Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract An ecological model is developed to estimate annual net primary productivity in twelve North American biomes. Existing models were combined which together address canopy photosynthesis in response to light, temperature, and moisture availability, and account for respiration. Normalized Difference Vegetation Index (NDVI) data from the NOAA-7 Advanced Very High Resolution Radiometer (AVHRR) sensor provided estimates of canopy-intercepted photosynthetically active radiation for the twelve-month study period. Estimates of NPP generated from the model compare favorably with figures reported in the literature. However the model estimates are, in general, less than reported figures, suggesting the possibility of systematic error in the model. Difficulties confronted in specifying certain model parameters are discussed as possible sources of error. The results of this study suggest the promise of remotely sensed measurements for macroscale evaluation and modeling of primary productivity. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> This paper reports on aspects of the design and philosophy of the Ecotron, an integrated series of 16 controlled environmental chambers at the NERC Centre for Population Biology. The Ecotron serves as an experimental means for analysing population and community dynamics and ecosystem processes under controlled physical conditions. Within the chambers, terrestrial experimental communities are assembled into foodwebs of desired complexity from a pool of species selected for their preadaptations to the physical conditions of the Ecotron. These species include decomposers (earthworms, snails, microarthropods and microbes), primary producers (16 species of plants), primary consumers (four species of herbivorous arthropods), and secondary consumers (four species of parasitoids). The design of the Ecotron is unique in several aspects with respect to its blend of biology and technology. It supports small, dynamic communities of up to 30 plant and metazoan species, thereby making it among the more biologically complex controlled environmental systems currently in use. Its architecture permits replication and variation of spatial scale in experimental design. Its artificial climate simulates natural environmental conditions within chambers allowing experimental control over light, water, temperature, humidity, and in the near future CO $_{2}$ and UV-B radiation. Sensors monitor both macro- and micro-environmental conditions of a number of physical factors within the chambers. Preliminary experiments show the Ecotron to be an excellent facility for long-term population and community-level experiments. We discuss the results of one of these early experiments and briefly consider ongoing and future experiments. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract The world's forest ecosystems are in a state of permanent flux at a variety of spatial and temporal scales. Monitoring techniques based on multispectral satellite‐acquired data have demonstrated potential as a means to detect, identify, and map changes in forest cover. This paper, which reviews the methods and the results of digital change detection primarily in temperate forest ecosystems, has two major components. First, the different perspectives from which the variability in the change event has been approached are summarized, and the appropriate choice of digital imagery acquisition dates and interval length for change detection are discussed. In the second part, preprocessing routines to establish a more direct linkage between digital remote sensing data and biophysical phenomena, and the actual change detection methods themselves are reviewed and critically assessed. A case study in temperate forests (north‐central U.S.A.) then serves as an illustration of how the different change detectio... <s> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Predicting the effects of anthropogenic changes in climate, atmospheric composition and land use on vegetation patterns has been a central concern of recent ecological research. This aim has revived the search for classification schemes that can be to group plant species according to their response to specified environmental factors. One way forward is to adopt a hierarchical classification, where different sets of traits are examined depending on growth form. Also, at the level of interpretation, the environmental context and purpose of functional classifications need to be specified explicitly, so that global generalizations can be made by comparing across environments functional classifications derived from similar methodologies. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> A long-term research programme, conducted mainly in northern England, has involved field surveys (1965-77), laboratory screening (1974-96), monitoring of permanent plots (1958 to date) and manipulative experiments (1987 to date). The so-called C-S-R classification of plant functional types developed from all this activity. Patterns of covariation among the traits used in the classification have recently been validated in this journal. The C-S-R classification appears to be applicable to vegetation in general. It thus has considerable potential for interpreting and predicting vegetation and ecosystem properties on a world-wide scale. However, to realize this potential we need to develop simplified procedures to extrapolate the C-S-R system to the many species which have not been the subject of previous ecological investigation. Here we describe a rapid method for attribution of C-S-R type and we test its accuracy in Britain by comparing it with an independent classification based upon more laborious procedures. The new method allocates a functional type to an unknown herbaceous subject using few, simple predictor variables. We have developed spreadsheets to perform all of the necessary calculations. These may be downloaded from the UCPE website at http://www.shef.ac.uk/uni/academic/N-Q/ nuocpe, or obtained by direct application to the E-mail address ucpe@sheffield.ac.uk. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised ... <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Landsat 7 ETM+ provides an opportunity to extend the area and frequency with which we are able to monitor the Earth's surface with fine spatial resolution data. To take advantage of this opportunity it is necessary to move beyond the traditional image-by-image approach to data analysis. A new approach to monitoring large areas is to extend the application of a trained image classifier to data beyond its original temporal, spatial, and sensor domains. A map of forest change in the Cascade Range of Oregon developed with methods based on such generalization shows accuracies comparable to a map produced with current state-of-the-art methods. A test of generalization across sensors to monitor forest change in the Rocky Mountains indicates that Landsat 7 ETM+ data can be combined with earlier Landsat 5 TM data without retraining the classifier. Methods based on generalization require less time and effort than conventional methods and as a result may allow monitoring of larger areas or more frequent monitoring at reduced cost. One key component to achieving this goal is the improved availability and affordability of Landsat 7 imagery. These results highlight the value of the existing Landsat archive and the importance for continuity in the Landsat Program. <s> BIB007 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> In central Amazonian white-water floodplains (vairzea), different forest types become established in relation to the flood-level gradient. The forma- tions are characterized by typical patterns of species composition, and their archi- tecture results in different light reflectance patterns, which can be detected by Landsat TM image data. Ground checking comprised a detailed forest inventory of 4 ha, with Digital Elevation Models (DEM) being generated for all sites. The results indicate that, at the average flood level of 3 m, species diversity and archi- tecture of the forests changes, thus justifying the classification into the categories of low vairzea (vairzea baixa) and high varzea (vairzea alta). In a first step to scale up, the study sites were observed by aerial photography. Tree heights, crown sizes, the projected crown area coverage and the gap frequencies provide information, which confirms a remotely sensed classification into three different forest types. The structure of low vairzea depends on the successional stage, and species divers- ity increases with increasing age of the formations. In high varzea, only one succes- sional stage was found and species diversity is higher than in all low-varzea forma- tions. The more complex architecture of the high-vairzea forest results in a more diffuse behaviour pattern in pixel distribution, when scanned by TM image data. <s> BIB008 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB009 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> The process of forest fragmentation, a common phenomenon occurring in tropical forests, not only results into continuously forest getting fragmented but also brings about several physical and biological changes in the environment of forests. Consequently, there is a loss of biodiversity due to change in habitat conditions. These remnant fragments provide the last hope for biodiversity conservation. The present study deals with the impact of decreasing patch size of a fragmented forest on the diversity of the tropical dry deciduous forests in Vindhyan highlands, India. There is considerable change in the vegetation cover of this region owing to rapid industrialization and urbanization, which has also contributed to forest fragmentation. In the present study, remotely sensed data has been used to describe the changes brought about in vegetated areas over a period of 10 years as a result of fragmentation and its impact on biodiversity was assessed. Further, in order to assess the loss of species with respect to the reduction in patch size, species area curves for various change areas were analysed. It was observed that the rate of decrease in the number of species is faster in the case of negative change areas as compared to the positive change areas of the region. Various diversity indices also support this observation. Such an analysis would help in formulating appropriate conservation measures for the region. <s> BIB010 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract The incorporation of landscape ecological and fragmentation analyses within remote sensing science has expanded the inferential capabilities of such research. This issue presents a series of papers on the use of landscape ecological techniques to explore the relationship between land cover and land use spatial pattern and process in an international, comparative context. Methodologically, researchers seek to link spatial pattern to land use process by integrating geographic information systems (GIS), socio-economic, and remote sensing techniques with landscape ecological approaches. This issue brings together papers at the forefront of this research effort, and illustrates the diversity of methods necessary to evaluate the complex linkages between pattern and process in landscapes across the world. The analyses focus on major forces interacting at the earth’s surface, such as the interface of agricultural and urban land, agriculture and forestry, and other pertinent topics dealing with environmental policy and management. Empirical analyses stem from many different ecological, social and institutional contexts within the Americas, Africa, and Asia. <s> BIB011 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> We used airborne imaging spectroscopy and photon transport modeling to determine how biological invasion altered the chemistry of forest canopies across a Hawaiian montane rain forest landscape. The nitrogen-fixing tree Myrica faya doubled canopy nitrogen concentrations and water content as it replaced native forest, whereas the understory herb Hedychium gardnerianum reduced nitrogen concentrations in the forest overstory and substantially increased aboveground water content. This remote sensing approach indicates the geographic extent, intensity, and biogeochemical impacts of two distinct invaders; its wider application could enhance the role of remote sensing in ecosystem analysis and management. <s> BIB012 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Satellite and high-altitude aerial remote sensing have been used to measure dense infestations of invasive weeds over very large areas but have limited resolution and cannot be used to detect sparsely distributed weeds. Ground-based methods have provided detailed measurements of invasive weeds but can measure only limited areas. Here we test a novel approach that uses a lightweight airplane, flying at 72 km ? h 21 and 100-m altitude, to rapidly collect high-resolution images over relatively large areas. We obtained 1 987 images, each representing 48.5 m 2 of mixed-grass prairie with 2-mm resolution (ground sample distance). From these images we were able to reliably measure small patches and even individual plants of the invasive forb Dalmatian toadflax (Linaria dalmatica [L.] P. Mill.). Ground-based measurements of aboveground toadflax biomass were highly correlated (R 2 . 