Integrating spatial and legacy data to understand archaeological sites in their landscape. A case study from Unguja Ukuu, Zanzibar.

Spatial analysis is paramount for understanding, monitoring, and conserving ancient settlements and cultural landscapes. Advancing remote sensing and prospection techniques are expanding the methodological frame of archaeological settlement analysis by enabling remote, landscape‐scale approaches to mapping and investigation. Whilst particularly effective in arid lands and areas with sparse or open ground cover, such as vegetation and buildings, these approaches remain peripheral in tropical environments because of technical and contextual challenges. In tropical Eastern Africa, for example, scales, resolution and visibility are often compromised by thick vegetation cover, inadequate access to, if not lack of, imagery resources and technologies, and the availability of comparative archaeological data for interpretation. This paper presents the initial results of spatial analysis, using historic landscape characterisation, remote sensing, published and legacy data, and a pilot ground survey to examine the earliest settlement of Zanzibar, Unguja Ukuu. Comparing multiple strands of evidence in a Geographic Information System (GIS), we use each as a test on the others to draw out the strengths and weaknesses of each technique in the context of tropical and coastal Eastern Africa. Drone photogrammetry, geophysical prospection, and ground survey were compared with legacy remote sensing resources and the results of a coring survey conducted across the site during the 1990s into a GIS platform to produce multi‐phase hypothetical maps of the archaeological site in the context of its potential resource landscape. These were then tested against the results of recent excavations. The discussion highlights the challenges and potential of combining these techniques in the context of Eastern Africa and provides some suggested methods for doing so. We show that remote sensing techniques give an insight into current landscapes but are less useful in understanding or modelling how sites would have fitted into their surroundings in the past, when conditions were potentially very different. [ABSTRACT FROM AUTHOR]