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108 results for “satellite imagery”
Sentinel-2 Satellite Imagery Based Forest Fire Monitoring
<p><strong>Forest Fire in Villages near Berlin - Normalized Burn Ratio (NBR)</strong></p> <p>Villages in Treuenbrietzen (Frohnsdorf, Klausdorf and Tiefenbrunnen) around 50 km southwest of Berlin have been severely affected by recent unpredicted wildfire and the size of the burned area is about of 400 hectares, which started to spread on 23rd of August, 2018. More than 500 people had to leave their homes as a result of the fire in Treuenbrietzen and the burning fire with dense smoke continued for days. This year Europe has faced a long hot dry summer with almost no rain and as a consequence some European countries like Germany are on high alert regarding possible forest fires.</p>
Dataset: Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery
<div> <div> <div> <p>This repository containts the datasets from the article "Mapping saltmarsh communities in South Portugal using high spatiotemporal resolution satellite imagery" (Submitted). The dataset was used in a workflow used to create saltmarsh maps for the Algarve region (South Portugal), focused on the 4 main costal systems of the region: Alvor, Arade, Ria Formosa and Guadiana.</p> <p> </p> <p>For a description of the methodology see the article [link] and Github repo [link].</p> <p> </p> <h1>Repository content</h1> <h2>1. system-masks.zip</h2> <p>Contains 4 <code>geojson</code>files with a polygon which delimits the areas included in the study. The files are named after the respective systems that they delimit. Any region outside of these polygons were not used in the analysis.</p> <p><strong>CRS</strong> - EPSG:4326</p> <h2>2. manual-clean-up-masks.gpkg</h2> <p>Polygons which were manually created to mask out (exclude) pixels which were classified as saltmarsh, but are clearly not.</p> <p>File contains a single layer with 52 polygons and one variable.</p> <p><strong>Variables:</strong></p> <ul> <li>system [<em>string</em>] - Which system the polygon delimits</li> </ul> <h2>3. saltmarsh-training-data.gpkg</h2> <p>Data used for supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, target classes, or predictor classes.</p> <p>File contains a single layer with 2448 points and 18 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the quadrat was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>train [<em>boolean</em>] - Was quadrat used in the train (TRUE) or test (FALSE) stage of model training?</li> <li>ndvi [<em>numerical</em>] - Normalized Difference Vegetation Index, calculated from the satellite image mosaic as (nir – red) / (nir + red).</li> <li>ndwi_high [<em>numerical</em>] - Normalized Difference Water Index estimated from images at high tide, calculated as (green – nir) / (green + nir)</li> <li>ndwi_low [<em>numerical</em>] - Normalized Difference Water Index estimated from images at low tide, calculated as (green – nir) / (green + nir)</li> <li>subtime [<em>numerical</em>] - Fraction of time that a cell is estimated to be submerged in water over one year.</li> <li>coastal_blue [<em>numerical</em>] - Surface reflectance values at 443 nm.</li> <li>blue [<em>numerical</em>] - Surface reflectance values at 490 nm.</li> <li>green_i [<em>numerical</em>] - Surface reflectance values at 531 nm.</li> <li>green [<em>numerical</em>] - Surface reflectance values at 565 nm.</li> <li>yellow [<em>numerical</em>] - Surface reflectance values at 610 nm.</li> <li>red [<em>numerical</em>] - Surface reflectance values at 665 nm.</li> <li>rededge [<em>numerical</em>] - Surface reflectance values at 705 nm.</li> <li>nir [<em>numerical</em>] - Surface reflectance values at 865 nm.</li> </ul> <h2>4. saltmarsh-transect-metadata.csv</h2> <p>Comma-delimited file with information about vegetation sampling transects. Each row represents one transect.</p> <p>File contains 6 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>site [<em>string</em>] - Name of the site within the study system. This was used exclusively to plan transects.</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>date [<em>date yyyy-mm-dd</em>] - Date of transect sampling.</li> <li>notes [<em>string</em>] - Notes taken during transect sampling and which might be relevant to understand data issues.