0.93) with point-intercept and visual-estimate cover measurements from aerial images. The time required to analyze images ranged from 4 to 45 seconds for presence/absence data and from 1 to 6 minutes for cover data. Toadflax was present in 795 of 1 987 images but exceeded 1% cover in only 99 images. Given the observed variation among images in toadflax cover, at least 400 images were needed to precisely estimate the mean toadflax cover of 0.2%. These results suggest that such high-resolution aerial imagery could be used to obtain detailed measurements of many invasive weed populations. It may be most useful for identifying incipient weed infestations and expanding the scale at which population-level attributes of weed populations can be effectively measured. <s> BIB013 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB014 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> article Variation in the foliar chemistry of humid tropical forests is poorly understood, and airborne imaging spectroscopy could provide useful information at leaf and canopy scales. However, variation in canopy structure affects our ability to estimate foliar properties from airborne spectrometer data, yet these structural affects remain poorly quantified. Using leaf spectral (400-2500 nm) and chemical data collected from 162 Australian tropical forest species, along with partial least squares (PLS) analysis and canopy radiative transfer modeling, we determined the strength of the relationship between canopy reflectance and foliar properties under conditions of varying canopy structure. At the leaf level, chlorophylls, carotenoids and specific leaf area (SLA) were highly correlated with leaf spectral reflectance (r=0.90-0.91). Foliar nutrients and water were also well represented by the leaf spectra (r=0.79-0.85). When the leaf spectra were incorporated into the canopy radiative transfer simulations with an idealistic leaf area index (LAI)=5.0, correlations between canopy reflectance spectra and leaf properties increased in strength by 4-18%. The effects of random LAI (=3.0-6.5) variation on the retrieval of leaf properties remained minimal, particularly for pigments and SLA (r=0.92-0.93). In contrast, correlations between leaf nitrogen (N) and canopy reflectance estimates decreased from r=0.87 at constant LAI=5 to r=0.65 with randomly varying LAI=3.0-6.5. Progressive increases in the structural variability among simulated tree crowns had relatively little effect on pigment, SLA and water predictions. However, N and phosphorus (P) were more sensitive to canopy structural variability. Our modeling results suggest that multiple leaf chemicals and SLA can be estimated from leaf and canopy reflectance spectroscopy, and that the high-LAI canopies found in tropical forests enhance the signal via multiple scattering. Finally, the two factors we found to most negatively impact leaf chemical predictions from canopy reflectance were variation in LAI <s> BIB015 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB016 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Tree canopies play an enormous role in the maintenance of tropical forest diversity and ecosystem function, and are therefore central to conservation, management, and resource policy development in tropical regions. However, high-resolution mapping of tropical forest canopies is very difficult, because traditional field, airborne, and satellite measurements cannot resolve the number of canopy species, or particular species of interest, over the large regional scales commensurate with conservation goals and strategies. Newer technologies, such as imaging spectroscopy and light detection and ranging (lidar), are just now reaching performance levels that will allow monitoring of tropical forest diversity from the air, but the methods for applying these technologies are not yet ready. Here, we present concepts that combine chemical and spectral remote sensing perspectives to facilitate canopy diversity mapping. Using examples from our ongoing work in the Hawaiian Islands, we demonstrate how a new “airborne sp... <s> BIB017 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets. <s> BIB018 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract. The current paper reviews the experimental setup of the CarboEurope Experimental Strategy (CERES) campaigns with the aim of providing an overview of the instrumentation used, the data-set and associated modelling. It then assesses progress in the field of regional observation and modelling of carbon fluxes, bringing the papers of this special issue into a somewhat broader context of analysis. Instrumental progress has been obtained in the field of remotely monitoring from tall towers and the experimental planning. Flux measurements from aircraft are now capable, within some constraints, to provide regular regional observations of fluxes of CO 2 , latent and sensible heat. Considerable effort still needs to be put into calibrating the surface schemes of models, as they have direct impact on the input of energy, moisture and carbon fluxes in the boundary layer. Overall, the mesoscale models appear to be capable of simulating the large scale dynamics of the region, but in the fine detail, like the precise horizontal and vertical CO 2 field differences between the models still exist. These errors translate directly into transport uncertainty, when the forward simulations are used in inverse mode. Quantification of this uncertainty, including that of inadequate boundary layer height modelling, still remains a major challenge for state of the art mesoscale models. Progress in inverse models has been slow, but has shown that it is possible to estimate some of the errors involved, and that using the combination of observations. Overall, the capability to produce regional, high-resolution estimates of carbon exchange, exist in potential, but the routine application will require considerable effort, both in the experimental as in the modelling domain. <s> BIB019 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Questions: What is the optimum combination of image dates across a growing season for tree species differentiation in multi-spectral data and how does species composition affect overstorey canopy density? ::: ::: Location: Monks Wood, Cambridgeshire, eastern England, UK. ::: ::: Methods: Six overstorey tree species were mapped using five Airborne Thematic Mapper images acquired across the 2003 growing season (17 March, 30 May, 16 July, 23 September, 27 October). After image pre-processing, supervised maximum likelihood classification was performed on the images and on all two-, three-, four- and five-date combinations. Relationships between tree species composition and canopy density were assessed using regression analyses. ::: ::: Results: The image with the greatest tree species discrimination was acquired on 27/10 when the overstorey species were in different stages of leaf tinting and fall. In this image, tree species were mapped with an overall classification accuracy (OCA) of 71% (kappa 0.63). A similar OCA was achieved from the other four images combined (OCA 72%, kappa 0.64). The highest classification accuracy was achieved by combining three images: 17 March, 16 July, 27 October. This achieved an OCA of 84% (kappa 0.79), increasing to 88% (kappa 0.85) after a post-classification clump and sieve procedure. Canopy height and percentage cover of oak explained 72% of variance in canopy density. ::: ::: Conclusions: The ability to discriminate and map temperate deciduous tree species in airborne multi-spectral imagery is increased using time-series data. An autumn image supplemented with an image from both the green-up and full-leaf phases was optimum. The derived tree species map provides a more powerful ecological tool for determining woodland structural/compositional relationships than field-based measures. <s> BIB020 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> In land change science studies, a cover type is defined by land surface attributes, specifically including the types of vegetation, topography and human structures, which makes it difficult to characterize land cover as discrete classes. One of the challenges in characterizing a land-cover type is to distinguish variability within the class from actual land-cover transformation. The spread of plant invasions in tropical systems is affected by seasonal variations and disturbances such as agricultural activities and fires, making it difficult to determine the spread through thematic classifications. In this paper, we estimate the changes in spatial extent and seasonal variation of bracken fern invasion in Southern Yucatan from 1989 to 2005 by using a linear mixture model (LMM), a widely used method in the classification of remotely sensed data. The results show an increase in areas affected by bracken from 40 km 2 in 1989 to almost 80 km 2 in 2000. Lower estimates of the invasion resulted from data acquired at the end of the dry season (March―May), when bracken mixes with secondary vegetation or is removed by fires. The accuracy of the maps is estimated through the use of sketch maps offarmer's parcels and field data collected from 2000 to 2001. Understanding the spatial distribution and annual variability of bracken fern cover in the region is critical to determining the relation between disturbances such as fire and forest recovery. Using LMM may enhance this understanding by giving a more accurate picture of the extent and distribution of bracken fern invasion. <s> BIB021 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with 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> BIB022 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB023 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract We introduce and test LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery), a new approach to extract spectral trajectories of land surface change from yearly Landsat time-series stacks (LTS). The method brings together two themes in time-series analysis of LTS: capture of short-duration events and smoothing of long-term trends. Our strategy is founded on the recognition that change is not simply a contrast between conditions at two points in time, but rather a continual process operating at both fast and slow rates on landscapes. This concept requires both new algorithms to extract change and new interpretation tools to validate those algorithms. The challenge is to resolve salient features of the time series while eliminating noise introduced by ephemeral changes in illumination, phenology, atmospheric condition, and geometric registration. In the LandTrendr approach, we use relative radiometric normalization and simple cloud screening rules to create on-the-fly mosaics of multiple images per year, and extract temporal trajectories of spectral data on a pixel-by-pixel basis. We then apply temporal segmentation strategies with both regression-based and point-to-point fitting of spectral indices as a function of time, allowing capture of both slowly-evolving processes, such as regrowth, and abrupt events, such as forest harvest. Because any temporal trajectory pattern is allowable, we use control parameters and threshold-based filtering to reduce the role of false positive detections. No suitable reference data are available to assess the role of these control parameters or to test overall algorithm performance. Therefore, we also developed a companion interpretation approach founded on the same conceptual framework of capturing both long and short-duration processes, and developed a software tool to apply this concept to expert interpretation and segmentation of spectral trajectories (TimeSync, described in a companion paper by Cohen et al., 2010). These data were used as a truth set against which to evaluate the behavior of the LandTrendr algorithms applied to three spectral indices. We applied the LandTrendr algorithms to several hundred points across western Oregon and Washington (U.S.A.). Because of the diversity of potential outputs from the LTS data, we evaluated algorithm performance against summary metrics for disturbance, recovery, and stability, both for capture of events and longer-duration processes. Despite the apparent complexity of parameters, our results suggest a simple grouping of parameters along a single axis that balances the detection of abrupt events with capture of long-duration trends. Overall algorithm performance was good, capturing a wide range of disturbance and recovery phenomena, even when evaluated against a truth set that contained new targets (recovery and stability) with much subtler thresholds of change than available from prior validation datasets. Temporal segmentation of the archive appears to be a feasible and robust means of increasing information extraction from the Landsat archive. <s> BIB024 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> article i nfo Availability of free, high quality Landsat data portends a new era in remote sensing change detection. Using dense (~annual) Landsat time series (LTS), we can now characterize vegetation change over large areas at an annual time step and at the spatial grain of anthropogenic disturbance. Additionally, we expect more accurate detection of subtle disturbances and improved characterization in terms of both timing and intensity. For Landsat change detection in this new era of dense LTS, new detection algorithms are required, and new approaches are needed to calibrate those algorithms and to examine the veracity of their output. This paper addresses that need by presenting a new tool called TimeSync for syncing algorithm and human interpretations of LTS. The tool consists of four components: (1) a chip window within which an area of user- defined size around an area of interest (i.e., plot) is displayed as a time series of image chips which are viewed simultaneously, (2) a trajectory window within which the plot spectral properties are displayed as a trajectory of Landsat band reflectance or index through time in any band or index desired, (3) a Google Earth window where a recent high-resolution image of the plot and its neighborhood can be viewed for context, and (4) an Access database where observations about the LTS for the plot of interest are entered. In this paper, we describe how to use TimeSync to collect data over forested plots in Oregon and Washington, USA, examine the data collected with it, and then compare those data with the output from a new LTS algorithm, LandTrendr, described in a companion paper (Kennedy et al., 2010). For any given plot, both TimeSync and LandTrendr partitioned its spectral trajectory into linear sequential segments. Depending on the direction of spectral change associated with any given segment in a trajectory, the segment was assigned a label of disturbance, recovery, or stable. Each segment was associated with a start and end vertex which describe its duration. We explore a variety of ways to summarize the trajectory data and compare those summaries derived from both TimeSync and LandTrendr. One comparison, involving start vertex date and segment label, provides a direct linkage to existing change detection validation approaches that rely on contingency (error) matrices and kappa statistics. All other comparisons are unique to this study, and provide a rich set of means by which to examine algorithm veracity. One of the strengths of TimeSync is its flexibility with respect to sample design, particularly the ability to sample an area of interest with statistical validity through space and time. This is in comparison to the use of existing reference data (e.g., field or airphoto data), which, at best, exist for only parts of the area of interest, for only specific time periods, or are restricted thematically. The extant data, even though biased in their representation, can be used to ascertain the veracity of TimeSync interpretation of change. We demonstrate that process here, learning that what we cannot see with TimeSync are those changes that are not expressed in the forest canopy (e.g., pre-commercial harvest or understory burning) and that these extant reference datasets have numerous omissions that render them less than desirable for representing truth. <s> BIB025 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Community ecologists have long recognized the importance of phenology (the timing of periodic life-history events) in structuring communities. Phenological differences between exotic and native species may contribute to the success of invaders, yet a general theory for how phenology may shape invasions has not been developed. Shifts toward longer growing seasons, tracked by plant and animal species worldwide, heighten the need for this analysis. The concurrent availability of extensive citizen-science and long-term datasets has created tremendous opportunities to test the relationship between phenology and invasion. Here, we (1) extend major theories within community and invasion biology to include phenology, (2) develop a predictive framework to test these theories, and (3) outline available data resources to test predictions. By creating an integrated framework, we show how new analyses