Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning

نویسندگان

چکیده

The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production more accurate data about effects wildland fire, enabling land managers to make informed decisions. ability detect trees in hyperspatial enables calculation canopy cover. A comparison post-fire cover and pre-fire sources such as LANDFIRE project tree mortality, which is a major indicator burn severity. mask region-based convolutional neural network was trained classify groups pixels orthomosaic acquired with system. classification summarized at 30 m, resulting raster. then compared preceding calculating how much reduced due fire. Canopy reduction allows mapping severity while also identifying where surface, passive crown, active crown fire occurred within perimeter. mapped through this effort lower than Cover product, literature indicated typically over reported. Assessment on reflects observations made both ground truthing efforts well associated sUAS orthomosaic.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13020290