Abstract
In our world of rapid change, floods are a growing threat. In this context, flood extent mapping is important for damage and accurate and timely information about flood-affected areas are needed. Due to the suitability of remote sensing in mapping flood inundation, multi-source satellite images have been used in recent years. This paper presents a new approach to representing and modeling uncertainty in spatio-temporal information of satellite images using belief function theory for flooded areas extraction. Interval approaches and the associated arithmetic operations are used for modeling the uncertainty of spatial locations and temporal durations from 2D dimensional model in order to take the direction of flow in the flood. Based on topological spatial relationships, the similarity for the spatio-temporal inundation events is performed and the similar intervals are fused. Detailed examples are given to illustrate each step. A case study of flood risk in Chad Lake from 2003 to 2019 using Sentinel-2, MODIS and Landsat-8 images shows how spatio-temporal uncertainty can be problematic and bias the results of even the simplest flood mapping, and how coupling a Dempster–Shafer approach with topological relationships nevertheless offers a useful solution.
•A new approach for modeling uncertain spatio-temporal information is proposed.•Information with uncertain location and duration of floods are investigated.•A set of spatio-temporal relations between uncertain flood events is introduced.•Similar information about flooding events are merged.•Experimental results show the accuracy and robustness of our approach.