Improving remote sensing flood assessment using volunteered geographical data
Date
2013-03-19
Authors
Schnebele, E.
Cervone, G.
Journal Title
Journal ISSN
Volume Title
Publisher
Copernicus Publications
Abstract
A new methodology for the generation of flood hazard maps is presented fusing remote sensing and volunteered geographical data. Water pixels are identified utilizing a machine learning classification of two Landsat remote sensing scenes, acquired before and during the flooding event as well as a digital elevation model paired with river gage data. A statistical model computes the probability of flooded areas as a function of the number of adjacent pixels classified as water. Volunteered data obtained through Google news, videos and photos are added to modify the contour regions. It is shown that even a small amount of volunteered ground data can dramatically improve results.
Description
Keywords
Remote sensing, Volunteered ground data, Statistical flood model, Flood hazard maps
Citation
Schnebele, E. and Cervone, G.: Improving remote sensing flood assessment using volunteered geographical data, Nat. Hazards Earth Syst. Sci., 13, 669-677, doi:10.5194/nhess-13-669-2013, 2013.