Every year, floods cause enormous damage and loss of human life all over the world. Regarding the European Union, extreme floods are the most common types of natural disasters (44% of the total in the last decade), and in the future, the number of flash floods is expected to rise. Recent works of the authors have focused on the development of a straightforward and efficient processing algorithm for analyzing and mapping flood damages using optical remotely sensed satellite data and digital terrain models. In this paper, some improvements of the processing technique, both regarding the flood mapping and the water depth estimation, are presented. With respect to the first issue, a new data transformation is introduced, replacing the spectraltemporal principal component analysis (STPCA) with the spectraltemporal minimum noise fraction (STMNF) transformation, while the peak water depth is obtained through more sophisticated interpolation methods. The STMNF-based technique was applied to the data collected for the worst flood of the 20th Century that struck Piemonte Region, Italy, in 1994. Regarding the flood mapping, the STMNF method allowed an overall accuracy of 97.09% with a kappa coefficient of 0.889 to be established, obtaining a user accuracy of 85.76%, and a producer accuracy of 95.96%, with a lower commission error if compared to the previous STPCA method. Regarding the water depth computation, the best results were obtained using the second-order composed splines interpolator, obtaining an overall agreement with ground reference data of about 83%.
Rapid Response Flood Assessment Using Minimum Noise Fraction and Composed Spline Interpolation
Villa P
2007
Abstract
Every year, floods cause enormous damage and loss of human life all over the world. Regarding the European Union, extreme floods are the most common types of natural disasters (44% of the total in the last decade), and in the future, the number of flash floods is expected to rise. Recent works of the authors have focused on the development of a straightforward and efficient processing algorithm for analyzing and mapping flood damages using optical remotely sensed satellite data and digital terrain models. In this paper, some improvements of the processing technique, both regarding the flood mapping and the water depth estimation, are presented. With respect to the first issue, a new data transformation is introduced, replacing the spectraltemporal principal component analysis (STPCA) with the spectraltemporal minimum noise fraction (STMNF) transformation, while the peak water depth is obtained through more sophisticated interpolation methods. The STMNF-based technique was applied to the data collected for the worst flood of the 20th Century that struck Piemonte Region, Italy, in 1994. Regarding the flood mapping, the STMNF method allowed an overall accuracy of 97.09% with a kappa coefficient of 0.889 to be established, obtaining a user accuracy of 85.76%, and a producer accuracy of 95.96%, with a lower commission error if compared to the previous STPCA method. Regarding the water depth computation, the best results were obtained using the second-order composed splines interpolator, obtaining an overall agreement with ground reference data of about 83%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
