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501 results for “Remote Sensing”
Data and code for "Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"
<p>Data and code for <br>"Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand"</p> <p>All R code used for the analysis is provided in the folder <em>code</em>. <br>Data and intermediate results are provided or stored in the folders <em>data </em>and <em>tmp_data</em>.<br>All results including figures will be stored in the folder <em>results</em>. </p> <p>R version: 4.3.1</p> <p>To carry out the entire analysis the code should be run in the provided order:</p> <p>1) Code to run non-metric multidimensional scaling (NMDS) for habitat groups and <br>produce Fig. 1b (habitat map and legend for Fig 1a: data/eunis_gl_habitat_ger_990m.tif,eunis_gl_habitat_ger_990m_legend.clr)<br> <br>2) Code to generate grassland vitality maps and time series from 1985 to 2021 (Fig. 3). <br>Grassland vitality maps on 30m for all grasslands in Germany provided in data/glv_1985-2021.zip.</p> <p>3) Code to model relation of grassland vitality to five drought indices (VPD, temperature, CWB, soil moisture, precipitation),<br>output are Fig. 4, Fig. S1, Tab. 1.</p> <p>4) Code for trend analysis of drought sensitivity based on 5-, 10-, and 15-year moving windows, output are Fig. 5, Fig. S2. </p> <p>5) Code to model drought sensitivity of grassland habitat groups and habitat types, output are Fig. 6 and table with sensitivity per habitat type. </p>
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine
<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> <p>The framework extraction process can be reffered from the following publication:</p> <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: <em>Forests</em> <strong>2024</strong>, <em>15</em>(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> <p>Access the framework code from: </p> <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> <p>The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example:</p> <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>
New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities
<p>The dataset is the vegetation type map for India as per <a href="https://www.sciencedirect.com/science/article/pii/S0303243415000574?via%3Dihub">Roy et 2015 "<span>New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities". </span></a></p> <p><span>The dataset consistes of two files- (1) a raster GIS file at 60m spatial resolution (EPSG 32643 WGS 84/ UTM Zone 34) in which each pixel value means a vegetation class as defined and mapped in Roy et al., 2015 and (2) a csv file which contains information matching the pixel value with the vegetation type. <br><br>For all additional information, please refer to the pper reviewed publication. </span></p>
Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: the case of Prague
<p>This is supplementary data for a paper called ‘Where is the heat threat in a city? Different perspectives of people-oriented and remote sensing methods, the case of Prague’ to be submitted to the journal Heliyon. Dataset contains three folders:</p> <ol> <li><strong>LST</strong> <br>– Layer Landsat_ecostress_data = vector polygon layer containing fishnet which includes values from 15 sattelite images from 3 different sources - ECOSTRESS, Landsat 8 and Landsat 9<br>Attribute percent_av contains mean percentile from all 15 images</li> <li><strong>Participatory mapping</strong><br>Raw data from participatory mapping campaign held on August 2022 in Prague-Holešovice</li> <li><strong>Thermal walk</strong><br>Layer Data_app = Raw data from thermal walk held on August 3rd 2022 (Declared time in the attribute table is UTC)<br>Layer DataApp_corr = Corrected coordinates from thermal walk (For these corrections, precisely prepared routes were used. Data were post-processed using an algorithm for a minimization of the distance between the measured and expected point location. The measured point was moved using its normal on-route position and his distance was compared with the previous point. If the distance was larger than 115% of expected, the point was moved closer, and when the distance was smaller than 85% of expected, the point was moved forward.)</li> </ol> <p> </p> <p><em>Acknowledgements: This work was supported by the Faculty of Science, Palacký University Olomouc internal grant IGA_PrF_2024_022 – Novel approaches to studying the human thermal environment in urban areas. This work was also supported by Strategy AV21 project ‘City as Lab of changes’, financed by the Czech Academy of Sciences.</em></p>
PVLAB: Remotely Sensed Surface Currents from Field Experiments (2013, 2018, 2021, 2022)
