VEGETATION FIRE FUELS MAPPING IN THE SAN DIEGO CITY CANYONS – A METHOD COMPARISON

Fire risk is a major threat to life, property and natural resources in southern California. Recent fire disasters occurred in autumn 2003 and 2007. Fire risk management deals with these hazards, input data are collected, analyzed and evaluated. One of the most important input data is the vegetation density in the endangered areas. Here we describe methods to map vegetation density forming five hazard classes. The main objective of this study is to explore the benefits of using remote sensed data for the accurate classification of vegetation in San Diego city canyons. Three very high resolution remote sensing data sets (< 1 m) were used in comparison: scanned color infrared film (CIR) airborne, digital multi-spectral airborne (ADS40) and digital multi-spectral satellite imagery (QuickBird). Different classification approaches (e.g. pixel-based, segment-based and knowledge-based) were tested and analyzed to separate the vegetation into five hazard classes. Accuracy assessment indicated low overall accuracies of 58 % on average. With regard to an optimized classification result in particular unsupervised and segment-based classification can be recommended. The overall accuracy for these two methods reached around 62 %. The use of specially selected reference areas for validation helped to increase the accuracies up to 81 %. Also a separating between three instead of five different hazard classes resulted in accuracies around 80 %. Furthermore it could be shown that all three data sets can be used for successful classification procedures. The resulting fire risk maps can help to reduce or prevent fire hazards. The maps are a basis for the brush management of the Fir