## 1 Introduction  
Landslides are a type of earth-surface mass wasting driven by the force of gravity [1]. In alpine forest regions, the distribution and development of landslides are related to various factors, such as regional topographic conditions, geological settings, and human activities [2, 3]. In terms of topographic conditions, slope relief controls the gravitational potential energy of slope masses, indicating the ability of landslide formation [4]. Slope degree influences the free surface of slopes, which is associated with slope displacement [5]. Geological conditions including slope material properties and tectonic development are considered as internal controlling factors for landslide formation [6]. Slope material properties, such as rock or soil type, thickness, and strength, determine the possibility of a slope evolving into a landslide [7, 8]. Regional tectonics including earthquakes and active faults contribute to landslide development over long time periods by weakening the strata and initiating dynamic loadings on slopes [9]. Human activities introduce some changes in the geological and topographic conditions of the slope and, thus, accelerate the development of landslides [10; 11; 12]. Understanding the relationship between landslides and the above-mentioned factors is, thus, crucial for determining the regional development of landslides and taking apposite measures to mitigate and prevent landslide hazards. Many studies investigated factors affecting landslide development through various means, such as physical tests [13; 14; 15; 16], numerical simulations [17; 18; 19], and statistical methods [20; 21]. At present, the most effective method has not yet been established because the characteristics of landslides and influencing factors vary from region to region. Nevertheless, statistical methods are considered feasible at the regional scale. This is because (1) physical model-based methods are more applicable to the study of individual landslides, (2) numerical simulation-based methods demand high computational costs, and (3) both physical and numerical modeling require high-resolution updating of geological structures and environmental conditions, which are difficult to implement at the regional scale. Building an accurate, up-to-date, and complete landslide inventory is a prerequisite for landslide development analysis at the regional scale using statistical methods [20]. However, compared to relatively low-altitude areas, an alpine forest region is characterized by complex topography, high altitude, steep terrain, and high climate sensitivity [22]. These specific features pose challenges to the construction of a complete landslide inventory. Traditionally, landslides in alpine forest regions are usually identified through morphological evidence such as cracks, collapse, and ground deformation [23]. Such evidence of landslides might be hidden or nonvisible in the early stage of landslide evolution [24]. For example, if a landslide area is densely vegetated, cracks and deformation are difficult to observe [25]. For slow-moving landslides, morphological evidence may not be sufficiently distinct [26]. Further, the terrain condition in alpine forest regions may also limit the identification of landslides in field geological surveys [27]. Specifically, ridge-top landslides in alpine forest regions are often located at the top of mountains with high relief and no road access and are barely detectable [28]. For instance, the Maoxian landslide in southwest China, which was a massive landslide with a volume of approximately 1.8 \(\times\) 10\({}^{7}\) m\({}^{3}\) (Chinese Government, 2017) that eventually claimed 83 lives [29; 30], was not identified through a field survey because the sliding body was located at the inaccessible top of the Fugui Mountain [31]. In addition, the development of landslides is a dynamic process that can be triggered by many external factors such as earthquakes and intense rainfall [32].