Abstract Forest canopies act as intermediaries in radiation energy exchange between the atmosphere and the snow surface. The size, location and distribution of forest discontinuities are important controls on forest shortwave radiation transmission and subsequent snow surface shading and radiation energy exchange between the atmosphere and the canopy, but challenges arise when accounting for these vegetation characteristics at large spatial scales. Airborne LiDAR datasets contain detailed information about canopy structure across large spatial scales which can be exploited within 2D transmission models. However, airborne LiDAR data typically does not resolve lower canopy elements, leading to unrealistic depictions of individual trees. We present a methodology to enhance airborne LiDAR data by calculating additional trunk and branch points based on segmentation of a canopy height model, allowing more accurate estimates of canopy shortwave transmissivity. To demonstrate this, we deployed a computationally efficient 2D radiation transfer modelling framework that calculates direct and diffuse radiation from a set of distributed synthetic hemispherical images. The model can predict incoming direct and diffuse solar radiation at the snow surface at high spatial (meter-scale) and temporal (minute-scale) resolutions. Comparison between synthetic and real hemispherical photographs showed that synthetic images, if based on enhanced LiDAR data, featured canopy and individual tree crowns that were much denser than the original LiDAR portrays, improving the representation of vegetation structure especially within dense environments and along canopy edges. Corresponding modelled total shortwave radiation matched well with spatially gridded measurements from a moving pyranometer at two sites, where model RMSE was reduced to 59 and 29 W m−2 from 181 and 138 W m−2, respectively, compared to the same transmission model with the original LiDAR data. Maps of snow surface shading patterns corresponded well to those seen in aerial photographs, showing the enhanced LiDAR data can be used to solve complex spatiotemporal patterns of sub-canopy incoming radiation. This work demonstrates that canopy structure information from the lower canopy is an important aspect for accurate radiation transfer modelling, and methods presented here can successfully mitigate problems inherent in many airborne LiDAR datasets to improve spatially distributed estimates of sub-canopy shortwave radiation.

中文翻译：

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增强机载 LiDAR 数据以改进短波传输模型中的森林结构表示

摘要 森林冠层作为大气和雪面​​之间辐射能量交换的中介。森林间断的大小、位置和分布是森林短波辐射传输以及随后的雪面遮蔽和大气与冠层之间辐射能量交换的重要控制因素，但在大空间尺度上考虑这些植被特征时会出现挑战。机载 LiDAR 数据集包含有关大空间尺度的冠层结构的详细信息，可在 2D 传输模型中加以利用。然而，机载 LiDAR 数据通常无法解析较低的树冠元素，导致对单个树木的描绘不切实际。我们提出了一种方法，通过基于冠层高度模型的分割计算额外的主干和分支点来增强机载 LiDAR 数据，从而更准确地估计冠层短波透射率。为了证明这一点，我们部署了一个计算效率高的 2D 辐射传输建模框架，该框架从一组分布式合成半球图像计算直接和漫射辐射。该模型可以在高空间（米尺度）和时间（分钟尺度）分辨率下预测雪表面的入射直接和漫射太阳辐射。合成和真实半球照片之间的比较表明，如果基于增强的 LiDAR 数据，合成图像的特征是树冠和单个树冠比原始 LiDAR 描绘的要密集得多，改善植被结构的表现，尤其是在密集环境和冠层边缘。相应的模拟总短波辐射与来自两个站点的移动总辐射表的空间网格测量结果相匹配，与相同的传输模型相比，模型 RMSE 分别从 181 和 138 W m-2 降低到 59 W m-2 和 29 W m-2原始 LiDAR 数据。雪面阴影模式的地图与航空照片中看到的情况非常吻合，表明增强的 LiDAR 数据可用于解决次冠层入射辐射的复杂时空模式。这项工作表明，来自下层冠层的冠层结构信息是准确辐射传输建模的一个重要方面，