Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Summary ::: ::: ::: 1 ::: Maps of averaged plant indicator values can supply spatially explicit information about environmental gradients and are therefore tools of broad relevance in applied ecology. However, field-based mapping demands extensive sampling and thus most assessments are at the level of plots rather than covering a whole area. To address these limitations, this study evaluated the potential of remote sensing to produce Ellenberg indicator maps. ::: ::: 2 ::: Ellenberg indicator values for water supply, soil pH and soil fertility were derived using species data from vegetation plots in montane rangeland. The extrapolation of these data was accomplished by partial least-squares (PLS) regression between indicator values and plot reflectance values extracted from airborne ‘hyperspectral’ imagery. Applying the regression to the imagery resulted in three largely accurate maps giving the spatial distribution of certain soil attributes as indicated by Ellenberg values (R2 = 0·58–0·68 in cross-validation). ::: ::: 3 ::: Synthesis and applications. Mean indicator values express floristic composition as a single, comparable, continuous and mappable variable. This makes them an appealing tool for vegetation monitoring. Imaging spectroscopy provides fast access to spatially contiguous and explicit information about soil conditions as indicated by plants. The technique allows the investigation to advance beyond plots and supplies indicator maps at a stand-level resolution. The mapped gradients of environmental attributes are of direct relevance to plant growth. The maps depict life-span growth conditions rather than ‘snap-shots’ given by measurements that are often limited in space and time, cost intensive and difficult to implement. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Abstract Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <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> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Assessing the level of diversity in plant communities from field-based data is difficult for a number of practical reasons: (1) establishing the number of sampling units to be investigated can be difficult; (2) the choice of sample design can impact on results; and (3) defining the population of concern can be challenging. Satellite remote sensing (SRS) is one of the most cost-effective approaches to identify biodiversity hotspots and predict changes in species composition. This is because, in contrast to field-based methods, it allows for complete spatial coverages of the Earth's surface under study over a short period of time. Furthermore, SRS provides repeated measures, thus making it possible to study temporal changes in biodiversity. Here, we provide a concise review of the potential of satellites to help track changes in plant species diversity, and provide, for the first time, an overview of the potential pitfalls associated with the misuse of satellite imagery to predict species diversity. Our work shows that, while the assessment of alpha-diversity is relatively straightforward, calculation of beta-diversity (variation in species composition between adjacent locations) is challenging, making it difficult to reliably estimate gamma-diversity (total diversity at the landscape or regional level). We conclude that an increased collaboration between the remote sensing and biodiversity communities is needed in order to properly address future challenges and developments. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Oland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R2 values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape. <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Abstract Remotely sensed image interpretation or classification of tropical forests can be severely hampered by the effects of the bidirectional reflection distribution function (BRDF). Even for narrow swath sensors like Landsat TM/ETM+, the influence of reflectance anisotropy can be sufficiently strong to introduce a cross-track reflectance gradient. If the BRDF could be assumed to be linear for the limited swath of Landsat, it would be possible to remove this gradient during image preprocessing using a simple empirical method. However, the existence of natural gradients in reflectance caused by spatial variation in floristic composition of the forest can restrict the applicability of such simple corrections. Here we use floristic information over Peruvian and Brazilian Amazonia acquired through field surveys, complemented with information from geological maps, to investigate the interaction of real floristic gradients and the effect of reflectance anisotropy on the observed reflectances in Landsat data. In addition, we test the assumption of linearity of the BRDF for a limited swath width, and whether different primary non-inundated forest types are characterized by different magnitudes of the directional reflectance gradient. Our results show that a linear function is adequate to empirically correct for view angle effects, and that the magnitude of the across-track reflectance gradient is independent of floristic composition in the non-inundated forests we studied. This makes a routine correction of view angle effects possible. However, floristic variation complicates the issue, because different forest types have different mean reflectances. This must be taken into account when deriving the correction function in order to avoid eliminating natural gradients. <s> BIB007 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> A more comprehensive