## 1 Introduction  
The global biodiversity crisis has emphasized the contribution of biodiversity to ecosystem stability and resilience and highlighted monitoring needs at multiple scales [1, 2]. In this context, functional diversity has emerged as an essential dimension of biodiversity due to its potential to link ecosystem processes and function with taxonomic, phenotypic, and genomic diversity [3, 4]. Remotely sensed multispectral data collected along the visible (VIS) through the long-wave infrared (LWIR) regions has already made possible the characterization of ecosystem traits and functions [5, 6, 7, 8, 9, 10, 11] and helped monitor the Earth's \"pulse\". Simultaneously, important developments in imaging spectroscopy are making possible the establishment of relationships between field-based leaf- and plant-level functional trait measurements with air- and spaceborne hyperspectral data to investigate functional diversity and ecosystem function at unprecedented spectral scales [12; 13; 14; 15; 16]. Here, we posit that these developments, together with similar ones focusing on soils and lithologies [17; 18; 19; 20; 21; 22], can help understand relationships between functional diversity, ecosystem processes, landsl, and, more broadly, disturbance dynamics of mountainousscapes (Figure 1). Investigating the aforementioned relationships will greatly benefit from a new generation of spaceborne hyperspectral missions (Files S1 and S2: Table S1) [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33], yet there are two sets of challenges that need to be recognized. The first set stems from the nature of mountain environments and the landslides themselves. Atmospheric, topographic, and bidirectional reflectance distribution function (BRDF) effects are ubiquitous in mountainous areas. This may require extensive image pre-processing to correct for water vapor, aerosol content, multiple scattering, cloud and terrain shadows, and mask clouds to ultimately retrieve sun-lit pixels and evaluate surface reflectance [34; 35; 36] and also the inclusion of topographic effects in radiative transfer models [37]. The latter may improve simulations of canopy reflectance and emissivity along the optical to thermal regions. In addition, landslides, depending upon the stage of development, may contain a mixture of vegetated pixels and bare soil that will influence the spectral characteristics of vegetation and substrate. Vegetation studies based on hyperspectral data recommend  
Figure 1: An ecosystem-centered view of landslides can benefit from the integration of remotely sensed hyperspectral and LiDAR data to better understand ecosystem-soil-lithology interactions at multiple scales. (**a**) At local scales, plant functional traits may contribute to slope resistance/susceptibility to failure (before landslides) and landslide recovery (after landslides). Plant physiological and soil/lithological attributes can be derived from hyperspectral data, whereas plant above-ground morphological traits can be derived from LiDAR data. The latter may be used to develop models establishing relationships with below-ground morphological traits. (**b**) The study of landslides comes with different challenges associated with the nature of mountainous terrain and the landslides themselves. Both rainfall and earthquake events can trigger hundreds to thousands of landslides over areas of variable extents within which landslides exhibit a large variability in size. This may require upscaling or downscaling hyperspectral and LiDAR data to address different sets of questions or overcome limitations of the data. (**c**) At regional scales, plant functional traits and derived measures of functional diversity can be used to better understand the contribution of ecosystem-soil-lithology interactions to slope stability as well as variation in the developmental trajectories of landslides at the scale of landslide populations and landslide assemblages. pixels to eliminate background reflectance [38], whereas soil/rock studies recommend the removal of vegetated pixels [39].