Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> There is growing recognition that classifying terrestrial plant species on the basis of their function (into 'functional types') rather than their higher taxonomic identity, is a promising way forward for tackling important ecological questions at the scale of ecosystems, landscapes or biomes. These questions include those on vegetation responses to and vegetation effects on, environmental changes (e.g. changes in climate, atmospheric chemistry, land use or other disturbances). There is also growing consensus about a shortlist of plant traits that should underlie such functional plant classifications, because they have strong predictive power of important ecosystem responses to environmental change and/or they themselves have strong impacts on ecosystem processes. The: most favoured traits are those that are also relatively easy and inexpensive to measure for large numbers of plant species. Large international research efforts, promoted by the IGBP-GCTE Programme, are underway to screen predominant plant species in various ecosystems and biomes worldwide for such traits. This paper provides an international methodological protocol aimed at standardising this research effort, based on consensus among a broad group of scientists in this field. It features a practical handbook with step-by-step recipes, with relatively brief information about the ecological context, for 28 functional traits recognised as critical for tackling large-scale ecological questions. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> The biogeographic variation of life has predominantly been studied using taxonomy, but this focus is changing. There is a resurging interest in understanding patterns in the distribution not only of taxa but also of the traits those taxa possess. Patterns of trait variation shed light on fundamental questions in biology, including why organisms live where they do and how they will respond to environmental change. Technological advances such as environmental genomics place microbial ecology in a unique position to move trait-based biogeography forward. We anticipate that as trait-based biogeography continues to evolve, micro- and macroorganisms will be studied in concert, establishing a science that is informed by and relevant to all domains of life. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> Conceptually, plant functional types represent a classification scheme between species and broad vegetation types. Historically, these were based on physiological, structural and/or phenological properties, whereas recently, they have reflected plant responses to resources or environmental conditions. Often, an underlying assumption, based on an economic analogy, is that the functional role of vegetation can be identified by linked sets of morphological and physiological traits constrained by resources, based on the hypothesis of functional convergence. Using these concepts, ecologists have defined a variety of functional traits that are often context dependent, and the diversity of proposed traits demonstrates the lack of agreement on universal categories. Historically, remotely sensed data have been interpreted in ways that parallel these observations, often focused on the categorization of vegetation into discrete types, often dependent on the sampling scale. At the same time, current thinking in both ecology and remote sensing has moved towards viewing vegetation as a continuum rather than as discrete classes. The capabilities of new remote sensing instruments have led us to propose a new concept of optically distinguishable functional types ('optical types') as a unique way to address the scale dependence of this problem. This would ensure more direct relationships between ecological information and remote sensing observations. <s> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> A B S T R A C T Plant trait data have been used in various studies related to ecosystem functioning, community ecology, and assessment of ecosystem services. Evidences are that plant scientists agree on a set of key plant traits, which are relatively easy to measure and have a stable and strong predictive response to ecosystem functions. However, the field measurements of plant trait data are still limited to small area, to a certain moment in time and to certain number of species only. Therefore, remote sensing (RS) offers potential to complement or even replace field measurements of some plant traits. It offers instantaneous spatially contiguous information, covers larger areas and in case of satellite observations profits from their revisit capacity. In this review, we first introduce RS concepts of light–vegetation interactions, RS instruments for vegetation studies, RS methods, and scaling between field and RS observations. Further we discuss in detail current achievements and challenges of optical RS for mapping of key plant traits. We concentrate our discussion on three categorical plant traits (plant growth and life forms, flammability properties and photosynthetic pathways and activity) and on five continuous plant traits (plant height, leaf phenology, leaf mass per area, nitrogen and phosphorous concentration or content). We review existing literature to determine the retrieval accuracy of the continuous plant traits. The relative estimation error using RS ranged between 10% and 45% of measured mean value, i.e. around 10% for plant height of tall canopies, 20% for plant height of short canopies, 15% for plant nitrogen, 25% for plant phosphorus content/ concentration, and 45% for leaf mass per area estimates. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> Understanding, modeling, and predicting the impact of global change on ecosystem functioning across biogeographical gradients can benefit from enhanced capacity to represent biota as a continuous distribution of traits. However, this is a challenge for the field of biogeography historically grounded on the species concept. Here we focus on the newly emergent field of functional biogeography: the study of the geographic distribution of trait diversity across organizational levels. We show how functional biogeography bridges species-based biogeography and earth science to provide ideas and tools to help explain gradients in multifaceted diversity (including species, functional, and phylogenetic diversities), predict ecosystem functioning and services worldwide, and infuse regional and global conservation programs with a functional basis. Although much recent progress has been made possible because of the rising of multiple data streams, new developments in ecoinformatics, and new methodological advances, future directions should provide a theoretical and comprehensive framework for the scaling of biotic interactions across trophic levels