Mapping the severity of fire using object-based classification of IKONOS imagery

Abstract

Mapping fire severity is necessary in order (1) to locate areas in need of special or intense post-fire management; (2) to allow the study of fire impact and vegetation recovery; and (3) to validate fire risk and fire behaviour models. The present study aimed to develop a method to map the severity of fire by employing post-fire IKONOS imagery. The objective was to develop an object-oriented model that would distinguish between different degrees of fire severity and to assess the accuracy of the model by employing field-collected data. The work comprised five main consecutive steps, namely, field data collection, data preprocessing, image segmentation, image classification and accuracy assessment. An adapted version of the FIREMON (fire effects monitoring and inventory protocol) landscape assessment method was employed to quantitatively record fire severity in the field. As a result, two different datasets were used: one for training and developing the classification rules, and another one for assessing the accuracy of the classification. Separate and independent data were used for training and for accuracy assessment. Overall accuracy was estimated to be 83%, while the Kappa Index of Agreement obtained was 0.74. The main source of inaccuracy was the inability of IKONOS to penetrate the dense canopy of unburned vegetation. The main conclusion drawn from the present work was that object-based classification applied to IKONOS imagery has the potential to produce accurate maps of fire severity, especially in the case of the open Mediterranean forest.