</li> </ul> <h2>5. saltmarsh-vegetation-quadrats.gpkg</h2> <p>Data used for to create vegetation clusters (<code>cluster</code>) and saltmarsh community types (<code>marsh_type</code>). The later was used as the target class in the supervised model training. Each row represents one quadrat, and each column contains either quadrat identifiers, or presence/absence of species.</p> <p>File contains a single layer with 2448 points and 32 variables.</p> <p><strong>Variables:</strong></p> <ul> <li>water_system [<em>string</em>] - Study system in which the transect was sampled</li> <li>transect [<em>string</em>] - Name of transect in which the quadrat was sampled</li> <li>transect_set [<em>string</em>] - Which set of transects was this transect sampled in? Set A was performed in 2019, set B in 2023.</li> <li>quad_id [<em>integer</em>] - Unique identifier per quadrat</li> <li>distance_from_water <em>[integer]</em> - Distance from start of quadrat, which was the point closes to the water where saltmarsh was found for that transect.</li> <li>cluster [<em>integer</em>] - Vegetation cluster identified via hierarchical clustering. They are nested within <code>water_system</code>, and the same number within different systems will not correspond to the same vegetation type.</li> <li>marsh_type [<em>string</em>] - Functional groupings of saltmarsh vegetation (low, middle or high), created by grouping <code>cluster</code> based on niche of the defined clusters.</li> <li>Arthrocaulon.macrostachyum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Tripolium.pannonicum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.halimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Cistanche.phelypaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Atriplex.portulacoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limbarda.crithmoides [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Juncus.effusus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limoniastrum.monopetalum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Myriolimon.ferulaceum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Limonium.vulgare [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Phragmites.australis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Polygonum.maritimum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Puccinellia.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.procumbens [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.europaea [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Caroxylon.vermiculatum [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.fruticosa [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Salicornia.perennis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Bolboschoenus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.maritimus [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Spergularia.bocconei [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Suaeda.vera [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Triglochin.maritima [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> <li>Sporobolus.montevidensis [<em>boolean</em>] - Presence (1) or absence (0) of the species with the variable name.</li> </ul> <h2>6. predicted-map.tif</h2> <p>Geotiff file with a single layer for predicted saltmarsh community. Values are:<br> - <em>no data</em> - Not saltmarsh<br> - <em>1</em> - Low saltmarsh<br> - <em>2</em> - Middle saltmarsh<br> - <em>3</em> - High saltmarsh</p> <p><strong>CRS</strong> - EPSG:32629</p> </div> </div> </div>
Ground-truthing of satellite imagery to track harmful algal blooms in Pigeon Lake, Alberta, Canada 2017-2022
This data was collected to create a calibrated model that would enable the use of satellite imagery to track harmful algal blooms by using chlorophyll a estimates as a proxy for cyanobacteria in the lake. Samples from Pigeon Lake were collected on the same day that the Sentinel-2 satellite would pass over the lake. These samples were analyzed for different algal pigments and enumerated to genus level to ensure that the satellite imagery was of cyanobacteria rather than different algal groups. An algorithm was developed which we termed the three band index (TBI) that best matched with the cholorophyll a from the in situ samples. This model was used on satellite imagery from 2017-2022 of Pigeon Lake to get chlorophyll a estimates for every 20 x 20 pixel of each image of the lake. This pixel data was used to determine different bloom metrics like the intensity, the area (extent) and severity.
