The potential of airborne laser scanning (ALS) and multispectral remote sensing data to aid in generating improved wind damage risk maps over large forested areas is demonstrated. This article outlines a framework to generate such maps, primarily utilizing the horizontal structural information contained in the ALS data. Validation was done over an area in Eastern Finland that had experienced sporadic wind damage. Wind is the most prominent disturbance element for Finnish forests. Hence, tools are needed to generate wind damage risk maps for large forested areas, and their possible changes under planned silvicultural operations. (1) How effective are ALS-based forest variables (e.g. distance to upwind forest stand edge, gap size) for identifying high wind damage risk areas? (2) Can robust estimates of predicted critical wind speeds for uprooting of trees be derived from these variables? (3) Can these critical wind speed estimates be improved using wind multipliers, which factor in topography and terrain roughness effects? We first outline a framework to generate several wind damage risk–related parameters from remote sensing data (ALS + multispectral). Then, we assess if such parameters have predictive power. That is, whether they help differentiate between damaged and background points. This verification exercise used 42 wind damaged points spread over a large area. Parameters derived from remote sensing data are shown to have predictive power. Risk models based on critical wind speeds are not that robust, but show potential for improvement. Overall, this work described a framework to get several wind risk–related parameters from remote sensing data. These parameters are shown to have potential in generating wind damage risk maps over large forested areas.

中文翻译：

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融合机载激光扫描和多光谱数据在改进芬兰受管理森林景观风害风险评估中的效用

展示了机载激光扫描 (ALS) 和多光谱遥感数据在帮助生成大面积森林地区的改进风灾风险地图方面的潜力。本文概述了生成此类地图的框架，主要利用 ALS 数据中包含的水平结构信息。验证是在芬兰东部经历过零星风害的地区进行的。风是芬兰森林最突出的干扰因素。因此，需要工具来生成大面积森林地区的风害风险图，以及它们在计划造林作业下可能发生的变化。(1) 基于 ALS 的森林变量（例如到逆风林分边缘的距离，间隙大小）用于识别高风害风险区域？(2) 能否从这些变量中推导出对树木连根拔起的预测临界风速的稳健估计？(3) 是否可以使用风乘法器来改进这些临界风速估计值，这会影响地形和地形粗糙度的影响吗？我们首先概述了一个框架，用于从遥感数据（ALS + 多光谱）生成几个与风害风险相关的参数。然后，我们评估这些参数是否具有预测能力。也就是说，它们是否有助于区分受损点和背景点。该验证练习使用了大面积分布的 42 个风损点。来自遥感数据的参数显示具有预测能力。基于临界风速的风险模型并不那么可靠，但显示出改进的潜力。全面的，这项工作描述了一个框架，可以从遥感数据中获取几个与风风险相关的参数。这些参数被证明具有在大片森林地区生成风害风险图的潜力。