Extensive gaps in terrestrial laser scanning (TLS) point cloud data can primarily be classified into two categories: occlusions and dropouts. These gaps adversely affect derived products such as 3D surface models and digital elevation models (DEMs), requiring interpolation to produce a spatially continuous surface for many types of analyses. Ultimately, the relative proportion of occlusions in a TLS survey is an indicator of the survey quality. Recognizing that regions of a scanned scene occluded from one scan position are likely visible from another point of view, a prevalence of occlusions can indicate an insufficient number of scans and/or poor scanner placement. Conversely, a prevalence of dropouts is ordinarily not indicative of survey quality, as a scanner operator cannot usually control the presence of specular reflective or absorbent surfaces in a scanned scene. To this end, this manuscript presents a novel methodology to determine data completeness by properly classifying and quantifying the proportion of the site that consists of point returns and the two types of data gaps. Knowledge of the data gap origin can not only facilitate the judgement of TLS survey quality, but it can also identify pooled water when water reflections are the main source of dropouts in a scene, which is important for ecological research, such as habitat modeling. The proposed data gap classification methodology was successfully applied to DEMs for two study sites: (1) A controlled test site established by the authors for the proof of concept of classification of occlusions and dropouts and (2) a rocky intertidal environment (Rabbit Rock) presenting immense challenges to develop a topographic model due to significant tidal fluctuations, pooled water bodies, and rugged terrain generating many occlusions.

中文翻译：

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地面激光扫描衍生的数字高程模型的数据间隙分类

地面激光扫描（TLS）点云数据中的巨大差距主要可以分为两类：遮挡和漏失。这些间隙会对3D曲面模型和数字高程模型（DEM）等派生产品产生不利影响，需要进行插值才能为多种类型的分析生成空间连续的表面。最终，TLS调查中遮挡物的相对比例是调查质量的指标。认识到从一个扫描位置遮挡的扫描场景区域可能从另一角度来看是可见的，因此，遮挡的流行可以表明扫描次数不足和/或扫描仪放置不佳。相反，辍学率通常并不表示调查质量，因为扫描仪操作员通常无法控制被扫描场景中镜面反射或吸收性表面的存在。为此，该手稿提出了一种新颖的方法，可以通过正确分类和量化由点收益和两种数据缺口组成的站点比例来确定数据完整性。了解数据间隙的起源不仅可以帮助评估TLS调查质量，而且还可以在水反射是场景中丢失的主要来源时识别池水，这对于生态研究（例如栖息地建模）很重要。所提出的数据缺口分类方法已成功应用于两个研究地点的DEM：