<p>This archive contains 2-min mean remotely sensed surface currents generated using optical imagery and PIV (Dooley et al., 2024) from field experiments (2013, 2018, 2021, 2022) occurring at the U.S. Army Corps of Engineers Field Research Facility, in Duck, NC. The README.txt file contains information regarding variables found in the MATLAB (PVLAB_PIV_SurfaceFlows.mat) file, including estimate locations, units, and timestamps.</p> <p>Estimates were generated at times with appropriate conditions for remote sensing (e.g., sufficient foam tracer) that were within 1-day of measured bathymetry at the field site. Additional data for the field site (obtained by the USACE Field Research Facility or by NOAA) including the measured bathymetry and wave conditions can be found at: https://chlthredds.erdc.dren.mil/thredds/catalog/frf/catalog.html</p> <p>Remote sensing estimates are not perfect and errors in filtering out bad data are possible. Please reach out to Ciara Dooley at cdooley@whoi.edu, Steve Elgar at selgar@mac.com, and Britt Raubenheimer at braubenheimer@whoi.edu if you have any questions. Additionally, the optical imagery (large dataset, many TBs) used to generate flow estimates can be accessed by contacting the authors.</p> <p> </p>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
Atlantic bluefin tuna (Thunnus thynnus) presence and pseudoabsence locations with 13 remotely sensed environmental variables
<p>Atlantic bluefin tuna (<em>Thunnus thynnus</em>; ABFT) are a highly important fisheries species of economic and conservation concern. Understanding their distributions, particularly under climate change is imperative for effective management. Here we assemble a dataset of 4,216 true presence locations for ABFT tagged with pop-up satellite archival tags off the west coast of Ireland between 2016-2021 and 392,009 pseudoabsence locations simulated by a series of correlated random walk modelling. The dataset also contains remotely sensed data at each location for 13 environmental variables including: bathymetry (m), rugosity (m), absolute dynamic topography (m) and it's spatial standard deviation, log surface chlorophyll-a (Log mg m-3), mixed layer depth (m), mean primary productivity over the top 200m of the water column (mg m-3), mean oxygen over the top 200m of the water column (mmol m-3), sea level height anomaly (m) and it's standard deviation, sea surface temperature (°C) and it's spatial standard deviation, and log eddy kinetic energy (Log m<sup>2 </sup>s<sup>-2</sup>).</p>
Figure 1 in Climate Changes of the Temperature of the Surface and Level of the Black Sea by the Data of Remote Sensing at the Coast of the Krasnodar Krai and the Republic of Abkhazia
Figure 1. The Black Sea coast of the Krasnodar Krai and the Republic of Abkhazia.
Dataset used in Kittel et al., 2017 (https://doi.org/10.5194/hess-2017-549), including model files and processed remote sensing observations (CryoSat-2 radar altimetry and GRACE total water storage)
<p>Dataset used in</p> <p>Kittel, C. M. M., Nielsen, K., Tøttrup, C., Bauer-Gottwein, P., 2017.Informing a hydrological model of the Ogooué with multi-mission remote sensing data. Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-549</p> <p>The dataset contains</p> <p>Model files:</p> <ul> <li>River delineation of the Ogooué river based on the SRTM 3 arc-second DEM </li> <li>Climate input data for the Ogooué model subbasins (TRMM and FEWS-RFE precipitation and ECMWF temperature)</li> <li>Parameter files</li> </ul> <p>Processed remote sensing data:</p> <ul> <li>CryoSat-2 satellite altimetry data over the Ogooué River from July 2010 to February 2015</li> <li><strong> </strong>Water mask derived from Sentinel-1 SAR, used to filter CryoSat-2 data</li> <li>GRACE TWS time series for the Ogooué basin</li> </ul> <p>The data is provided in a .zip file with a README.txt file providing additional information and details on the data, including where to obtain similar/original datasets.</p> <p>(c) Author(s) and Technical University of Denmark (DTU) 2018</p>
Datasets associated with: Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography
<p>Data associated with the paper 'Comparing temperature data sources for use in species distribution models: From in-situ logging to remote sensing. Global Ecology and Biogeography' by Lembrechts JJ et al., published in Global Ecology and Biogeography.</p> <p>Contains a dataset containing all extracted and measured temperature variables for all 106 measurement plots (climatedata), as well as the climate and species data used in the Species Distribution Models (SDMs). </p> <p>For details on the content of the table, see the readme-file, for details on methodology, see the original paper. </p>
