Purpose of the Review

Many LiDAR remote sensing studies over the past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in the final data products. Here, we performed a structured literature review to analyze relevant studies on these topics published in the last decade and the main motivations and applications for fusion, and the methods used. We discuss the findings with a panel of experts and report important lessons, main challenges, and future directions.

Recent Findings

LiDAR fusion with other datasets, including multispectral, hyperspectral, and radar, is found to be useful for a variety of applications in the literature, both at individual tree level and at area level, for tree/crown segmentation, aboveground biomass assessments, canopy height, tree species identification, structural parameters, and fuel load assessments etc. In most cases, gains are achieved in improving the accuracy (e.g. better tree species classifications), and spatial-temporal resolution (e.g. for canopy height). However, questions remain regarding whether the marginal improvements reported in a range of studies are worth the extra investment, specifically from an operational point of view. We also provide a clear definition of “data fusion” to inform the scientific community on data fusion, combination, and integration.

Summary

This review provides a positive outlook for LiDAR fusion applications in the decade to come, while raising questions about the trade-off between benefits versus the time and effort needed for collecting and combining multiple datasets.

中文翻译：

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用于改进森林属性估计的 LiDAR 数据融合：综述

 审查的目的

过去十年的许多激光雷达遥感研究都承诺数据融合是提高最终数据产品的准确性、时空分辨率和信息提取的潜在途径。在这里，我们进行了结构化文献综述，分析了过去十年发表的这些主题的相关研究以及融合的主要动机和应用以及所使用的方法。我们与专家小组讨论研究结果，并报告重要的教训、主要挑战和未来的方向。

 最近的发现

LiDAR 与其他数据集（包括多光谱、高光谱和雷达）的融合被发现可用于文献中的各种应用，无论是在单树水平还是在区域水平，用于树木/树冠分割、地上生物量评估、树冠高度、树种识别、结构参数和燃料负荷评估等。在大多数情况下，可以通过提高准确性（例如更好的树种分类）和时空分辨率（例如树冠高度）来实现收益。然而，关于一系列研究中报告的边际改进是否值得额外投资，特别是从运营角度来看，仍然存在疑问。我们还提供了“数据融合”的明确定义，以便科学界了解数据融合、组合和集成。

 概括

这篇评论为未来十年的激光雷达融合应用提供了积极的前景，同时提出了关于收益与收集和组合多个数据集所需的时间和精力之间的权衡的问题。