Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data

Faculty/Professorship: Digital Geoarchaelogy  
Author(s): Comer, Douglas; Patsolic, Jesse; Megarry, William; Herrera Malatesta, Eduardo; Hofman, Corinne L.; Sonnemann, Till  
Title of the Journal: Geosciences
Corporate Body: MDPI
Publisher Information: Basel
Year of publication: 2017
Volume: 7
Issue: 4 (127)
Pages: 15
Language(s): English
DOI: 10.3390/geosciences7040127
Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, detectable by analyzing remote sensing imagery. A variety of pre-processed, very diverse data sets went through a process of image registration, with the intention to combine multispectral bands to feed two different semi-automatic direct detection algorithms: a posterior probability, and a frequentist approach. Two 5 × 5 km2 areas in the northwestern Dominican Republic with diverse environments, having sufficient imagery coverage, and a representative number of known indigenous site locations, served each for one approach. Buffers around the locations of known sites, as well as areas with no likely archaeological evidence were used as samples. The resulting maps offer quantifiable statistical outcomes of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. These still very experimental and rather unvalidated trials, as they have not been subsequently groundtruthed, show variable potential of this method in diverse environments.
Keywords: remote sensing, direct detection, GIS mapping, Caribbean archaeology, landscape archaeology
Peer Reviewed: Ja
International Distribution: Ja
Open Access Journal: Ja
Type: Article
Year of publication: 21. December 2017