Data Gap Classification for Terrestrial Laser Scanning-Derived Digital Elevation Models

Matthew S O’Banion,William C Wright,Jeff P Hollenbeck,Michael J Olsen

doi:10.3390/ijgi9120749

Abstract

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.

Highlights

Data gaps/voids are a common occurrence that plague remote sensing data including terrestrial laser scanning (TLS) 3D point cloud data
Results of the data gap classification (Figure 12 and Table 2) indicated that for digital elevation models (DEMs) RR1, ~61% of the site was occupied by elevation data (TLS returns), ~36% was occupied by dropouts, but only ~2.6%
For DEM RR2, ~72% of the site was occupied by TLS returns, ~25% was occupied by dropouts, and we saw a similar but slightly lower relative percentage of occlusions at around ~2.4%

Summary

Introduction

Data gaps/voids (i.e., the absence of data) are a common occurrence that plague remote sensing data including terrestrial laser scanning (TLS) 3D point cloud data. TLS point cloud data gaps can have an adverse effect on subsequent point cloud-derived products, including digital surface models (DSMs), bare-earth digital elevation models (DEMs), triangulated surface meshes, and 3D solid models, among others. A point cloud data gap of significant size and extent is unable to provide geometric or radiometric information to the chosen spatially continuous product; assumptions must be made to span the data gap, which inherently adds uncertainty to the derived product. TLS data gaps stem from two primary sources (Figure 1): A line-of-sight obstacle resulting in an occlusion, and a dropout [1] resulting from a specular reflective or absorbent surface preventing the ISPRS Int. J.

Methods

Results

Conclusion