A landslide encompasses a wide range of mass movements and can be defined as the downslope movement of soil, rock, or earth (Highland & Bobrowsky, 2008). The movements can be triggered by various external activities such as intense rainfall, earthquakes, changes in water level, waves, or stream erosion, which lead to a decrease in shear strength and an increase in shear stress on the slope (Dai et al. 2002). Landslides can also be caused by anthropogenic activities such as excavation, road construction, and land use changes. These factors often induce small, shallow landslides, but abrupt changes to the slope surface such as poor construction and planning can result in larger, more dangerous landslides (Jaboyedoff et al. 2016). Watersheds that have been recently affected by wildfires can be highly susceptible to rainfall-triggered landslides that usually occur within a short time following the burn (Degraff et al. 2015; Kean et al. 2011). This study focuses on rainfall-triggered landslides as they are the most frequent and cause loss of life and destruction of property across the globe (Froude & Petley 2018). Rapid urbanization can increase the risk for landslides, especially along poorly constructed roads and deforested areas in mountainous regions (Forbes et al. 2012). However, better land management of forests and cultivated areas can produce a decrease in rainfall-triggered landslide susceptibility (Pisano et al. 2017).
In the Lower Mekong River Basin (LMRB) in Southeast Asia, the monsoon season brings an increase in flood and landslide disasters due to an increase in rainfall from large storms in combination with the complex topography of the region. The LMRB has experienced extensive changes from urban and agricultural expansion, deforestation, river damming, and natural disasters such as flood and drought. In this region, changes in Land Use and Land Cover (LULC) are largely influenced by agricultural prices, road accessibility, construction projects, and climate change (Spruce et al. 2020). Spruce et al. (2020) assessed LULC changes in the Lower Mekong using two maps from 1997 to 2010. In their analysis, 2.5% of the total area of permanent agriculture decreased which could be associated with the abandonment of crops or converting cropland to forests. They also identified an 6.7% increase of the total area categorized by scrub/shrub/herbaceous which could be attributed to abandoned cropland reverting back to forest. Looking at the changes between LULC classes, some cropland had changed to deciduous forest/scrub over time between 1997 and 2010. Changes in land cover can have variable impacts on landslide susceptibility. In some cases, human impacts such as deforestation and mining serve to exacerbate instability on slopes (Winter et al. 2010). In other examples, thoughtful engineering and planning can serve to stabilize slopes (Prastica et al. 2019; Yan et al. 2019).
The preconditions for landslides vary, but changes in land use and land cover (LULC) have been shown to have local impacts (e.g., Glade 2003; Hewawasam 2010; Mugagga et al. 2012; Reichenbach et al. 2014). Pisano et al. (2017) evaluated how land cover change affects slope stability over time in the Italian Southern Apennine Mountains by treating land cover as a dynamic variable, unlike many other landslides susceptibility studies that consider land cover as a static variable. This study found that a decrease in forest and cultivated land and increase in barren, pasture, and shrub land led to an increase in landslide susceptibility. A study by Persichillo et al. (2017) assessed shallow landslides in the northern Apennine Mountains in Italy in areas with land abandonment and changes in land management practices from 1954 to 2012. They found that cultivated lands that were abandoned and allowed to gradually recover naturally was the land cover change scenario most susceptible to landslides and land cover was the most predisposing factor in all study areas. Similarly, Deng et al. (2018) investigated landslide distribution and agricultural abandonment in several provinces in China’s mountainous areas. They concluded that more landslides occurred in areas with high incidence of agricultural abandonment. Furthermore, the effects of land use changes on landslides were analyzed in a landslide-prone region in Northeast Turkey with mountainous topography and high rainfall frequency by Karsli et al. (2009). Land cover changes and landslides were identified using aerial images taken in 1973 and 2002. Their results indicated that the land cover type played an important role in landslide occurrence as 95% of the landslides identified from the imagery were in areas with acidic soil weakened by fertilizer use in agriculture.
Frequency Ratio (FR) analysis is a common method used to assess the relationship between susceptibility and the occurrence of a landslide event (Gariano et al. 2018; Pourghasemi et al. 2013). A study by Silalahi et al. (2019) used GIS mapping and FR analysis to assess the effects of contributing factors on landslides in Bogor, Indonesia. Their results indicated land cover as one of the most important factors contributing to landslides as well as lithology and soil type in this area. Additionally, Khan et al. (2019) used the FR to create a landslide susceptibility index which was used to produce a susceptibility map for northern Pakistan. Their study found barren land and irrigated agricultural land to have the highest FR values of the land cover classifications, however distance to roads was found to have the highest overall FR value. These and other studies use FR to assess conditioning factors and create susceptibility maps but however, rarely incorporate land cover as a dynamic variable. Additionally, statistical methods like Logistic Regression (LR) are effective in identifying input variable importance/significance and several studies have considered LULC within this framework (e.g., Reichenbach et al. 2018; Lee & Sambath 2006; Shahabi et al. 2014; Bai et al. 2010; Das & Lepcha 2019; Bornaetxea et al. 2018). LR has been an effective tool for developing landslide susceptibility maps and highlighting the significance of contributing variables however, few studies have considered how LULC can be considered dynamically in these models to explain changes over time in a region. Hemasinghe et al. (2018) analyzed susceptibility in mountainous regions predisposed to landslides in Sri Lanka. Their study examined slope, aspect, lithology, land cover, distance to rivers and roads as predictor variables. Land cover was determined to be the most influential factor in the study area. Known landslide locations were used to validate their susceptibility map, and a majority (76%) of the landslide points were in high and extremely high susceptibility areas. Land cover is similarly used as a static predictor variable in many other susceptibility studies, uniquely this study will treat land cover change as a dynamic variable that changes over time.
The question posed in this study is - how do changes in land use and land cover (LULC) impact landslide susceptibility in the LMRB? We evaluate these interactions using several new landslide event inventories mapped between 2015–2018 that provide information within areas of Vietnam, Myanmar, and Laos (Amatya et al. 2021)(Fig. 1). Additionally, this study closely examines the effects of LULC change on landslide occurrence, a dynamic which is not completely understood in the LMRB (Shu et al. 2019). Our work seeks to understand the relationship of changing LULC over time and how this impacts susceptibility. The relationship between LULC changes and landslide occurrence will be analyzed using Frequency Ratio (FR) analysis and Logistic Regression (LR) modeling. The FR will be used to closely examine the frequency of landslides within the various LULC change scenarios present in each of the landslide inventory locations. This study will use the LR models to compare LULC changes with other contributing factors like slope, forest loss, soil properties described in Table 2. The FR and LR results will be compared to determine any similarities regarding the significance of LULC changes on landslide occurrence. Results of this work are important as population expansion, road development and farming continue to increase (and hence changes in land cover) in the LMRB (Spruce et al. 2018). This work is part of a broader effort to characterize landslide susceptibility, hazard, and exposure within the LMRB for decision making at the country and municipal level using satellite remote sensing products.