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11 results for “inundation maps”
Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data
<p><strong>Overview:</strong> The Global Inundation Extent from Multi-Satellites (GIEMS; Prigent et al. 2007, Papa et al. 2010) downscaled at 15 arc-second (GIEMS-D15; Fluet-Chouinard et al. 2015) was produced through the downscaling of the GIEMS database (natively at 0.25°). The downscaling procedure predicts the location of surface water cover with an inundation ranking surface generated by bagged decision trees. The decision trees were trained on binary presence/absence of wetland in the GLC2000 global land cover map (Bartholomé & Belward 2005) and used 13 topographic and hydrographic predictors derived from the SRTM-derived HydroSHEDS database (Lehner, Verdin & Jarvis 2008). The downscaling technique to three temporal aggregation of the GIEMS dataset representing three states of land surface inundation extents: mean annual minimum (MA<sub>Min</sub>; total area, 6.5 × 106 km<sup>2</sup>), mean annual maximum (MA<sub>Max</sub>; 12.1 × 106 km<sup>2</sup>), and long-term maximum (LT<sub>Max</sub>; 17.3 × 106 km<sup>2</sup>). The area of MAMin and MAMax from GIEMS were supplemented with the minimum area value from lakes, river and reservoirs from GLWD (Lehner & Döll 2004; classes 1,2,3). LTMax was corrected as the mean area from 3-year rolling maximum from GIEMS and the total wetland area from GLWD (classes 1-12). The accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates. Yet, a comparison against independent regional wetland maps showed adequate agreement over large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than originally offered by GIEMS, allowing for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems.</p> <p><strong>Projection:</strong> WGS84 (EPSG:4326)</p> <p><strong>Geographic extent:</strong></p> <ul> <li>Longitude: -180° to 180°</li> <li>Latitude: -56° to 84°</li> </ul> <p><strong>Spatial resolution: </strong>15 arc-second (500m at equator)</p> <p><strong>Legend</strong> (for discrete pixel values):</p> <ul> <li>0 = Upland</li> <li>1 = Mean Annual Minimum (MA<sub>Min</sub>)</li> <li>2 = Mean Annual Maximum (MA<sub>Max</sub>)</li> <li>3 = Long Term Maximum (LT<sub>Max</sub>)</li> </ul>
Inundation maps of Donana for 23 dates within the period 2015/12/19 to 2017/08/20 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2015/12/19 to 2017/08/20 were generated for Donana based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Donana_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Danube Delta for 10 dates within the period 2016/10/05 to 2017/08/01 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/10/05 to 2017/08/01 were generated for Danube Delta based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Danube_Delta_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. The regions, which are manually denoted as affected by clouds, are denoted with 2. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Camargue for 47 dates within the period 2016/02/09 to 2018/06/19 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/02/09 to 2018/06/19 were generated for Camargue based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Camargue_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Inundation map of Mandra's flood event (Athens, Greece) 2017
<p>In this dataset we manually reproduced and we share as a shape file the flood extent derived by the National Observatory of Athens BEYOND team, through the processing of a WordlView-4 satellite high resolution (31 cm) image on 21/11/2017 and with ground observations, of the flood event which hit Mandra, Athens, Greece in 15th of November 2017 (Bellos et al., 2020).</p> <p>It is available online by BEYOND team at <a href="http://beyond">http://beyond</a> <a href="http://eocenter.eu/index.php/web-services/floodhub">eocenter.eu/index.php/web-services/floodhub</a>). </p> <p>Another one flood dataset is provided in:</p> <p>https://zenodo.org/record/7140750</p> <p>References</p> <p>Bellos, V., Papageorgaki, I., Kourtis, I., Vangelis, H., Kalogiros, I., Tsakiris, G. (2020).<br> Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm.<br> Natural Hazards, 101(3), 711-726.</p> <p>The dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/): CC BY-NC-SA) You are free to: Share — copy and redistribute the material in any medium or format; Adapt — remix, transform, and build upon the material, for not commercial use, under the following terms:</p> <p>1) Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.</p> <p>2) You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> <p>3) ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.</p>
