1.5 to 4 million hectares of land burns in wildfire across the United States each year, contributing to post-fire erosion, ecosystem degradation and loss of wildlife habitat. Unmanned Aircraft Systems (UAS) and sensor miniaturization offer a new paradigm, providing an affordable, safe, and responsive on-demand tool for monitoring fire effects at a much finer spatial resolution than is possible with current technology. Using spectroscopic analysis of a variety of live as well as combusted vegetation samples to identify the spectral separability of vegetation classes, an optimal set of spectra was selected to be utilized by machine learning classifiers. This approach allows high resolution mapping of wildland fire severity and extent.
Keywords: fire ecology; image classification; machine learning; remote sensing.