There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.

中文翻译：

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无人飞行系统 (UAS) 运动结构冠层燃料参数：美国加利福尼亚州森林的多站点区域建模


在美国加利福尼亚州的地中海和温带气候森林中，迫切需要进行信息灵通的管理，以减少野火危害并恢复火灾的有益生态作用。这些努力依赖于生物量和冠层燃料参数的高空间和时间分辨率数据的可获取性，例如冠层基高（CBH）、平均冠层高度、冠层容重（CBD）、冠层覆盖度和叶面积指数（LAI）。使用无人航空系统运动结构（UAS-SfM）进行遥感由于其可访问性、相对较低的成本以及高时间节奏的可能性，因此为该应用提供了一种有前景的技术。然而，迄今为止，这种方法尚未在加州各地发现的复杂森林类型中进行研究。在这项研究中，我们检查了从 UAS-SfM 获得的结构和多光谱信息的能力，结合机器学习方法，使用跨代表多种森林类型的多个地点的基于区域的方法来模拟地上生物量和森林冠层燃料结构参数在加利福尼亚。