Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> 2. <s> Aims ::: The three-strategy (CSR) model proposed by Grime constitutes one of the most established systems for plant functional types. The primary strategies (competitive ability, adaptation to severe stress and adaptation to disturbance) relate to the productivity and level of disturbance at a given site. Accordingly, their change in space and time may serve as an illustration and measure of key processes such as succession, eutrophication and habitat shift. Here, we make use of the known links between the three primary strategies to traits with potential relevance for canopy reflectance, and test whether remote sensing is able to reproduce the spatial pattern of strategy types. ::: ::: Location ::: A raised bog and minerotrophic fen complex, Murnauer Moos, Germany. ::: ::: Methods ::: Field data on the distribution of plant strategies in sample plots were regressed against canopy reflectance using partial least squares regression. The resulting models were validated and applied to airborne hyperspectral imagery on a per pixel basis. The resulting local maps for each strategy type and their combined representation in an RGB colour composite were interpreted in terms of plant species composition and environmental constraints. ::: ::: Results ::: All three primary strategy types could be mapped using remote sensing. Reflectance spectra related to competitive ability and adaptation to severe stress suggest that typical traits linked to these strategies exerted a direct influence. On the other hand, species with low cover values played a decisive role for the strength of the statistical relationship between reflectance and strategies. Because these species have a low impact on canopy reflectance, their contribution is better explained by their role as proxies for covarying variables such as the total cover of dead plant material. ::: ::: Conclusions ::: Our study demonstrates the potential to detect community strategy type composition using hyperspectral remote sensing, providing direct insights into spatial ecological patterns. By illustrating the exposure to stress, competition and disturbance, the derived maps of functional traits are potentially useful for applications in nature management and for the monitoring of functional shifts in ecosystems. As a next step, they can be easily combined into maps of functional diversity. Upcoming satellites with higher spectral resolution will improve access to this kind of spatial information. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> 2. <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
Plant traits and plant trait variations are therefore proxies of the state, abiotic and biotic limitations, ecological interactions, processes and pressures which affect forest plant species and communities BIB002 BIB001  ].