Soil moisture index (SMI) estimation from landsat 8 images for prediction and monitoring landslide occurrences in Ulu Kelang, Selangor, Malaysia

Noraisyah Tajudin, Norsuzila Ya'acob, Darmawaty Mohd Ali, Nor Aizam Adnan

Abstract


Soil moisture is one of the contributing factors that accelerates soil erosion and landslide events. This is 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. Remote sensing techniques present possibilities for providing distributed soil moisture data at different scales and varying temporal resolutions to overcome that limitation. 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 the normalized difference vegetation index (NDVI) using Geographic Information System (GIS) software. Overall results indicated that rainfall distribution is high during Inter-Monsoon (IM) season followed by Northeast Monsoon (NEM) season and Southwest Monsoon (SWM) season. High rainfall distribution is a direct contributor towards SMI condition. The months of February, April, and June 2017 were selected as the case study to analyze the soil moisture condition for all seasons. Results from simulation show that April is known to have the highest SMI estimation season (wet season). Thus, it was selected to be the best SMI mapping parameter to be applied for prediction and monitoring analysis of landslide events in Ulu Kelang, Selangor.

Keywords


landsat 8; landslide; rainfall; remote sensing; soil moisture index (SMI);



DOI: http://doi.org/10.11591/ijece.v11i3.pp%25p
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ISSN 2088-8708, e-ISSN 2722-2578