Mapping of forest species and tree density using new Earth Observation sensors for wildfire applications

摘要

The success of any decision support system for managing wildfires lies on its ability to simulate fire evolution. Therefore, accurate information on the natural fuel material in any area of interest is necessary. The present study aims to provide methodological tools to explore in depth the potential of new Earth Observation data for horizontal mapping of vegetated areas. Two approaches are mainly described. The first one deals with the classification of ASTER visible, near- and short-wave infrared images in a detailed nomenclature including both different species and tree densities. This is important for wildfire studies since the same vegetation classes may represent completely different risk ignition levels depending on their morphological characteristics (i.e., trees height and density). The improvement of class separability using hyperspectral images acquired by Hyperion is also investigated. The second approach refers to a pattern recognition software tool for single tree counting using a very high spatial resolution image acquired by IKONOS-2 satellite. According to this approach, the regions dense in plants are identified by applying a suitable thresholding on the image. The resulted regions are further processed in order to estimate the number and location of single trees based on a pre-specified crown size per stratified zone. The outcome of the latter approach may provide direct evidence of tree density relating to ground biomass. Finally, the two approaches are used in a complementary manner to explore the possibilities offered by new sensor technology to override past limitations in species and fuel classification due to inadequate spectral/spatial resolution. The pilot application area is Mount. Pendeli and the east side of Mount. Parnitha, in the prefecture of Attiki, Greece.