Mapping hurricane Katrina’s widespread destruction in New Orleans using multi-sensor data and the normalized difference change detection technique (NDCD)

This paper introduces a new and general method for change detection based on the normalized difference change detection (NDCD) technique. A case study shows the use of the NDCD technique for flood mapping. Flood maps for the city of New Orleans (Louisiana, USA) resulting from the passage of hurricane Katrina in 2005 were produced from the data processing of SPOT-4/HRVIR and Landsat-5/TM images and the maps’ accuracies were verified using as ground truth the flood extension map of the city of New Orleans produced at the Dartmouth Flood Observatory (Dartmouth College, USA). The potentialities and performances of the NDCD technique in flood mapping were also compared to other standard change detection methods such as: i) the near-infrared normalized difference, ii) unsupervised post-classification comparison, iii) Change Vector Analysis , and iv) Spectral-Temporal Minimum Noise Fraction. Results show that the NDCD technique led to better results than all the others change detection methods here considered when using the SPOT-4/HRVIR data, while for the Landsat-5/TM data processing the closeness of the post-flood image to Katrina landfall negatively influenced the overall performances. However, with respect to flood mapping in the urban area only, which may be of major interest in most cases, the NDCD technique performed better than all the other change detection methods here considered also when using the Landsat-5/TM data.