Towards an open pipeline for the detection of Critical Infrastructure from satellite imagery – A case study on electrical substations in The Netherlands
<p><strong>Abstract.</strong> Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI prevents their incorporation in large-scale risk assessment studies. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMaps, processes the imagery and assets' masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The best metrics achieved are an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable AI.</p>
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
Satellite-derived chlorophyll-a concentrations for Lake Harsha (USA) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Harsha (USA) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
SeasoNet: A Seasonal Scene Classification, Segmentation and Retrieval Dataset for Satellite Imagery over Germany
<p>This dataset consists of 1,759,830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database (CLC) 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019. The patches were taken from 519,547 unique locations, covering the whole surface area of Germany, with each patch covering an area of 1.2km x 1.2km. The set is split into two overlapping grids, consisting of roughly 880,000 samples each, which are shifted by half the patch size in both dimensions. The images in each of the both grids themselves do not overlap.</p> <p><strong>Contents</strong></p> <p>Each sample includes:</p> <ul> <li>3 10m resolution bands (RGB), 120px x 120px</li> <li>1 10m resolution band (infrared), 120px x 120px</li> <li>6 20m resolution bands, 60px x 60px</li> <li>2 60m resolution bands, 20xp x 20px</li> <li>1 pixel-level label map</li> <li>2 binary masks for cloud and snow coverage</li> <li>2 binary masks for easy and medium segmentation difficulties, marks areas <300px and <100px respectively</li> <li>1 JSON-file containing additional meta-information</li> </ul> <p>The meta.csv contains the following information about each sample:</p> <ul> <li>Which season it belongs to</li> <li>Which of the two grids it belongs to</li> <li>Coordinates of the patch center</li> <li>Whether it was acquired from Sentinel-2 Satellite A or B</li> <li>Date and time of image acquisition</li> <li>Snow and cloud coverage percentages</li> <li>Image-level multi-class labels</li> <li>Three additional image-level urbanization labels, based on the center pixel (details below)</li> <li>The path to the sample</li> </ul> <p><strong>Classes</strong></p> <table> <thead> <tr> <th scope="col">ID</th> <th scope="col">Class</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>Continuous urban fabric</td> </tr> <tr> <td>2</td> <td>Discontinuous urban fabric</td> </tr> <tr> <td>3</td> <td>Industrial or commercial units</td> </tr> <tr> <td>4</td> <td>Road and rail networks and associated land</td> </tr> <tr> <td>5</td> <td>Port areas</td> </tr> <tr> <td>6</td> <td>Airports</td> </tr> <tr> <td>7</td> <td>Mineral extraction sites</td> </tr> <tr> <td>8</td> <td>Dump sites</td> </tr> <tr> <td>9</td> <td>Construction sites</td> </tr> <tr> <td>10</td> <td>Green urban areas</td> </tr> <tr> <td>11</td> <td>Sport and leisure facilities</td> </tr> <tr> <td>12</td> <td>Non-irrigated arable land</td> </tr> <tr> <td>13</td> <td>Vineyards</td> </tr> <tr> <td>14</td> <td>Fruit trees and berry plantations</td> </tr> <tr> <td>15</td> <td>Pastures</td> </tr> <tr> <td>16</td> <td>Broad-leaved forest</td> </tr> <tr> <td>17</td> <td>Coniferous forest</td> </tr> <tr> <td>18</td> <td>Mixed forest</td> </tr> <tr> <td>19</td> <td>Natural grasslands</td> </tr> <tr> <td>20</td> <td>Moors and heathland</td> </tr> <tr> <td>21</td> <td>Transitional woodland/shrub</td> </tr> <tr> <td>22</td> <td>Beaches, dunes, sands</td> </tr> <tr> <td>23</td> <td>Bare rock</td> </tr> <tr> <td>24</td> <td>Sparsely vegetated areas</td> </tr> <tr> <td>25</td> <td>Inland marshes</td> </tr> <tr> <td>26</td> <td>Peat bogs</td> </tr> <tr> <td>27</td> <td>Salt marshes</td> </tr> <tr> <td>28</td> <td>Intertidal flats</td> </tr> <tr> <td>29</td> <td>Water courses</td> </tr> <tr> <td>30</td> <td>Water bodies</td> </tr> <tr> <td>31</td> <td>Coastal lagoons</td> </tr> <tr> <td>32</td> <td>Estuaries</td> </tr> <tr> <td>33</td> <td>Sea and ocean</td> </tr> </tbody> </table> <p><strong>Urbanization classes</strong></p> <ul> <li><strong>SLRAUM</strong> <ul> <li>0: None</li> <li>1: Ländlicher Raum (~ rural area)</li> <li>2: Städtischer Raum (~ urban area)</li> </ul> </li> <li><strong>RTYP3</strong> <ul> <li>0: None</li> <li>1: Ländliche Regionen (~ rural areas)</li> <li>2: Regionen mit Verstädterungsansätzen (~ urbanizing areas)</li> <li>3: Städtische Regionen (~ urban areas)</li> </ul> </li> <li><strong>KTYP4</strong> <ul> <li>0: