Ralf HESSE
(State Office for Cultural Heritage Baden-Württemberg, Germany)

Outline:

Analysis of the first results of the project “LIDAR prospection of archaeological sites in Baden-Württemberg” show distinct spatial patterns.

Abstract:

Since May 2009, the State Office for Cultural Heritage Baden-Württemberg runs a project aimed at the complete archaeological prospection of the federal state Baden-Württemberg using high-resolution airborne LIDAR (light detection and ranging) data, i.e. an area of 35 751 km2. The goal of this project is the verification and extension of the existing archaeological data base. A methodology for the extraction of local relief anomalies as well as tools and workflows for data management and processing were developed and implemented in order to allow an efficient handling of the enormous amounts of data.

By May 2010, data processing had been completed and the prospection of approximately 4000 km2 had already yielded more than 60 000 potential sites. This compares to approximately 6000 previously known sites and find spots in the same area. Most of the identified features can be related to resource extraction and production (e.g., agricultural terraces, ridge and furrow, kiln podia, mining and quarrying sites); others provide information on transport (sunken roads), defence (ditch and rampart) or funeral practices (burial mounds).

All features exhibit distinct spatial patterns which allow further conclusions e.g. regarding resource use and settlement patterns. Here, the spatial patterns of selected classes of archaeological features will be analysed. This includes topographic parameters (elevation, slope, aspect) as well as interrelationships (e.g. clustering). Such information regarding the distribution of archaeological features is a crucial prerequisite for predictive modelling. All steps from LIDAR data acquisition and processing to spatial analysis and predictive modelling rely heavily on efficient digital technology.

Keywords:

LIDAR, archaeological prospection, spatial patterns