Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
501
datasets available to search
ShareScore release 0.7.1
Dataset results
501 results for “Remote Sensing”
Chapter 29. Supplementary material. Remote sensing of invasive Australian Acacia species: State of the art and future perspectives
<p>This is online supplementary material for the Chapter "Remote sensing of invasive Australian Acacia species: State of the art and future perspectives" authored by A Große-Stoltenberg, I Lizarazo, G Brundu, VP Gonçalves, LP Osco, C Masemola, J Müllerová, C Werner, I Kotze, and J Oldeland, corresponding to Chapter 29 In: "Wattles: Australian Acacia species around the world". Eds: D.M. Richardson, J.J. Le Roux, and E. Marchante (CABI, UK, 2023).”</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>
Locating Manning-based Satellite Gauging Reach for River Discharge Estimation from Remotely Sensed Imagery
<p>River discharge is critical for understanding river hydrological condition and water resource management. With the advance of earth observation technologies, estimating river discharge through remote sensing using Manning's Equation has become more and more popular for filling the gaps in gauging observations. However, finding a Manning-based Satellite Gauging Reach (MSGR) that can successfully transfer satellite signals into river discharge based on the Equation is not easy and lacks proper guidance. Theredore, we provide a practical approach for locating MSGR.<br> The manuscript is currently under review by Water Resources Research.<br> This repositry is the data for reproducing the figures in our manuscript.</p> <p>This repositry consists of four part. They are the corresponding data and results at MSGR, CR_FSE, CR_SCT locations, and elevation values along river centerline for calculating riverbed slope.</p> <p>MSGR: Manning-based Satellite Gauging Reach<br> CR_FSE: comparison reach with unsatisfactory FSE<br> CR_SCT: comparison reach with unsatisfactory SCT<br> FSE: Fluctuation degree of surface water extent<br> SCT: Stability of Channel Terrain</p>
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>
RapidEye and Landsat remote sensing measures for Sabah Biodiversity Experiment plots
<p>Experiments under controlled conditions have established that ecosystem functioning is generally positively related to levels of biodiversity but it is unclear how widespread these effects are in real-world settings and whether they can be harnessed for ecosystem restoration. We used a long-term, field-scale tropical restoration experiment to test how the diversity of planted trees affected recovery of a 500-ha area of selectively logged forest measured using multiple sources of satellite data. Replanting using species-rich mixtures of tree seedlings with higher phylogenetic and functional diversity accelerated restoration of remote sensing estimates of aboveground biomass, canopy cover and Leaf Area Index. Our results are consistent with a positive relationship between biodiversity and ecosystem functioning in the lowland dipterocarp rainforests of SE Asia and demonstrate that using diverse mixtures of species can enhance their initial recovery after logging.</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>
Remote Sensing and Geomorphological Covariates and Labels of main Peruvian Glaciers
<p>Abstract: The Peruvian Glacier Classification Dataset is a comprehensive collection designed to facilitate research and analysis in the field of glaciology and remote sensing. Comprising a total of 9 rasterStacks, each representing distinct glacier regions within Peru, the dataset offers a rich set of 25 morphological and Landsat 8 derived covariates for in-depth study. These covariates have been meticulously selected to capture a wide range of features and characteristics associated with glaciers and their surrounding environments. Moreover, the dataset includes true labels indicating the classification of each pixel into Glacier and Non-Glacier classes, as determined by the National Inventory of 2017.</p> <p>Description: The Peruvian Glacier Classification Dataset presents a valuable resource for researchers, scientists, and practitioners interested in glacial dynamics, environmental monitoring, and geospatial analysis. Comprising 9 main glacier regions within Peru (Blanca, Central, Huallanca, Huayhuasha, Huaytapallana, Raura,Urubamba, Vilcabamba,Vilcanota), the dataset provides an extensive collection of covariates derived from both morphological features and Landsat 8 satellite imagery. These covariates have been processed and curated to offer comprehensive insights into the intricate nature of glaciers and their surroundings.