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239 results for “remote sensing data”
Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"
<p>This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.</p>
Data Archive for: Hurricane Laura (2020): A Comparison of Drop Size Distribution Moments Using Ground and Radar Remote Sensing Retrieval Methods
<p>This archive corresponds to the data described in Brauer et al. (2021) to be published in <em>Journal of Geophysical Research: Atmospheres.</em> Please see the included readme.txt file for details about each data file.</p>
A STP-HSI index method for urban built-up area extraction based on multi-source remote sensing data
<p>The changes of urban built-up areas can reflect the process of urbanization, and it can reflect the population, economy, and cultural development of the city. Therefore, accurate and timely extraction of urban built-up areas plays an important role in the dynamic management of the city. In the existing research, single-source remote sensing data is used to extract urban built-up areas, and there is a problem that the spectrum of urban areas and non-urban areas is easily confused. Multi-source remote sensing data, including luojia-1 remote sensing data, Landsat 8 OLI remote sensing data, etc., can make up for the spectrum confusing issues.</p> <p>We fuse the time series information of night light remote sensing data, neighborhood information and point of interest (POI) data in spatial dimension, and propose a built-up area extraction method that integrates night light time and space information and POI information.</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- Derived data 2020 & 2021
<p>This is derived data from a remote sensing experiment performed in 2020 and 2021. This repository contains .csv and .R files that can be used to replicate the analysis presented here:</p> <p><a href="https://zenodo.org/record/6859791#.Y-K6ky-B1z8">https://zenodo.org/record/6859791#.Y-K6ky-B1z8</a></p> <p>If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p>
Utilizing traditional and remote sensing techniques to assess Colorado potato beetle host preference in the Columbia Basin -- 2020 Data
<p>This is a remote sensing dataset collected in 2020 that contains orthomosaic images, shape files, analysis scripts, and derived numerical data from each plot. Data was collected using the protocol described here:</p> <p><a href="https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1">https://www.protocols.io/view/usda-ars-potato-genetics-lab-drone-data-collection-bp2l6148dvqe/v1</a></p> <p>Provided are "field map" files that denote the location and contents of each plot, a folder from each date that contains the 5 band orthomosiac, surface model image, a cropped and rotated image, shape files indicating the location of each plot, and derived data. The analysis can be replicated by following along with workflow listed in file named: rondon_cpb_2020.R. Derived data from this experiment can be found it the file named: "Rondon_CPB_data_2020_UAS_all.csv"<br> <br> If you have any questions or comments regarding this dataset please contact Dr. Max Feldman via email: max.feldman@usda.gov</p>
Data associated with: Applying remote sensing for large-landscape problems: Inventorying and tracking habitat recovery for a broadly distributed Species At Risk
<ol> <li> <p><span>Anthropogenic habitat alteration is leading to the reduction of global biodiversity. Consequently, there is an imminent need to understand the state and trend of habitat alteration across broad areas. In North America, habitat alteration has been linked to the decline of threatened woodland caribou. As such, habitat protection and restoration are critical measures to support recovery of self-sustaining caribou populations. Broad estimates of habitat change through time have set the stage for understanding the status of caribou habitat. However, the lack of updated and detailed data on post-disturbance vegetation recovery is an impediment to recovery planning and monitoring restoration effectiveness. Advances in remote sensing tools to collect high-resolution data at large spatial scales are beginning to enable ecological studies in new ways to support ecosystem-based and species-based management.</span></p> </li> <li> <p><span>We used semi-automated and manual methodologies to fuse photogrammetry point clouds (PPC) from high-resolution aerial imagery with wide-area Light Detection and Ranging (LiDAR) data to quantify vegetation structure (height, density, class) on disturbances associated with caribou declines. We also compared vegetation heights estimated from the semi-automated PPC-LiDAR fusion to heights estimated in the field, using stereoscopic interpretation, and using multi-channel TiTAN LiDAR.</span></p> </li> <li> <p><span>Vegetation regrowth was occurring on many of the disturbance types, though there was local variability in the type, height, and density of vegetation. Heights estimated using PPC-LiDAR fusion were highly correlated (r ≥ 0.87 in all cases) with heights estimated using stereomodels, TiTAN multi-channel LiDAR, and field measurements. </span></p> </li> <li> <p>We demonstrated that PPC-LiDAR fusion can be operationalized over large areas to collect comprehensive and consistent vegetation data across landscape levels, providing opportunities to link fine-resolution remote sensing to landscape-scale ecological studies. Crucially, these data can be used to estimate rates of habitat recovery at resolutions that are not feasible using more commonly used satellite-based sensors, bridging the gap between resolution and extent. Such data are needed to achieve effective and efficient habitat monitoring to support caribou recovery efforts, as well as a myriad of additional forest management needs.</p> </li> </ol>
Data from: Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales
<p class="MsoNormal">The increasing availability of satellite imagery has supported a rapid expansion in forward-looking studies seeking to track and predict how climate change will influence wild population dynamics. However, these data can also be used in retrospect to provide additional context for historical data in the absence of contemporaneous environmental measurements. We used 167 Landsat-5 Thematic Mapper (TM) images spanning 13 years to identify environmental drivers of fitness and population size in a well-characterized population of banner-tailed kangaroo rats (<em>Dipodomys spectabilis</em>) in the southwestern United States. We found evidence of two decoupled processes that may be driving population dynamics in opposing directions over distinct time frames. Specifically, increasing mean surface temperature corresponded to increased individual fitness, where fitness is defined as the number of offspring produced by a single individual. This result contrasts with our findings for population size, where increasing surface temperature led to decreased numbers of active mounds. These relationships between surface temperature and (i) individual fitness and (ii) population size would not have been identified in the absence of remotely sensed data, indicating that such information can be used to test existing hypotheses and generate new ecological predictions regarding fitness at multiple spatial scales and degrees of sampling effort. To our knowledge, this study is the first to directly link remotely sensed environmental data to individual fitness in a nearly exhaustively sampled population, opening a new avenue for incorporating remote sensing data into eco-evolutionary studies.</p>
Data from: Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing
<p><span>Current climate change species response models usually do not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree species European beech (<em>Fagus sylvatica</em> L.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016–2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that the addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.</span></p>
Data to: Remotely sensed tree height and density explain global gliding vertebrate richness
<p>In vertebrates, gliding evolved as a mode of energy-efficient locomotion to move between trees. Gliding vertebrate richness is hypothesised to increase with tree height and decrease with tree density but empirical evidence for this is scarce, especially at a global scale. Here, we test the ability of tree height and density to explain species richness of gliding vertebrates globally compared to richness of all vertebrates, while controlling for biogeographical and climatic factors. We compiled a global database of 193 gliding amphibians, mammals and reptiles and created maps of species richness from extent-of-occurrence range maps. We paired species richness of gliding vertebrates with spatial estimates of global tree height and density and biogeographical regions as covariates to account for ecological differences among global regions. We used univariate linear and multivariate generalised linear mixed-effect models to evaluate relationships between species richness and tree height and density and the interaction between both. We found that richness of all gliding vertebrate species increased significantly with tree height, while results for richness of amphibians, mammals and reptiles alone indicated mixed responses, especially among different biogeographical regions. Mixed-effect models mirrored these results for richness of all species combined, while also revealing the mixed responses to tree height and density of richness of amphibians, mammals and reptiles. Richness of all vertebrate species – gliding and non-gliding – also increased with tree height and density but at a lesser rate than richness of gliding vertebrates indicating a greater influence of forest structure on richness patterns of gliding vertebrates. Our results support hypotheses stating that gliding in vertebrates globally evolved in tall forests as energy-efficient locomotion between trees and provide further evidence for the importance of forest structure to explain the distribution of gliding vertebrates.</p>
Remotely sensed environmental and grazing data at Jalama Canyon Ranch
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Topographic data to support the analysis of error and uncertainty that degrade topographic corrections of remotely sensed data
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Data from: Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing
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Data from: Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales
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Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
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Data from: Groundwater and remotely sensed phenology reveal vulnerability of riparian trees to drought
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Data associated with: Applying remote sensing for large-landscape problems: Inventorying and tracking habitat recovery for a broadly distributed Species At Risk
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Data to: Remotely sensed tree height and density explain global gliding vertebrate richness
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Data from: Hyperspectral imaging has a limited ability to remotely sense the onset of beech bark disease
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Data from: Integrating environmental DNA metabarcoding and remote sensing reveals known and novel fish diversity hotspots in a World Heritage Area
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Data for: Remote sensing reveals the importance of adjacent seminatural habitat and irrigation method on aphid biocontrol in arid agroecosystems
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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.