None</li> <li>1: Dünn besiedelte ländliche Kreise</li> <li>2: Kreisfreie Großstädte</li> <li>3: Ländliche Kreise mit Verdichtungsansätzen</li> <li>4: Städtische Kreise</li> </ul> </li> </ul> <p>Further information on the urbanization classes can be found here:</p> <p><strong>SLRAUM</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/staedtischer-laendlicher-raum/kreistypen.html</a></p> <p><strong>RTYP3</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/regionen/siedlungsstrukturelle-regionstypen/regionstypen.html</a></p> <p><strong>KTYP4</strong></p> <p><a href="https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html">https://www.bbsr.bund.de/BBSR/DE/forschung/raumbeobachtung/Raumabgrenzungen/deutschland/kreise/siedlungsstrukturelle-kreistypen/kreistypen.html</a></p> <p><strong>License of landcover model</strong></p> <p>Bundesamt für Kartographie und Geodäsie</p> <p>dl-de/by-2-0 from <a href="https://www.govdata.de/dl-de/by-2-0">https://www.govdata.de/dl-de/by-2-0</a></p> <p>© GeoBasis-DE / <strong>BKG</strong> 2022</p> <p><strong>Source of landcover model</strong></p> <p><a href="https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/">https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/</a></p>
Gummern - Digital elevation model 0.5 m, created from Pléiades Neo tri-stereo satellite imagery
<h2>Abstract</h2> <p>Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation.</p> <p>This depositry contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Gummern_PleiadesNeo_DSM05m & Gummern_PleiadesNeo_GCPs</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Digital surface model, spatial resolution 0.5 m, produced from Pleiades Neo Tri-Stereo imagery & Ground control points for Pleiades Neo tri-stereo orientation</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>surface model, Pleiades Neo, tri-stereo</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Gummern</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Gummern_PleiadesNeo_GCPs</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Elevation & GNSS</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>1.10.2023 (27.10.2023 – GNSS)</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>1.12.2023 (6.11.2023 – GNSS)</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Raster / Vector</p> </td> </tr> <tr> <td> <p>Fromat</p> </td> <td> <p>GeoTIFF / CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>0.30m (0.01m – GNSS)</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 25833</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>UL</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Dejan Grigillo (dejan.grigillo@fgg.uni-lj.si)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Ground-Truthing Satellite Imagery with Phenological Observations: Visual Observations from Grasslands at the Sevilleta National Wildlife Refuge, New Mexico
Phenology is the study of recurring natural phenomena. The seasonal "greening-up" and "greening-down" of dominant vegetation can be used as a predictor for a variety of processes and variables at local to global scales. The use of satellites to monitor land surface phenology is important for understanding local and regional ecosystem variability, identifying change over time, and potentially predicting ecosystem response to short and long-term changes in climate. However, the relationship between how phenology is expressed on the ground and how it is interpreted from satellites is poorly understood because phenological stages do not always correspond well to changes in spectral reflectance. In this study, we explored the relationship between greenness as measured by digital camera, the human eye, and ASTER imagery in two perennial grasslands at the Sevilleta National Wildlife Refuge in central New Mexico.
Textured 3D model over Morenci Mine and Shisper glacier using SkySat and PlanetScope satellite imagery
<p>Supplementary material of our research paper entitled "Optimization of optical image geometric modeling, application to topography extraction and topographic change measurements using PlanetScope and SkySat imagery".</p> <p>Flyover animation of 3D model extracted over Morinci Mine using SkySat tri-stereo.</p> <p>Flyover animation of 3D model extracted over Shisper glacier using multi-date PlanetScopeimages.</p> <p> </p>
Analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Networks
<p>This multi-spectral satellite image data set is associated with our recent work on analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Neural Networks.</p> <p>The images were extracted using an automated Python script from Google Maps Static API, based on sample locations in four East African capital cities namely Kampala, Nairobi, Dar es Salaam, and Kigali.</p> <p>Other data sets associated with this work, that is, ESRI shapefiles for administrative level 1 and OpenStreetMap data for the named cities may be downloaded directly from the respective URLs provided in the manuscript.</p>
