Open AccessLANDSLIDE SUSCEPTIBILITY MODELLING IN SELECTED STATES ACROSS SE. NIGERIA
Author(s) -
Ruth Oghenerukevwe Eyankware Ulakpa,
V.U.D. Okwu,
Kevin Ejike Chukwu,
Moses Oghenenyoreme Eyankware
Publication year - 2020
Publication title -
environment and ecosystem science
Language(s) - English
Resource type - Journals
eISSN - 2521-0882
pISSN - 2521-0483
DOI - 10.26480/ees.01.2020.23.27
Subject(s) - landslide , lineament , digital elevation model , geology , remote sensing , vegetation (pathology) , elevation (ballistics) , landslide classification , drainage , cartography , geomorphology , geography , seismology , tectonics , medicine , ecology , geometry , mathematics , pathology , biology
Identification and mapping of landslide is essential for landslide risk and hazard assessment. This paper gives information on the uses of landsat imagery for mapping landslide areas ranging in size from safe area to highly prone areas. Landslide mitigation largely depends on the understanding of the nature of the factors namely: slope, soil type, lineament, lineament density, elevation, rainfall and vegetation. These factors have direct bearing on the occurrence of landslide. Identification of these factors is of paramount importance in setting out appropriate and strategic landslides control measures. 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, while Digital Elevation Model (DEM) and Google EarthPro TM were used to produce slope, drainage, lineament and elevation. From the processed landsat 8 imagery, landslide susceptibility map was produced, and landslide was category into various class; low, medium and high. From the study, it was observed that Enugu and Anambra state ranges from high to medium in terms of landslide susceptibility, Imo state ranges from medium to low.