Near Real-time and archival data of High-resolution (10 m) flood inundation dataset over the Contiguous United States, developed based on the Sentinel-1 SAR imagery (2016-current) archive, using an automated Radar Produced Inundation Diary (RAPID) algorithm.

Activation time (UTC): 2018-03-08 14:20:00
Event time (UTC): 2018-03-08 00:00:00
Event type: Humanitarian (Population displacement (IDP))

Activation reason:
The Danish Emergency Management Agency (DEMA) is planning to build a coordination hub in Bangladesh in relation with Rohingya refugees. The Copernicus EMS Rapid Mapping Service has been triggered to produce Reference Maps based on recent optical satellite imagery that will be used for the initial assessment on the Areas of Interest.

Reference products: 5
Delineation products: 0
Grading products: 0

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

Replication Data for the Paper "Rapid Mapping of Global Flood Precursors and Impacts Using a Novel GRACE Five-Day Solution" in Nature Communications: This dataset is a crucial resource for verifying and replicating the research findings presented in the paper. Replication Data Files: 1- "File DFO_GlobalFloods.csv: This is the data file containing information about global floods. The original data source can be accessed at https://floodobservatory.colorado.edu/. The floods in this file are organized based on their durations." 2- "File PrCR_ReCR_Rate_11_2023_Figures3_5.csv: This file contains the results of an event coincidence analysis for both precursor coincidence rates and response coincidence rates, as depicted in Figures 3 and 5." 3- "File DJF_results_11_2023_Figure_6.csv: Describes the results of response coincidence rates for the Atmospheric Teleconnection-Water System (ATWS) following heavy winter rainfall." 4- "File JJA_results_11_2023_Figure_6.csv: Describes the results of response coincidence rates for the Total Water Storage (ATWS) following intense summer rainfall." 5- "File MAM_results_11_2023_Figure_6.csv: Describes the results of response coincidence rates for the Total Water Storage (ATWS) following intense spring rainfall." 6- "File SON_results_11_2023_Figure_6.csv: Describes the results of response coincidence rates for the Total Water Storage (ATWS) following intense fall rainfall." 7- "File RawData_5D_ATWS_GlobalFloods.csv: This file contains antecedent total water storage data that is necessary to generate the results shown in Figures 1, 3, and 5 for a total of 3,272 flood events." The ATWS quantifies the fraction of wet storage relative to historical maxima for the period spanning from 2002 to 2021. This is accomplished by accumulating the data using a weighted sum of the last 6 epochs and the current epocsh. (Manuscript Methods) 8- "File RawData_5D_TWS_GlobalFloods.csv: The CSR-5D solution provides total water storage data for 3,372 flood events, averaged over a 3-degree around the flood locations." Unit in mm

Raw data, intermediate results, and Jupyter notebook used in the following journal publication: Rapid mapping of polarization switching through complete information acquisition Suhas Somnath, Alex Belianinov, Sergei V. Kalinin & Stephen Jesse Nature Communications volume 7, Article number: 13290 (2016) doi: 10.1038/ncomms13290

Rapid Mapping (RM) Daten werden im Falle eines großräumigen Naturereignisses von swisstopo im Auftrag des Bundesamtes für Umwelt BAFU für die Ereignisdokumentation und unter gewissen Bedingungen auch für die Ereignisbewältigung erstellt. Die Daten basieren auf verschiedenen Sensoren und weisen unterschiedliche Eigenschaften auf.

Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.

Activation time (UTC): 2017-05-02 14:19:00
Event time (UTC): 2017-05-13 11:00:00
Event type: Humanitarian (Security)

Activation reason:
In occasion of Fátima 100th Anniversary, Portugal will receive the visit of Pope Francisco on May 13th . As it is aspected a huge number of pilgrims to attend the cerimonies, the preparations for the event are particularly complex, involving several actors such as Police Forces, Medical Emergency, Firemen, Volunteers. An update map is needed to assess and eventually identify chunk nodes, possible evacuation areas, possible helicopter landing areas.