of long-term datasets could advance the fields of community ecology and invasion biology, while developing novel strategies for invasive species management. Although we focus here on terrestrial plants, our framework has clear extensions to animal communities and aquatic ecosystems as well. <s> BIB026 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> How plants interact with sunlight is central to the existence of life and provides a window to the functioning of ecosystems. Although the basic properties of leaf spectra have been known for decades, interpreting canopy-level spectra is more challenging because leaf-level effects are complicated by a host of stem- and canopy-level traits. Progress has been made through empirical analyses and models, although both methods have been hampered by a series of persistent challenges. Here, I review current understanding of plant spectral properties with respect to sources of uncertainty at leaf to canopy scales. I also discuss the role of evolutionary convergence in plant functioning and the difficulty of identifying individual properties among a suite of interrelated traits. A pattern that emerges suggests a synergy among the scattering effects of leaf-, stem- and canopy-level traits that becomes most apparent in the near-infrared (NIR) region. This explains the widespread and well-known importance of the NIR region in vegetation remote sensing, but presents an interesting paradox that has yet to be fully explored: that we can often gain more insight about the functioning of plants by examining wavelengths that are not used in photosynthesis than by examining those that are. <s> BIB027 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Airborne light detection and ranging (LiDAR) is fast turning the corner from demonstration technology to a key tool for assessing carbon stocks in tropical forests. With its ability to penetrate tropical forest canopies and detect three-dimensional forest structure, LiDAR may prove to be a major component of international strategies to measure and account for carbon emissions from and uptake by tropical forests. To date, however, basic ecological information such as height-diameter allometry and stand-level wood density have not been mechanistically incorporated into methods for mapping forest carbon at regional and global scales. A better incorporation of these structural patterns in forests may reduce the considerable time needed to calibrate airborne data with ground-based forest inventory plots, which presently necessitate exhaustive measurements of tree diameters and heights, as well as tree identifications for wood density estimation. Here, we develop a new approach that can facilitate rapid LiDAR calibration with minimal field data. Throughout four tropical regions (Panama, Peru, Madagascar, and Hawaii), we were able to predict aboveground carbon density estimated in field inventory plots using a single universal LiDAR model (r ( 2 ) = 0.80, RMSE = 27.6 Mg C ha(-1)). This model is comparable in predictive power to locally calibrated models, but relies on limited inputs of basal area and wood density information for a given region, rather than on traditional plot inventories. With this approach, we propose to radically decrease the time required to calibrate airborne LiDAR data and thus increase the output of high-resolution carbon maps, supporting tropical forest conservation and climate mitigation policy. <s> BIB028 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Tree species diversity is a key parameter to describe forest ecosystems. It is, for example, important for issues such as wildlife habitat modeling and close-to-nature forest management. We examined the suitability of 8-band WorldView-2 satellite data for the identification of 10 tree species in a temperate forest in Austria. We performed a Random Forest (RF) classification (object-based and pixel-based) using spectra of manually delineated sunlit regions of tree crowns. The overall accuracy for classifying 10 tree species was around 82% (8 bands, object-based). The class-specific producer’s accuracies ranged between 33% (European hornbeam) and 94% (European beech) and the user’s accuracies between 57% (European hornbeam) and 92% (Lawson’s cypress). The object-based approach outperformed the pixel-based approach. We could show that the 4 new WorldView-2 bands (Coastal, Yellow, Red Edge, and Near Infrared 2) have only limited impact on classification accuracy if only the 4 main tree species (Norway spruce, Scots pine, European beech, and English oak) are to be separated. However, classification accuracy increased significantly using the full spectral resolution if further tree species were included. Beside the impact on overall classification accuracy, the importance of the spectral bands was evaluated with two measures provided by RF. An in-depth analysis of the RF output was carried out to evaluate the impact of reference data quality and the resulting reliability of final class assignments. Finally, an extensive literature review on tree species classification comprising about 20 studies is presented. <s> BIB029 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB030 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Landsat occupies a unique position in the constellation of civilian earth observation satellites, with a long and rich scientific and applications heritage. With nearly 40 years of continuous observation – since launch of the first satellite in 1972 – the Landsat program has benefited from insightful technical specification, robust engineering, and the necessary infrastructure for data archive and dissemination. Chiefly, the spatial and spectral resolutions have proven of broad utility and have remained largely stable over the life of the program. The foresighted acquisition and maintenance of a global image archive has proven to be of unmatched value, providing a window into the past and fueling the monitoring and modeling of global land cover and ecological change. In this paper we discuss the evolution of the Landsat program as a global monitoring mission, highlighting in particular the recent change to an open (free) data policy. The new data policy is revolutionizing the use of Landsat data, spurring the creation of robust standard products and new science and applications approaches. Open data access also promotes increased international collaboration to meet the Earth observing needs of the 21st century. <s> BIB031 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> We present a generic spatially explicit modeling framework to estimate carbon emissions from deforestation (INPE-EM). The framework incorporates the temporal dynamics related to the deforestation process and accounts for the biophysical and socioeconomic heterogeneity of the region under study. We build an emission model for the Brazilian Amazon combining annual maps of new clearings, four maps of biomass, and a set of alternative parameters based on the recent literature. The most important results are as follows: (a) Using different biomass maps leads to large differences in estimates of emission; for the entire region of the Brazilian Amazon in the last decade, emission estimates of primary forest deforestation range from 0.21 to 0.26 Pg C yr � 1 . (b) Secondary vegetation growth presents a small impact on emission balance because of the short duration of secondary vegetation. In average, the balance is only 5% smaller than the primary forest deforestation emissions. (c) Deforestation rates decreased significantly in the Brazilian Amazon in recent years, from 27 Mkm 2 in 2004 to 7 Mkm 2 in 2010. INPE-EM process-based estimates reflect this decrease even though the agricultural frontier is moving to areas of higher biomass. The decrease is slower than a non-process instantaneous model would estimate as it considers residual emissions (slash, wood products, and secondary vegetation). The average balance, considering all biomass, decreases from 0.28 in 2004 to 0.15 Pg C yr � 1 in 2009; the nonprocess model estimates a decrease from 0.33 to 0.10 Pg C yr � 1 . We conclude that the INPE-EM is a powerful tool for representing deforestation-driven carbon emissions. Biomass estimates are still the largest source of uncertainty in the effective use of this type of model for informing mechanisms such as REDD+. The results also indicate that efforts to reduce emissions should focus not only on controlling primary forest deforestation but also on creating incentives for the restoration of secondary forests. <s> BIB032 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Sources of variation among the chemical and spectral properties of tropical forest canopies are poorly understood, yet chemical traits reveal potential ecosystem and phylogenetic controls, and spectral linkages to chemical traits are needed for remote sensing of functional and biological diversity. We analyzed 21 leaf traits in 395 fully sunlit canopies, representing 232 species and multiple growth forms, in a lowland mixed dipterocarp forest of Sarawak, Malaysia. Leaf traits related to light capture and growth (for example, photosynthetic pigments, nutrients) were up to 55% lower, and defense traits (for example, phenols, lignin) were 15–40% higher, in the dominant family Dipterocarpaceae and in its genus Shorea, as compared to all other canopy species. The chemical variation within Dipterocarpaceae and Shorea was equivalent to that of all other canopy species combined, highlighting the role that a single phylogenetic branch can play in creating canopy chemical diversity. Seventeen of 21 traits had more than 50% of their variation explained by taxonomic grouping, and at least 16 traits show a connection to remotely sensed spectroscopic signatures (RMSE < 15%). It is through these chemical-to-spectral linkages that studies of functional and biological diversity interactions become possible at larger spatial scales, thereby improving our understanding of the role of species in tropical forest ecosystem dynamics. <s> BIB033 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Tree species mapping in forest areas is an important topic in forest inventory. In recent years, several studies have been carried out using different types of hyperspectral sensors under various forest conditions. The aim of this work was to evaluate the potential of two high spectral and spatial resolution hyperspectral sensors (HySpex-VNIR 1600 and HySpex-SWIR 320i), operating at different wavelengths, for tree species classification of boreal forests. To address this objective, many experiments were carried out, taking into consideration: 1) three classifiers (support vector machines (SVM), random forest (RF), and Gaussian maximum likelihood); 2) two spatial resolutions (1.5 m and 0.4 m pixel sizes); 3) two subsets of spectral bands (all and a selection); and 4) two spatial levels (pixel and tree levels). The study area is characterized by the presence of four classes 1) Norway spruce, 2) Scots pine, together with 3) scattered Birch and 4) other broadleaves. Our results showed that: 1) the HySpex VNIR 1600 sensor is effective in boreal tree species classification with kappa accuracies over 0.8 (with Pine and Spruce reaching producer's accuracies higher than 95%); 2) the role of the HySpex-SWIR 320i is limited, and its bands alone are able to properly separate only Pine and Spruce species; 3) the spatial resolution has a strong effect on the classification accuracy (an overall decrease of more than 20% between 0.4 m and 1.5 m spatial resolution); and 4) there is no significant difference between SVM or RF classifiers. <s> BIB034 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Summary 1. Three main directions of adaptive specialization are evident in the world flora, reflecting fundamental trade-offs between economics (conservative vs. acquisitive investment of resources) and size. The current method of ordinating plants according to these trade-offs, CSR classification, cannot be applied to the woody species that dominate many terrestrial ecosystems. 2. We aimed to produce a novel CSR classification method applicable to vascular plants in general. 3. Principal components analysis (PCA) of variation in a range of plant traits for 678 angiosperm, gymnosperm and pteridophyte species was used to determine the limits to multivariate space occupied by functionally diverse species. From this calibration, correlations between PCA axes and values of leaf dry matter content (LDMC; as an index of conservatism in life history), specific leaf area (SLA; indicative of acquisitive economics) and leaf area (LA; photosynthetic organ size) were used to produce predictor regressions from which target species could be compared against the multivariate space. A spreadsheet was developed that returned ternary coordinates and tertiary CSR strategies for target subjects based on LA, LDMC and SLA values. 4. The method allowed classification of target species within a triangular space corresponding to Grime’s theoretical CSR triangle and was sufficiently precise to distinguish strategies between species within genera and within populations of species. It was also largely in agreement with previous methods of CSR classification for herbaceous species. 