4.6 Article

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

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PLOS ONE
卷 16, 期 1, 页码 -

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PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0245170

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  1. European Research Council (ERC) under the European Union [714842]
  2. European Research Council (ERC) [714842] Funding Source: European Research Council (ERC)

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The study presents two models for predicting karstic cave features in the Inner Asian Mountain Corridor (IAMC) and conducted field surveys from 2017 to 2019, identifying 105 cave and rockshelter features. The model-led approach significantly reduces the target area for foot survey, providing an important tool for archaeological research in the region.
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 units 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 105 cave and rockshelter features from 2017-2019. We conclude that this model-led approach significantly reduces the target area for foot survey.

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