 |
| Titel |
Improving remote sensing flood assessment using volunteered geographical data |
| VerfasserIn |
E. Schnebele, G. Cervone |
| Medientyp |
Artikel
|
| Sprache |
Englisch
|
| ISSN |
1561-8633
|
| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 3 ; Nr. 13, no. 3 (2013-03-19), S.669-677 |
| Datensatznummer |
250018388
|
| Publikation (Nr.) |
 copernicus.org/nhess-13-669-2013.pdf |
|
|
|
|
|
| Zusammenfassung |
| 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. |
| |
|
| Teil von |
|
|
|
|
|
|
|
|