5. Rapid CSR classification of woody and herbaceous vascular plants is now possible, potentially allowing primary plant functional types and ecosystem processes to be investigated over landscape scales. <s> BIB035 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract We evaluated the potential of a multi-temporal Multiple Endmember Spectral Mixture Analysis (MESMA) for invasive species mapping in Hawaiian rainforests. Earth Observing-1 Hyperion time series data were compiled in a single image cube and ingested into MESMA. While the temporal analysis provided a way to incorporate species phenology, a feature selection technique automatically identified the best time and best spectral feature set to optimize the separability among the native and invasive tree species in our study area. We initiated an alternative Separability Index (SI)-based feature selection approach in which a boundary condition reduced the amount of correlation in the selected spectral subset. We hypothesized that redundant spectral information could be avoided, and improved plant detection accuracy could be achieved, with reduced computational time due to the selection of fewer bands in the mixture analysis. Our analysis showed a systematic increase in the invasive species detection success when we compared the output of multi-temporal MESMA (Kappa = 0.78) with that of the traditional unitemporal approach (Kappa = 0.51–0.69). Even for unitemporal MESMA, in which only a single input image was used, the band selection strategy was beneficial both in plant detection accuracy and computational time. We could further demonstrate that, despite a lack of imagery covering all phenological events, a proper band selection strategy can emphasize subtle spectral and phenological differences between species and can thereby partly compensate for this lack of data. This creates opportunities for mapping in areas where cloud cover is a limiting factor for building extended spectral image time series. This approach is sufficiently general and inherently adaptive, thereby supporting species mapping using Hyperion and forthcoming space-borne imaging spectrometers. <s> BIB036 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Remote sensing image analysis is increasingly being used as a tool for mapping invasive plant species. Resulting distribution maps can be used to target management of early infestations and to model future invasion risk. Remote identification of invasive plants based on differences in spectral signatures is the most common approach, typically using hyperspectral data. But several studies have found that textural and phenological differences are also effective approaches for identifying invasive plants. I review examples of remote detection of invasive plants based on spectral, textural and phenological analysis and highlight circumstances where the different approaches are likely to be most effective. I also review sources and availability of remotely sensed data that could be used for mapping and suggest field data collection approaches that would support the analysis of remotely sensed data. Remote mapping of biological invasions remains a relatively specialized research topic, but the distinct cover, morphology and/or seasonality of many invaded versus native ecosystems suggests that more species could be detected remotely. Remote sensing can sometimes support early detection and rapid response directly, however, accurately detecting small, nascent populations is a challenge. However, even maps of heavily infested areas can provide a valuable tool for risk assessment by increasing knowledge about temporal and spatial patterns and predictors of invasion. <s> BIB037 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Aim ::: ::: Determining the spatial-temporal spread of an invasive plant is vital for understanding long-term impacts. However, invasions have rarely been directly documented given the resources required and the need for substantial foresight. One method widely used is historical photography interpretation, but this can be hard to verify. We attempt to improve this method by linking historical aerial photos to a paleobotanical analysis of pollen cores. ::: ::: ::: ::: Location ::: ::: Laurentian Great Lakes coastal wetlands, United States of America. ::: ::: ::: ::: Methods ::: ::: We chose invasive cattail ( Typha) as our model species because it is identifiable from aerial imagery and has persistent, identifiable pollen, and its ecological impacts appear to be time-dependent. We used Geographic Information Systems, aerial photo-interpretation and field verification to post-dict the invasion history of Typha in several wetland ecosystems. Using 210 Pb and 137 Cs sediment dating and pollen classification, we correlated the temporal dominance of Typha to our estimates of per cent coverage at one site. The pollen record was then used to estimate the Typha invasion dynamics for dates earlier than those for which aerial photos were available. ::: ::: ::: ::: Results ::: ::: Typha spread through time in all study wetlands. Typha pollen dominance increased through time corresponding with increased spatial dominance. Hybrid cattail, T. × glauca increased in pollen abundance relative to T. angustifolia pollen through time. ::: ::: ::: ::: Main conclusions ::: ::: This study illustrates the value of generating historical invasion maps with publically available aerial imagery and linking these maps with paleobotanical data to study recent (< 100 years) invasions. We determined rates of Typha expansion in two coastal wetland types, validated our mapping methods and modelled the relationship between pollen abundance and wetland coverage, enhancing the temporal precision and breadth of analyses. Our methodology should be replicable with similar invasive plant species. The combination of pollen records and historical photography promises to be a valuable additional tool for determining invasion dynamics. <s> BIB038 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change. <s> BIB039 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with 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> BIB040 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun. <s> BIB041 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract An accurate map of forest types is important for proper usage and management of forestry resources. Medium resolution satellite images (e.g., Landsat) have been widely used for forest type mapping because they are able to cover large areas more efficiently than the traditional forest inventory. However, the results of a detailed forest type classification based on these images are still not satisfactory. To improve forest mapping accuracy, this study proposed an operational method to get detailed forest types from dense Landsat time-series incorporating with or without topographic information provided by DEM. This method integrated a feature selection and a training-sample-adding procedure into a hierarchical classification framework. The proposed method has been tested in Vinton County of southeastern Ohio. The detailed forest types include pine forest, oak forest, and mixed-mesophytic forest. The proposed method was trained and validated using ground samples from field plots. The three forest types were classified with an overall accuracy of 90.52% using dense Landsat time-series, while topographic information can only slightly improve the accuracy to 92.63%. Moreover, the comparison between results of using Landsat time-series and a single image reveals that time-series data can largely improve the accuracy of forest type mapping, indicating the importance of phenological information contained in multi-seasonal images for discriminating different forest types. Thanks to zero cost of all input remotely sensed datasets and ease of implementation, this approach has the potential to be applied to map forest types at regional or global scales. <s> BIB042 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Forest disturbances in central Europe caused by fungal pests may result in widespread tree mortality. To assess the state of health and to detect disturbances of entire forest ecosystems, up-to-date knowledge of the tree species diversity is essential. The German state Mecklenburg–Vorpommern is severely affected by ash (Fraxinus excelsior) dieback caused by the fungal pathogen Hymenoscyphus pseudoalbidus. In this study, species diversity and the magnitude of ash mortality was assessed by classifying seven different tree species and multiple levels of damaged ash. The study is based on a multispectral WorldView-2 (WV-2) scene and uses object-based supervised classification methods based on multinomial logistic regressions. Besides the original multispectral image, a set of remote sensing indices (RSI) was derived, which significantly improved the accuracies of classifying different levels of damaged ash but only slightly improved tree species classification. The large number of features was reduced by three approaches, of which the linear discriminant analysis (LDA) clearly outperformed the more commonly used principal component analysis (PCA) and a stepwise selection method. Promising overall accuracies (83%) for classifying seven tree species and (73%) for classifying four different levels of damaged ash were obtained. Detailed tree damage and tree species maps were visually inspected using aerial images. The results are of high relevance for forest managers to plan appropriate cutting and reforestation measures to decrease ash dieback over entire regions. <s> BIB043 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Knowledge of tree species distribution is important worldwide for sustainable forest management and resource evaluation. The accuracy and information content of species maps produced using remote sensing images vary with scale, sensor (optical, microwave, LiDAR), classification algorithm, verification design and natural conditions like tree age, forest structure and density. Imaging spectroscopy reduces the inaccuracies making use of the detailed spectral response. However, the scale effect still has a strong influence and cannot be neglected. This study aims to bridge the knowledge gap in understanding the scale effect in imaging spectroscopy when moving from 4 to 30 m pixel size for tree species mapping, keeping in mind that most current and future hyperspectral satellite based sensors work with spatial resolution around 30 m or more. Two airborne (HyMAP) and one spaceborne (Hyperion) imaging spectroscopy dataset with pixel sizes of 4, 8 and 30 m, respectively were available to examine the effect of scale over a central European forest. The forest under examination is a typical managed forest with relatively homogenous stands featuring mostly two canopy layers. Normalized digital surface model (nDSM) derived from LiDAR data was used additionally to examine the effect of height information in tree species mapping. Six different sets of predictor variables (reflectance value of all bands, selected components of a Minimum Noise Fraction (MNF), Vegetation Indices (VI) and each of these sets combined with LiDAR derived height) were explored at each scale. Supervised kernel based (Support Vector Machines) and ensemble based (Random Forest) machine learning algorithms were applied on the dataset to investigate the effect of the classifier. Iterative bootstrap-validation with 100 iterations was performed for classification model building and testing for all the trials. For scale, analysis of overall classification accuracy and kappa values indicated that 8 m spatial resolution (reaching kappa values of over 0.83) slightly outperformed the results obtained from 4 m for the study area and five tree species under examination. The 30 m resolution Hyperion image produced sound results (kappa values of over 0.70), which in some areas of the test site were comparable with the higher spatial resolution imagery when qualitatively assessing the map outputs. Considering input predictor sets, MNF bands performed best at 4 and 8 m resolution. Optical bands were found to be best for 30 m spatial resolution. Classification with MNF as input predictors produced better visual appearance of tree species patches when compared with reference maps. Based on the analysis, it was concluded that there is no significant effect of height information on tree species classification accuracies for the present framework and study area. Furthermore, in the examined cases there was no single best choice among the two classifiers across scales and predictors. It can be concluded that tree species mapping from imaging spectroscopy for forest sites comparable to the one under investigation is possible with reliable accuracies not only from airborne but also from spaceborne imaging spectroscopy datasets. <s> BIB044 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Landsat 8, a NASA and USGS collaboration, acquires global moderate-resolution measurements of the Earth's terrestrial and polar regions in the visible, near-infrared, short wave, and thermal infrared. Landsat 8 extends the remarkable 40 year Landsat record and has enhanced capabilities including new spectral bands in the blue and cirrus cloud-detection portion of the spectrum, two thermal bands, improved sensor signal-to-noise performance and associated improvements in radiometric resolution, and an improved duty cycle that allows collection of a significantly greater number of images per day. This paper introduces the current (2012–2017) Landsat Science Team's efforts to establish an initial understanding of Landsat 8 capabilities and the steps ahead in support of priorities identified by the team. Preliminary evaluation of Landsat 8 capabilities and identification of new science and applications opportunities are described with respect to calibration and radiometric characterization; surface reflectance; surface albedo; surface temperature, evapotranspiration and drought; agriculture; land cover, condition, disturbance and change; fresh and coastal water; and snow and ice. Insights into the development of derived ‘higher-level’ Landsat products are provided in recognition of the growing need for consistently processed, moderate spatial resolution, large area, long-term terrestrial data records for resource management and for climate and global change studies. The paper concludes with future prospects, emphasizing the opportunities for land imaging constellations by combining Landsat data with data collected from other international sensing systems, and consideration of successor Landsat mission requirements. <s> BIB045 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with 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> BIB046 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB047 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Mapping anthropogenic forest disturbances has largely been focused on distinct delineations of events of deforestation using optical satellite images. In the tropics, frequent cloud cover and the challenge of quantifying forest degradation remain problematic. In this study, we detect processes of deforestation, forest degradation and successional dynamics, using long-wavelength radar (L-band from ALOS PALSAR) backscatter. We present a detection algorithm that allows for repeated disturbances on the same land, and identifies areas with slow- and fast-recovering changes in backscatter in close spatial and temporal proximity. In the study area in Madre de Dios, Peru, 2.3% of land was found to be disturbed over three years, with a false positive rate of 0.3% of area. A low, but significant, detection rate of degradation from sparse and small-scale selective logging was achieved. Disturbances were most common along the tri-national Interoceanic Highway, as well as in mining areas and areas under no land use allocation. A continuous spatial gradient of disturbance was observed, highlighting artefacts arising from imposing discrete boundaries on deforestation events. The magnitude of initial radar backscatter, and backscatter decrease, suggested that large-scale deforestation was likely in areas with initially low biomass, either naturally or since already under anthropogenic use. Further, backscatter increases following disturbance suggested that radar can be used to characterize successional disturbance dynamics, such as biomass accumulation in lands post-abandonment. The presented radar-based detection algorithm is spatially and temporally scalable, and can support monitoring degradation and deforestation in tropical rainforests with the use of products from ALOS-2 and the future SAOCOM and BIOMASS missions. <s> BIB048 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Remote sensing allows for cost- and time-efficient monitoring of landscapes vital to the conservation of natural resources, ecosystems, and biodiversity. This review synthesizes and recommends best practice change detection methods for land management groups to monitor chief ecological change indicators currently monitored in United States protected areas. The indicators frequently monitored via change detection and reviewed here include: land use/land cover, disturbance, and phenology. Landsat data products are recommended for monitoring land use/land cover and disturbance, due to their continuous data