Sonnemann, T.F.; Comer, D.C.; Patsolic, J.L.; Megarry, W.P.; Herrera Malatesta, E.; Hofman, C.L. Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data. Geosciences2017, 7, 127.
Sonnemann, T.F.; Comer, D.C.; Patsolic, J.L.; Megarry, W.P.; Herrera Malatesta, E.; Hofman, C.L. Semi-Automatic Detection of Indigenous Settlement Features on Hispaniola through Remote Sensing Data. Geosciences 2017, 7, 127.
Abstract
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, which can be detected by analysis of remote sensing imagery. A variety of data sets were investigated, with the intention to combine multispectral bands to feed a direct detection algorithm, providing a semi-automatic process to cross-correlate the datasets. Sampling was done using locations of known sites, as well as areas with no archaeological evidence. The pre-processed very diverse remote sensing data sets have gone through a process of image registration. The algorithm was applied in the northwestern Dominican Republic on areas that included different types of environments, chosen for having sufficient imagery coverage, and a representative number of known locations of indigenous sites. The resulting maps present quantifiable statistical results of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. The results show the variable potential of this method in diverse environments.
Keywords
Remote sensing; direct detection; GIS mapping; Caribbean Archaeology; landscape archaeology
Subject
ARTS & HUMANITIES, Archaeology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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