The development and availability of capable point cloud software creates a new archaeological forefront in landscape point-cloud data-capture and visualization. Terrestrial scanning via Light Detection and Ranging (LiDAR) can therefore be a worthwhile diagnostic tool for field archaeology if efficient methodologies for overcoming its mechanical, time, and processing limitations can be established. This thesis investigates the viability of terrestrial LiDAR for field archaeological purposes, and outlines a new methodology by which its limitations can be overcome and it can be effectively used for archaeological data capture in harsh environments under time constraints. Building off of the success of the efficient data collection and processing methodologies created to resolve the scanner's limitations, this thesis will then explore areas of archaeology where landscape point clouds and expedient digital documentation might be used (i.e. Rescue LiDAR and temporal scanning), utilizing test case studies in southern Jordan. The integrated laser scanning methodology presented here is intended to solve three problems--one technical, one anthropological, and one educational. In streamlining methodologies, a digital conservation workflow for archaeological and world cultural heritage field sites is established. With the ability to preserve endangered data and return it to the lab, a myriad of anthropologically significant data which would be impossible to discern in the field can be gleaned. In this thesis, various ways in which point cloud models may be subjected to quantitative analysis to study will be indicated. Furthermore, the capacity of point clouds for public archaeology purposes, phenomenological perspective, and educational dissemination will be discussed

The combination of 3D acquisition (terrestrial and airborne LiDAR, structured light, structure-from-motion) and 2D imaging (photographic, multispectral, panoramic, orthorectified, reflectance transformation) techniques allows the geometry, appearance and other aspects of culturally significant sites to be objectively documented. Traditionally, these data are usually transformed into models such as 3D textured meshes before they are visualized or analyzed—an often time- and effortintensive process. We propose a system for the direct visualization and analysis of such data, allowing the different aspects recorded to be layered together, and covisualized with annotations and other relevant information. We describe the required technical foundations, including gigapoint and gigapixel visualization pipelines that enable the dynamic layering of high-resolution imagery over massive minimally-processed LiDAR point clouds that serve as the base spatial layer. In particular, we introduce the pointbuffer—a GPU-resident view-dependent point cache—as the foundation of our gigapoint pipeline, and outline the use of virtual texturing for draping of gigapixel imagery onto point clouds. Finally, we present case studies from sites in Jordan and Italy.