Sea ice and wave breaking patterns during the Terra Nova Bay Polynya event on September 19, 2019, based on very high resolution satellite imagery
<p>Results of the WorldView-2 (WV2) panchromatic image analysis, described in Herman and Bradtke 2023 "Fetch-limited, strongly forced wind waves in waters with frazil and grease ice — spectral modelling and satellite observations in an Antarctic coastal polynya". The image, which cover part of the polynya in Terra Nova Bay, was taken on September 19, 2019 at 21:22 UTC. The spatial resolution of source data is 0.5 m.</p> <p>The zip file contains:</p> <ol> <li> “AOI” folder with the analyzed area outline in SHAPEFILE format</li> <li>“whitecaps” folder with breaking wave signatures (polygons) in SHAPEFILE format</li> <li>Sea ice (1) - water (2) mask in GeoTIFF format (0 means NoData)</li> </ol> <p>Data are provided in WGS 1984 / UTM Zone 58S projection (EPSG:32758)</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
Data for 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'
<div>This dataset contains avalanche deposit outlines (as shapefiles) derived for the study 'Mapping and characterization of avalanches on mountain glaciers with Sentinel-1 satellite imagery'</div> <div> </div> <div>They were outlined at three different sites (Mt Blanc, Everest and Hispar regions) for the periods 11/2016-10/2021 (Mt Blanc) and 11/2017-10/2022 (Everest and Hispar). The time period is indicated in the file name.</div> <div> </div> <div>For each dataset we give the raw outlines (Automated_outlines_dates), the manually updated (Automated_outlines_dates_ManualUpd) and the manually updated after accounting for surface elevation change (Automated_outlines_dates_ManualUpd_shifted). </div> <div> </div> <div>In order to know which scenes were used for the mapping (if no avalanche was detected, we did not provide a shapefile, but this doesn't been that there is a gap in the Sentinel-1 time series), we provide a Sentinel1_date file that shows all the Sentinel-1 RGB pairs that we used to detect the avalanches.</div> <div> </div> <div>We also provide as geotiffs the temporally aggregated outlines (Automated_outlines_dates_ManualUpd_shifted_aggregated; over one specific year yn - from 01/11/yn-1 to 01/11/yn - or the full study period):</div> <div>- as heatmaps (where the value of each pixel corresponds to the number of avalanches that occured) </div> <div>- as binary maps of deposits (where 1 is when an avalanche occured over the time period and 0 is where none were detected).</div> <div> </div> <div> </div> <div>Finally we provide a csv file for each region with metrics per glacier:</div> <div> </div> <div>RGI ID</div> <div>Glacier size (in m^2)</div> <div>Catchment size (in m^2)</div> <div>Area of slopes steeper than 30° (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the ascending obits (in m^2)</div> <div>The area of total deposits detected (by summing all the pixels of the deposit binary maps) in the descending obits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the ascending orbits (in m^2)</div> <div>The avalanche activity detected (by summing all pixels of the heat maps) in the descending orbits (in m^2)</div> <div>The area of the glacier visible in the ascending orbits (in m^2)</div> <div>The area of the glacier visible in the descending orbits (in m^2)</div> <div> </div> <div> </div> <div>The main Google Earth Engine and Matlab scripts used to pre-process the Sentinel-1 GRD images and to map the avalanches are available on GitHub: https://github.com/MarinKneib/S1_avalanches</div> <div> </div>
Water sample analysis and satellite imagery of a thermo-erosion gully and its surroundings in Adventdalen, Svalbard.
<h2><strong>Data description</strong></h2> <p>This dataset is part of the supplemental information to the paper "Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff" by Parmentier et al. (2024). It includes the analysis of water quality in and around a thermo-erosion gully on the high-Arctic archipelago of Svalbard, and three satellite images that give an overview of the wider area around this gully in the context of a snow fence experiment (Cooper et al. 2011). More details are provided in Parmentier et al. (2024).