Decision-Based Fusion for Improved Fluvial Landscape Classification Using Digital Aerial Photographs and Forward Looking Infrared Images

Abstract

Riverine landscapes associated with large dynamic flood-plains provide a complex array of habitats for fish. Mapping and quantitative assessment of the habitats poses a major challenge. This study uses high spatial resolution airborne digital photographs and forward-looking infrared (FLIR) images, simultaneously acquired in spring 2005 over a 12 river kilometer section of the Unuk River in Southeast Alaska to map macro habitat indicators for Pacific salmon such as large woody debris (LWD), water channels, sand/gravel bars, and riparian vegetation. Image processing revealed that LWD could best be extracted using contextual information from the digital photos. River channel water had prominent shadows cast from neighboring trees and could not be accurately classified using digital photos, but could be well-delineated using unsupervised classification of the FLIR images. All other classes showed up well using supervised classification of digital photos. Using a decision-based fusion approach, the best individual classification results obtained from the digital photos and FLIR images to generate an improved fluvial landscape classification map (land-cover map). Using a decision-based fusion method resulted in an overall classification accuracy of the study area to 84.29 percent, compared to 77.00 percent using supervised classification of aerial photos alone. This appears to be first time that high-resolution airborne thermal images have been used for fluvial landscape classification, and the study clearly demonstrates the value of using thermal images and decision-based fusion approach for improved land-cover classification.