DECISION MAKING ON DISASTER MANAGEMENT IN AGRICULTURE WITH SENTINEL APPLICATIONS

• Main Authors: G. Doxani, S. Siachalou, Z. Mitraka, P. Patias
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-08-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W8/121/2019/isprs-archives-XLII-3-W8-121-2019.pdf

Climate change and increase of extreme weather events, besides the numerous consequences, affect significantly and put in risk the agriculture sectors. Natural disasters, such as floods and wildfires, are responsible for a great loss in agriculture production. National governments together with international bodies make an important effort to cooperate towards the response and resilience when a disaster occurs. In this frame the European Earth Observation Programme - Copernicus provides a series of observation data, in-situ measurements and services related, amongst others, to different types of disasters. Concerning the availability of this big volume of observation data, the aim of DiAS (Disaster and Agriculture Sentinel Applications) project is to revise the existing knowledge on remote sensing methods for mapping the extent of natural and/or man-made disaster over agricultural areas and propose improvements. The developed methodology will be implemented in a Decision Support System (DSS), which will be freely available and easy-to-use by non-experts. In this paper, the developed methodology focuses on mapping floods over agricultural areas. Sentinel-1 and Sentinel-2 imagery are used as input information for the comparison analysis before and after the event. The reference for results’ evaluation is the corresponding information delivered by Copernicus Emergency Management Service (EMS). Although, the evaluation results are in good agreement when they could be used, a reference of higher accuracy is needed in order to estimate accurately the quality of the output products.