English | Español
The objective of this Recommended Practice is to determine the extent of flooded areas. The usage of Synthetic Aperture Radar (SAR) satellite imagery for flood extent mapping constitutes a viable solution with fast image processing, providing near real-time flood information to relief agencies for supporting humanitarian action. The high data reliability as well as the absence of geographical constraints, such as site accessibility, emphasize the technology’s potential in the field.
This Jupyter Notebook covers the full processing chain from data query and download up to the export of a final flood mask product by utilizing open access Sentinel-1 SAR data. The tool's workflow follows the UN-SPIDER Recommended Practice on Radar-based Flood Mapping and is illustrated below. More detailed information regarding user inputs and processing steps can be found within the Jupyter Notebook.
Alternative versions have been optimized for the use with Binder and Google Colab. As cloud computing-based environments for Jupyter Notebooks, they take advantage of external technical resources and thus allow this tool to be applied using devices with limited computing power, including phones and tablets, and in areas with scarce bandwidth. These versions can directly be accessed and used by clicking the respective icons below.
The tool's workflow is demonstrated using the example of the Ulua Basin, Honduras, after the tropical cyclone Eta with Sentinel-1 data from November 11, 2020. Click here to see the full tutorial.
This tool was created to support the UN-SPIDER Knowledge Portal.
"The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the United Nations."
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent