Reliable, accurate and timely forest mapping for wildfire management using ASTER and Hyperion satellite imagery

Abstract

The present study aims to explore the potential and effectiveness of new Earth Observation data for mapping the vegetation composition and structure and thus provide accurate forest maps to be used in fire propagation simulation models and fire risk assessment. Land cover classification of ASTER and Hyperion images is performed in a detailed nomenclature including different vegetation types and densities since the same vegetation type may give fires with different behaviour as a result of differences in fuel continuity. The results suggest that both datasets can provide highly accurate maps with an overall accuracy of 85% for ASTER and 93% for Hyperion classification. Although Hyperion is superior to ASTER in terms of overall accuracy, the latter provided a higher thematic accuracy identifying one additional class compared to Hyperion. The evaluation of the classification results in terms of cost and technical characteristics suggest that both datasets are suitable for use in wildfire management tools, depending on the specific user needs, and they could also be used complementary if a combination of high thematic accuracy and locally high spatial accuracy is needed.