Vegetation Recovery, Susceptibility Mapping, and Modeling of Landslides Using Remote Sensing and GIS

Abstract

Landslides may significantly damage communities and ecosystems. This study integrated remote sensing, field data collection, and Geographic Information System (GIS) techniques to map and model landslide susceptibility and vegetation recovery patterns. Remote sensing is the method of acquiring information about an object or activity without physically interconnecting can be referred to as remote sensing. Understanding the vegetation recovery process following landslides is crucial for assessing long-term stability and ecological restoration. The study implemented the Analytical Hierarchy Process (AHP) to assess the landslide susceptibility of different Agro-ecological regions in Sri Lanka, by analyzing data of 17 landslides occurred in 2014, 2016, and 2017. Alupathagala, Wanniwatta, Athwalthota, Niggaha, Digana, Ambalanpitiya, Hali-Ela, Meeriyabedda, Walapane, Aranayaka, Ambalakanda, and Ratanapura were among the places classified as having a very high risk of landslide susceptibility. Kobewela, Rilpola, and Imbulpe displayed a high risk and moderate risk, respectively Diddeniya Pahala and Bopetta were listed as having a moderate risk. Different levels of risk were caused by soil type, slope, aspect, rainfall, lithology, land use, and distance to streams and roads. The study evaluated recovery times, highlighting the distinctive patterns in different Agro-ecological zones. In the majority of instances, it took three to five years for the vegetative cover to return to pre-landslide values. However, the vegetation has not yet recovered in Alupathagala, Hailiela, or Imbulpe. The study revealed that vegetation cover changes in landslides are not only depend on the Agro-ecological zone, but also involve complex interactions within the same zone. In conclusion it can be stated, it is important to understand the complex interactions and causative factors that lead to vegetation recovery and risk of landslides.