## 2 Functional Traits--From Field to Remotely Sensed Observations  
In a broad sense, functional traits refer to attributes that play a role in the establishment, growth, survival, reproduction, and ultimately fitness of an organism [57]. In a narrow sense, functional traits represent a subset of biochemical, physiological, morphological, and behavioral attributes that mediate organisms' responses to environmental stressors, including disturbance, and have a marked effect on ecosystem functions and ecosystem stability at multiple scales [58; 59; 60; 61; 62]. Thus, upscaling functional trait measurements to regions, e.g., [63; 64], can help understand underlying causes of the observed variability and consequences under global changes. In this regard, imaging spectroscopy is providing unique opportunities to identify spectral features [65; 66] representative of leaf- and plant-level traits [55; 67; 68] that mediate ecosystem functions (Figure 2; [12; 15]) and explore their variability with regard to geomorphic [69] and environmental [70; 71] gradients, landscape heterogeneity [72], and spread of invasive species [73; 74]. In addition, this work may help understand the consequences that changes in the underlying drivers may have on trait distributions and ultimately ecosystem functions, which becomes important for monitoring and forecasting purposes [75; 76; 77] as well as for calibrating ecosystem and biosphere models [50; 78; 79; 80]. Figure 2: Workflows to derive (**a**) functional traits and (**b**) functional diversity metrics from remotely sensed (RS) data. (**a**) Stage 1a: Field Data. Leaf samples are collected from field plots and undergo analytical work; spectra are collected from leaf samples or plant canopies. Stage 1b: RS Data. Optical and LiDAR data collected at sampling sites provide spectral and morphological information of vegetation; these data may require various levels of preprocessing. Stage 2: Retrieval of functional traits (FT). Leaf and canopy traits are retrieved from reflectance data using different modeling approaches. Stage 3a: Scaling traits to pixels and scenes. Involves the combination of models developed under Stage 2 with RS data. Stage 3b: Scaling traits to regions. Involves model average spectra as a function of environmental variables. Stage 4: Spectral Features. These are images  
representing plant traits, principal components, or spectral species that can be used to estimate different diversity metrics. (**b**) Stage 5: Functional diversity (FD). FD is estimated for communities or neighborhoods of pixels and may be based on various approaches. Functional groups are derived from clustering of pixels of functional trait maps from known sites/plots. Functional diversity metrics are based on one or multiple metrics derived from two broad sets of approaches, namely Community Weighted Means (CWM) and Trait Space Indices; these metrics describe diversity at local (within or alpha diversity) scales. The contribution of local, landscape, and regional processes to FD can be examined by partitioning the total FD of a region (gamma; \(\gamma\)SD) into additive alpha (within community FD; \(\alpha\)SD) and beta (between community FD; \(\beta\)SD) components. Boxes in peach color represent input and output data whereas boxes in blue represent processes. The selection of leaf- and plant-level traits (Figure 2a, Stage 1a, 1b) that collectively translate into critical ecosystem processes and functions, the retrieval of this information from optical data (Figure 2a, Stage 2), and scaling from leaves and plants to pixels and plots (Figure 2a, Stage 3) are central in the definition of spectral features (Figure 2b Stage 4).