Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <s> ○ Specific leaf area (leaf area to dry mass ratio), leaf dry matter content (leaf dry mass to saturated fresh mass ratio) and leaf nitrogen concentration (LNC) have been proposed as indicators of plant resource use in data bases of plant functional traits. ○ We tested whether species ranking based on these traits was repeatable by studying spatio-temporal variations in specific leaf area and leaf dry matter content of water-saturated leaves (SLA SAT and LDMC SAT ), as well as in LNC, for 57 herbaceous and woody species (or subsets thereof) growing under the Mediterranean climate of southern France. ○ Interseason and intersite variations were more pronounced than interannual variations, but species ranking for a given trait remained mostly consistent in space and time. Classifications based on LDMC SAT were generally more repeatable across years and sites, whereas those based on SLA SAT were more stable over seasons. LNC usually gave the least repeatable classifications. ○ Species rankings were not completely similar for the three traits. Discussion of reproducibility, ease of trait measurement, as well as trait-function relationships led us to propose that measurements of the leaf traits, SLA SAT and/or LDMC SAT , were the most suitable in large screening programmes. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <s> Summary 1. The concept of plant functional type proposes that species can be grouped according to common responses to the environment and/or common effects on ecosystem processes. However, the knowledge of relationships between traits associated with the response of plants to environmental factors such as resources and disturbances (response traits), and traits that determine effects of plants on ecosystem functions (effect traits), such as biogeochemical cycling or propensity to disturbance, remains rudimentary. 2. We present a framework using concepts and results from community ecology, ecosystem ecology and evolutionary biology to provide this linkage. Ecosystem functioning is the end result of the operation of multiple environmental filters in a hierarchy of scales which, by selecting individuals with appropriate responses, result in assemblages with varying trait composition. Functional linkages and trade-offs among traits, each of which relates to one or several processes, determine whether or not filtering by different factors gives a match, and whether ecosystem effects can be easily deduced <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <s> † Background and Aims A standardized methodology to assess the impacts of land-use changes on vegetation and ecosystem functioning is presented. It assumes that species traits are central to these impacts, and is designed to be applicable in different historical, climatic contexts and local settings. Preliminary results are presented to show its applicability. † Methods Eleven sites, representative of various types of land-use changes occurring in marginal agro-ecosystems across Europe and Israel, were selected. Climatic data were obtained at the site level; soil data, disturbance and nutrition indices were described at the plot level within sites. Sixteen traits describing plant stature, leaf characteristics and reproductive phase were recorded on the most abundant species of each treatment. These data were combined with species abundance to calculate trait values weighed by the abundance of species in the communities. The ecosystem properties selected were components of above-ground net primary productivity and decomposition of litter. † Key Results The wide variety of land-use systems that characterize marginal landscapes across Europe was reflected by the different disturbance indices, and were also reflected in soil and/or nutrient availability gradients. The trait toolkit allowed us to describe adequately the functional response of vegetation to land-use changes, but we suggest that some traits (vegetative plant height, stem dry matter content) should be omitted in studies involving mainly herbaceous species. Using the example of the relationship between leaf dry matter content and aboveground dead material, we demonstrate how the data collected may be used to analyse direct effects of climate and land use on ecosystem properties vs. indirect effects via changes in plant traits. † Conclusions This work shows the applicability of a set of protocols that can be widely applied to assess the impacts of global change drivers on species, communities and ecosystems. <s> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <s> Worldwide agriculture is one of the main drivers of biodiversity decline. Effective conservation strategies depend on the type of relationship between biodiversity and land-use intensity, but to date the shape of this relationship is unknown. We linked plant species richness with nitrogen (N) input as an indicator of land-use intensity on 130 grasslands and 141 arable fields in six European countries. Using Poisson regression, we found that plant species richness was significantly negatively related to N input on both field types after the effects of confounding environmental factors had been accounted for. Subsequent analyses showed that exponentially declining relationships provided a better fit than linear or unimodal relationships and that this was largely the result of the response of rare species (relative cover less than 1%). Our results indicate that conservation benefits are disproportionally more costly on high-intensity than on low-intensity farmland. For example, reducing N inputs from 75 to 0 and 400 to 60kgha-1yr-1 resulted in about the same estimated species gain for arable plants. Conservation initiatives are most (cost-)effective if they are preferentially implemented in extensively farmed areas that still support high levels of biodiversity. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <s> Statistical relations between the species composition of plant assemblages and canopy reflectance are frequently employed in remote sensing for mapping vegetation at local scales. Reflectance is influenced by species composition but also affected by dynamics such as seasonal vegetation development or plant stress. Due to this variability in time and space, doubts are frequently raised with respect to the transferability of statistical relations in remote sensing of plant assemblages. Hence, this study addresses the stability of statistical relations between species composition and reflectance despite of spatiotemporally changing vegetation conditions. We established permanent plots at three temperate sites (nutrient-poor grassland, wet heath, and floodplain meadow). We measured canopy reflectance at multiple dates over the vegetation period using a field spectrometer with hyperspectral resolution. Simultaneously, plant species composition and other vegetation and surface parameters that may exert influence on reflectance were recorded. Species composition was statistically related to the corresponding reflectance data using ordination (Isometric Feature Mapping) and cross-validated regression models (Partial Least Squares Regression). Time series of model fits as well as regression coefficients were used to estimate the temporal stability of the models. Model fits were further compared to changes in vegetation conditions. Model residuals were tested for co-variable influences. Finally, we tested the transferability of the statistical relations in time. Results showed that species composition could be modeled with rather high accuracies (R² in validation up to 0.78 and for only three measurements lower than 0.5), with the highest fits near the vegetation optimum (i.e., the date with maximum cover of photosynthetically active vegetation). The transferability in time varied with the vegetation type. Uncertainties in the models were strongly related to variable canopy height and to the occurrence of litter. Since such spatial heterogeneities may be a result of non-stationary processes, we conclude that statistical methods taking into account such effects may further improve the accuracy of vegetation mapping. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <s> Abstract For quantifying and modelling of landscape patterns, the patch matrix model (PMM) and the gradient model (GM) are fundamental concepts of landscape ecology. While the PMM model has been the backbone for our advances in landscape ecology, it may also hamper truly universal insights into process–pattern relationships. The PMM describes landscape structures as a mosaic of discretely delineated homogenous areas. This requires simplifications and assumptions which may even result in errors which propagate through subsequent analyses and may reduce our ability to understand effects of landscape structure on ecological processes. Alternative approaches to represent landscape structure should therefore be evaluated. The GM represents continuous surface characteristics without arbitrary vegetation or land-use classification and therefore does not require delineation of discrete areas with sharp boundaries. The GM therefore lends itself to be a more realistic representation of a particular surface characteristic. In the paper PMM and GM are compared regarding their prospects and limitations. Suggestions are made regarding the potential use and implementation of both approaches for process–pattern analysis. The ecological and anthropogenic process itself and its characteristics under investigation is decisive for: (i) the selection of discrete and/or continuous indicators, (ii) the type of the quantitative pattern analysis approach to be used (PMM/GM) and (iii) the data and the scale required in the analysis. Process characteristics and their effects on pattern characteristics in space and time are decisive for the applicability of the PMM or of the GM approach. A low hemeroby (high naturalness and low human pressure on landscapes) allows for high internal-heterogeneity in space and over time within patterns. Such landscapes can be captured with the GM approach. A high hemeroby reduces heterogeneity in space and time within patterns. For such landscapes we recommend the PMM model. <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Drivers and Process Effects on Forest Health <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
While the taxonomic and phylogenetic structures of the forest determine its resistance and resilience, it is the characteristics of the drivers and processes that determine the impact of the disturbances. In FES, there are multiple levels of interacting biological entities, which underlie abiotic and anthropogenic processes and pressures like deforestation, N and S depositions, land-use, or climate change, which causes extreme events such as heavy precipitation, high temperatures or high ozone concentrations. All of these can affect FH in a different way at different spatial and temporal scales (Figure 1 ). It is not just the process or driver itself that is crucial for understanding the effect on FH, but also its characteristics that determine the levels (e.g., genetic, molecular or community), direction and reversibility of the effects. Important process characteristics for FH include the range, scope, duration, intensity, continuity, dominance, or overlap BIB006 , which can alter the taxonomic, structural or functional diversity of FES on different spatio-temporal scales. Land-use changes, deforestation or fire can alter forest traits in their overall complexity  BIB002 BIB001 BIB003 BIB004 . Furthermore, anthropogenic management can lead to a change in the spectrum of species and, thus, also in plant and plant community traits over space and time. When using RS for monitoring, we assume that these alterations are reflected in changes of the spectral response of RS signals. Garnier et al., BIB003 showed a direct relationship between plant characteristics (e.g., plant height and flowering phenology) as a response to land-use changes and disturbance regimes. The short and long-term dynamics resulting from disturbance in soil and vegetation can also lead to adaptations  and Lausch BIB007 ). While the taxonomic and phylogenetic structures of the forest determine its resistance and resilience, it is the characteristics of the drivers and processes that determine the impact of the disturbances. In FES, there are multiple levels of interacting biological entities, which underlie abiotic and anthropogenic processes and pressures like deforestation, N and S depositions, land-use, or climate change, which causes extreme events such as heavy precipitation, high temperatures or high ozone concentrations. All of these can affect FH in a different way at different spatial and temporal scales ( Figure 1 ). It is not just the process or driver itself that is crucial for understanding the effect on FH, but also its characteristics that determine the levels (e.g., genetic, molecular or community), direction and reversibility of the effects. Important process characteristics for FH include the range, scope, duration, intensity, continuity, dominance, or overlap BIB006 , which can alter the taxonomic, structural or functional diversity of FES on different spatio-temporal scales. Land-use changes, deforestation or fire can alter forest traits in their overall complexity  BIB002 BIB001 BIB003 BIB004 . Furthermore, anthropogenic management can lead to a change in the spectrum of species and, thus, also in plant and plant community traits over space and time. When using RS for monitoring, we assume that these alterations are reflected in changes of the spectral response of RS signals. Garnier et al., BIB003 showed a direct relationship between plant characteristics (e.g., plant height and flowering phenology) as a response to land-use changes and disturbance regimes. The short and long-term dynamics resulting from disturbance in soil and vegetation can also lead to adaptations Remote Sens. 2016, 8, 1029 5 of 43 and often systematic, complex and sometimes even irreversible shifts in species compositions, configurations or distributions BIB005 . Moreover, deforestation, clearing, harvesting and disturbances often cause a complete and/or irreversible loss of entire forest communities and ecosystems, producing complex changes in the characteristics of the species or communities BIB003 .