and its ecological implications. <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <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> BIB007 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Spectral Traits & Spectral Trait Variation Paradigms for Quantifying Forest Health Using RS <s> Earth is home to a remarkable diversity of plant forms and life histories, yet comparatively few essential trait combinations have proved evolutionarily viable in today's terrestrial biosphere. By analysing worldwide variation in six major traits critical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled, we found that occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs. Three-quarters of trait variation is captured in a two-dimensional global spectrum of plant form and function. One major dimension within this plane reflects the size of whole plants and their parts; the other represents the leaf economics spectrum, which balances leaf construction costs against growth potential. The global plant trait spectrum provides a backdrop for elucidating constraints on evolution, for functionally qualifying species and ecosystems, and for improving models that predict future vegetation based on continuous variation in plant form and function. <s> BIB008
RS technology can only record entities of FH based on physical characteristics. This means that the reflectance and scattering properties in individuals, plants, communities, habitats and biomes are "based on principles of spectroscopy across the electromagnetic spectrum from visible to microwave bands" BIB003 . The use of RS technology with optical, thermal, active and passive RS to record indicators of FH follows a specific characteristic and is subject to specific parameters that significantly influence quality as well as reproducibility (Figures 2-4 ). Step 1: Remote sensors record the amount of electromagnetic radiance at the sensor in different wavebands and this radiance represents the traits of abiotic and biotic surfaces from which the radiance emanated. Biotic traits are biochemical, physiological, morphological, structural, phenological or functional characteristics of plants, populations or communities BIB007 BIB001 BIB004 BIB008 . With the help of biotic traits, we are able to describe, explain and predict (1) why forest plants and communities "live where they do" BIB002 ; and (2) how forest plants respond to different natural and anthropogenic processes, drivers, stress factors and resource limitations. Furthermore, traits form the only link between in-situ terrestrial forest monitoring approaches and RS approaches to quantify and understand indicators of FH. Not all of the biotic traits which are basically used in in-situ species trait approaches BIB004 BIB006 can be detected by RS BIB003 BIB005 . Therefore, traits that can be recorded using RS are in contrast to the in-situ species trait approaches, referred to as "Spectral Traits" (ST) and "Spectral Trait Variations" (STV) BIB007 . Traits that cannot be recorded using RS are referred to as "Non-Spectral Traits" (N-ST, Figures 2 and 3 ). N-ST are for example: (i) when the traits are underground or when they cannot be detected by a given STV are changes to Spectral Traits (ST) in terms of physiology, senescence and phenology, but also caused by stress, disturbances and the resource limitations of plants, populations and communities, which can be directly or indirectly recorded by remote-sensing techniques in space and over time. Step 2: In addition to recording and quantifying spectral traits using RS, taxonomically different species, populations, communities, habitats or biomes of FES ( Figure 2 ) can be distinguished and monitored with RS if the biochemical, biophysical, morphometric, geometric, morphological or functional spectral traits among forest species or communities differ. If it is not • ST are anatomical, morphological, biochemical, biophysical, physiological, structural, phenological or functional, etc. characteristics of plants, populations and communities that are influenced by phylogenetic, taxonomic, populations and communities characteristics, which can be directly or indirectly recorded using remote-sensing techniques in space. • N-ST are anatomical, morphological, biochemical, biophysical, physiological, structural, phenological or functional, etc. characteristics of plants, populations and communities that are influenced by phylogenetic, taxonomic, populations and communities characteristics, which cannot be directly or indirectly recorded using remote-sensing techniques in space. • STV are changes to Spectral Traits (ST) in terms of physiology, senescence and phenology, but also caused by stress, disturbances and the resource limitations of plants, populations and communities, which can be directly or indirectly recorded by remote-sensing techniques in space and over time. possible to distinguish the spectral traits of the species or communities, then processes in seasonal changes of the species like senescence, phenology, physiological changes (gas exchange, photosynthetic activity or evapotranspiration, changes in 3D geometry), changes in biomass or seasonal changes in geometry can help with this distinction. Other supporting information may also aid this process, including the ability to identify traits against the soil background, the filling of specific ecological niches, resource constraints, specific adaptation mechanisms or ecological strategy types. The spatial composition and configuration such as the distribution, size, shape, aggregation and extent of forest spectral traits also play a crucial role when it comes to distinguishing among forest species. Step 1 records spectral traits (ST) and spectral trait variations (STV); Step 2 distinguishes species, populations, communities and biomes of FES depending on the spatial, spectral, radiometric, angular and temporal resolution of the RS techniques, the distribution of ST/STV in space and their temporal changes, the choice of the modelling method (classification; biophysical/chemical parameter estimation) and the geographic data representation (pixel-based or (geographic) object-based) and the appropriateness of the RS algorithm and its assumptions for the given spectral traits. Step 1 records spectral traits (ST) and spectral trait variations (STV); Step 2 distinguishes species, populations, communities and biomes of FES depending on the spatial, spectral, radiometric, angular and temporal resolution of the RS techniques, the distribution of ST/STV in space and their temporal changes, the choice of the modelling method (classification; biophysical/chemical parameter estimation) and the geographic data representation (pixel-based or (geographic) object-based) and the appropriateness of the RS algorithm and its assumptions for the given spectral traits.