</p> <h2><strong>Background</strong></h2> <p>Thicker snow cover in permafrost areas causes deeper active layers and thaw subsidence, which alter local hydrology and may amplify the loss of soil carbon. However, the potential for changes in snow cover and surface runoff to mobilize permafrost carbon remains poorly quantified. The data presented here is part of a study that showed that a snow fence experiment on High-Arctic Svalbard inadvertently led to surface subsidence through warming, and extensive downstream erosion due to increased surface runoff. Within a decade of artificially-raised snow depths, several ice wedges collapsed, forming a 50 m long and 1.5 m deep thermo-erosion gully in the landscape. We estimate that 1.1 to 3.3 tons C may have eroded, and that the gully is a hotspot for processing of mobilised aquatic carbon. Our study show that interactions among snow, runoff and permafrost thaw form an important driver of soil carbon loss.</p> <h2><strong>Water samples</strong></h2> <p>The following datafile includes the analysis of several water samples taken in and near a thermo-erosion gully on Svalbard on August 5<sup>th</sup> and 6<sup>th</sup>, 2017. These were analyzed for dissolved organic carbon (DOC), particulate organic carbon (POC), particulate nitrogen (PN) content, and stable carbon isotope ratios δ<sup>13</sup>C-DOC and δ<sup>13</sup>C-POC. In addition, temperature, pH, oxygen, and electrical conductivity were measured in the field on the day of sampling. This data is provided in the following Excel file that also includes the latitude and longitude for each sample point: </p> <ul> <li>Parmentier et al - 2024 - Water Sample Analysis.xlsx</li> </ul> <h3><strong> </strong><strong>Sample analysis</strong></h3> <p>A full description of the analysis is repeated here from the supplemental information in the accompanying publication (Parmentier et al. 2024). The water samples were filtered on the day of collection through a pre-combusted glass fiber filter with pore size of 0.7 µm (Whatman, Grade GF/F). After filtration, the filters were packed in aluminum foil and frozen for later analysis of the collected particulate matter. From the filtrate, three samples of ~50 ml were taken and immediately frozen for transport.</p> <p>The filtered water samples were analyzed for their dissolved organic carbon (DOC) content and their stable carbon isotope ratio δ<sup>13</sup>C-DOC. This combined analysis was carried out at the labs of UCLouvain, Belgium with an Aurora 1030W TOC Carbon Analyzer, from OI Analytical, coupled to an IRMS (Thermo delta V Advantage). In the Aurora 1030W, the water samples were purged with H<sub>3</sub>PO<sub>4</sub>(phosphoric acid) to remove any dissolved inorganic carbon (DIC). Afterwards, Na<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (sodium persulfate) was added to the heated sample (97 °C) to oxidize any DOC to CO<sub>2</sub>. With N<sub>2</sub> as the carrier gas, the CO<sub>2</sub> was transferred to the analyzing units where the total concentration and δ<sup>13</sup>C-DOC of the CO<sub>2</sub> were detected. The δ<sup>13</sup>C-DOC samples were calibrated against the certified standard IAEA-CH-6 (-10.449 ± 0.033 ‰VPDB) and an internal sucrose standard (-26.99 +/- 0.04 ‰). The DOC measurements were calibrated against a concentration range (n=8) of the same standards (Morana et al., 2015).</p> <p> The particulate matter retained on the filters was analyzed for particulate organic carbon (POC) and particulate nitrogen (PN) concentrations, as well as δ<sup>13</sup>C-POC. The glass fiber filters were subsampled and repeatedly acidified with HCl (1.5 M) in pre-combusted Ag capsules to remove carbonates. Analyses were performed at the Stable Isotope Facility of the University of California in Davis using an Elementar Vario EL Cube (Elementar Analysensysteme GmbH, Hanau, Germany) connected to a PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK). Isotope ratios of δ<sup>13</sup>C are reported relative to the international standard VPDB (Vienna PeeDee Belemnite).</p> <h2><strong>Satellite imagery</strong></h2> <p>To show the development of the thermo-erosion gully over time, we provide three high resolution satellite images from the Digital Globe constellation of satellites. The areal extent of these images covers the entire snow fence experiment in the valley of Adventdalen on Svalbard. They were acquired on August 5<sup>th</sup>, 2011, August 30<sup>th</sup>, 2013, and July 9<sup>th</sup>, 2015 by the WorldView-2, GeoEye-1 and WorldView-3 satellites, respectively. These images are provided as GeoTiffs – projected in the UTM 33X coordinate system:</p> <ul> <li>SnoEco_2011AUG05_WV2_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2013AUG30_GE1_MUL_Pansharpened_bco_rcs_dobj.tif</li> <li>SnoEco_2015JUL09_WV3_MUL_Pansharpened_bco_rcs_dobj.tif</li> </ul> <p>Each of these files includes the following color bands: </p> <ul> <li>Band 1: Blue</li> <li>Band 2: Green</li> <li>Band 3: Red</li> <li>Band 4: Near Infrared</li> </ul> <p>In addition, the images are clipped to the following coordinate bounds (in UTM 33X):</p> <ul> <li> <p><span>x<sub>min</sub>, x<sub>max</sub></span><span>: 523740, 524825</span></p> </li> <li> <p><span>y<sub>min</sub>, y<sub>max</sub></span><span>: 8677150, 8678100</span></p> </li> </ul> <p>For full details on these satellite products, we refer to DigitalGlobe/Maxar.