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Improving understanding of how biodiversity and ecosystems respond to environmental change is necessary to guide policy and management. To this end, the bioDISCOVERY project of the international programme on global change, Future Earth, initiates and supports international networks of scientists to advance research on monitoring and observations, scenarios and models, and assessments of biodiversity and ecosystems. bioDISCOVERY activities seek collective solutions to key research challenges, and provide support for the international science community by participating in the development of global databases. This global working-group approach is essential for directing cutting-edge science toward supporting international policies, addressing urgent environmental issues, and closing research gaps through transdisciplinary integration and mobilisation of the scientific community. [ABSTRACT FROM AUTHOR]

Forest canopy structure influences and partitions the energy fluxes between the atmosphere and vegetation. It serves as an indicator of a variety of biophysical variables and ecosystem goods and services. Airborne laser scanning (ALS) can simultaneously provide horizontal and vertical information on canopy structure. Existing approaches to assess canopy structure often focus on in situ collected structural variables and require a substantial set of prior information about stand characteristics. They also rely on pre-defined spatial units and are usually dependent on site-specific model calibrations. We propose a method to provide quantitative canopy-structure descriptors on different scales, retrieved from ALS data. The approach includes (i) a sensitivity assessment and a quantification of ALS-derived canopy-structure information dependent on ALS data properties, (ii) an automatic determination of the most feasible spatial unit for canopy-structure characterization, and (iii) the derivation of canopy-structure types (CSTs) using a hierarchical, multi-scale classification approach based on Bayesian robust mixture models (BRMM), satisfying structurally homogenous criteria without the use of in situ calibration information. The CSTs resulted in retrievals of canopy layering (single-, two-, and multi-layered canopies) and canopy types (deciduous or evergreen canopies). Retrievals classified seven CSTs with accuracies ranging from 52% to 82% user accuracy (canopy layering) and 89–99% user accuracy (canopy type). The method supports a data-driven approach, allowing for an efficient monitoring of canopy structure. [ABSTRACT FROM AUTHOR]

We present the Airborne Prism Experiment (APEX), its calibration and subsequent radiometric measurements as well as Earth science applications derived from this data. APEX is a dispersive pushbroom imaging spectrometer covering the solar reflected wavelength range between 372 and 2540 nm with nominal 312 (max. 532) spectral bands. APEX is calibrated using a combination of laboratory, in-flight and vicarious calibration approaches. These are complemented by using a forward and inverse radiative transfer modeling approach, suitable to further validate APEX data. We establish traceability of APEX radiances to a primary calibration standard, including uncertainty analysis. We also discuss the instrument simulation process ranging from initial specifications to performance validation. In a second part, we present Earth science applications using APEX. They include geometric and atmospheric compensated as well as reflectance anisotropy minimized Level 2 data. Further, we discuss retrieval of aerosol optical depth as well as vertical column density of NOx, a radiance data-based coupled canopy–atmosphere model, and finally measuring sun-induced chlorophyll fluorescence (Fs) and infer plant pigment content. The results report on all APEX specifications including validation. APEX radiances are traceable to a primary standard with < 4% uncertainty and with an average SNR of > 625 for all spectral bands. Radiance based vicarious calibration is traceable to a secondary standard with ≤ 6.5% uncertainty. Except for inferring plant pigment content, all applications are validated using in-situ measurement approaches and modeling. Even relatively broad APEX bands (FWHM of 6 nm at 760 nm) can assess Fs with modeling agreements as high as R 2 = 0.87 (relative RMSE = 27.76%). We conclude on the use of high resolution imaging spectrometers and suggest further development of imaging spectrometers supporting science grade spectroscopy measurements. [ABSTRACT FROM AUTHOR]