Drought impacts on Australian vegetation during the Millennium Drought measured with multi-source spaceborne remote sensing
<p>During the period from 1997 to 2009, Australia experienced a severe and persistent drought known as the Millennium Drought (MD). Major water shortages were reported across the continent as were various field accounts of tree mortality and dieback, but large-area assessment has been lacking. Given uncertain projections of future drought conditions in South-East Australia, analysis of the MD presents a valuable opportunity to assess possible impacts of these future trends. In this study, we analyzed the <strong>magnitude and sensitivity of vegetation responses to the MD</strong> with satellite-derived information including the fraction of photosynthetically absorbed radiation (FPAR), photosynthetic vegetation cover (PVC), canopy density derived from vegetation optical depth (VOD) and aboveground biomass carbon (ABC). <strong>Bioclimatic diferences in drought impacts and sensitivity</strong> were examined as well. Here are generated datasets and related codes for processing. Drought impcats and sensitivities for FPAR and PVC are too large to be uploaded but users could calculate their own results with the codes provided here. See Metadata.txt for details.</p>
Muti-type Aircraft of Remote Sensing Images: MTARSI
<p>MTARSI has a total of 9'385 remote sensing images acquired from Google Earth satellite imagery and manually expanded, including 20~different types of aircraft covering 36~airports.<br> The new data set is made up of the following 20 aircraft types: B-1, B-2, B-29, B-52, Boeing, C-130, C-135, C-17, C-5, E-3, F-16, F-22, KC-10, C-21, U-2, A-10, A-26, P-63, T-6, T-43.<br> All the sample images are carefully labeled by seven specialists in the field of remote sensing images interpretation. <br> Each image contains one and only one complete aircraft.</p>
Development of a global 30-m impervious surface map using multi-source and multi-temporal remote sensing datasets with the Google Earth Engine platform
<p>An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.</p>
Data/Code for: Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation
<p>This archive contains data and postprocessed results used in the article "Sediment dynamics in the energetic nearshore zone: Acoustic remote sensing and model validation" by G. Wilson, P. Dickhudt & J. Aldrich. All data and code in this archive is copyright of the authors. Please contact the authors prior to publishing new results or derivative works based on data/code from this archive.</p>
DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images
<p><span>This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. </span><span>The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. </span><span>Figure 8 </span><span>shows </span><span>the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability. </span></p>
Mapping Soil Organic Carbon in the World's Largest Arid Mangrove Forest (Indus Delta, Pakistan): A Multi-Sensor Remote Sensing and Machine Learning Approach
<p>Mangrove forests play a crucial role in carbon sequestration, especially in arid regions where their ability to store carbon in soil is vital for mitigating climate change. The Indus Delta in Pakistan, the world’s largest arid mangrove forest system, lacks spatially explicit data on Soil Organic Carbon (SOC) despite its importance for conservation and carbon budgeting. This study aims to establish a baseline SOC map 2020 at 10 m spatial resolution using Sentinel-1 (Synthetic Aperture Radar) and Sentinel-2 (MultiSpectral Instrument) satellite imagery, integrated with in-situ soil sampling. SOC predictions were made using a Classification and Regression Tree (CART) machine learning model within the Google Earth Engine platform, leveraging 40 predictor variables, including spectral bands and derived indices. A total of 53 topsoil (0-10 cm) samples were collected in February 2020 across the Indus Delta, and SOC was analyzed using the Walkley-Black method. The results showed an average SOC value of 65.88 Mg C ha⁻¹ with substantial spatial variability, ranging from 15.06 Mg C ha⁻¹ to 138.03 Mg C ha⁻¹ with a total of 0.91 Pg C. The CART model demonstrated high accuracy, with an R² of 0.95 and an RMSE of 9.18 Mg C ha⁻¹. However, the region faces challenges such as seawater intrusion and salinity, which threaten its ability to sequester carbon. With the first high-resolution SOC map for the Indus Delta, this study provides valuable insights for ecosystem management, conservation planning, and carbon budgeting. These findings of this study have the potential to significantly influence initiatives like REDD+ and Blue Carbon projects, which aim to enhance carbon sequestration while addressing the ecological challenges facing Pakistan’s mangroves</p>