<strong> </strong></p> <h3><strong>Image processing</strong></h3> <p>The satellite imagery was processed according to DigitalGlobe guidelines and calibration coefficient adjustment factors. The radiometrically corrected source images were first converted to top-of-the-atmosphere spectral radiance, and thereafter to top-of-the-atmosphere reflectance. Following this processing, each color band of the image was pansharpened (using Bicubic interpolation) with the RCS algorithm in the Orfeo ToolBox of QGIS 2.18 to increase the horizontal resolution to ~50 cm. To reduce haze effects, the images were further corrected through a dark object subtraction (bottom 1 percentile of the blue band) which was applied to each band separately. Subsequent negative values were set to zero.<strong> </strong></p> <h2><strong>Acknowledgments</strong></h2> <p>This research was funded by the Research Council of Norway (RCN; grant agreement 230970), and the FRAM - Terrestrial flagship (362255 and 642018). F.J.W.P. and S.W. received additional funding from the RCN (grant agreement 323945). The high-resolution satellite imagery comes courtesy of the DigitalGlobe Foundation. We thank UCLouvain and the University of California, Davis for assisting in the sample analysis.<strong> </strong></p> <h2><strong>References</strong></h2> <p>Cooper, E. J., Dullinger, S., & Semenchuk, P. (2011). Late snowmelt delays plant development and results in lower reproductive success in the High Arctic. <em>Plant Science</em>, 180(1), 157–167. https://doi.org/10.1016/j.plantsci.2010.09.005</p> <p>Morana, C., Darchambeau, F., Roland, F. A. E., Borges, A. V., Muvundja, F., Kelemen, Z., et al. (2015). Biogeochemistry of a large and deep tropical lake (Lake Kivu, East Africa: insights from a stable isotope study covering an annual cycle. <em>Biogeosciences</em>, 12(16), 4953–4963. https://doi.org/10.5194/bg-12-4953-2015</p> <p>Parmentier, F. J. W., Nilsen, L, Tømmervik, H., Meisel, O. H., Bröder, L., Vonk, J. E., Westermann, S., Semenchuk, P. R., Cooper, E. J., Rapid Ice-Wedge Collapse and Permafrost Carbon Loss Triggered by Increased Snow Depth and Surface Runoff, <em>Geophysical Research Letters</em>, In press</p>
Satellite-derived chlorophyll-a concentrations for Lake Hume (Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of Lake Hume (Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery
<p>This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery.</p> <p>Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following:</p> <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. <em>Remote Sensing of Environment, 270</em>, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. <em>Frontiers in Remote Sensing, 1</em></li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. <em>Remote Sensing of Environment, 240</em>, 111604</li> </ul>
Generating Imperviousness Maps from Multispectral Sentinel-2 Satellite Imagery
<p>This dataset contains a list of Sentinel-2 tiles covering Italy for the year 2017. For each tile, a corresponding ground truth GeoTIFF is present which contains a clip of the soil consumption provided by ISPRA (<a href="https://www.isprambiente.gov.it">https://www.isprambiente.gov.it</a>).</p> <p>Dataset can be used to train a Machine Learning model to extract imperviousness maps using Sentinel-2 satellite images.</p> <p>More details can be found reading the paper: </p> <p>Giacco, G., Marrone, S., Langella, G., & Sansone, C. (2022). ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery. <em>Future Internet</em>, <em>14</em>(10), 278.</p>
Fig. 6 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 6. Abundance (mean number of burrows/100 × 5 m transect) of S. citellus in 4 colonies in the study area in summer (for the period 2017–2021) N = Luda Yana; –– l –– = Belotrup; ---- l ---- = Panagyurski kolonii; u = Beli Manastiri
Fig. 5 in Long Term (1985-2018) Changes Of The Habitat Suitability Of European Souslik Assessed By Maxent Modelling Based On Landsat Satellite Imagery - A Case Study From A Mountain Landscape Of Central Bulgaria
Fig. 5. Changes in the habitat suitability in the study area (white – not suitable, black – high suitability) of European souslik assessed by maxent modelling based on data from 2006–2018 (B) and extrapolated for the period 1985–2005 (A). The results are presented in
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.