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Prediction and Verification of Distribution Potential of the Debris Landforms in the Southwest Region of the Korean Peninsula

한반도 서남부 암설사면지형의 분포가능성 예측 및 검증

  • Lee, Seong-Ho (Department of Geography, Kongju National University) ;
  • Jang, Dong-Ho (Department of Geography, Kongju National University)
  • Received : 2020.06.09
  • Accepted : 2020.06.29
  • Published : 2020.06.30

Abstract

This study evaluated a debris landform distribution potential area map in the southwest region of the Korean peninsula. A GIS spatial integration technique and logistic regression method were used to produce a distribution potential area map. Seven topographic and environmental factors were considered for analysis and 28 different data set were combined and used to get most effective results. Moreover, in an accuracy assessment, the extracted results of the Distribution Potential area were evaluated by conducting a cross-validation module. Block stream showed the highest accuracy in the combination No. 6, and that DEM (digital elevation model) and TWI (topographic wetness index) have relatively high influences on the production of the Block stream Distribution Potential area map. Talus showed the highest accuracy in the combination No. 13. We also found that slope, TWI and geology have relatively high influences on the production of the Talus Distribution Potential area map. In addition, fieldwork confirmed the accuracy of the input data that were used in this study, and the slope and geology were also similar. It was also determined that these input data were relatively accurate. In the case of angularity, the block stream was composed of sub-rounded and sub-angular systems and Talus showed differences according to the terrain formation. Although the results of the rebound strain measurement using a Schmidt's hammer did not shown any difference in topographic conditions, it is determined that the rebound strain results reflected the underlying geological setting.

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Acknowledgement

본 연구는 2017년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2017S1A5A2A01026274).