Data from systematic review of uses of remote sensing in disease ecology
<p>These data accompany the paper "The potential of remote sensing for improved infectious disease ecology research and practice" by Teitelbaum, C., Ferraz, A., De La Cruz, S.E.W., Gilmour, M.E., and Brosnan, I.G.. Each .csv file contains data from primary articles that used remote sensing to study disease ecology. Studies are identified by a unique study ID in each table; relationships between tables are usually many-to-many, except for the biobliographic details, which contains only one entry per article. The metadata.csv file describes columns in all sheets.</p>
SynRS3D : A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery
<h1><strong>SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery</strong></h1> <h3><strong>Neural Information Processing Systems (Spotlight), 2024</strong></h3> <p>For more details, please refer to our <a href="https://arxiv.org/pdf/2406.18151">paper</a> and visit our <a href="https://github.com/JTRNEO/SynRS3D">GitHub repository</a>.</p> <h2><strong>Overview</strong></h2> <p><strong>TL;DR:</strong><br>SynRS3D is a comprehensive synthetic remote sensing dataset designed to improve global 3D semantic understanding from monocular high-resolution imagery. It includes data for three key tasks:</p> <ul> <li>Height estimation</li> <li>Land cover mapping</li> <li>Building change detection</li> </ul> <h2><strong>Dataset Structure</strong></h2> <p>The dataset consists of 17 folders and includes a total of 69,667 images at a resolution of 512x512. After downloading and extracting the files, ensure the directory structure follows this format:</p> <p>${DATASET_ROOT} # Example: /home/username/project/SynRS3D/data/grid_g05_mid_v1<br>├── opt # RGB images (.tif), also used as post-event images for building change detection<br>├── pre_opt # RGB images (.tif), used as pre-event images for building change detection<br>├── gt_nDSM # Normalized Digital Surface Model (nDSM) images (.tif)<br>├── gt_ss_mask # Land cover mapping labels (.tif)<br>├── gt_cd_mask # Building change detection masks (.tif, 0 = no change, 255 = change area)<br>└── train.txt # List of training data filenames</p> <p>The land cover mapping labels (`gt_ss_mask`) are mapped to the following categories:</p> <ul> <li>Bareland: 1</li> <li>Rangeland: 2</li> <li>Developed Space: 3</li> <li>Road: 4</li> <li>Trees: 5</li> <li>Water: 6</li> <li>Agriculture land: 7</li> <li>Buildings: 8</li> </ul> <h2><strong>Image Breakdown by Folder</strong></h2> <p>The dataset is organized into grid-like and irregular terrain. It includes a range of ground sampling distances (GSDs) and variations in building heights. The folder naming convention indicates these characteristics: <br>- `grid` = grid-like terrain <br>- `terrain` = irregular terrain <br>- `g005`, `g05`, `g1` = GSD ranges (0.05m–0.3m, 0.3m–0.6m, and 0.6m–1m, respectively) <br>- `low`, `mid`, `high` = building height variations</p> <p>The dataset includes the following image counts:</p> <p>- 1,430 images – `terrain_g05_mid_v1`<br>- 10,000 images – `grid_g05_mid_v2`<br>- 2,354 images – `terrain_g05_low_v1`<br>- 3,707 images – `terrain_g05_high_v1`<br>- 880 images – `terrain_g005_mid_v1`<br>- 2,127 images – `terrain_g005_low_v1`<br>- 11,325 images – `grid_g005_mid_v2`<br>- 1,212 images – `terrain_g005_high_v1`<br>- 348 images – `terrain_g1_mid_v1`<br>- 4,285 images – `terrain_g1_low_v1`<br>- 904 images – `terrain_g1_high_v1`<br>- 3,000 images – `grid_g005_mid_v1`<br>- 2,997 images – `grid_g005_low_v1`<br>- 4,000 images – `grid_g005_high_v1`<br>- 7,000 images – `grid_g05_mid_v1`<br>- 7,098 images – `grid_g05_low_v1`<br>- 7,000 images – `grid_g05_high_v1`</p> <h2><strong>Citation</strong></h2> <p>If you find SynRS3D useful in your research, please consider citing:</p> <div> <div>@article{song2024synrs3d,</div> <div>title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery},</div> <div>author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto},</div> <div>journal={arXiv preprint arXiv:2406.18151},</div> <div>year={2024}</div> <div>}</div> </div> <h2><strong>Contact</strong></h2> <p>For any questions or feedback, feel free to reach out via email: <strong> song@ms.k.u-tokyo.ac.jp</strong>.</p> <p>Enjoy using SynRS3D!</p>
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.