Soil moisture index estimation from Landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

• Main Authors: Adnan, N.A (Author), Ali, D.M (Author), Tajudin, N. (Author), Ya’acob, N. (Author)
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2021
• Series: International Journal of Electrical and Computer Engineering
• Subjects: L Remote sensing; Landsat 8; Landslide; Rainfal; Soil moisture index (SMI)
• Online Access: View Fulltext in Publisher; View in Scopus

 Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events due to the increase in pore pressure which eventually reduces the soil strength. For landslide prediction and monitoring purposes, large-scale measurement involves estimating the soil moisture. However, estimation of soil moisture usually involves point-based measurements at a particular site and time, which is difficult to capture the spatial and temporal soil moisture dynamics. This paper presents the estimation of the SMI using Landsat 8 images for prediction and monitoring of landslide events in Ulu Kelang, Selangor. The selected SMI map for dry, moist, and wet seasons are obtained from climatology rainfall analysis over 20-year periods (1998-2017). SMI is assessed based on remote sensing data which are land surface temperature (LST) and normalized difference vegetation index (NDVI) using GIS software. Overall results indicated that rainfall distribution is high during inter-monsoon (IM), followed by northeast monsoon (NEM) and southwest monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. Results from simulation show that April 2017 is known to have the highest SMI estimation season and selected to be the best SMI mapping parameter to be applied for prediction and monitoring of landslide events. © 2021 Institute of Advanced Engineering and Science. All rights reserved.