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A Gis-Based Approach For Long˗term Prediction Of Landslide; A Case Study Of Three States In Se. Nigeria

Author

Listed:
  • R. O. E. Ulakpa

    (Department of Geography and Meteorology, Faculty of Environmental Science, Enugu State University of Science and Technology, Enugu State, Nigeria)

  • V.U.D. Okwu

    (Department of Geography and Meteorology, Faculty of Environmental Science, Enugu State University of Science and Technology, Enugu State, Nigeria)

  • K. E Chukwu

    (Department of Geography and Meteorology, Faculty of Environmental Science, Enugu State University of Science and Technology, Enugu State, Nigeria)

  • N. T. Iwueke

    (Department of Geography and Meteorology, Faculty of Environmental Science, Enugu State University of Science and Technology, Enugu State, Nigeria)

Abstract

his article emphasizes past occurrence of landslide and prediction of landslide across three selected in southeastern Nigeria using remote sensing and Digital Elevation Map (DEM).Images for this study was downloaded by using remote sensing with Landsat 8 ETM and aerial photos using ArcGIS 10.7 and Surfer 8 software. The study showed occurrence of landslide from 1970 to 2019 and predict landside from 2019 to 2050 with emphasis on area where landslide has occurred and is currently occurring. Findings revealed that Nsukka is more in the very high range, next is Oji – River and Udi is said to be the least in terms of landslide occurrences. This is followed by Anambra state, in Anambra it was observed that Orumba North falls between high, medium, low to very low range, Anaocha falls between the very high, high, medium to low range while Aguata falls between the very high, high, medium to low range. Classification according to susceptibility zone indicates that Aguata is more in the veryhigh range, next is Anaocha and least is Orumba North. In Imo state, it was also observed that Ideato North falls between the high, medium to low, Ideato South also falls between the high, medium to low range while Orlu falls between medium, low to very low range.

Suggested Citation

  • R. O. E. Ulakpa & V.U.D. Okwu & K. E Chukwu & N. T. Iwueke, 2020. "A Gis-Based Approach For Long˗term Prediction Of Landslide; A Case Study Of Three States In Se. Nigeria," Environmental Contaminants Reviews (ECR), Zibeline International Publishing, vol. 3(2), pages 66-70, March.
    • Handle: RePEc:zib:zbnecr:v:3:y:2020:i:2:p:66-70
    DOI: 10.26480/ecr.02.2020.66.70
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