SYNERGISTIC USE OF OPTICAL AND RADAR DATA FOR RAPID MAPPING OF FOREST FIRES IN THE EUROPEAN MEDITERRANEAN

• Main Authors: E.-M. Bernhard, E. Stein, A. Twele, M. Gähler
• Format: Article
• Language: English
• Published: Copernicus Publications 2012-09-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-4-W19/27/2011/isprsarchives-XXXVIII-4-W19-27-2011.pdf

In a classical approach, optical data are being used for forest fire detection in a rush mode. Difficulties arise due to persistent cloud coverage, haze layers and smoke plumes. In contrast, radar measurements offer high acquisition rates because of their ability to penetrate clouds and their independence of sun illumination. However, a visual interpretation of radar data is generally less intuitive than optical imagery for an untrained image analyst. Thus the main focus of our work was to combine the advantages of both data types and to develop a robust and fast but at the same time precise and transferable algorithm for burned area detection in the European Mediterranean region. Object-based change detection approaches and a synergistic use of optical and radar data can improve detection capabilities. The optical part of the algorithm covers very high resolution satellite images (like SPOT 5) including index calculation such as MSAVI, BAI and NDSWIR in single-temporal approaches and their temporal differences in multi- temporal approaches. Within the scope of both methodologies the burned area can be detected with an accuracy higher than 90%. In line with other authors (Libonati et al., 2011; Pereira et al., 1999) our work confirms the middle infrared band as crucial for burned area detection. The radar algorithm was based on TerraSAR-X StripMap data acquired before and after the forest fires. Different polarisations (VV and HH) have been used to improve the forest fire mapping capability. In addition, a comparison of burned and unburned areas was performed using different backscatter coefficients. These change detection techniques were based on image differences, image ratios and index calculation. The image segmentation was performed by using the new calculated layers. The burned area was then classified via a threshold given by the pre- and post- disaster differences. The classification result achieved an accuracy of 78%. This result shows the limitations of burned area mapping with microwaves. Therefore a combination of the optical and the radar technique, which takes advantage of both the optical accuracy and the ability of microwaves to penetrate clouds, led to the design and implementation of a single- and multi-temporal, object-based and semi-operational tool for burned area mapping.