The use of SPOT VEGETATION data in a classification tree approach for burnt area mapping in Australian savanna

An algorithm to map burnt areas has been developed for SPOT VEGETATION (VGT) data in Australian woodland savannas. A time series of daily VGT images (15 May to 15 July 1999) was composited into 10-day periods by applying a minimum value criterion to the near-infrared band (0.78–0.89 mm). The algorithm was developed using a classification tree methodology that was confirmed as a powerful means of image classification. This methodology allowed the identification of three classes of burnt surfaces that appear to be differentiated by the proportion of the pixel that is burnt, the intensity of the fire and the density of the tree layer. The performance of the algorithm was assessed by classification of one VGT composite image (31 May–9 June) using, as representative of the ground truth, burnt areas extracted from two Landsat TM scenes (9 June). We randomly extracted 30 windows (each of ~14 km by 14 km) for which we compared the percentage of area burnt as derived from TM and VGT. The estimated mean absolute deviation in the percentage of the area burnt in each window is ±6.3%. In the area common to the two datasets a total amount of 6473 km2 was estimated to be burnt in the VGT classification against 7536 km2 that was burnt according to TM images. The accuracy of the classification was found to vary with the vegetation type being the most accurate estimate in low woodland with an underestimation error of 8.6%. These results show that VGT could be a very useful sensor for burnt area mapping over large woodland areas, although the low spatial resolution and the lack of a thermal band can be a limitation in certain conditions (e.g. understorey burns). The same methodology will be applied to map burnt areas for the entire Australian continent.