Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Conclusions <s> The world's ecosystems are losing biodiversity fast. A satellite mission designed to track changes in plant functional diversity around the globe could deepen our understanding of the pace and consequences of this change, and how to manage it. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Conclusions <s> Abstract Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales. This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research. EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models. <s> BIB002
FH is characterized by a high degree of stability, elasticity and resistance to stress, disturbances and resource limitations, when a scale-and FES-dependent taxonomic, phylogenetic, structural as well as functional diversity is maintained or at least restored after the impacts of stress. To monitor and understand the entire complexity of FH, its driving forces and mechanisms, it is imperative to establish a cost-effective, extensive, repeatable and standardised FH monitoring program. Remote sensing is a key technology in these programs. RS as a physically-based system records spectral traits as well as spectral trait variations in FES. Since ST and STV are proxies for stress, disturbances and resource limitations can be found on all spatio-temporal scales. The ST/STV approach with RS techniques is suitable for monitoring the status and changes of the taxonomic, structural and functional diversity in FES. Structural as well as functional disturbances to FH can be effectively recorded with RS techniques using the ST/STV approach, whereas the ability to monitor stress in the taxonomic and phylogenetic diversity of forest animal and plant species is limited with RS techniques. Extensive investigations have established the constraints under which the taxonomic, structural and functional monitoring of forest species, populations and communities is possible with RS. In this way, the characteristics ST and STV, the shape, density and distribution of ST/STV, different sensor characteristics, the choice of the classification method and the use of different RS algorithms all influence accuracy in the monitoring of indicators of FH. RS is suitable for recording disturbance processes to FH (from short-term to long-term processes), which is not (or only insufficiently) possible with terrestrial FH surveys. In contrast to RS, however, terrestrial FH surveys can record precise taxonomic and phylogenetic information about forest species, making crucial information on phylogeny, tolerances, resilience and threshold values available, which are important input variables for calibrating RS data as well as understanding stress and spectral response processes. It should therefore be the goal of future research on FH to link terrestrial and RS-based approaches BIB001 BIB002 . In doing so the ST/STV paradigm might be used as an interface between both information sources. Future research on RS technologies should focus on making previously non-recordable yet relevant non-spectral traits (N-ST) recordable in the future through new technologies and multi-sensor approaches. Since numerous constraints influence the ability to ascertain FH indicators using RS, the standardised recording of FH indicators using terrestrial and RS-based approaches will be one of the greatest challenges in the future if we are to gain a better understanding of FH and forest ecosystems. Furthermore, compared to the Essential Biodiversity Variables (EBV) for FH, the "Essential Forest Health Variables" (EFHV) also need to be clearly defined, so that comparable indicators of FH can be monitored on all spatio-temporal scales. Author Contributions: A.L. was responsible for the main part of this review analysis, writing and production of the figures. M.H. and P.M. provided extensive contributions on recording terrestrial forest health as well as substantial input about methods used for implementing sensors in the context of forest health. D.J.K. and S.E. added their knowledge about extensive methodology aspects for using optical, RADAR and LiDAR as well as modelling forest health with remote-sensing techniques. All authors checked and contributed to the final text.