Multi-sensor, multi-band and multi-temporal remote sensing data can be very useful in precise flood monitoring. In this paper, we describe DAFNE, a Matlab (R)-based, open source toolbox, to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of producing time series of output flood maps, and thus follow the evolution of single or recurrent flood events. Here, an application of the toolbox is illustrated to delineate a flood map, close to the peak of inundation occurred in April 2015 on the Strymonas river (Greece), from multi-band optical and SAR data.
An Open-Source Tool for the Integration of Remotely Sensed Information and Hydro-Geomorphic Parameters for Precise Monitoring of Inundations
Refice A;D'Addabbo A;Pasquariello G;Lovergine F
2018
Abstract
Multi-sensor, multi-band and multi-temporal remote sensing data can be very useful in precise flood monitoring. In this paper, we describe DAFNE, a Matlab (R)-based, open source toolbox, to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of producing time series of output flood maps, and thus follow the evolution of single or recurrent flood events. Here, an application of the toolbox is illustrated to delineate a flood map, close to the peak of inundation occurred in April 2015 on the Strymonas river (Greece), from multi-band optical and SAR data.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
