Finding karstic caves and rockshelters in the Inner Asian mountain corridor using predictive modelling and field survey

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Authors

Patrick Cuthbertson, Tobias Ullman, Christian Büdel, Aristeidis Varis, Abay Namen, Reimar Seltmann, Denné Reed, Zhaken Taimagambetov, Radu Iovita 

Abstract

The area of the Inner Asian Mountain Corridor (IAMC) follows the foothills and piedmont zones around the northern limits of Asia’s interior mountains, connecting two important areas for human evolution: the Fergana valley and the Siberian Altai. Prior research has suggested the IAMC may have provided an area of connected refugia from harsh climates during the Pleistocene. To date, this region contains very few secure, dateable Pleistocene sites, but its widely available carbonate deposits present an opportunity for discovering cave sites, which generally preserve longer sequences and organic remains. Here we present two models for predicting karstic cave and rockshelter features in the Kazakh portion of the IAMC. The 2018 model used a combination of lithological data and unsupervised landform classification, while the 2019 model used feature locations from the results of our 2017-2018 field surveys in a supervised classification using a minimum-distance classifier and morphometric features derived from the ASTER digital elevation model (DEM). We present the results of two seasons of survey using two iterations of the karstic cave models (2018 and 2019), and evaluate their performance during survey. In total, we identified 96 cave and rockshelter features from 2017-2019. We conclude that this model-led approach significantly reduces the target area for foot survey.

DOI

https://doi.org/10.31223/osf.io/vz3b5

Subjects

Geographic Information Sciences, Geography, Physical and Environmental Geography, Social and Behavioral Sciences, Spatial Science

Keywords

GIS, digital elevation model, field survey, geographic information systems, karst, landform classification, supervised classification, unsupervised classification

Dates

Published: 2020-07-10 08:14

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License

CC BY Attribution 4.0 International

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