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首页> 外文OA文献 >Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA
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Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA

机译：使用小型无人机的野火遥感：火灾后映射，大湾植被回收和损伤分析，密西西比州/阿拉巴马州，美国

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摘要

Wildfires can be beneficial for native vegetation. However, wildfires can impact property values, human safety, and ecosystem function. Resource managers require safe, easy to use, timely, and cost-effective methods for quantifying wildfire damage and regeneration. In this work, we demonstrate an approach using an unmanned aerial system (UAS) equipped with a MicaSense RedEdge multispectral sensor to classify and estimate wildfire damage in a coastal marsh. We collected approximately 7.2 km2 of five-band multispectral imagery after a wildfire event in February 2016, which was used to create a photogrammetry-based digital surface model (DSM) and orthomosaic for object-based classification analysis. Airborne light detection and ranging data were used to validate the accuracy of the DSM. Four-band airborne imagery from pre- and post-fire were used to estimate pre-fire health, post-fire damage, and track the vegetation recovery process. Immediate and long-term post-fire classifications, area, and volume of burned regions were produced to track the revegetation progress. The UAS-based classification produced from normalized difference vegetation index and DSM was compared to the Landsat-based Burned Area Reflectance Classification. Experimental results show the potential of using UAS and the presented approach compared to satellite-based mapping in terms of classification accuracies, turnaround time, and spatial and temporal resolutions.
机译：野火可能有利于本地植被。然而，野火会影响物业价值，人力安全和生态系统功能。资源管理人员需要安全，易于使用，及时和经济高效的方法，用于量化野火损坏和再生。在这项工作中，我们展示了一种方法，使用了配备有云母发布多光谱传感器的无人机系统（UAS），在沿海沼泽中进行分类和估算野火损坏。在2016年2月的野火活动之后，我们在野火活动之后收集了大约7.2平方公里的五频谱图像，用于创建基于摄影测量的数字表面模型（DSM）和基于对象的分类分析的正轨。使用空中光检测和测距数据来验证DSM的准确性。来自预先和火灾后的四频空气传播图像用于估算火灾前的健康，火灾后损伤，并跟踪植被恢复过程。制作了立即和长期的火灾后分类，面积和烧毁区域的体积，以跟踪恢复进展。将由归一化差异植被指数和DSM产生的基于UAS的分类与基于Landsat的烧毁区域反射率分类进行了比较。实验结果表明，在分类精度，周转时间和空间和时间分辨率方面，使用基于卫星绘图的卫星绘图的潜力和所提出的方法。

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