches are needed. We used a method combining RGB imagery acquired using an Unmanned Aerial Vehicle (UAV), Sentinel-2 data and field surveys to determine the carbon stock of an unmanaged forest in the UNESCO World Heritage Site wilderness area <em>Dürrenstein-Lassingtal</em> in Austria. The entry-level consumer drone (DJI Mavic Mini) and free of charge Sentinel-2 multispectral datasets were used for the evaluation. We merged the Sentinel-2 derived vegetation index NDVI with aerial photogrammetry data and used an orthomosaic and a Digital Surface Model (DSM) to map the extent of woodland in the study area. The Random Forest (RF) Machine Learning (ML) algorithm was used to classify land cover. Based on the acquired field data, the average carbon stock per hectare of forest was determined to be 371.423 ± 51.106 t of CO<sub>2</sub> and applied to the ML-generated classification. An overall accuracy of 80.8% with a Cohen’s kappa value of 0.74 was achieved for the land cover classification, while the carbon stock of the living Above-Ground Biomass (AGB) was estimated with an accuracy of -1.0% (± 5.9%). In conclusion, the proposed approach demonstrated that the combination of low-cost remote sensing data and field work can predict above-ground biomass with high accuracy. The results and the estimation error distribution highlight the importance of accurate field data.</span></p>
Data used in the article "Remote sensing of a levitated superconductor with a flux-tunable microwave cavity"
<p>Data from the manuscript, a Python-based script to create PDF figures, and the resulting PDF files for convenience are provided.</p>
SpectralGPT: The first remote sensing foundation model customized for spectral data
<p>SpectralGPT is the first purpose-built foundation model designed explicitly for spectral RS data. It considers unique characteristics of spectral data, i.e., spatial-spectral coupling and spectral sequentiality, in the MAE framework with a simple yet effective 3D GPT network.</p> <p>We will gradually release the trained models (SpectralGPT, SpectralGPT+), the new benchmark dataset (SegMunich) for the downstream task of semantic segmentation, original code, and implementation instructions.</p>
Data analysis of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset contains an explanation of data analysis for creating a flood vulnerability map of Samarinda Seberang District. The dataset contains sub-criteria for each flood parameter and its score value. In addition, this dataset contains the weight value of each parameter, flood vulnerability level and its coloring, and the results of calculating the area of each vulnerability level.</p>
Data from: Modelling the area of occupancy of habitat types with remote sensing
1. A current challenge of biodiversity and conservation is the estimation of the spatial extent of habitat types across broad territories. In the absence of fine-resolution maps, predictive modelling helps in assessing the spatial distribution of vegetation cover. However, such approaches are still uncommon in regional planning and management. Here, we present a framework for mapping the area of occupancy (AOO) of habitat types that allows highly suitable estimates at different scales. 2. We model the potential AOO with abiotic variables related to topography and climate, resulting in broad AOO estimates that are subsequently downscaled to the local AOO with remote sensing. The combination of individual local AOO estimates allows the defining of the realized AOO, comprising locations with a high likelihood of occurrence and low uncertainty for each habitat. We applied this framework to mapping 24 protected habitat types of Natura 2000 sites in northern Spain. 3. Local and realized AOO were highly accurate, with a 70% overall accuracy for the realized AOO. Remote sensing data, and especially LiDAR, were the most important predictors in habitat types related to forests and shrubs, followed by rock outcrops and pastures. Environmental variables were also relevant for specific habitats subject to abiotic constraints. 4. The combination of ecological modelling with remote sensing offers multiple advantages over traditional field surveys and image interpretation, allowing the harmonization of habitat maps across large regions and through time. This is particularly useful for implementing conservation actions under Natura 2000 principles or assessing IUCN criteria for ecosystems.
Data Sharing - Article GIScience e Remote Sensing
<p>Confidence Interval: Comparative of the Delta Models.</p>
Adopt a Pixel 3 km: A Multiscale Data Set Linking Remotely Sensed Land Cover Imagery with Field Based Citizen Science Observation
<p>These datasets were used in an article submitted to the journal Frontiers in Climate in 2021: <a href="https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full">https://www.frontiersin.org/articles/10.3389/fclim.2021.658063/full</a></p> <p>Further supplemental links (including general information about GLOBE data) can be accessed at <a href="https://observer.globe.gov/get-data/mosquito-habitat-data">https://observer.globe.gov/get-data/mosquito-habitat-data</a>.</p>
Cross-Scene Hyperspectral Remote Sensing Wetland image data
<p>Two representative study areas in China, i.e., Yancheng and Huanghekou (i.e, Yellow River Estuary) wetlands, are selected.<br> For Yancheng wetland, there are two HSIs acquired by the Advanced Hyperspectral Imager (AHSI) aboard on China's Ziyuan1-02D (ZY1-02D) and GaoFen-5 (GF-5) satellites, respectively. For Huanghekou wetland, there are also two HSIs acquired by the AHSI aboard on China's ZY1-02D satellite in June 28, 2020 and September 29, 2021, respectively.</p>
Data set for "Implications of lidar depolarized signal for deformed raindrops remote sensing"
<p>This is the dataset used in the paper "Implications of lidar depolarized signal for deformed raindrops remote sensing"</p>
Data from: Integrating remotely sensed fires for predicting deforestation for REDD+
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Data from: Multi-decadal time series of remotely sensed vegetation improves prediction of soil carbon in a subtropical grassland
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Data from: Remotely-sensed primary productivity shows that domestic and native herbivores combined are overgrazing Patagonia
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Data from: Remote sensing of plant trait responses to field-based plant–soil feedback using UAV-based optical sensors
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Data from: Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data
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Data from: Remotely sensed data informs red list evaluations and conservation priorities in southeast Asia
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Data-Linking remote sensing data to the estimation of pollination services in agroecosystems
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Data from: Remote sensing data predict indicators of soil functioning in semi-arid steppes, central Spain
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Data from: Evaluating the ability of community‐protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data
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Data from: Modelling the area of occupancy of habitat types with remote sensing
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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)
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.