understanding of the factors governing tropical tree community turnover at different spatial scales is needed to support land-management and biodiversity conservation. We used new forest inventory data from 263 permanent plots in the Carnegie Biodiversity-Biomass Forest Plot Network spanning the eastern Andes to the western Amazonian lowlands of Peru to examine environmental factors driving genus-level canopy tree compositional variation at regional and landscape scales. Across the full plot network, constrained ordination analysis indicated that all environmental variables together explained 23.8% of the variation in community composition, while soil, topographic, and climatic variables each explained 15.2, 10.9, and 17.0%, respectively. A satellite-derived metric of cloudiness was the single strongest predictor of community turnover, and constrained ordination revealed a primary gradient of environmentally-driven community turnover spanning from cloudy, high elevation sites to warm, wet, lowland sites. For three focal landscapes within the region, local environmental variation explained 13.4–30.8% of compositional variation. Community turnover at the landscape scale was strongly driven by topo-edaphic factors in the two lowland landscapes examined and strongly driven by potential insolation and topography in the montane landscape. At the regional scale, we found that the portion of compositional variation that was uniquely explained by spatial variation was relatively small (2.7%), and was effectively zero within the three focal landscapes. Overall, our results show strong canopy tree compositional turnover in response to environmental gradients at both regional and landscape scales, though the most important environmental drivers differed between scales and among landscapes. Our results also highlight the usefulness of key satellite-derived environmental covariates that should be considered when conducting biodiversity analyses in tropical forests. <s> BIB008 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Abstract To answer new scientific and ecological questions and monitor multiple forest changes, a fine scale characterization of these ecosystems is needed, and could imply the mapping of specific species, of detailed forest types, and of functional composition. This characterization can be now provided by the novel Earth Observation tools. This study aims to contribute to understanding the innovation in forest and ecological research that can be brought in by advanced remote sensing instruments, and proposes the guild mapping approach as a tool to efficiently monitor the varied tropical forest resources. We evaluated, in tropical Ghanaian forests, the ability of airborne hyperspectral and simulated multispectral Sentinel-2 data, and derived vegetation indices and textures, to: distinguish between two different forest types; to discriminate among selected dominant species; and to separate trees species grouped according to their functional guilds: Pioneer, Non Pioneer Light Demanding, and Shade Bearer. We then produced guild classification maps for each area using hyperspectral data. Our results showed that with both hyperspectral and simulated Sentinel-2 data these discrimination tasks can be successfully accomplished. Results also stressed the importance of texture features, especially if using the lower spectral and spatial Sentinel-2 resolution data, and highlighted the important role of the new Sentinel-2 data for ecological monitoring. Classification results showed a statistically significant improvement in overall accuracy using Support Vector Machine, over Maximum Likelihood approach. We proposed the functional guilds mapping as an innovative approach to: (i) monitor compositional changes, especially with respect to the effects of global climate change on forests, and particularly in the tropical biome where the occurrence of hundreds of species prevents mapping activities at species level; (ii) support large-scale forest inventories. The imminent Sentinel-2 data could serve to open the road for the development of new concepts and methods in forestry and ecological research. <s> BIB009 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Plant trait measurements are needed for evaluating ecological responses to environmental conditions and for ecosystem process model development, parameterization, and testing. We present a standardized dataset integrating measurements from projects conducted by the Terrestrial Ecosystem Research and Regional Analysis- Pacific Northwest (TERRA-PNW) research group between 1999 and 2014 across Oregon and Northern California, where measurements were collected for scaling and modeling regional terrestrial carbon processes with models such as Biome-BGC and the Community Land Model. The dataset contains measurements of specific leaf area, leaf longevity, leaf carbon and nitrogen for 35 tree and shrub species derived from more than 1,200 branch samples collected from over 200 forest plots, including several AmeriFlux sites. The dataset also contains plot-level measurements of forest composition, structure (e.g., tree biomass), and productivity, as well as measurements of soil structure (e.g., bulk density) and chemistry (e.g., carbon). Publically-archiving regional datasets of standardized, co-located, and geo-referenced plant trait measurements will advance the ability of earth system models to capture species-level climate sensitivity at regional to global scales. <s> BIB010 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> Understanding the mechanisms underlying ecosystem resilience - why some