accessibility free of cost since 1972. Data from the Moderate Resolution Imaging Spectrometer (MODIS) are recommended for monitoring changes in phenology due to its 1–2 day return interval at any given location. Best-practice remote sensing methods are stressed, such as careful validation of results, either by combination of remotely sensed datasets with high resolution imagery or in situ data, in order to increase accuracy and to better align the remotely sensed data to the scale of the on-the-ground processes. Reported results should always be presented with utmost clarity in a manner that is both applicable to managers and understood by the general public. Increased collaborations between ecologists, land managers, conservation groups, and scientists are compulsory for successful integration of remote sensing-based monitoring, which is vital for effective conservation in protected areas. Remote sensing change detection quantifies the effects of humans on a landscape scale without creating further disturbances to ecologically sensitive areas; the results of which can be used for efficient conservation management into the future. <s> BIB049 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Leaf dark respiration (Rdark ) is an important yet poorly quantified component of the global carbon cycle. Given this, we analyzed a new global database of Rdark and associated leaf traits. Data for 899 species were compiled from 100 sites (from the Arctic to the tropics). Several woody and nonwoody plant functional types (PFTs) were represented. Mixed-effects models were used to disentangle sources of variation in Rdark . Area-based Rdark at the prevailing average daily growth temperature (T) of each site increased only twofold from the Arctic to the tropics, despite a 20°C increase in growing T (8-28°C). By contrast, Rdark at a standard T (25°C, Rdark (25) ) was threefold higher in the Arctic than in the tropics, and twofold higher at arid than at mesic sites. Species and PFTs at cold sites exhibited higher Rdark (25) at a given photosynthetic capacity (Vcmax (25) ) or leaf nitrogen concentration ([N]) than species at warmer sites. Rdark (25) values at any given Vcmax (25) or [N] were higher in herbs than in woody plants. The results highlight variation in Rdark among species and across global gradients in T and aridity. In addition to their ecological significance, the results provide a framework for improving representation of Rdark in terrestrial biosphere models (TBMs) and associated land-surface components of Earth system models (ESMs). <s> BIB050 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Good forest management requires comprehensive and reliable inventory data spanning large areas. Forest management has increasingly relied on remote sensing, specifically light detection and ranging (LiDAR). However, due to the high costs associated with data collection and processing, wall-to-wall LiDAR data is rarely obtained for forests. In contrast, multispectral imagery from optical sensors often covers large extents but they fail to capture detail below the forest canopy and do not directly measure structural attributes. To take advantage of the complementary benefits of different sensors, active LiDAR and passive optical sensors have been combined and applied to problem-solving in a forestry context for over a decade. A review of the literature shows that fusion of different sensors has resulted in superior performance relative to individual sensors for classifying and delineating forest areas (up to 20 % accuracy improvement), identifying species (up to 21 % accuracy improvement), and estimating forest volume and biomass (up to 55 % accuracy improvement). In contrast, sensor fusion achieved only minor improvements for tree or forest height estimation (1–7 % accuracy improvement); this is likely because LiDAR alone is already so effective. This review was unable to draw conclusions on the performance of sensor fusion for forest age and productivity assessment due to the limited number of studies. The lack of results in these areas presents an opportunity for future research. The literature clearly demonstrates the utility of integrating LiDAR and optical data for many aspects of forest description. Perhaps the greatest challenge moving forward will be to operationalise the research such that forestry companies and governments can take advantage of the benefits of data fusion. <s> BIB051 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Fragmentation alters forest canopy structure through various mechanisms, which in turn drive subsequent changes to biogeochemical processes and biological diversity. Using repeated airborne LiDAR (Light Detection and Ranging) mappings, we investigated the size distribution and dynamics of forest canopy gaps across a topical montane forest landscape in Hawaii naturally fragmented by lava >100 yr ago. The combined effects of long-term isolation were evaluated using edge-proximity patterns in the distribution of gaps identified in year 1 (“existing gaps”). In addition, using the data from year 2, we investigated patterns in the loss of canopy (“new gaps”) and regrowth of canopy (“filled gaps”). The size distributions of the three gap types were modelled using the power-law exponent, λ. We found that fragmentation has resulted in some large changes in gap dynamics, with both the total area of gaps and the relative proportion of large existing gaps increased with distance to fragment edge. In models of power-law distribution scaling, λ estimates decreased to asymptotic values within 20 m of fragment edges for existing gaps. The size distributions of new and filled gaps were surprisingly similar. However, the total area of filled gaps was less than that of new gaps, and this difference was greatest near fragment edges. From these results, we conclude that fragmentation may be capable of continuously altering gap-phase dynamics of a forest for more than a century. <s> BIB052 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide. <s> BIB053 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress. <s> BIB054 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Sun-induced chlorophyll fluorescence (SIF) is a radiation flux emitted from chlorophyll molecules and is considered an indicator of the actual functional state of plant photosynthesis. The remote measurement of SIF opens a new perspective to assess actual photosynthesis at larger, ecologically relevant scales and provides an alternative approach to study the terrestrial carbon cycle. Recent studies demonstrated the reliability of measured SIF signals and showed significant relationships between SIF and gross primary production (GPP) at ecosystem and global scales. Despite these encouraging results, understanding the complex mechanisms between SIF and GPP remains challenging before SIF can be finally utilized to constrain estimates of GPP. In this study, we present a comprehensive assessment of the relationship between far-red SIF retrieved at 760 nm (SIF760) and GPP, and its transferability across three structurally and physiologically contrasting ecosystems: perennial grassland, cropland and mixed temperate forest. We use multi-temporal imaging spectroscopy (IS) data acquired with the Airborne Prism EXperiment (APEX) sensor as well as eddy covariance (EC) flux tower data to evaluate the relationship between SIF760 and GPPEC. We use simulations performed with the coupled photosynthesis–fluorescence model SCOPE to prove trends obtained from our observational data and to assess apparent confounding factors such as physiological and structural interferences or temporal scaling effects. Observed relationships between SIF760 and GPPEC were asymptotic and ecosystem-specific, i.e., perennial grassland (R2 = 0.59, rRMSE = 27.1%), cropland (R2 = 0.88, rRMSE = 3.5%) and mixed temperate forest (R2 = 0.48, rRMSE = 15.88%). We demonstrate that asymptotic leaf level relationships between SIF760 and GPPEC became more linear at canopy level and scaled with temporal aggregation. We conclude that remote sensing of SIF provides a new observational approach to decrease uncertainties in estimating GPP across ecosystems but requires dedicated strategies to compensate for the various confounding factors impacting SIF–GPP relationships. Our findings help in bridging the gap between mechanistic understanding at leaf level and ecosystem-specific observations of the relationships between SIF and GPP. <s> BIB055 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Remote estimation of Sun-induced chlorophyll fluorescence emitted by terrestrial vegetation can provide an unparalleled opportunity to track spatiotemporal variations of photosynthetic efficiency. Here we provide the first direct experimental evidence that the two peaks of the chlorophyll fluorescence spectrum can be accurately mapped from high-resolution radiance spectra and that the signal is linked to variations in actual photosynthetic efficiency. Red and far red fluorescence measured using a novel airborne imaging spectrometer over a grass carpet treated with an herbicide known to inhibit photosynthesis was significantly higher than the corresponding signal from an equivalent untreated grass carpet. The reflectance signal of the two grass carpets was indistinguishable, confirming that the fast dynamic changes in fluorescence emission were related to variations in the functional status of actual photosynthesis induced by herbicide application. Our results from a controlled experiment at the local scale illustrate the potential for the global mapping of terrestrial photosynthesis through space-borne measurements of chlorophyll fluorescence. <s> BIB056 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract In 2007, the NASA Hyperspectral InfraRed Imager (HyspIRI) mission was recommended in Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond (Decadal Survey) to address critical science questions in multiple areas, in particular ecosystems and natural hazards. HyspIRI is comprised of two instruments, a visible to short-wavelength infrared (VSWIR) imaging spectrometer and a thermal infrared (TIR) multispectral imager, together with an Intelligent Payload Module (IPM) for onboard processing and rapid downlink of selected data. The VSWIR instrument will have 10 nm contiguous bands and cover the 380–2500 nm spectral range with 30 m spatial resolution and a revisit of 16 days. The TIR instrument will have 8 discrete bands in the 4–13 μm range with 60 m spatial resolution and a revisit of 5 days. With these two instruments in low Earth orbit, HyspIRI will be able to address key science and applications questions in a wide array of fields, ranging from ecosystem function and diversity to human health and urbanization. <s> BIB057 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Remote sensing of vegetation function and traits has advanced significantly over the past half-century in the capacity to retrieve useful plant biochemical, physiological and structural quantities across a range of spatial and temporal scales. However, the translation of remote sensing signals into meaningful descriptors of vegetation function and traits is still associated with large uncertainties due to complex interactions between leaf, canopy, and atmospheric mediums, and significant challenges in the treatment of confounding factors in spectrum-trait relations. This editorial provides (1) a background on major advances in the remote sensing of vegetation, (2) a detailed timeline and description of relevant historical and planned satellite missions, and (3) an outline of remaining challenges, upcoming opportunities and key research objectives to be tackled. The introduction sets the stage for thirteen Special Issue papers here that focus on novel approaches for exploiting current and future advancements in remote sensor technologies. The described enhancements in spectral, spatial and temporal resolution and radiometric performance provide exciting opportunities to significantly advance the ability to accurately monitor and model the state and function of vegetation canopies at multiple scales on a timely basis. <s> BIB058 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract The success of remote sensing approaches to assess tree species diversity in a heterogeneously mixed forest stand depends on the availability of both appropriate data and suitable classification algorithms. To separate the high number of in total ten broadleaf tree species in a small structured floodplain forest, the Leipzig Riverside Forest, we introduce a majority based classification approach for Discriminant Analysis based on Partial Least Squares (PLS-DA), which was tested against Random Forest (RF) and Support Vector Machines (SVM). The classifier performance was tested on different sets of airborne hyperspectral image data (AISA DUAL) that were acquired on single dates in August and September and also stacked to a composite product. Shadowed gaps and shadowed crown parts were eliminated via spectral mixture analysis (SMA) prior to the pixel-based classification. Training and validation sets were defined spectrally with the conditioned Latin hypercube method as a stratified random sampling procedure. In the validation, PLS-DA consistently outperformed the RF and SVM approaches on all datasets. The additional use of spectral variable selection (CARS, “competitive adaptive reweighted sampling”) combined with PLS-DA further improved classification accuracies. Up to 78.4% overall accuracy was achieved for the stacked dataset. The image recorded in August provided slightly higher accuracies than the September image, regardless of the applied classifier. <s> BIB059 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Spatially explicit information on tree species composition of managed and natural forests, plantations and urban vegetation provides valuable information for nature conservationists as well as for forest and urban managers and is frequently required over large spatial extents. Over the last four decades, advances in remote sensing technology have enabled the classification of tree species from several sensor types. While studies using remote sensing data to classify and map tree species reach back several decades, a recent review on the status, potentials, challenges and outlooks in this realm is missing. Here, we search for major trends in remote sensing techniques for tree species classification and discuss the effectiveness of different sensors and algorithms based on a literature review. This review demonstrates that the number of studies focusing on tree species classification has increased constantly over the last four decades and promising local scale approaches have been presented for several sensor types. However, there are few examples for tree species classifications over large geographic extents, and bridging the gap between current approaches and tree species inventories over large geographic extents is still one of the biggest challenges of this research field. Furthermore, we found only few studies which systematically described and examined the traits that drive the observed variance in the remote sensing signal and thereby enable or hamper species classifications. Most studies followed data-driven approaches and pursued an