</p> <p>Key Features:</p> <ol> <li> <p>Morphological Covariates: The dataset encompasses a diverse array of morphological features extracted from high-resolution elevation data calculated with SAGA GIS. </p> </li> <li> <p>Landsat 8 Derived Covariates: Landsat Covariates corresponding to the period 2017–2018 obtained from Landsat Collection 2 Level 2 and Tier 1 surface reflectance (SR) products (Vermote et al., 2016) available online: https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance (accessed on 1 July 2023). procesing with GoogleEarth Engine study. https://code.earthengine.google.com/fea31b7f2a3fdbc1065644616819134a. This includes the following covariates: BLUE, GREEN, RED, NIR, SWIR1, SWIR2, NDFI, ndsi, ndvi, NDWI , NDWIns, NDSInw, nbr2, VNSIR, NDMI, TCG .</p> </li> <li> <p>True Labels: Ground truth information is provided through accurate classification labels for each pixel, classifying it as either belonging to the Glacier or Non-Glacier class. These labels are based on the authoritative National Inventory of 2017, enhancing the reliability and usability of the dataset.</p> </li> </ol>
Remote sensing of the light-obscuring smoke properties in real-scale fires using a photometric measurement method
<p>This data set complements the article <a href="https://doi.org/10.1007/s10694-023-01470-z">https://doi.org/10.1007/s10694-023-01470-z</a>.</p> <p>The data set contains the raw image data of three representative experiments and the corresponding input configuration data files for the LEDSA simulations.</p> <p>The data set contains:</p> <p>- The processed LEDSA simulation data of all experiments, mainly consisting of the measured and normalized intensities of all LEDs and the computed extinction coefficients according to the applied layer discretization (ledsa_simulations.zip).</p> <p>- The recorded measurement data of all experiments from three MIREX devices (MIREX.zip).</p> <p>- An exemplary data set consisting of the LEDSA raw data as RAW image files from three representative experiments in shortened form, containing every third image (image_data_{...}.zip).</p> <p>- LEDSA Input and Config files for analysis and simulation of the provided exemplary image data (ledsa_simulations_short.zip).</p> <p>- Python scripts for reading and post-processing the LESDA simulation data (postprocessing_tools.zip)</p> <p>- Juptyter Lab Python scripts that have been used to create the plots in the associated paper (data_analysis.zip).</p> <p>- The figures from the associated paper (figures.zip).</p> <p>Notes</p> <p>In order to run the Python scripts in Juptyer Labs properly, the given directory structure must be maintained, resulting when all archives are unpacked on the same hierarchical level.</p> <p> </p> <p>The authors gratefully acknowledge the financial support of the German Federal Ministry of Education and Research. The extensive LEDSA computations were performed largely on the high-performance computer system funded as part of the CoBra project with the grant number 13N15497.</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>
Roost-dataset: a remote sensing object detection dataset
<p>We release a multi-channel weather radar sensing dataset with roost annotations, for the purpose of ecological analyses and developing visual object detection and tracking models to recognize biological phenomena in radar data. Please refer to https://github.com/darkecology/roost-dataset for more details. Here we upload arrays rendered from weather radar data. Radar scan lists and roost annotations are released in json files of a COCO-like format in the roost-dataset Github repository.</p>
Remotely sensed environmental and grazing data at Jalama Canyon Ranch
Open the record for dataset details and reuse information.
Topographic data to support the analysis of error and uncertainty that degrade topographic corrections of remotely sensed data
Open the record for dataset details and reuse information.
Remote sensing and field information aid in predicting the presence of the terrestrial orchid Cyclopogon lute-albus
Open the record for dataset details and reuse information.
Surprise Canyon Creek wild and scenic river remote sensing and geospatial database
Open the record for dataset details and reuse information.
Data from: Phenotypic drought stress prediction of European beech (Fagus sylvatica) by genomic prediction and remote sensing
Open the record for dataset details and reuse information.
Scripts from: Remotely sensed microhabitat characteristics associated with Haematopus palliatus (American Oystercatcher) nest-site selection can inform beach habitat restoration along the U.S. Atlantic Coast
Open the record for dataset details and reuse information.
RapidEye and Landsat remote sensing measures for Sabah Biodiversity Experiment plots
Open the record for dataset details and reuse information.
Data from: Remotely sensed environmental measurements detect decoupled processes driving population dynamics at contrasting scales
Open the record for dataset details and reuse information.
Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up
Open the record for dataset details and reuse information.
Data from: Groundwater and remotely sensed phenology reveal vulnerability of riparian trees to drought
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
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.