Inundation Maps - Polyphytos open surface water reservoir - WQeMS raster products
<p>Within this dataset, inundation maps of the Polyphytos open surface water reservoir in Greece for the years 2021 and 2022 are available in GeoTIFF raster format. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project as intermediate products. Each raster file in the dataset is named according to the date in which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps.</p>
Rapid mapping of flood inundation by deep learning-based image super-resolution
<div> <div># Rapid mapping of flood inundation by deep learning-based image super-resolution</div> <div># Developer: Wenke Song</div> <div># The University of Hong Kong</div> <div># Contact email: songwk@connect.hku.hk</div> <div># MIT License</div> <div># Copyright (c) 2024 songwk0924</div> <div> </div> <div>There are two folders in the compressed file: Trained_model and Test_cases:</div> <div>(1) Trained_model</div> <div> model_d_DenseUnet.pth, for predicting the maximum water depth;</div> <div> model_v_DenseUnet.pth, for predicting the maximum velocity.</div> <div> </div> <div>(2) Test_cases</div> <div> Test_d_r1.npy, Test_d_r2.npy, Test_d_r3.npy: Input features for predicting maximum water depth of rainfall events r1-r3;</div> <div> Test_v_r1.npy, Test_v_r2.npy, Test_v_r3.npy: Input features for predicting maximum velocity of rainfall events r1-r3; <div> </div> </div> <div> bathy_mat_5m_0p.csv: Elevation data to create mask layer;</div> <div> Fine_grid_flood_maps (2DSWEs):</div> <div> hmax_r1.asc, hmax_r2.asc, hmax_r3.asc, maximum water depth simulated by 2DSWEs of rainfall events r1-r3;</div> <div> velmax_r1.asc, velmax_r2.asc, velmax_r3.asc, maximum velocity simulated by 2DSWEs of rainfall events r1-r3;</div> <div> </div> <div><span>The aforementioned data will be used as input for model prediction (Prediction.py). </span></div> <div><a name="OLE_LINK902"></a><a name="OLE_LINK909"></a><a href="https://github.com/songwk0924/Flood-inundation-mapping"><span>https://github.com/songwk0924/Flood-inundation-mapping</span></a></div> </div>
Processing steps to generate a Digital Surface Model based on SPOT-7 tri-stereo images published in the study "An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar inundation areas at volcan Copahue (Argentina & Chile)" in the Journal of South American Earth Sciences https://doi.org/10.1016/j.jsames.2022.104138
<p>The Digital Surface Model (DSM) was created from SPOT-7 tri-stereo images for the Copahue volcano between the border of Argentina and Chile. Two versions of the DSM are provided: an unfiltered product and a final, filtered product. The final product has a spatial resolution of 5-m and was used for lahar inundation modeling for the Copahue volcano (Viotto, Toyos, and Bookhagen 2022, <a href="https://doi.org/10.1016/j.jsames.2022.104138">https://doi.org/10.1016/j.jsames.2022.104138</a> : An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at Volcán Copahue (Argentina & Chile). <em>Journal of South American Earth Sciences</em> ). The dataset provided should be cited together with the article. </p> <p><strong>DSM processing </strong></p> <p>The source images were given by a SPOT-7 snow- and cloud-free triplet (Nadir, Backward and Forward) of 1.5 m spatial resolution from 19 April 2018 (SPOT Image, Airbus Defence and Space GmbH, distributed by CONAE; Dataset ID: <em>SEN_SPOT7_20180419_142955500_000</em>, delivered by CONAE as <em>DS_SPOT7_20180419</em>).</p> <p>The data were processed with the suite of digital photogrammetry tools AMES Stereo Pipeline ASP (Beyer et al., 2018). The procedure for the generation of the DSM is summarized by following steps: </p> <ol> <li> <p>The orbital parameters (RCP models) were adjusted using the bundle adjustment tool with no ground control points, since they were unavailable.