Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> Abstract Analyses of various biophysical and biochemical factors affecting plant canopy reflectance have been carried out over the past few decades, yet the relative importance of these factors has not been adequately addressed. A combination of field and modeling techniques were used to quantify the relative contribution of leaf, stem, and litter optical properties (incorporating known variation in foliar biochemical properties) and canopy structural attributes to nadir-viewed vegetation reflectance data. Variability in tissue optical properties was wavelength-dependent. For green foliage, the lowest variation was in the visible (VIS) spectral region and the highest in the near-infrared (NIR). For standing litter material, minimum variation occurred in the VIS/NIR, while the largest differences were observed in the shortwave-IR (SWIR). Woody stem material showed opposite trends, with lowest variation in the SWIR and highest in the NIR. Leaf area index (LAI) and leaf angle distribution (LAD) were the dominant controls on canopy reflectance data with the exception of soil reflectance and vegetation cover in sparse canopies. Leaf optical properties (and thus foliar chemistry) were expressed most directly at the canopy level in the NIR, but LAI and LAD strongly controlled the relationship between leaf and canopy spectral characteristics. Stem material played a small but significant role in determining canopy reflectance in woody plant canopies, especially those with LAI <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> The paper revises and clarifies definitions of the term scale and scaling conversions for imaging spectroscopy of soil and vegetation. We demonstrate a new four-dimensional scale concept that includes not only spatial but also the spectral, directional and temporal components. Three scaling remote sensing techniques are reviewed: (1) radiative transfer, (2) spectral (un)mixing, and (3) data fusion. Relevant case studies are given in the context of their up- and/or down-scaling abilities over the soil/vegetation surfaces and a multi-source approach is proposed for their integration. ::: Radiative transfer (RT) models are described to show their capacity for spatial, spectral up-scaling, and directional down-scaling within a heterogeneous environment. Spectral information and spectral derivatives, like vegetation indices (e.g. TCARI/OSAVI), can be scaled and even tested by their means. Radiative transfer of an experimental Norway spruce (Picea abies (L.) Karst.) research plot in the Czech Republic was simulated by the Discrete Anisotropic Radiative Transfer (DART) model to prove relevance of the correct object optical properties scaled up to image data at two different spatial resolutions. Interconnection of the successive modelling levels in vegetation is shown. A future development in measurement and simulation of the leaf directional spectral properties is discussed. ::: We describe linear and/or non-linear spectral mixing techniques and unmixing methods that demonstrate spatial down-scaling. Relevance of proper selection or acquisition of the spectral endmembers using spectral libraries, field measurements, and pure pixels of the hyperspectral image is highlighted. An extensive list of advanced unmixing techniques, a particular example of unmixing a reflective optics system imaging spectrometer (ROSIS) image from Spain, and examples of other mixture applications give insight into the present status of scaling capabilities. ::: Simultaneous spatial and temporal down-scaling by means of a data fusion technique is described. A demonstrative example is given for the moderate resolution imaging spectroradiometer (MODIS) and LANDSAT Thematic Mapper (TM) data from Brazil. Corresponding spectral bands of both sensors were fused via a pyramidal wavelet transform in Fourier space. New spectral and temporal information of the resultant image can be used for thematic classification or qualitative mapping. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <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> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R2 of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> The benefits of multi-angular observations for mapping vegetation and their structural and biochemical characteristics are widely recognised. This paper examines the potential of a digital aerial frame camera flown on a standard aerial survey for deriving information on the bidirectional reflectance characteristics of forest and how this information can be used in forest classification. The Rahman-Pinty-Verstraete (RPV) model was fitted to sampled angular observations. For each sample plot at least 10 angular observations were available. The RPV model concentrates the information in a few meaningful parameters and minimizes sensor noise and other perturbing factors. Relying on the fitted model parameters, it is demonstrated that the multi-angular data permits a better discrimination of five forest types as compared to the sole use of spectral information. Compared to the spectral model, the overall classification accuracy (Kappa) increased from 0.702 (0.622) to 0.872 (0.837), when multi-angular data was used. For a reliable approximation of the underlying Bidirectional Reflectance Distribution Function (BRDF) a `balanced' observation geometry is mandatory. In particular, available measurements should cover the backward and forward scattering directions well. Otherwise, the RPV model fits the angular observations well, but the retrieved parameters do not represent the `true' BRDF of the observed target. Under such `unbalanced' conditions a drastic reduction in classification accuracy was observed. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> One of the major uncertainties in predicting climate change comes from a full accounting of carbon-cycle feedbacks, which roughly double physical feedbacks (1, 2). Most of this uncertainty is a result of the many pathways and time scales at which ecosystems interact with the climate system and how these will respond to change (3). The relationship between leaf nitrogen and the carbon cycle is key to many ecosystem processes because photosynthesis provides the energy and carbon-cycle molecules for growth and reproduction (4⇓⇓–7) and decomposition for nutrient cycling (7, 8). Ecologists have long recognized that nitrogen was the most limited nutrient for plant growth (9, 10). Quantifying changes in canopy nitrogen content provides direct information about ecosystem functioning and a method to detect and monitor changes in response to climate forcing (9, 10); thus, it has been a long-term objective for airborne and spaceborne imaging spectroscopy (11⇓–13). Several papers have reported direct detection of canopy nitrogen from airborne imaging spectrometers (14⇓⇓–17). Ollinger (18) argues that selective pressure on plant competition for light, water, and nutrients should result in suites of biochemical and structural traits that integrate their functional strategies. Thus, structural traits affecting light scattering “over scales ranging from cells to canopies” (18) will be convergent with their biochemical traits. Knyazikhin et al. … <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions. <s> BIB007 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> Abstract Forest mapping based on remote sensing data usually relies on purely spectral information ignoring that the observed signal may be substantially influenced by angular effects. On the other hand, it is known that for forest canopies the variation of reflectance with the sun-view-geometry is significant. The study examines different approaches to extract spectro-directional information from airborne imagery and explores its use in forest type classification. The images were acquired in a standard aerial survey. They were taken with a common forward and side overlap with the result that each point on the ground is observed from several view directions. To obtain directional information that might be useful in forest type classification, two widely used semi-empirical models of the bidirectional reflectance distribution function (BRDF) were tested, i.e., the Rahman–Pinty–Verstraete model (RPV; with 3 and 4 parameters) (Rahman et al., 1993) and the RossThick-LiSparse model (RTLS; with 3 and 5 parameters) (Wanner et al., 1995). For each sample plot, observations from at least 10 view directions were available. The models were inverted against the observations applying a look-up table approach. The estimated model parameters were used 1) directly as explanatory variables in the classification, and 2) to estimate the bidirectional reflectance factor (BRF) for selected sun-view-geometries, referred to as modeled BRFs. The BRFs also served as explanatory variables in the classification. For the classification the Random Forests classifier was used. Both models proved to provide information that can be successfully used in forest type classification outperforming conventional (single-angle) multi-spectral data. The 3-parameter version of the RPV model was found most effective. Compared to the conventional dataset, the overall classification accuracy increased from 72% (kappa: 0.64) to 85% (kappa: 0.81), when the parameters of this model were used as explanatory variables. The 4-parameter version of RPV and the 5-parameter version of RTLS fitted the observed data more accurately than the corresponding 3-parameter versions. However, the classification accuracies obtained with the estimated model parameters were significantly lower than those obtained with the 3-parameter models. All tested models yielded higher classification accuracies with the modeled BRFs than with the model parameters. Again, the highest classification accuracy was obtained with the RPV-3P model (overall accuracy: 92%; kappa: 0.89). The classification accuracy varied with the sun-view-geometry. These differences could partly be explained by the angular sampling scheme and partly by the models' capability to estimate the reflectance at angles that were not observed. <s> BIB008 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <s> Abstract This paper describes a novel method to derive 3D hyperspectral information from lightweight snapshot cameras for unmanned aerial vehicles for vegetation monitoring. Snapshot cameras record an image cube with one spectral and two spatial dimensions with every exposure. First, we describe and apply methods to radiometrically characterize and calibrate these cameras. Then, we introduce our processing chain to derive 3D hyperspectral information from the calibrated image cubes based on structure from motion. The approach includes a novel way for quality assurance of the data which is used to assess the quality of the hyperspectral data for every single pixel in the final data product. The result is a hyperspectral digital surface model as a representation of the surface in 3D space linked with the hyperspectral information emitted and reflected by the objects covered by the surface. In this study we use the hyperspectral camera Cubert UHD 185-Firefly, which collects 125 bands from 450 to 950 nm. The obtained data product has a spatial resolution of approximately 1 cm for the spatial and 21 cm for the hyperspectral information. The radiometric calibration yields good results with less than 1% offset in reflectance compared to an ASD FieldSpec 3 for most of the spectral range. The quality assurance information shows that the radiometric precision is better