A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Satellite‐based flood mapping in the boreal region for improving situational awareness
Abstract Space‐borne remote sensing techniques enable near real‐time mapping of floods cost‐efficiently. Synthetic aperture radar (SAR) and optical sensors are the most suitable for flood detection. However, SAR has become more popular, due to the independence of sunlight and weather conditions, and the increasing data availability. Typical spring floods occurred in northern Finland during 2018. Various remote sensing sources were utilised for monitoring and damage estimation of the flooding. Floods were mapped with the SAR‐based Finnish Flood Centre's Flood Detection Algorithm (FC‐FloDA), a standard threshold‐based approach applied to Sentinel‐1, and a visual interpretation of Sentinel‐2 images. In addition, flood maps from the Copernicus Emergency Management Service (EMS) and aerial photographs from the city of Tornio were ordered. The flood products and interpretations were compared, and a deeper accuracy assessment was conducted on the FC‐FloDA maps. FC‐FloDA was, in general, the most successful in detecting floods within the test areas. The EMS product and the Sentinel‐1 interpretation worked well in open areas, but did not detect floods in forests. The superiority of Flood Centre's product is mainly based on the adaptation of the algorithm to northern boreal environments and the selection of an optimal polarisation for detecting floods also under tree canopies.
Satellite‐based flood mapping in the boreal region for improving situational awareness
Abstract Space‐borne remote sensing techniques enable near real‐time mapping of floods cost‐efficiently. Synthetic aperture radar (SAR) and optical sensors are the most suitable for flood detection. However, SAR has become more popular, due to the independence of sunlight and weather conditions, and the increasing data availability. Typical spring floods occurred in northern Finland during 2018. Various remote sensing sources were utilised for monitoring and damage estimation of the flooding. Floods were mapped with the SAR‐based Finnish Flood Centre's Flood Detection Algorithm (FC‐FloDA), a standard threshold‐based approach applied to Sentinel‐1, and a visual interpretation of Sentinel‐2 images. In addition, flood maps from the Copernicus Emergency Management Service (EMS) and aerial photographs from the city of Tornio were ordered. The flood products and interpretations were compared, and a deeper accuracy assessment was conducted on the FC‐FloDA maps. FC‐FloDA was, in general, the most successful in detecting floods within the test areas. The EMS product and the Sentinel‐1 interpretation worked well in open areas, but did not detect floods in forests. The superiority of Flood Centre's product is mainly based on the adaptation of the algorithm to northern boreal environments and the selection of an optimal polarisation for detecting floods also under tree canopies.
Satellite‐based flood mapping in the boreal region for improving situational awareness
Juval Cohen (author) / Kirsikka Heinilä (author) / Mikko Huokuna (author) / Sari Metsämäki (author) / Jyri Heilimo (author) / Mikko Sane (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Satellite‐based flood mapping in the boreal region for improving situational awareness
| Wiley | 2022
Improving Real-time Situational Awareness During Flood Events
| British Library Conference Proceedings | 2012
| Wiley | 2011
Improving Situational Awareness in a Fire Structure
British Library Online Contents | 2008