optimization of classification accuracy, while a concrete hypothesis or a targeted application was missing in all but a few exceptional studies. We recommend that future research efforts focus stronger on the causal understanding of why tree species classification approaches work under certain conditions or – maybe even more important - why they do not work in other cases. This might require more complex field acquisitions than those typically used in the reviewed studies. At the same time, we recommend reducing the number of purely data-driven studies and algorithm-benchmarking studies as these studies are of limited value, especially if the experimental design is limited, e.g. the tree population is not representative and only a few sensors or acquisition settings are simultaneously investigated. <s> BIB060 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Accurately mapping the spatial distribution of tree species in tropical environments provides valuable insights for ecologists and forest managers. This process may play an important role in reducing fieldwork costs, monitoring changes in canopy biodiversity, and locating parent trees to collect seeds for forest restoration efforts. However, mapping tree species in tropical forests with remote sensing data is a challenge because of high floristic and spectral diversity. In this research, we discriminated and mapped tree species in tropical seasonal semi-deciduous forests (Brazilian Atlantic Forest Biome) by using airborne hyperspectral and simulated multispectral data in the 450 to 2400 nm wavelength range. After quantifying the spectral variability within and among individual tree crowns of eight species, three supervised machine learning classifiers were applied to discriminate the species at the pixel level. Linear Discriminant Analysis outperformed Support Vector Machines with Linear and Radial Basis Function (RBF-SVMs) kernels and Random Forests in almost all the tested cases. An average classification accuracy of 70% was obtained when using the visible/near-infrared (VNIR, 450–919 nm) bands. The inclusion of shortwave infrared bands (SWIR, 1045–2400 nm) increased the accuracy to 84%. Narrow-band vegetation indices (VIs) were also tested and increased the classification accuracy by up to 5% when combined with VNIR features. Furthermore, the spectral bands of the WorldView-3 (WV-3) satellite sensor were simulated for classification purposes. WV-3 VNIR bands provided an accuracy of 57.4%, which increased to 74.8% when using WV-3 SWIR bands. We also tested the production of species maps by using an object-oriented approach that integrated a novel segmentation algorithm that was tailored to delineate tree crowns and label high class membership pixels inside each object. In this scenario, RBF-SVMs produced the best species maps, correctly identifying 84.9% of crowns with hyperspectral data and 78.5% with simulated WV-3 data. The use of a reduced set of hyperspectral bands, which were selected with stepwise regression, did not significantly affect the classification accuracies but allowed us to depict the most important wavelengths to discriminate the species. These wavelengths were located around the green reflectance peak (550 nm), at the red absorption feature (650 nm) and in the SWIR range at 1200, 1700, 2100 and 2300 nm. These encouraging results suggest the feasibility of the proposed approach for mapping pioneering and climax tree species in the Brazilian Atlantic Forest Biome, highlighting its potential use in forest recovery and inventory initiatives. <s> BIB061 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Remote sensing data allow large scale observation of forested ecosystems. Forest assessment benefits from information about individual trees. Multibaseline SAR interferometry (InSAR) is able to generate dense point clouds of forest canopies, similar to airborne laser scanning (ALS). This type of point cloud was generated using data from the Ka-band MEMPHIS system, acquired over a mainly coniferous forest near Vordemwald in the Swiss Midlands. This point cloud was segmented using an advanced clustering technique to detect individual trees and derive their positions, heights, and crown diameters. To evaluate the InSAR point cloud properties and limitations, it was compared to products derived from ALS and stereo-photogrammetry. All point clouds showed similar geolocation accuracies with 0.2–0.3 m relative shifts. Both InSAR and photogrammetry techniques yielded points predominantly located in the upper levels of the forest vegetation, while ALS provided points from the top of the canopy down to the understory and forest floor. The canopy height models agreed very well with each other, with R 2 values between 0.84 and 0.89. The detected trees and their estimated physical and structural parameters were validated by comparing them to reference forestry data. A detection rate of ~ 90% was achieved for larger trees, corresponding to half of the reference trees. The smaller trees were detected with a success rate of ~ 50%. The tree height was slightly underestimated, with a R 2 value of 0.63. The estimated crown diameter agreed on an average sense, however with a relatively low R 2 value of 0.19. Very high success rates (> 90%) were obtained when matching the trees detected from the InSAR-data with those detected from the ALS- and photogrammetry-data. There, InSAR tree heights were in the mean 1–1.5 m lower, with high R 2 values ranging between 0.8 and 0.9. Our results demonstrate the use of millimeter wave SAR interferometry data as an alternative to ALS- and photogrammetry-based data for forest monitoring. <s> BIB062 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Leverhulme Trust; European Research Council [GEM-TRAITS (321131)]; Natural Environment Research Council [NE/J023418/ 1]; Division of Environmental Biology [1146206, 1457804] <s> BIB063 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Invasive plant species spread presents a challenge, requiring better mapping and monitoring for control. Remote sensing RS provides an efficient tool to map invasive plants in diverse ecosystems. Yet applications of RS for invasive plant mapping largely rely on spatial and spectral patterns. The use of invasive plant functional traits can improve RS mapping, using ecological insights on processes and functions associated with invasion. We summarize research utilizing plant functional traits in RS mapping of invasive species from the years 2000 to 2014. Based on this review, we summarize plant traits that can be related to spatial and spectral properties, and used to discriminate invasive alien plants from native vegetation. Phenological and structural plant traits have been relatively well exploited via RS for invasion studies. In comparison, there has been limited utilization of physiological traits with the exception of properties such as nitrogen content. This is an area that merits further research attention, via the linkage of ecophysiological field research with RS. <s> BIB064 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Human activities in tropical forests, such as logging or shifting cultivation, largely affect forest biodiversity. These disturbances often create small patches of successional vegetation and heterogeneous spatial patterns. Airborne LiDAR can detect small-scale disturbances, which are undetectable by conventional remote sensing. Here, our aim was to evaluate the similarity in community composition of tree assemblages after human disturbances in a tropical rainforest in northern Borneo using small-footprint airborne LiDAR. We derived 16 variables related to the height distribution and canopy characteristics from airborne LiDAR data. The similarity in community composition among plots was calculated using ordination analysis (nonmetric multidimensional scaling) based on the number of trees of each species. LiDAR-derived variables were significantly correlated with the similarity in community composition; the strongest correlation was with the canopy laser penetration rate down to 1-m height above ground (rs = âˆ’ 0.81, p < 0.001). The predictive model for similarity in community composition developed using the canopy laser penetration rate at 0-m height and the maximum height had R2adj = 0.71 (p < 0.001). We applied this equation to the entire study area and compared the output with the human disturbance histories. A predictive map based on the equation suggested that the similarity in community composition changes in proportion to the degree of human disturbances. Our findings indicate that the similarity attributed to human disturbances in tropical forests can be predicted and monitored by means of airborne LiDAR. This approach could be combined with ground-based monitoring data to map the patterns of biodiversity in tropical rainforest. <s> BIB065 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB066 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract South East Asia has the highest rate of lowland forest loss of any tropical region, with logging and deforestation for conversion to plantation agriculture being flagged as the most urgent threats. Detecting and mapping logging impacts on forest structure is a primary conservation concern, as these impacts feed through to changes in biodiversity and ecosystem functions. Here, we test whether high-spatial resolution satellite remote sensing can be used to map the responses of aboveground live tree biomass (AGB), canopy leaf area index (LAI) and fractional vegetation cover (FCover) to selective logging and deforestation in Malaysian Borneo. We measured these attributes in permanent vegetation plots in rainforest and oil palm plantations across the degradation landscape of the Stability of Altered Forest Ecosystems project. We found significant mathematical relationships between field-measured structure and satellite-derived spectral and texture information, explaining up to 62% of variation in biophysical structure across forest and oil palm plots. These relationships held at different aggregation levels from plots to forest disturbance types and oil palms allowing us to map aboveground biomass and canopy structure across the degradation landscape. The maps reveal considerable spatial variation in the impacts of previous logging, a pattern that was less clear when considering field data alone. Up-scaled maps revealed a pronounced decline in aboveground live tree biomass with increasing disturbance, impacts which are also clearly visible in the field data even a decade after logging. Field data demonstrate a rapid recovery in forest canopy structure with the canopy recovering to pre-disturbance levels a decade after logging. Yet, up-scaled maps show that both LAI and FCover are still reduced in logged compared to primary forest stands and markedly lower in oil palm stands. While uncertainties remain, these maps can now be utilised to identify conservation win–wins, especially when combining them with ongoing biodiversity surveys and measurements of carbon sequestration, hydrological cycles and microclimate. <s> BIB067 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km 2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively. <s> BIB068 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization. <s> BIB069 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Implementation of policies to reduce forest loss challenges the Earth observation community to improve forest monitoring. An important avenue for progress is the use of new satellite missions and the combining of optical and synthetic aperture radar sensor data. <s> BIB070 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract The great success of the Brazilian deforestation programme “PRODES digital” has shown the importance of annual deforestation information for understanding and mitigating deforestation and its consequences in Brazil. However, there is a lack of similar information on deforestation for the 1990s and 1980s. Such maps are essential to understand deforestation frontier development and related carbon emissions. This study aims at extending the deforestation mapping record backwards into the 1990s and 1980s for one of the major deforestation frontiers in the Amazon. We use an image compositing approach to transform 2224 Landsat images in a spatially continuous and cloud free annual time series of Tasseled Cap Wetness metrics from 1984 to 2012. We then employ a random forest classifier to derive annual deforestation patterns. Our final deforestation map has an overall accuracy of 85% with half of the overall deforestation being detected before the year 2000. The results show for the first time detailed patterns of the expanding deforestation frontier before the 2000s. The high degree of automatization exhibits the great potential for mapping the whole Amazon biome using long-term and freely accessible remote sensing collections, such as the Landsat archive and forthcoming Sentinel-2 data. <s> BIB071 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales. <s> BIB072 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted. <s> BIB073 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with 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> BIB074 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract The newly launched Landsat 8 satellite continues the long and extremely important record of Earth observation from the Landsat program. We analyzed differences between Landsat 7 and Landsat 8 surface reflectances and cirrus cloud characterization to address how substitutable Landsat 8 observations are within this long archive. Comparison of surface reflectance estimates acquired near simultaneously during Landsat 8's underflight orbital placement shows that Landsat 8 surface reflectance is consistently darker in the blue, green, and red bands and brighter in the near infrared than in Landsat 7. Vegetation indices that rely on the visible and near infrared bands should be used with caution as individual biases in index components can be amplified to create large biases in vegetation indices. We also analyzed time series datasets from the Landsat Climate Data Record (CDR) surface reflectance product across four scenes that contained only Landsat 7 data, Landsat 7 data and only Landsat 8 data post-launch, and Landsat 7 data and data from both sensors post-launch to investigate how sensor differences propagate in time series analysis. If left uncorrected or unexplained, the difference in reflectance between Landsat 7 and Landsat 8 creates spurious time trends in visible wavelengths and in the Normalized Difference Vegetation Index (NDVI). The introduction of Landsat 8 into time series of Landsat 7 data also biases the mean reflectance or vegetation index value as measured by a time series model intercept while increasing the Root Mean Squared Error of such models. We characterized the spectral reflectance of cirrus clouds in the underflight data that were omitted from Landsat 7 cloud masks but were detected in Landsat 8's cloud mask due to the use of the newly added cirrus band. While these cirrus cloud observations missed in Landsat 7's cloud mask are only slightly brighter in the visible bands, a simulation of time series containing Landsat 8 data that does not use the cirrus band shows that