## 4 Diversity, Landslide Dynamics, and Mountainscapes
### Plant Traits, Ecosystem Function, Montane Ecosystems, and Landsliding  
In mountainous environments, multi- and hyperspectral remote sensing has contributed to the characterization of plant communities [177; 178; 179; 180], land cover/land use [181; 182; 183; 184], tree line dynamics [34], and disturbance regimes including deforestation, forest die-off, mining, and landsl [185; 186; 187; 188; 189; 190]. In particular, the efforts towards landslide mapping have been substantial not only in terms of the number of papers but also the use of diverse machine learning methods (Figure 1; [45]). In contrast, the use of multi- and hyperspectral remote sensing to characterize functional traits, functional diversity, and ecosystem function in mountainous environments, particularly in the context of landslide studies, has been limited. The first set of studies has used vegetation indices and canopy chemical traits derived from whole spectra (Figure 2; Files S1 and S2: Table S2) to study responses of montane ecosystems to climate variability and to characterize components of the carbon and, to a lesser extent, nutrient cycles. For example, phenological metrics derived from the Normalized Vegetation Index (NDVI) have been used to infer temperature sensitivity of mountain vegetation [191]. Similarly, NDVI and the Enhanced Vegetation Index (EVI) have been used in carbon cycling studies to estimate above-ground biomass (AGB) and leaf area index (LAI) in forest and non-forest montane ecosystems [9; 192; 193; 194; 195]. The quantification of canopy nitrogen and lignin at landscape- and regional-scales through air- and spaceborne hyperspectral data collected over temperate and tropical hilly and mountainous ecosystems has improved our understanding of nutrient dynamics [70; 76; 196; 197]. The characterization of leaf chemistry through airborne hyperspectral data in the eastern Andes of Peru and Mount Kinabalu in Malaysia has provided unique insights into the cycling of rock-derived nutrients [53; 82; 113; 198]. Altogether, the aforementioned work has improved our understanding of the contribution of abiotic (temperature, geomorphic, and topographic settings) and biotic (species composition) factors driving trait variability and ecosystem, soil, geomorphic, and hydrologic processes. The second set--Biodiversity-Ecosystem Function (BEF) studies--has estimated functional diversity metrics from remotely sensed traits to derive large-scale relationships between functional diversity and ecosystem functions (Files S1 and S2: Table S2). Work conducted in the Laegern Mountain of Switzerland estimated functional richness (FRic), functional evenness (FEve), and functional divergence (FDiv) (Figure 2b, Stage 5; Files S1 and S2: Table S2) from physiological (chlorophyll, leaf carotenoids, and equivalent water thickness) and morphological (canopy height, plant area index, and foliage height diversity) traits and showed that FRic responded more strongly to environmental conditions than FEve and FDiv [15; 40]. More specifically, FRic was highest on the south aspect and lowest at the mountain ridge. At this same site, [PERSON] and collaborators [199] simulated gross primary productivity (GPP) and found that it was positively related to FRic; yet there were large differences among sites differing with elevation. A fourth study conducted in the southern aspect of Mt. Shennongjia in China using similar traits and functional diversity indices showed that FRic was highest at mid and high elevations [47]. Perhaps more importantly, this work revealed a humped-back relationship between aboveground biomass (AGB) and FRic derived from morphological traits and a more complex, non-linear relationship between productivity (as reflected by NDVI) and FRic derived from physiological traits.