Geospatial Capacity Building for Flood...
URL: https://isprs-archives.copernicus.org/articles/XLVIII-5-2024/1/2024/
This study focuses on the development of a people-centred early warning system (EWS) against floods in the Sirba River basin between Niger and Burkina Faso. This densely populated area has witnessed an increase in extreme flooding events in recent years. Several flood forecasting systems in the Sahel exist, although there is no EWS that integrates the four components of people-centredEWSs, namely risk knowledge, monitoring and warning service, dissemination and communication, and response capacity. The proposed EWS, named SLAPIS, includes a risk knowledge component that involves defining four levels of vigilance. Its monitoring and alert component involves a user-friendly web application containing real-time data collected through automatic stations. TheEWS communicate seamlessly with the national alert system. The response capacity is strengthened through the creation of a floodzone atlas. In this framework, the EWS integrates significant geoinformatics in preparation of local risk reduction plans and the a wareness of local communities. In the SLAPIS case study, multi-temporal classifications were conducted using Sentinel-2 data and high-resolution images (approximately 10 cm) generated through Structure from Motion (SfM) techniques. Digital Terrain Model(DTM) creation for hydraulic model calibration employed a multiscale approach, incorporating GNSS survey data processed via Precise Point Positioning (PPP), HydroSHEDS (approximately 100m resolution), and commercial 10m resolution data. All information was calibrated, harmonised, and integrated into the EWS model, which is accessible via a web platform. Capacity building encompassed direct training and field implementation to streamline the primary EWS generation steps.
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追加情報
| フィールド | 値 |
|---|---|
| 最終更新日 | 2024年11月22日 |
| メタデータ最終更新日時 | 2024年11月22日 |
| 作成日 | 2024年11月22日 |
| データ形式 | |
| ライセンス | クリエイティブ・コモンズ 表示 |
| Id | b12a0b74-2508-449d-862a-1dd01ebb5917 |
| Package id | c5af00bc-9e72-42f2-bd13-5f0296fbc05a |
| State | active |