Reconstructing the protohistoric landscape of Trieste Karst (north-eastern Italy) through airborne LiDAR remote sensing

Highlights

• LiDAR-derived images allowed to identify unknown archaeological features.

• Combined techniques were applied to the study of past Karst landscape.

• Gis-based spatial analyses allowed to evaluate possible relationships among sites.

• Results suggest the existence of small-scale clusters of protohistoric sites.

Abstract

The archaeological landscape of Trieste Karst (north-eastern Italy) is rich in protohistoric hill-forts built from the Early/Middle Bronze Age to the Late Iron Age. Airborne LiDAR data have been analysed for the archaeological prospection of a central Karst sector and several spatial analyses have been carried out. The high-resolution LiDAR-derived images allowed to identify several previously unknown fortified structures, ranging from small lookouts to large settlements within a complex landscape including enclosures, ancient clearance stone cairns and field divisions. Our results allowed to recognize small-scale clusters of protohistoric sites visually interconnected and arranged around a central large site. These territorial entities, considerably smaller than those previously proposed by other scholars for the Karst Bronze Age, probably had a role in the strategic control over routes leading from the coast to the inner Karst across the central Karst ridge. According to available chronological data, the formation and occupation of such cultural landscape could be tentatively dated to the Bronze Age.

Introduction

Airborne LiDAR remote sensing has changed the archeological prospecting of emerging features, even in forested environments, allowing the recognition and mapping of archeological evidence, from small and ephemeral structures, often not recognizable from the ground, to large sites. The relation between large settlements, settlements and minor sites, settlements and the surrounding territory and infrastructures can be identified and measured better than before (Holden et al., 2002, Bewley, 2003, Crutchley, 2006, Challis et al., 2008, Chase et al., 2012). However, the possibility to collect a huge amount of information about the past landscape in a relatively short time poses a number of methodological questions. In particular, LiDAR-derived images show a complex palimpsest of overlapping structures interpenetrated with natural landforms on a geological background which is often difficult to interpret.

Applications of LiDAR to Trieste Karst (north-eastern Italy) has shown its efficacy in an environment that is conservative due to relatively low sedimentation and dissolution/erosion rates (Bernardini et al., 2013) as well as to the relatively low human impact, leading to the discovery and description of several previously undetected fortified sites. These include small outposts and enclosures (Bernardini et al., 2013) as well as a group of early Roman military fortifications (Bernardini et al., 2015, Bernardini and Vinci, 2016).

In this paper we will focus on the central sector of the Trieste Karst plateau. The physical environment features Upper Cretaceous limestone outcropping at an average altitude of about 300 m asl with several peaks reaching more than 500 m asl and a high density of dolines (Cucchi and Piano, 2013, Jurkovšek et al., 2016). In cultural terms, past archaeological studies on the Bronze Age Karst landscape have recognized this area as the western sector of a territorial entity dominated by the hill-fort of Monrupino surrounded by those controlled by the hill-forts of Contovello, S. Leonardo, Tomaj and Povir (Novaković, 2005). In addition, the good number of excavated hill-forts, greater than in most other adjacent Karst areas, and the presence of fairly well preserved other types of structures, only preliminary described (Bernardini et al., 2013), make the examined area particularly suitable to a detailed analysis of the protohistoric landscape and of small-scale settlement patterns (Fig. 1). For these purposes, we first carried out an extensive study that integrates LiDAR remote sensing with other sources of information such as excavations data, historical maps, aerial photographs and systematic surface survey. We then performed several GIS-based spatial analyses, some of them carried out in a probabilistic framework.