Abstract: Leaf area index (LAI) and chlorophyll content (Cab) are important vegetation variables which can be monitored using remote sensing (RS). Physically-based approaches have higher transferability and are therefore better suited than empirically-based approaches for estimating LAI and Cab at global scales. These approaches, however, require the inversion of radiative transfer (RT) models, which is an ill-posed and underdetermined problem. Four regularization methods have been proposed, allowing finding stable solutions: 1) model coupling, 2) using a priori information (e.g. Bayesian approaches), 3) spatial constraints (e.g. using objects), and 4) temporal constraints. For mono-temporal data, only the first three methods can be applied. In an earlier study, we presented a Bayesian object-based algorithm for inverting the SLC-MODTRAN4 coupled canopy-atmosphere RT model, and compared it with a Bayesian LUT inversion. The results showed that the object-based approach provided more accurate LAI estimates. This study, however, heavily relied on expert knowledge about the objects and vegetation classes. Therefore, in this new contribution, we investigated the applicability of the Bayesian object-based inversion of the SLC-MODTRAN4 model to a situation where no such knowledge was available. The case study used a 16×22km2 simulated top-of-atmosphere image of the upcoming Sentinel-2 sensor, covering the area near the city of Zurich, Switzerland. Seven APEX radiance images were nadir-normalized using the parametric Li–Ross model, spectrally and spatially resampled to Sentinel-2 specifications, geometrically corrected, and mosaicked. The atmospheric effects between APEX flight height and top-of-atmosphere level were added based on two MODTRAN4 simulations. The vegetation objects were identified and delineated using a segmentation algorithm, and classified in four levels of brightness in the visible domain. The LAI and Cab maps obtained from the Bayesian object-based inversion of the coupled SLC-MODTRAN4 model presented realistic spatial patterns. The impact of the parametric Li–Ross nadir-normalization was evaluated by comparing 1) the angular signatures of the SLC-MODTRAN4 and Li–Ross models, and 2) the LAI and Cab maps obtained from a Li–Ross nadir-normalized image (using nadir viewing geometry) and from the original image (using the original viewing geometry). The differences in angular signatures were small but systematic, and the differences between the LAI and Cab maps increased from the center towards the edges of the across-track direction. The results of this study contribute to preparing the RS community for the arrival of Sentinel-2 data in the near future, and generalize the applicability of the Bayesian object-based approach for estimating vegetation variables to cases where no field data are available. [Copyright &y& Elsevier]

Highlights: [•] We review up-to-date remote sensing methods to estimate important plant traits. [•] We introduce basic concepts of remote sensing of vegetation and scaling mechanisms. [•] Plant height and nitrogen are retrieved with high accuracy from remote sensing. [•] Phosphorus and leaf mass per area are retrieved with lower accuracies. [Copyright &y& Elsevier]

Abstract: An important bio-indicator of actual plant health status, the foliar content of chlorophyll a and b (Cab), can be estimated using imaging spectroscopy. For forest canopies, however, the relationship between the spectral response and leaf chemistry is confounded by factors such as background (e.g. understory), canopy structure, and the presence of non-photosynthetic vegetation (NPV, e.g. woody elements)—particularly the appreciable amounts of standing and fallen dead wood found in older forests. We present a sensitivity analysis for the estimation of chlorophyll content in woody coniferous canopies using radiative transfer modeling, and use the modeled top-of-canopy reflectance data to analyze the contribution of woody elements, leaf area index (LAI), and crown cover (CC) to the retrieval of foliar Cab content. The radiative transfer model used comprises two linked submodels: one at leaf level (PROSPECT) and one at canopy level (FLIGHT). This generated bidirectional reflectance data according to the band settings of the Compact High Resolution Imaging Spectrometer (CHRIS) from which chlorophyll indices were calculated. Most of the chlorophyll indices outperformed single wavelengths in predicting Cab content at canopy level, with best results obtained by the Maccioni index ([R 780 − R 710]/[R 780 − R 680]). We demonstrate the performance of this index with respect to structural information on three distinct coniferous forest types (young, early mature and old-growth stands). The modeling results suggest that the spectral variation due to variation in canopy chlorophyll content is best captured for stands with medium dense canopies. However, the strength of the up-scaled Cab signal weakens with increasing crown NPV scattering elements, especially when crown cover exceeds 30%. LAI exerts the least perturbations. We conclude that the spectral influence of woody elements is an important variable that should be considered in radiative transfer approaches when retrieving foliar pigment estimates in heterogeneous stands, particularly if the stands are partly defoliated or long-lived. [Copyright &y& Elsevier]

Abstract: We use the Li-Strahler geometric-optical model combined with a scaling-based approach to detect forest structural changes in the Three Gorges region of China. The physical-based Li-Strahler model can be inverted to retrieve forest structural properties. One of the main input variables for the inverted model is the fractional component of sunlit background, which is calculated by using pure reflectance spectra (endmembers) of surface components. In this study, we extract these endmembers from moderate spatial resolution MODIS data using two scaling-based methods (namely, a regional based linear unmixing and a purest-pixel approach) relying on corresponding high spatial resolution Landsat TM images. Then, the forest structural property crown closure (CC) is estimated by inverting the Li-Strahler model based on the extracted endmembers. Changes in CC are mapped using MODIS mosaics dated 2002 and 2004 for the whole Three Gorges region. Validation of the estimated CC using 25 sample sites indicates that the regional scaling-based endmembers extracted using linear unmixing are more suitable to be used in combination with the inverted Li-Strahler model for monitoring the forest CC than the purest-pixel approach, and results in significantly better estimates in both years (R 2 2002 =0.614, RMSE2002 =6%, R 2 2004 =0.631 and RMSE2004 =5.2%). A change detection map of the model derived CC in 2002 and 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam. An increase in CC has been observed in other counties of the Three Gorges region, implying a preliminary positive feedback on certain policy measures taken safeguarding forest structure. [Copyright &y& Elsevier]