</p> </li> <li> <p>The scenes were map-projected onto the NASADEM (spatial resolution of 30 m) elevation dataset, assisted by the results of the orbital adjustment in Step 1.</p> </li> <li>The stereo correlation of the map-projected scenes including the results of the adjusted orbital parameters, was performed three times, using as first scene (i.e., primary image) the nadir (N), backward (B), and forward (F) images . In each run, the order of images to perform the stereo correlation was: N-F-B, F-N-B, and B-N-F. Thus, three point clouds were generated. Specific ASP correlator settings (other than defaults parameters; for details see the provided stereo-default file) were set in the following way: <em>Correlation Kernel</em>: 15 x 15 pixels; <em>Sub-pixel Refinement Kernel</em>: 21 x 21 pixels; <em>Subpixel Refinement Mode</em>: 2 (Weighted Affine Adaptive Window Correlator EM)</li> <li> <p>The three point clouds were merged into one point cloud with a regular grid of 5 m (unfiltered product, known as <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em>).</p> </li> </ol> <p>The quality of the final point cloud was assessed by comparing the unfiltered DSM with a spatial resolution of 12-m against the WorldDEM<sup>TM</sup> elevation dataset (Collins et al., 2015). The WorldDEM was provided by Airbus Defence and Space GmbH under license for the scope of the Viotto et al., 2022 study. The comparison of the pixel-to-pixel heights above the ellipsoid (WGS84) between the two datasets resulted in a mean difference of 0.67 m and a standard deviation of +/- 4.82 m. </p> <p>Comprehensive details on the methodologies evaluated to create the dataset with ASP, can be found in the corresponding master's thesis “Topografía digital y modelado de lahares en el Volcán Copahue, Argentina-Chile” from S. Viotto (link: https://rdu.unc.edu.ar/handle/11086/15384). Recommended literature about processing DEMs from SPOT imagery is given by Mueting et al., 2021 (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330">https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JF006330</a>). </p> <p><strong>Creation of the Final, Filtered DSM product</strong></p> <p>The corrections and improvements applied to the unfiltered product to create the final, filtered DSM (named DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif) are summarized by following steps. </p> <p> </p> <ol> <li> <p><em>Water Bodies Delineation</em></p> </li> </ol> <p>The delineation of the water bodies was based on a mask created from the free access water bodies datasets provided by the Instituto Geográfico Nacional of Argentina (<a href="https://www.ign.gob.ar/NuestrasActividades/InformacionGeoespacial/CapasSIG">https://www.ign.gob.ar/ NuestrasActividades/InformacionGeoespacia l/CapasSIG</a>) and by the Ministerio de Bienes Nacionales in Chile ( <a href="https://www.ide.cl/index.php/aguas-continentales/item/1508-catastro-de-lagos">https://www.ide.cl/index.php /aguas-continentales/item/1508-catastro-de-lagos</a>). A total of 45 lakes within the area of interest were considered. Lakes with areas below or equal to 25 m2 were smoothed with a median filter in the last step. Lakes with areas above this threshold were filled in with a constant value and their borders were smoothed with a median filter to provide smooth shorelines.</p> <p><em>2 . Void Filling</em></p> <p>Voids (other than water bodies) were filled with the tool “Close Gaps” from Saga GIS software. </p> <p><em>3. Smoothing</em></p> <p>Finally, the elevation dataset was smoothed with a median filter using a 3 x 3 pixel window, excluding water bodies filled in the step 1. </p> <p><strong>Final Remarks and Suggestion</strong></p> <p>The quality assessment of the final version by visual inspection of the hillshades suggested an improvement of the signal to noise ratio. However, the void filling process may be improved.