omission of cirrus clouds can result in anomalously brighter time series intercepts and positive time trends. Our results indicate that while Landsat 8 has improved on the legacy of previous sensors through increased radiometric resolution, better cloud identification, and better geometric accuracy, difference in reflectance between sensors in the current Landsat CDR product must be corrected or explained within time series analysis to avoid deleterious consequences. Future efforts should identify the contributions of target specific effects versus differences in atmospheric correction methods to better inform approaches to synthesize the two sensors. <s> BIB075 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract The pine forests in the southern portion of the Rocky Mountains are a heterogeneous mosaic of disturbance and recovery. The most extensive and intensive stress and mortality are received from human activity, fire, and mountain pine beetles (MPB; Dendroctonus ponderosae ). Understanding disturbance interactions and disturbance-succession pathways are crucial for adapting management strategies to mitigate their impacts and anticipate future ecosystem change. Driven by this goal, we assessed the forest disturbance and recovery history in the Southern Rocky Mountains Ecoregion using a 13-year time series of Landsat image stacks. An automated classification workflow that integrates temporal segmentation techniques and a random forest classifier was used to examine disturbance patterns. To enhance efficiency in selecting representative samples at the ecoregion scale, a new sampling strategy that takes advantage of the scene-overlap among adjacent Landsat images was designed. The segment-based assessment revealed that the overall accuracy for all 14 scenes varied from 73.6% to 92.5%, with a mean of 83.1%. A design-based inference indicated the average producer’s and user’s accuracies for MPB mortality were 85.4% and 82.5% respectively. We found that burn severity was largely unrelated to the severity of pre-fire beetle outbreaks in this region, where the severity of post-fire beetle outbreaks generally decreased in relation to burn severity. Approximately half the clear-cut and burned areas were in various stages of recovery, but the regeneration rate was much slower for MPB-disturbed sites. Pre-fire beetle outbreaks and subsequent fire produced positive compound effects on seedling reestablishment in this ecoregion. Taken together, these results emphasize that although multiple disturbances do play a role in the resilience mechanism of the serotinous lodgepole pine, the overall recovery could be slow due to the vast area of beetle mortality. <s> BIB076 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Abstract Native vegetation around the world is under threat from historical and ongoing clearance, overgrazing, invasive species, increasing soil and water salinity, altered fire regimes, poor land management and other factors, resulting in a degradation of natural ecosystem services. Consequently, maintaining and improving native vegetation condition is a target frequently adopted by natural resource managers and government agencies world-wide. Adequate monitoring of vegetation condition remains a prerequisite for environmental decision-making and for tracking progress towards management goals. Throughout we consider vegetation condition to include the compositional, structural and functional attributes of vegetation relative to undisturbed vegetation of the same type. Site-based methods have long been used to assess compositional, structural and functional attributes as indicators of vegetation condition, and these methods continue to be used widely today. With developing technologies, remote sensing methods are being employed increasingly for monitoring a range of remotely detectable properties of vegetation, and there is now a growing demand to explicitly integrate the two approaches for mapping and monitoring vegetation condition across a range of scales. Here we review the attributes of vegetation identified as important for monitoring vegetation condition, those indicators that are best measured using traditional site-based methods and those that are more readily detectable using remote sensing methods, including their application in operational programmes within Australia. Further to this we review recent literature on the integration of the two approaches for monitoring indicators of vegetation condition. We find that remote sensing methods have the advantage of offering broad scale automated and repeatable methods for monitoring indicators of vegetation condition, but when combined with detailed ecological site-based data, together can improve monitoring for answering ecological questions across a range of scales. Further work, however, is required to effectively integrate the two approaches for mapping and monitoring vegetation condition. <s> BIB077 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB078 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation. <s> BIB079 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Mapping aboveground carbon density in tropical forests can support CO2 emission monitoring and provide benefits for national resource management. Although LiDAR technology has been shown to be useful for assessing carbon density patterns, the accuracy and generality of calibrations of LiDAR-based aboveground carbon density (ACD) predictions with those obtained from field inventory techniques should be intensified in order to advance tropical forest carbon mapping. Here we present results from the application of a general ACD estimation model applied with small-footprint LiDAR data and field-based estimates of a 50-ha forest plot in Ecuador’s Yasuni National Park. Subplots used for calibration and validation of the general LiDAR equation were selected based on analysis of topographic position and spatial distribution of aboveground carbon stocks. The results showed that stratification of plot locations based on topography can improve the calibration and application of ACD estimation using airborne LiDAR (R2 = 0.94, RMSE = 5.81 Mg·C·ha−1, BIAS = 0.59). These results strongly suggest that a general LiDAR-based approach can be used for mapping aboveground carbon stocks in western lowland Amazonian forests. <s> BIB080 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Distributions of foliar nutrients across forest canopies can give insight into their plant functional diversity and improve our understanding of biogeochemical cycling. We used airborne remote sensing and partial least squares regression to quantify canopy foliar nitrogen (foliar N) across ~164 km2 of wet lowland tropical forest in the Osa Peninsula, Costa Rica. We determined the relative influence of climate and topography on the observed patterns of foliar N using a gradient boosting model technique. At a local scale, where climate and substrate were constant, we explored the influence of slope position on foliar N by quantifying foliar N on remnant terraces, their adjacent slopes, and knife-edged ridges. In addition, we climbed and sampled 540 trees and analyzed foliar N in order to quantify the role of species identity (phylogeny) and environmental factors in predicting foliar N. Observed foliar N heterogeneity reflected environmental factors working at multiple spatial scales. Across the larger landscape, elevation and precipitation had the highest relative influence on predicting foliar N (30% and 24%), followed by soils (15%), site exposure (9%), compound topographic index (8%), substrate (6%), and landscape dissection (6%). Phylogeny explained ~75% of the variation in the field collected foliar N data, suggesting that phylogeny largely underpins the response to the environmental factors. Taken together, these data suggest that a large fraction of the variance in foliar N across the landscape is proximately driven by species composition, though ultimately this is likely a response to abiotic factors such as climate and topography. Future work should focus on the mechanisms and feedbacks involved, and how shifts in climate may translate to changes in forest function. <s> BIB081 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets. <s> BIB082 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> The 2012-2015 drought has left California with severely reduced snowpack, soil moisture, ground water, and reservoir stocks, but the impact of this estimated millennial-scale event on forest health is unknown. We used airborne laser-guided spectroscopy and satellite-based models to assess losses in canopy water content of California's forests between 2011 and 2015. Approximately 10.6 million ha of forest containing up to 888 million large trees experienced measurable loss in canopy water content during this drought period. Severe canopy water losses of greater than 30% occurred over 1 million ha, affecting up to 58 million large trees. Our measurements exclude forests affected by fire between 2011 and 2015. If drought conditions continue or reoccur, even with temporary reprieves such as El Niño, we predict substantial future forest change. <s> BIB083 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> The Spectranomics approach to tropical forest remote sensing has established a link between foliar reflectance spectra and the phylogenetic composition of tropical canopy tree communities vis-a-vis the taxonomic organization of biochemical trait variation. However, a direct relationship between phylogenetic affiliation and foliar reflectance spectra of species has not been established. We sought to develop this relationship by quantifying the extent to which underlying patterns of phylogenetic structure drive interspecific variation among foliar reflectance spectra within three Neotropical canopy tree communities with varying levels of soil fertility. We interpreted the resulting spectral patterns of phylogenetic signal in the context of foliar biochemical traits that may contribute to the spectral-phylogenetic link. We utilized a multi-model ensemble to elucidate trait-spectral relationships, and quantified phylogenetic signal for spectral wavelengths and traits using Pagel’s lambda statistic. Foliar reflectance spectra showed evidence of phylogenetic influence primarily within the visible and shortwave infrared spectral regions. These regions were also selected by the multi-model ensemble as those most important to the quantitative prediction of several foliar biochemical traits. Patterns of phylogenetic organization of spectra and traits varied across sites and with soil fertility, indicative of the complex interactions between the environmental and phylogenetic controls underlying patterns of biodiversity. <s> BIB084 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Although canopy height has long been a focus of interest in ecology, it has remained difficult to study at large spatial scales. Recently, satellite-borne LiDAR equipment produced the first systematic high resolution maps of vegetation height worldwide. Here we show that this new resource reveals three marked modes in tropical canopy height ~40, ~12, and ~2 m corresponding to forest, savanna, and treeless landscapes. The distribution of these modes is consistent with the often hypothesized forest-savanna bistability and suggests that both states can be stable in areas with a mean annual precipitation between ~1,500 and ~2,000 mm. Although the canopy height states correspond largely to the much discussed tree cover states, there are differences, too. For instance, there are places with savanna-like sparse tree cover that have a forest-like high canopy, suggesting that rather than true savanna, those are thinned relicts of forest. This illustrates how complementary sets of remotely sensed indicators may provide increasingly sophisticated ways to study ecological phenomena at a global scale. <s> BIB085 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB086 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Evidence of shifting dominance among major forest disturbance agent classes regionally to globally has been emerging in the literature. For example, climate-related stress and secondary stressors on forests (e.g., insect and disease, fire) have dramatically increased since the turn of the century globally, while harvest rates in the western US and elsewhere have declined. For shifts to be quantified, accurate historical forest disturbance estimates are required as a baseline for examining current trends. We report annual disturbance rates (with uncertainties) in the aggregate and by major change causal agent class for the conterminous US and five geographic subregions between 1985 and 2012. Results are based on human interpretations of Landsat time series from a probability sample of 7200 plots (30 m) distributed throughout the study area. Forest disturbance information was recorded with a Landsat time series visualization and data collection tool that incorporates ancillary high-resolution data. National rates of disturbance varied between 1.5% and 4.5% of forest area per year, with trends being strongly affected by shifting dominance among specific disturbance agent influences at the regional scale. Throughout the time series, national harvest disturbance rates varied between one and two percent, and were largely a function of harvest in the more heavily forested regions of the US (Mountain West, Northeast, and Southeast). During the first part of the time series, national disturbance rates largely reflected trends in harvest disturbance. Beginning in the mid-90s, forest decline-related disturbances associated with diminishing forest health (e.g., physiological stress leading to tree canopy cover loss, increases in tree mortality above background levels), especially in the Mountain West and Lowland West regions of the US, increased dramatically. Consequently, national disturbance rates greatly increased by 2000, and remained high for much of the decade. Decline-related disturbance rates reached as high as 8% per year in the western regions during the early-2000s. Although low compared to harvest and decline, fire disturbance rates also increased in the early- to mid-2000s. We segmented annual decline-related disturbance rates to distinguish between newly impacted areas and areas undergoing gradual but consistent decline over multiple years. We also translated Landsat reflectance change into tree canopy cover change information for greater relevance to ecosystem modelers and forest managers, who can derive better understanding of forest-climate interactions and better adapt management strategies to changing climate regimes. Similar studies could be carried out for other countries where there are sufficient Landsat data and historic temporal snapshots of high-resolution imagery. <s> BIB087 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> In a recent Forum paper, Wardle (Journal of Vegetation Science, 2016) questions the value of biodiversity–ecosystem function (BEF) experiments with respect to their implications for biodiversity changes in real world communities. The main criticism is that the previous focus of BEF experiments on random species assemblages within each level of diversity has ‘limited the understanding of how natural communities respond to biodiversity loss.’ He concludes that a broader spectrum of approaches considering both non-random gains and losses of diversity is essential to advance this field of research. Wardle's paper is timely because of recent observations of frequent local and regional biodiversity changes across ecosystems. While we appreciate that new and complementary experimental approaches are required for advancing the field, we question criticisms regarding the validity of BEF experiments. Therefore, we respond by briefly reiterating previous arguments emphasizing the reasoning behind random species composition in BEF experiments. We describe how BEF experiments have identified important mechanisms that play a role in real world ecosystems, advancing our understanding of ecosystem responses to species gains and losses. We discuss recent examples where theory derived from BEF experiments enriched our understanding of the consequences of biodiversity changes in real world ecosystems and where comprehensive analyses and integrative modelling approaches confirmed patterns found in BEF experiments. Finally, we provide some promising directions in BEF research. <s> BIB088 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <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> BIB089 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Monitoring Stress on Vegetation in FES with RS <s> Detecting forest disturbances is an important task in formulating mitigation strategies for deforestation and forest degradation in the tropics. Our study investigated the use of Landsat time series imagery combined with a trajectory-based analysis for detecting forest disturbances resulting exclusively from selective logging in Myanmar. Selective logging was the only forest disturbance and degradation indicator used in this study as a causative force, and the results showed that the overall accuracy for forest disturbance detection based on selective logging was 83.0% in the study area. The areas affected by selective logging and other factors accounted for 4.7% and 5.4%, respectively, of the study area from 2000 to 2014. The detected disturbance areas were underestimated according to error assessments; however, a significant correlation between areas of disturbance and numbers of harvested trees during the logging year was observed, indicating the utility of a trajectory-based, annual Landsat imagery ti... <s> BIB090
Tree species richness and diversity are key parameters for describing the status, stability and the resilience of FES BIB059 . Numerous studies have mentioned the potential of RS technologies to record tree species taxonomy, richness and diversity BIB059 BIB034 BIB060 BIB029 BIB061 . As already described in detail in Chapter 3.1, the discrimination and monitoring of different taxa in forest plant species with RS is only possible when tree species can be differentiated from one another by their ST/STV (biochemical & biophysical ST, physiognomic & morphological ST or phenotypical ST, see Figure 2 ). Broadband RS sensors record less ST/STV than narrowband hyperspectral. Therefore, the forest tree species discrimination for broadband RS sensors such as Landsat, Spot, RapidEye, Worldview-2 and QuickBird is possible, but not as successful BIB029 BIB061 BIB042   BIB043 as narrowband hyperspectral RS data BIB017 BIB059 BIB034 BIB060 BIB047 . Immitzer et al., BIB029 used very high spatial resolution 8-band WordView-2 Satellite Data for classifying 10 tree species with an overall accuracy of around 82% (eight bands, object-based). Magnard et al., BIB062 used SAR interferometry data as an alternative to airborne laser scanning (ALS) or photogrammetry-based RS for discriminating various tree species. By implementing multi-sensor approaches (optical, thermal, RADAR, LiDAR) as well as multi-temporal approaches, the number and type of ST/STV is increased, whereby a greater number of tree species can be differentiated. In this way, Elatawneh  were able to improve the discrimination for forest tree species by including multi-temporal phenological stages. Hill et al., BIB020 also used time-series multi-spectral data for the discrimination of tree species in temperate deciduous forest, whereas Neyret et al., BIB063 integrated ST indicators such as the variation of leaf mass per area to investigate dominant species across two contrasting tropical gradients in the context of community assemblages. Pierce et al., BIB035 used the leaf economics and size traits of CSR plant functional types to classify woody and herbaceous vascular plants. Various authors have also used multi-sensor approaches whereby the fusion of optical, RADAR, thermal and LiDAR data BIB048 BIB044 extends the spectrum of recordable ST/STV, which ultimately leads to an improved discrimination of different tree species  . Invasive species: Various studies have shown that invasive plant species in FES can be mapped using RS techniques BIB012 BIB036 BIB037 BIB064 . Asner and Martin BIB015 found that the biochemistry of invasive species is very different compared to that of native species. The ST/STV are therefore crucial indicators of the spectral discrimination of invasive species  . Asner and Martin BIB015 used the biochemical and biophysical ST of leaves, while Schneider and Fernando BIB021 used plant pigments and leaf coloration for successful mapping. Blumenthal et al., BIB013 integrated structural ST in addition to purely biochemical ST or indicators of structural homogeneity, like those that emerge from the specific arrangement of monocultures BIB038 . Somers and Asner BIB036 , on the other hand, used multi-temporal Hyperion spaceborne imaging spectroscopy for invasive species mapping in Hawaiian rainforests. Apart from spectral biochemical, morphological and texture traits, other plant traits have also been used for species distinctions such as seasonality and phenology BIB037 BIB026 . In addition to recording tree species taxonomy and distribution, the monitoring of forest populations and forest community structures BIB008 or forest types is also possible using RS, although it is also subject to the same constraints as for the discrimination of tree species. Ioki et al., BIB065 assessed the similarity between tree community compositions in a tropical rainforest using airborne LiDAR RS data, whereas Laurin et al., BIB066 recorded tropical forest types, dominant species, and functional guilds in FES by using hyperspectral and simulated multispectral Sentinel-2 data. Plant functional types and plant strategy types: Environmental stress is a major factor, influencing plant traits and trait combinations  BIB004 . Recurring combinations of structural, physiological or phenological traits are used to group plant species into Plant Functional Types (PFTs) BIB009 BIB030  . PFTs are therefore important indicators of resource limitations or various stressors on FES, which can be measured using RS technologies BIB022 . The most important examples of PFTs are the CSR (Competitive, Ruderal, Stress tolerant) strategy types by  , where plant traits change depending on their stressors and disturbance regimes. According to this system, community composition in FES is established from ruderal plants, competitive plants, and stress-tolerant plants. Plant traits such as the dry matter content, the canopy height, the onset of flowering, the flowering period, lateral spread, specific leaf area and leaf dry weight are all crucial factors for the position and mapping of plant species in the CSR trait space BIB030  BIB089 BIB005 . Land Use Land Cover (LULC) in forests can cause changes in the taxonomic, structural and functional diversity. Changes in LULC influence forest plant and community traits, leading to trait variations in forest ecosystems in their entire complexity  BIB009 BIB014 BIB016 . For example, different forest management strategies are characterised by specific ST/STV. Pristine forests display a heterogeneous ST/STV in space and over time due to different forest tree species, different traits in growth forms, age, geometry or phenology and senescence compared to the relatively homogenous ST/STV in single species forest stands. Garnier et al., BIB014 were able to prove a direct relationship between plant traits (regeneration traits such as reproductive plant height and flowering phenology) as a response to land-use changes and disturbance regimes. Crucial changes to forest traits can be measured by spatial-temporal analyses of forest heterogeneity patterns BIB023 , 3D distribution of forest canopy species patches  , forest fragmentation  BIB010 BIB011   , mapping of forest structures BIB067 , or estimating forest area and forest biomass with TanDEM-X and RapidEye RS Data BIB068 . For more examples, see Table 1 . Monitoring forest cover changes or forest area dynamic on the local to the global scale has for some time now been extensively and successfully conducted using RS BIB039 BIB069 BIB003 BIB007  BIB070 BIB071  helped along by open access and forward-thinking data policies with free use and storage in databases and archives from different RS data and data products BIB031  BIB006 Furthermore, global environmental programs such as REDD+ (Reducing Emissions from forest Degradation and Deforestation) have been developed and require intensive forest cover monitoring. In addition to multi-sensor RS approaches with various RS sensors BIB072 , multi-temporal RS have frequently been employed for LULC monitoring (for an extensive discussion see BIB073 BIB074 . There are numerous remote-sensing data products and methods for the different types of RS data, which help us to use data for monitoring forest stress, disturbance and FH in a pre-processed and standardized way. These include the LandSat change detection products (CCDC), which integrate data from a 40-year monitoring period BIB075 BIB045 . Furthermore, LandTrend (Landsat Detection of Trends in Disturbance and Recovery,  ) is a trajectory-based land cover change detection method BIB024 which analyses time-series of Landsat RS data at once. The LandTrend algorithms can be used to analyse, monitor and describe sequences of disturbances and regrowth in FES. Comprehensive analyses of FH have been conducted based on LandTrend and LandSat Time Series (LTS) BIB025  BIB076 BIB090  . Comprehensive information and reviews of remote-sensing change detection and ecological monitoring can be found in BIB049 BIB077 . Functional and structural stress: RS approaches are crucial for assessing the structure and functioning of FES BIB078 BIB022 . This is based on functional traits sharing similar functions and showing similar effects and responses to environmental stressors, disturbances or resource limitations BIB009 BIB046 . Here, the ST/STV paradigm helps to understand the effects of stress on functional diversity such as interactions between the absorbed radiation (APAR) and plant productivity (net primary production, NPP), BIB001 , the relationship between biodiversity and carbon stocks BIB079 or interactions between canopy structure, canopy nitrogen and canopy near-infrared (near-IR) reflectance detected by RS BIB027 . Moreover, FES are crucial carbon pools and, therefore, important ecosystems in the global carbon cycle. Changes of carbon stocks are observed and monitored extensively using RS technologies BIB028 BIB079 BIB018 BIB032 BIB080 . Similarly, canopy foliar N distribution in FES BIB081 , the global variability in leaf respiration in relation to plant functional types, leaf traits and the climate BIB050 , the assessment of forest biomass BIB082 , or the loss of canopy water in FES BIB083 or the shifting distributions of chemical traits in forest  are all monitored using RS. Furthermore, the biochemical, structural and functional characteristics of ST/STV such as photosynthetic pathways, pigment and nitrogen content, 2/3 D plant/canopy vegetation height, or leaf phenology are crucial for assessing ecosystem functions and ecosystem services such as photosynthetic activities, primary production, gas exchange and climate regulation BIB040 . In addition to monitoring stress on functional diversity, it is also possible to monitor changes to structural diversity in FES using RS. This ranges from analysing the composition, configuration and distribution of phylogenetic structures BIB084 to molecular biochemical structures and patterns in plants   BIB033 to 2/3 D individual geometry and structure  BIB051 BIB085 , populations and community structures BIB047 BIB086 BIB066 and the recording of patterns and fragmentation in FES BIB052 . Further examples of monitoring stress and changes to the functional and structural diversity can also be recorded very well using RS. For an extensive list of other examples, see BIB053 BIB078  BIB074 BIB022 BIB087 . In the field of investigations on vegetation stress, rapid advances are currently taking place in the development of new RS technologies. For example, plant photosynthesis conditions and their changes are crucial for modelling carbon fluxes and water exchanges in soil-landscape-atmosphere modelling. However, the spatio-temporal variation of photosynthesis activity of the canopy vegetation shows great variations in seasonal, diurnal and spatial patterns but cannot be predicted sufficiently with conventional RS techniques BIB054 BIB019 . At the same time, forecasts on stress and shifts in the photosynthesis system are currently only indirectly possible through plant traits of greenness and the amount of chlorophyll. The development of sensors with a high spectral resolution (0.3-3.0 µm, Fluorescence Explorer Satellite, FLEX, ESA, 2018,  means that it is now possible for the first time to directly measure functional plant traits such as the solar-induced chlorophyll fluorescence signal as an indicator of photosynthesis conditions and efficiency in order to make better estimates of global photosynthetic activity, CO 2 fluxes and budgets  BIB054  BIB055 BIB056 . Furthermore, the new airborne instrument HyPlant was developed to monitor vegetation over a spectral range of 380-2500 nm (spectral resolution of 3-10 nm) and an additional segment, to monitor the chlorophyll fluorescence over the spectral region from 670 to 780 nm (with a high spectral resolution of 0.25 nm) as an indicator of photosynthesis conditions as well as to effectively detect stress and shifts in photosynthesis for vegetation BIB054 . Research at plant phenomics facilities BIB041 and the controlled environmental facility Ecotron BIB002 BIB088 have made tremendous contributions towards the development of FLEX and HyPlant sensors  . In the future, RS sensors will be enhanced with high spectral, spatial and temporal resolution, on the one hand, such as HyPlant, FLEX, EnMAP as well as the future hyperspectral/thermals combination HyspIRI (to be launched in 2020,  ) or the multispectral/hyperspectral RS combination HISIRI (Hyperspectral Imager Suite) and used in combination with other RS techniques (multi-sensor) such as RADAR or thermal radiometer (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS, scheduled for 2017, [501]), in order to increase the number of spectral traits and spectral trait variations of forest vegetation that can be recorded and to obtain better predictions about the resilience, stability or stress and instability of FES BIB057 BIB058 .