Reference products: 2
Delineation products: 0
Grading products: 0

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

Legacy product - no abstract available

We present a preliminary point inventory of the landslides associated with the M7.2 Nippes, Haiti, earthquake that occurred on August 14, 2021. The mapping was part of rapid response efforts to identify hazards for situational awareness and emergency response by humanitarian aid organizations. This inventory accompanies an Open-File Report detailing the hazards presented by the landslides triggered by the earthquake (Martinez et al., 2021). To map the landslides, we used mid- to high-resolution satellite imagery including Sentinel-2 (10-m resolution), WorldView (0.3-0.5-m resolution), Planet (2.7-4.0-m resolution), as well as a high-resolution (1.5 m) Digital Elevation Model (DEM) that was derived from lidar collected from 2014-2016 (HaitiData and The World Bank, 2021). We compared post-earthquake images to pre-earthquake images to assure the landslides were associated with the earthquake. Due to the varying quality of imagery used and our rapid mapping for the response, we estimate our accuracy of landslide head scarp points to be within tens of meters of their correct location at the top of the corresponding head scarp. For one of our more poorly orthorectified images, the root mean square error was calculated to be 45 m. This error is not representative of all images used, but it provides an upper limit on the positional accuracy of our mapping. Due to the large quantity of images utilized in our rapid mapping efforts, a formal and systematic assessment on the positional accuracy of the data has yet to be completed. We also referenced a grid of population data (Facebook Connectivity Lab and Center for International Earth Science Information Network, 2016) as well as OpenStreetMap data (OpenStreetMap, 2021) while mapping to determine the potential for human and infrastructure impacts. Specific hazards that were identified include landslide dams and roads that were undercut or covered by landslide debris. The inventory includes 4,893 landslides. This is a minimum, however, because high-resolution imagery remains unavailable in some areas. Additionally, there may be a few localized areas in our mapping area that did not have cloud free imagery.

For the ORR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pd-Ag(111) surface are stored in ads_PdAg_111_dft.db. For the SMR study, the final datasets of the DFT-relaxed adsorbate-alloy configurations for the Pt-Ni(111), (100) and (311) surfaces are stored in ads_PtNi_111_dft.db, ads_PtNi_100_dft.db and ads_PtNi_311_dft.db, respectively.

The 76,265 tasks (combining 15,253 SMR conditions with 5 exploration parameters) used for the BEM runs in the SMR study can be found in bem_smr_tasks.csv.

All the input files and scripts for BEM high-throughput screening (for both ORR and SMR studies), DFT calculations, EMT benchmarks, SGCMC simulations, structure generation and plotting (e.g. surface free energy diagrams and 2D phase diagrams) are all provided in inputs_and_scripts.zip.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 58.03 percent, this approach shows strong potential for generating crop type maps of current year in September.

Activation time (UTC): 2020-06-09 04:54:00
Event time (UTC): 2020-06-08 20:00:00
Event type: Flood (Riverine flood)

Activation reason:
There is an ongoing spring flood in the northern part of Norway due to snow melt from a winter with an exceptionally amount of snowfall. It is expected for some of the rivers to hit the peak in the very next days, while for some of the other rivers the peak is expected later, depending on temperature and rainfall.

Reference products: 0
Delineation products: 8
Grading products: 0

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

Landslide inventory data is related to the paper published in Landslides Journal: Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach Date Submitted: 2021-04-21

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 59.62 percent, this approach shows strong potential for generating crop type maps of current year in September.

Background

The TU Wien flood mapping algorithm is a Sentinel-1-based workflow using Bayes Inference at the pixel level. The algorithm is currently deployed in global operations under the Copernicus GFM project and have been shown to work generally well. However, the current approach has overestimation issues related to imperfect no-flood probability modeling. In a recent study, we proposed and compared an Exponential Filter derived from no-flood references versus the original Harmonic Model. We have conducted experiments on seven study sites for flooded and no-flood scenarios. A full description and discussion are found in the paper: Assessment of Time-Series-Derived No-Flood Reference for SAR-based Bayesian Flood Mapping.

