论文标题
通过深度学习监测野火对树种的影响
Monitoring the Impact of Wildfires on Tree Species with Deep Learning
论文作者
论文摘要
气候变化的影响之一是，野火在传统上被某些树种覆盖的区域的难度。在这里定制了一个深度学习模型，以对野火前后的四波段空中图像进行分类，以研究野火对树种的长期后果。树种标签是由手动划定的地图生成的五个土地覆盖类别：针叶树，硬木，灌木，再覆盖树和贫瘠的土地。该模型的准确度为92美元\％$，将模型应用于2009年至2018年的数据上的三个野火。该模型准确地描述了被野火损坏的区域，树种的变化以及燃烧区域的反弹。结果显示了影响当地生态系统的野火的明确证据，并且概述的方法可以帮助监测森林的变化，观察森林组成的变化并跟踪野火对树种的影响。
One of the impacts of climate change is the difficulty of tree regrowth after wildfires over areas that traditionally were covered by certain tree species. Here a deep learning model is customized to classify land covers from four-band aerial imagery before and after wildfires to study the prolonged consequences of wildfires on tree species. The tree species labels are generated from manually delineated maps for five land cover classes: Conifer, Hardwood, Shrub, ReforestedTree and Barren land. With an accuracy of $92\%$ on the test split, the model is applied to three wildfires on data from 2009 to 2018. The model accurately delineates areas damaged by wildfires, changes in tree species and rebound of burned areas. The result shows clear evidence of wildfires impacting the local ecosystem and the outlined approach can help monitor reforested areas, observe changes in forest composition and track wildfire impact on tree species.
