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Assessing visibility and geomorphological biases in regional field surveys: The case of Roman Aesernia
Author(s) -
Casarotto Anita,
Stek Tesse D.,
Pelgrom Jeremia,
Otterloo Ruud H.,
Sevink Jan
Publication year - 2017
Publication title -
geoarchaeology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.696
H-Index - 44
eISSN - 1520-6548
pISSN - 0883-6353
DOI - 10.1002/gea.21627
Subject(s) - visibility , field (mathematics) , settlement (finance) , interpretation (philosophy) , archaeology , scale (ratio) , vegetation (pathology) , survey data collection , remote sensing , geography , geology , physical geography , cartography , environmental resource management , computer science , environmental science , statistics , medicine , mathematics , pathology , meteorology , world wide web , pure mathematics , payment , programming language
Archaeological field survey data can be biased by many factors, such as ground visibility conditions (e.g. vegetation, plowing) and geomorphological processes (erosion, deposition). Both visibility and geomorphological factors need, therefore, to be assessed when patterns of settlement and location preferences are inferred from survey data. Although both factors have been taken into account in a variety of fieldwork projects and studies, their combined effects remain hard to predict. In this paper, we aim to address this issue by presenting a visualization method that helps in evaluating in combination the possible visibility and geomorphological effects in regional, site‐oriented field surveys. Capitalizing on first‐hand data on both archaeology and soil types produced by the recent Leiden University field survey project in the area of Isernia (Roman Aesernia, Central‐Southern Italy), we propose a combined application of statistical tests and geo‐pedological analysis to assess the extent and scale of the main biases possibly affecting the interpretation of the ancient settlement organization. Translating both sets of biases into GIS maps, we indicate the likelihood that negative field survey observations (absence of sites), in specific parts of the landscape, are genuine or rather distorted by biasing factors. The resulting “archaeological detectability” maps allow researchers to formally highlight critical surveyed zones where the recording of evidence is likely unreliable, and thus provide a filter through which archaeologists can calibrate their interpretations of field survey datasets.