Methodology

• We generated no-flood references using the Exponential Filter at various T-parameter values and the original Harmonic Model as a baseline.
• Flood maps were generated using the Bayes Inference-based SAR Flood mapping algorithm implemented in Python using the Yeoda software package. Flood maps using the various no-flood references for all available Sentinel-1 image acquisitions for a selected relative orbit per study site.
• Each flood map is compared with the reference CEMS Rapid Mapping or Sentinel Asia reference dataset to generate validation/confusion maps.

Technical details

• Datasets are stored in GeoTiff format using LZW Compression.
• Files are compressed in two bundles: 1) flood maps, 2) false positive count maps, and 3) validation results.
• Files are organized and tiled following the T3 Equi7Grid tilling system at 20m x 20m resolution.
  • Folder structure: dataset/map product>(continental)subgrid>tile>files.
  • The study covers the following study sites:
    • EU E039N027T: Scotland
    • AS E054N015T3: Vietnam
    • EU E054N006T3: Greece
    • EU E051N012T3: Slovenia
    • AS E024N027T3: India
    • OC E057N117T3: Philippines
    • EU E057N024T3: Latvia
• Files are named following the Yeoda file naming convention.
• Summary Accuracy Assessment Metrics are in CSV format.

Datasets:

• Flood: flood maps generated using different parameterizations of no-flood reference.
• FP_Count: false positive count maps.
• Validation results include:
  • Confusion maps were generated from the difference between the flood maps and the rasterized CEMS Rapid Mapping reference or Sentinel Asia datasets. Summary Accuracy Assessment Metrics in CSV format.
  • ERA5-LAND daily aggregates in CSV format.
  • Root Mean Square Error time-series analysis in CSV format.
  • False Positive Rate time-series analysis in CSV format.
• *Due to storage constraints, no flood reference is available upon request.

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existing software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethyl methanesulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results. Easymap is available at http://genetics.edu.umh.es/resources/easymap; its Quickstart Installation Guide details the recommended procedure for installation.

Activation time (UTC): 2019-05-28 15:30:00
Event time (UTC): 2019-04-09 00:00:00
Event type: Mass movement (Landslide)

Activation reason:
Starting in September 2018, massive landslides have occurred in Lusatian former mining area, in Saxony, Germany. Further slides occurred in March and April 2019. These landslides represent a major threat for the management of the whole water infrastructure,having also potentially a major impact on drinking water supplies for the greater Berlin/Brandenburg area.

Reference products: 0
Delineation products: 0
Grading products: 1

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

Raw data files in support of the key figures in the main manuscript (raw spectral files in Renishaw WiRE format and .txt format).

Activation time (UTC): 2012-11-21 15:00:00
Event time (UTC): 2012-11-19 12:00:00
Event type: Other

Activation reason:
Internally displaced persons due to military unrest in Goma

Reference products: 2
Delineation products: 2
Grading products: 0

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

MethodThis dataset includes a detailed example for using our method (described in paper linked to below) to digitize historical land-use maps in R.MapsWe also release all of the Swedish land-use maps that we digitized for this project. This includes the Economic Map of Sweden (Ekonomiska kartan) over Sweden's 15 southernmost counties (7069 25 km2 sheets), plus 11 sheets of the District Economic Map (Häradsekonomiska kartan - but see http://bolin.su.se/data/Cousins-2015 for more accurate manual digitization).SvenskaHär kan du ladda ner 7069 Ekonomiska kartblad som vi digitaliserade över södra Sverige. En kort beskrivning av metoden publicerades i tidningen Kart & Bildteknik (se länk nedan).--UpdatesVersion 2: The digitized Economic Maps have been resampled so that they are all at a 1m resolution. In the original version they were all very close to 1m but not exactly the same, which made mosaicking difficult. This should be easier now. We now also link to the published paper in Methods in Ecology and Evolution.For more information, please see the readme file. For help or collaboration, please contact alistair.auffret@natgeo.su.se. If you use the data here in your work or research, please cite the publication appropriately.