Fire type mapping using object-based classification of Ikonos imagery

Abstract

Distinguishing and mapping areas of surface and crown fire spread has significant applications in the study of fire behaviour, fire suppression, and fire effects. Satellite remote sensing has supplied a suitable alternative to conventional techniques for mapping the extent of burned areas, as well as for providing post-fire related information (such as the type and severity of burn). The aim of the present study was to develop an object-based classification model for mapping the type of fire using very high spatial resolution imagery (Ikonos). The specific objectives were: (i) to distinguish between surface burn and canopy burn; and (ii) to assess the accuracy of the classification results by employing field survey data. The methodology involved two consecutive steps, namely image segmentation and image classification. First, image objects were extracted at different scales using multi-resolution segmentation procedures, and then both spectral and contextual object information was employed to classify the objects. The accuracy assessment revealed very promising results (approximately 87% overall accuracy with a Kappa Index of Agreement of 0.74). Classification accuracy was mainly affected by the density of the canopy. This could be attributed to the inability of the optical sensors to penetrate dense canopy to detect fire-affected areas. The main conclusion drawn in the present study is that object-oriented classification can be used to accurately distinguish and map areas of surface and crown fire spread, especially those occurring in open Mediterranean forests.