Introduction to LiDAR in Geoarchaeology from a Technological Perspective

Abstract

LiDAR is a remote sensing method established in the geosciences for capturing highly accurate three-dimensional geodata. It is increasingly used to support geoarchaeological research due to a range of advantages, including survey-grade data quality, real 3D geodata, nonselective coverage of scenes with high measurement density, on-demand data capturing, and comprehensive filtering options based on geometric and radiometric information.

Different LiDAR measurement principles are used to derive 3D geodata, e.g., time-of-flight, phase shift, and structured light. The captured datasets include real 3D XYZ coordinates (point clouds). Depending on the sensor system, also radiometric information is gathered for each point, e.g., RGB, strength, and full waveform of the backscattered signal.

The processing of point clouds in general follows a similar workflow. After data acquisition, the point clouds are registered. Depending on available radiometric information and research question, the data is radiometrically calibrated. After removing outliers, the point cloud is filtered according to the requirements of the study, and derivative products (e.g., DTMs) are generated (Chaps. 12 and 13). Finally, geospatial analyses are conducted directly in the final point cloud or derived elevation models (e.g., raster datasets, TINs), and the data can be used for visualization purposes.

With LiDAR, a wide range of spatial scales can be captured: (1) Object scale: single objects and their surfaces can be captured in high detail, allowing for studies, e.g., of engravings. (2) On-site scale: specific areas or objects like excavation sites or building complexes are surveyed and documented with static or dynamic scanners applied on-site (TLS, ULS, etc.). Such datasets covering sites of tens or several hundreds of meters are used in studies which examine the local spatial relations of objects and their immediate surroundings (see also Chap. 13). (3) Off-site scale: on a spatial scale covering whole regions, off-site scanning, mainly from an airborne platform, is applied.

However, the LiDAR method has some shortcomings, e.g., high costs for equipment, trained personnel, and processing the large amounts of data. Here, low-cost approaches can offer complementary data sources.

The described workflow leads to important implications for DGA regarding the filtering and calculation of derivatives. There is no universal filter but only tailored filters, depending on the available data and the aim of the study. Furthermore, the original, raw point cloud should always be available to enable subsequent analyses with new methods or different aims and objects of interest.

Emerging fields offering new possibilities for LiDAR in DGA are, e.g., multi-platform and multi-sensor approaches, the combination of spatial scales, multi-wavelength devices, multi-temporal datasets, and refined filtering based on radiometric information, e.g., full waveform.