</p> <p><br> </p> <p><strong>Dataset Description</strong></p> <table align="center"> <caption> </caption> <tbody> <tr> <td>Digital Surface Models</td> <td> <p>No Data Value = -9999</p> <p>Format = float 32 bit</p> <p>File Format = GeoTiff</p> <p>Vertical Datum: WGS84</p> <p>Projection information: EPSG 32719 (UTM19S)</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>) </p> <p>Versions: </p> <ul> <li> <p>Unfiltered product: without corrections <em>DSM_Copahue_UTM19S_WGS84_5m_raw.tif</em></p> </li> <li> <p>Final, filtered product: smoothed and void filled <em>DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</em></p> </li> </ul> </td> </tr> <tr> <td>Water Bodies Mask</td> <td> <p>No Lake Value = 0</p> <p>Lakes Values = 1 to 45</p> <p>File Format= GeoTiff</p> <p>Spatial Resolution: 5m (subfix: <em>_5m</em>)</p> <p>Projection information : EPSG 32719 (UTM19S)</p> <p><em>WB_mask_5m_UTM19S.tif</em></p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>Repository structure</strong></p> <p>|__ 01_Scripts</p> <p> |+ run21_CopahueDSM_AMES_sviotto.sh</p> <p> |+ stereo.default</p> <p>|__ 02_DSMs</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_raw.tif</p> <p> |+ DSM_Copahue_UTM19S_WGS84_5m_VoidFilled.tif</p> <p> |+ WB_mask_5m_UTM19S.tif</p> <p><strong>References</strong></p> <p>Beyer, R. A., Alexandrov, O., & McMichael, S. (2018). The Ames Stereo Pipeline: NASA's open source software for deriving and processing terrain data. <em>Earth and Space Science</em>, 5, 537– 548. <a href="https://doi.org/10.1029/2018EA000409">https://doi.org/10.1029/2018EA000409</a></p> <p>Collins, J., Riegler, G., Schrader, H., Tinz, M., 2015. Applying terrain and hydrological editing to TanDEM-X data to create a consumer-ready worlddem product. Int. Arch. Photogram. Rem. Sens. Spatial Inf. Sci. 40 (7), 1149. https://doi.org/10.5194/isprsarchives-XL-7-W3-1149-2015.</p> <p>Mueting, A., Bookhagen, B., & Strecker, M. R. (2021). Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina. <em>Journal of Geophysical Research: Earth Surface</em>, 126, e2021JF006330. <a href="https://doi.org/10.1029/2021JF006330">https://doi.org/10.1029/2021JF006330</a></p> <p>Viotto, S., Toyos, G., & Bookhagen, B. (2022). An assessment of the effects of DEM quality and spatial resolution on a model for mapping lahar hazard inundation at volcán copahue (Argentina & Chile). Journal of South American Earth Sciences, 104138. https://doi.org/10.1016/j.jsames.2022.104138</p> <p> </p> <p> </p>
CYGNSS-based inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019
<p>Inundation maps of the Pantanal and the Sudd wetlands from June 2017 to December 2019 at 0.5<sup>o</sup> x 0.5<sup>o </sup>resolution developed using microwave remote sensing data from the Cyclone Global Navigation Satellite System (CYGNSS) constellation (https://cygnss.engin.umich.edu/), L1 v2.1.</p> <p>CYGNSS data used to produce these maps is available from NASA: https://podaac.jpl.nasa.gov/dataset/CYGNSS_L1_V2.1</p> <p>A complete description of the method developed to obtain these maps is available in the following preprint: https://www.essoar.org/doi/10.1002/essoar.10504845.1</p> <p> </p> <p> </p>
Spatial scale evaluation of forecast flood inundation maps
<p>Spatial scale evaluation of forecast flood inundation maps, data and code</p> <p>Creator: Helen Hooker[1] Publication Year: 2022</p> <p>Organisation(s): 1. Department of Meteorology, University of Reading, U.K</p> <p>Description: This dataset contains:</p> <p>- Python functions for a new scale-selective approach to forecast flood map evaluation.</p> <p>- SAR-derived observed flood maps used in the study.</p> <p>- JBA Consulting Flood Foresight forecast flood maps used in the study. </p> <p>Helen Hooker. (2022). Spatial scale evaluation of forecast flood inundation maps (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6011882</p> <p>Related publications:</p> <p>Spatial scale evaluation of forecast flood inundation maps; 2022; Journal of Hydrology (in preparation) Helen Hooker[1], Sarah L. Dance[1,2,3], David C. Mason[4], John Bevington[5], and Kay Shelton[5]</p> <ol> <li>Department of Meteorology, University of Reading, UK.</li> <li>Department of Mathematics and Statistics, University of Reading, UK.</li> <li>NCEO, University of Reading, UK.</li> <li>Department of Geography and Environmental Science, University of Reading, UK.</li> <li>JBA Consulting, UK.</li> </ol> <p>Correspondence: Helen Hooker (<a href="mailto:h.hooker@pgr.reading.ac.uk">h.hooker@pgr.reading.ac.uk</a>)</p>
Mapping of Inundation Extent in the Amazon Basin 2014-2017 with ALOS-2 PALSAR-2 ScanSAR Time-Series Data
<p>Figures and Tables </p>
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