Abstract
The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production of more accurate data about the effects of wildland fire, enabling land managers to make more informed decisions. The ability to detect trees in hyperspatial imagery enables the calculation of canopy cover. A comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover from sources such as the LANDFIRE project enables the calculation of tree mortality, which is a major indicator of burn severity. A mask region-based convolutional neural network was trained to classify trees as groups of pixels from a hyperspatial orthomosaic acquired with a small unmanned aircraft system. The tree classification is summarized at 30 m, resulting in a canopy cover raster. A post-fire canopy cover is then compared to LANDFIRE canopy cover preceding the fire, calculating how much the canopy was reduced due to the fire. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive crown, and active crown fire occurred within the burn perimeter. Canopy cover mapped through this effort was lower than the LANDFIRE Canopy Cover product, which literature indicated is typically over reported. Assessment of canopy reduction mapping on a wildland fire reflects observations made both from ground truthing efforts as well as observations made of the associated hyperspatial sUAS orthomosaic.
Highlights
This study investigates the use of machine learning to identify burn severity as a measure of canopy reduction
The difference between the pre-fire and post-fire canopy cover enables a calculation of canopy reduction resulting as an effect of the fire, which gives an indication of tree mortality which is a primary indicator of post-fire effects resulting from a wildland fire
The post-fire canopy cover is compared against pre-fire canopy cover from the LANDFIRE project to determine how much upper layer canopy cover reduction occurred due to the fire, giving an indication of tree mortality, which is a primary indicator of post-fire effects in forested ecosystems
Summary
This study investigates the use of machine learning to identify burn severity as a measure of canopy reduction. In situations where the amount of current post-fire canopy has not been significantly altered from what can be observed from the pre-fire conditions, it can be assumed that a fire was only consuming surface vegetation (or possibly the lower canopy) but did not consume the upper portion of the tree crowns in the canopy [6]. This low intensity fire low intensity surface fire results in no mortality of the trees in the upper canopy would result in no noticeable change of canopy cover between pre-fire and post-fire conditions. Another situation results from where all the upper tree crowns in the forest canopy have been either been consumed by a fire or the crown has been scorched by a more intense fire on the surface which extends into the tree crowns [6]
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