Abstract Title
Evaluation of Image Spatial Resolution for Machine Learning Mapping of Wildland Fire Effects
Abstract
Many different machine-learning algorithms have previously been used to map wildland fire effects using satellite imagery from the Landsat satellites with 30-meter spatial resolution. Small-unmanned aircraft systems (sUAS) can capture images with five-centimeter (hyperspatial) resolution. Consequently, the amount of data needing to be stored and analyzed is greatly increased. There is a need for more tools that focus on extracting actionable knowledge from hyperspatial imagery and providing timely information for management of wildland fires. This analysis shows that the accurate mapping of fire effects from hyperspatial imagery is increased. The classifier developed to do this analysis uses a support vector machine (SVM) to determine the burn severity by classifying image pixels into canopy crown, surface vegetation, white ash, and black ash.
Evaluation of Image Spatial Resolution for Machine Learning Mapping of Wildland Fire Effects
Many different machine-learning algorithms have previously been used to map wildland fire effects using satellite imagery from the Landsat satellites with 30-meter spatial resolution. Small-unmanned aircraft systems (sUAS) can capture images with five-centimeter (hyperspatial) resolution. Consequently, the amount of data needing to be stored and analyzed is greatly increased. There is a need for more tools that focus on extracting actionable knowledge from hyperspatial imagery and providing timely information for management of wildland fires. This analysis shows that the accurate mapping of fire effects from hyperspatial imagery is increased. The classifier developed to do this analysis uses a support vector machine (SVM) to determine the burn severity by classifying image pixels into canopy crown, surface vegetation, white ash, and black ash.