A new landslide detection approach using the combination of the normalized difference moisture index and slope classification analysis

Landslides are the most common natural disasters that are often found in tropical and mountainous areas. This study aimed to identify the potency of the combination of the normalized difference moisture index (NDMI) and slope analysis in the Cameron Highlands region of Pahang State, Malaysia. Thus, four years (2014, 2017, 2020, and 2022) of time series analysis of Landsat 8 images were applied to generate NDMI, normalized difference vegetation index (NDVI), and land surface temperature (LST) for landslide detection. The extraction of NDMI and slope values was carried out in a GIS environment. Finally, the accuracy of the model was measured using the kappa statistic based on the ground truth data. The results revealed that landslides were detected in around 2,234 ha of the study area. Most of the landslides occurred in the northern and western parts, which corresponded with steep slopes (>55%) and low NDMI (<0.1). The LST had a notable impact on slope stability, where landslide areas tended to have a high temperature. There was an increase in LST of about 3°C in the study period. The change from forest areas to built-up areas was one of the contributors to the land temperature increasing in the Cameron Highlands area.