than 0.13% for the derived data product. We apply the approach to data from a flight campaign in a barley experiment with different varieties during the growth stage heading (BBCH 52 – 59) to demonstrate the feasibility for vegetation monitoring in the context of precision agriculture. The plant parameters retrieved from the data product correspond to in-field measurements of a single date field campaign for plant height (R2 = 0.7), chlorophyll (BGI2, R2 = 0.52), LAI (RDVI, R2 = 0.32) and biomass (RDVI, R2 = 0.29). Our approach can also be applied for other image-frame cameras as long as the individual bands of the image cube are spatially co-registered beforehand. <s> BIB009 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <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> BIB010 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <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> BIB011 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Characteristics of RS Data for Quantifying FH <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> BIB012
RS data to record indicators of FH vary in their spatial, spectral, radiometric, temporal and directional resolution (Figures 2 and 4 ) and therefore in their ability to record forest ST/STV. Depending on the sensor platform, the spatial resolution may be millimeters (e.g., drone-based cameras), 0.5-2 m (airborne, hyperspectral sensors-AISA, HySPEX, APEX), 2-10 m (satellite sensors such as WorldView-2, RapidEye, Sentinel-2), 10-30 m (Spot, Landsat), and up to 250-1000 m or greater (MODIS and NOAA AVHRR). The spatial resolution, the characteristics and the density of forest ST/STV will determine the characteristics that are contained in one pixel. In this way, every pixel can contain a combined reflectivity of multiple characteristics of ST/STV BIB010 . Spectral resolution is also crucial for making qualitative distinctions among forest traits and taxonomic distinctions among forest species and communities. Narrowband hyperspectral RS sensors with a spectral range of 0.4-2.5 µm like the "Cubert UHD 185-Firefly" BIB009 , the airborne hyperspectral system sensors HySpex, HyMAP, AISA or satellite-based hyperspectral sensor EnMAP (Environmental Mapping and Analysis Program, to be launched in 2018, Guanter et al.,  are able to quantify many different biochemical and biophysical ST and STV in forest ecosystems  BIB003 BIB001 BIB006   (see also Figure 2 ). Furthermore, these sensors are able to taxonomically recognize more than 20-30 different tree species BIB011 compared to sensors with broad spectral bands such as Landsat, Spot, Aster, Sentinal-2 and RapidEye. The development of sensors with high spectral resolution (0.3-3.0 µm, Fluorescence Explorer Satellite, FLEX, ESA, 2018,  allows for the detection of important eco-physiological process traits such as photosynthesis and solar-induced chlorophyll fluorescence for a better understanding of global photosynthetic activity, CO 2 fluxes and budgets   . The temporal resolution of RS sensors is another decisive factor for improving the discrimination of species, populations, communities or forest types (see Chapter 4) as well as the quantification of ST/STV. Senescence, phenology, stress, disturbances and resource limitations are all factors that lead to changes in traits at very different time intervals. Therefore, it is essential to have sensors that can also record processes and STV at different temporal frequencies. Lausch et al.,  extensively discuss the implementation of in-situ RS methods such as plant phenomics facilities and the Ecotron (controlled environment facility), spectral measurements from towers or terrestrial wireless sensor networks (WSN), where a monitoring of ST/STV of FH is carried out at high temporal frequencies. Moreover, spaceborne RS are developed or can be used with a high temporal resolution and revisit time such BIB012 . Until today, only limited research has been conducted into the spatial, spectral or temporal resolution of such sensors for the assessment of ST/STV. Lausch et al., BIB007 BIB004 showed that for a given stable experimental design (the same sensor, time, recording frames and region) the spatial resolution is more important than the spectral resolution in discriminating the spatial heterogeneity in vegetation. Sensors with a low spatial resolution often have a higher temporal resolution (e.g., AVHRR, MODIS with 1-2 revisits/day). Sensors with high spatial and spectral resolution are generally limited in temporal resolution. However, these constraints are gradually being improved upon with the development of new sensor technologies (e.g., Sentinel 2-10 m, 20 m, and 60 m spatial resolution in the VNIR to SWIR spectral range, 5-day revisit time). In addition, more approaches are being developed that enable data merging from sensors with different spatial, spectral and temporal resolutions for the purpose of extracting information relevant to different indicators of FH  . Detecting indicators of forest health is not only based on spatial, spectral and temporal resolutions. Due to different growth forms, geometry, tree height and structural diversity as well as the heterogeneity of the tree stands such as tree density, crown shape and leaf orientation, diversity of tree height, the angular (or directional) resolution plays a major role BIB008 BIB005 BIB002 . According to Koukal [419] , "the directional domain of reflectance is mostly ignored, although the variation of reflectance properties with the sun and view angle (anisotropy) of forest canopies is substantial". If the anisotropy of reflectance is not taken into account in forest health models, it will lead to noise in the spectral signatures and thus to errors in the quantification and monitoring of ST/STV with RS BIB008 .