systems have an irreversible response to disturbances while others recover - is critical for conserving biodiversity and ecosystem function in the face of global change. Despite the widespread acceptance of a positive relationship between biodiversity and resilience, empirical evidence for this relationship remains fairly limited in scope and localized in scale. Assessing resilience at the large landscape and regional scales most relevant to land management and conservation practices has been limited by the ability to measure both diversity and resilience over large spatial scales. Here, we combined tools used in large-scale studies of biodiversity (remote sensing and trait databases) with theoretical advances developed from small-scale experiments to ask whether the functional diversity within a range of woodland and forest ecosystems influences the recovery of productivity after wildfires across the four-corner region of the United States. We additionally asked how environmental variation (topography, macroclimate) across this geographic region influences such resilience, either directly or indirectly via changes in functional diversity. Using path analysis, we found that functional diversity in regeneration traits (fire tolerance, fire resistance, resprout ability) was a stronger predictor of the recovery of productivity after wildfire than the functional diversity of seed mass or species richness. Moreover, slope, elevation, and aspect either directly or indirectly influenced the recovery of productivity, likely via their effect on microclimate, while macroclimate had no direct or indirect effects. Our study provides some of the first direct empirical evidence for functional diversity increasing resilience at large spatial scales. Our approach highlights the power of combining theory based on local-scale studies with tools used in studies at large spatial scales and trait databases to understand pressing environmental issues. <s> BIB011 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <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> BIB012 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> • <s> 1.The comparable and integrated nature of plant functional types and advances in high spectral resolution remote sensing techniques (i.e., imaging spectroscopy) make their combination highly interesting for spatially continuous and repeatable large-scale ecosystem monitoring. Depending on physical environment and stress, plants invest in co-varying biochemical and structural traits, influencing spectral characteristics of vegetation. These traits are assumed to bear a more direct causal relationship to plant functional types than to plant life/growth forms. However, the connection between a vegetation community's functional and spectral response remains to be established. ::: ::: 2.We assessed the correlation structure between i) biochemical and structural vegetation traits (biomass, dry matter content, nitrogen content, neutral detergent fibre content), ii) plant life/growth forms, and iii) seven plant functional types of two categories (strategy types, indicator values) collected in heterogeneous alpine grassland. We then used airborne imaging spectroscopy data from the same area to model and predict plant life/growth forms and plant functional types at the vegetation community level using partial least squares regression (PLSR), and validated our models based on an independent dataset. ::: ::: 3.We found high correlations between many of the biochemical and structural vegetation traits, plant life/growth forms and plant functional types tested. Using airborne imaging spectroscopy data, we successfully modelled and predicted most plant life/growth forms (R2 max. = 0.56) and all plant functional types (R2 max. = 0.62). However, model performance for plant life/growth forms decreased substantially during external validation and overall model consistency was low (average change in R2 = 72%), while plant functional type models were much more consistent (average change in R2 = 20%). Based on our findings, we developed a conceptual framework using the theory and methodology of vegetation ecology and imaging spectroscopy to link the vegetation community's functional to its spectral signature. ::: ::: 4.Our results encourage the use of plant functional types in imaging spectroscopy in order to aid the large-scale monitoring of ecosystems, which is particularly important given the increased availability of airborne data and the prospective launches of spaceborne instruments in the near future. ::: ::: This article is protected by copyright. All rights reserved. <s> BIB013
Changes in 2D or 3D structural spectral traits such as leaf arrangement and geometry, tree height or area, density, size, the shape of forest patches as well as fragmentation, complexity, homogeneity or the diversity of species or communities. Furthermore, forest canopy height, forest extent as well as the vertical and horizontal vegetation structure of forests BIB002 BIB005 . • Alterations in the stress levels and adaptation or the disturbance of spectral traits such as the protective mechanisms of leaf hair and cuticles. • Plant functional types (PFTs) or plant strategy types BIB003  BIB013 or the Ellenberg indicator plant species BIB001 BIB006 . • Turnover in species composition like spatial heterogeneity and dissimilarity BIB002 BIB004 BIB007 or spatial structure and distribution of species composition, tree community turnover BIB008 BIB009 BIB010 . • Spectral traits of productivity and diversity and disturbances BIB011 BIB010 . For more examples, see Table 1 and  BIB012 .