Spectroscopic Analysis for Mapping Wildland Fire Effects from Remotely Sensed Imagery.

IF 1.3 Q3 REMOTE SENSING
Dale A. Hamilton, M. Bowerman, J. Colwell, Greg Donohoe, B. Myers
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引用次数: 10

Abstract

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.
利用遥感影像绘制野火效果的光谱分析。
美国每年有150万至400万公顷的土地在野火中燃烧,导致火灾后的侵蚀、生态系统退化和野生动物栖息地的丧失。无人机系统(UAS)和传感器小型化提供了一种新的模式,提供了一个负担得起、安全且响应迅速的按需工具,用于以比当前技术更精细的空间分辨率监测火灾影响。使用对各种活的和燃烧的植被样本的光谱分析来识别植被类别的光谱可分性,选择了一组最佳光谱供机器学习分类器使用。这种方法可以对荒地火灾的严重程度和程度进行高分辨率的测绘。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.30
自引率
0.00%
发文量
2
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