Rapid Response Flood Assessment using Minimun Noise Fraction and Composed Spline Interpolation

Every year floods cause enormous damages and loss of human life all over the world. Regarding the European Union, extreme floods are the most common type 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 (DTMs). In this paper are presented some improvements of the processing technique, both regarding the flood mapping and the water depth estimation. With respect to the first issue a new data transformation is introduced, replacing the Spectral-Temporal Principal Component Analysis (STPCA) with the Spectral-Temporal 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 the Piemonte Region (Italy) in 1994. Regarding the flood mapping, the STMNF method allowed to establish an overall accuracy of 97.09% with a kappa coefficient of 0.889, 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 2nd order composed splines interpolator, obtaining an overall agreement with ground reference data of about 83%.