## 6 Concluding Remarks and Future Directions  
This review aimed at synthesizing established methods to retrieve functional traits from remotely sensed hyperspectral and point-cloud data, summarizing approaches to characterize functional diversity from remotely sensed functional traits, and elaborating on how methods and approaches used in different fields, together with an increasing availability of hyperspectral data, could support an \"ecosystem-centered\" view of landsl. We found that the extent to which hyperspectral and point-cloud data have been used to characterize functional traits, functional diversity, and rock attributes in mountainous regions has been limited. Moreover, there has been very little integration among the various sources of data to understand ecosystem-geomorphic interactions and, ultimately, the dynamics of mountainscapes mediated by landsl. We identified three dimensions of the \"ecosystem-centered\" view of landsl that can clearly benefit from new approaches and the availability of air- and spaceborne hyperspectral and point-cloud data. Improving our knowledge along these dimensions will help us to better understand mountainous regions and landsl and, ultimately, re-assess management practices and risk assessments. Mountain biomass are among the most diverse on Earth. At the same time, they are very susceptible to numerous drivers of change, including climate. Thus, at a time of rapid global changes, it becomes imperative to focus our attention on mountainouscapes.  
Supplementary Materials: The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/rs1711806/s1](https://www.mdpi.com/article/10.3390/rs1711806/s1)]([https://www.mdpi.com/article/10.3390/rs1711806/s1](https://www.mdpi.com/article/10.3390/rs1711806/s1)): File S1 is an Excel file with three worksheets: Table S1 lists new and planned hyperspectral spaceborne sensors, including characteristics; Table S2 synthesizes studies estimating functional diversity from functional traits [280], and Table S3 summarizes landslide studies characterizing Vegetation Recovery Rates (VRRs) over time. File S2 includes relevant information about the supplementary tables and references listed therein. File S3 includes Figure S1 depicting two sets of spectral features: principal components and indexes that are a surrogate of functional traits used in the RGB composites of main Figure 2.  
Conceptualization, A.K. and C.R.; writing--original draft preparation, A.K. and C.R.; writing--review and editing, A.K. and C.R. All authors have read and agreed to the published version of the manuscript.  
Support for this work was provided by NASA Cooperative Agreement 80 NSSC20M0052--PR Space Grant Consortium Fellowship (AK), NSF-DEB 155687 (CR) and NSF REPS Supplemental Funding (CR, AK), the Puerto Rico Science, Technology, and Research Trust Research Program(CR). The NHI-NIGMS Institutional Development Award (IDeA) INBRE P20 GM103475 supported computational infrastructure used in this project.  
No new data were created or analyzed in this study.  
We are thankful to [PERSON] and [PERSON] for contributing to lively discussions on functional traits, functional diversity, and remote sensing, to [PERSON] for clarifying questions regarding spectroscopy, to [PERSON] for clarifying questions on functional diversity calculations, to [PERSON] for technical support, to [PERSON] and [PERSON] for providing a friendly review of an early version of this manuscript, and several anonymous reviewers whose comments improved this manuscript.  
The authors declare no conflicts of interest.