The Intergovernmental Panel on Climate Change and the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services have presented unequivocal evidence for human induced climate change and biodiversity decline. Transformative societal change is required in response. However, while the Global Observing System for Climate has coordinated climate observations for these assessments, there has been no equivalent actor for the biodiversity assessment. Here we argue that a central agency for coordinated biodiversity observations can lead to an improved assessment process for biodiversity status and coupled climate - biodiversity observations in areas of mutual interest such as monitoring indicators of Nature's Contributions to People. A global biodiversity observation system has already begun to evolve through bottom up development of the Essential Biodiversity Variables. We propose recommendations on how to build on this progress through definition of user requirements, observation principles, creation of a community data basis and regional actions through existing networks. • The biodiversity and climate crises are interlinked and need joint societal responses • The biodiversity community urgently needs to improve global monitoring schemes • There are perceived differences in how biodiversity and climate change are measured, monitored and reported on • Joint climate-biodiversity observations have the potential to overcome differences and save observational costs for both communities [ABSTRACT FROM AUTHOR]

Grassland use intensity and its impact on biodiversity and water pollution is a topic of growing interest. In ecological studies, intensity of use has been assessed by means of three indicators: i) mowing frequency, ii) grazing intensity, and iii) fertilization input. A multidimensional approach is key for the understanding of intensification effects in terrestrial and water ecosystems. Remote sensing is a powerful tool to monitor management indicators. Nevertheless, interdependencies between remote sensing methods and between indicators require new approaches to assess intensity of use. The objective of this study is to monitor ecological indicators of land use intensity based on multispectral imagery using a multidimensional approach. We performed a multi-temporal analysis using a series of RapidEye images within a growing season in the Canton of Zurich, Switzerland, in 2013. We defined mowing frequency classes distinguishing spectral changes between pairs of images. The analysis of the whole image sequence within the growing season helped differentiate grazing intensities. Furthermore, we analysed the suitability of modelled livestock density based on remote sensing derived products to determine fertilizer input. Three grassland management practices were distinguished: i) medium intensive (46%), ii) low intensive (37%), and iii) high intensive (17%). We discuss the combination of high mowing frequency and fields with high grazing intensity to define areas prone to nutrient surpluses. Finally, we demonstrate that the estimation of interrelated indicators of grassland use intensity could be carried out preserving independence between methods. [ABSTRACT FROM AUTHOR]

Tree inventories, inter alia, need information on the tree species. Previous studies aimed at distinguishing tree species based on three dimensional tree structure metrics derived from airborne laser scanning (ALS) point clouds or based on features from full-waveform (FW) laser scanning data provided by today's sensors. Classifications based on FW features mainly use echo amplitude, pulse energy (hereafter referred to as energy) and width, which are typically retrieved by waveform decomposition, often performed using the symmetric Gaussian distribution function. However, for forested areas, the symmetry of the echo shape is potentially modified by multiple scattering and the distribution of scattering elements (e.g. leaves). We assess the potential of processing full-waveform ALS data such that the third and fourth statistical moments, i.e. the echo skewness and the echo kurtosis, can be retrieved in addition to the amplitude, energy and FWHM. We propose a waveform decomposition approach using the skew normal distribution (SND) function, which enables the modelling of skewed echoes. We investigate the difference of tree-crown aggregated SND derived FW features between seven tree species (969 individual trees) in a temperate mixed forest with the aim of detecting the most descriptive echo features. The such derived FW features are tested for species classification. The results reveal that the largest differences across the tree species are in the mean energy of the first echoes (15 out of 21 species pairs show differences), followed by the mean amplitude of the first echoes and the mean skewness of all echoes originating from a single crown (14 out of 21 species pairs show differences against each other for both features). The differentiation of coniferous and deciduous trees benefits from the features derived from the SND decomposition compared to the use of echo amplitude only (0.39 vs. 0.61 in Cohen's κ ). As the classification accuracy of the three dominant tree species within the test site only shows a small increase (0.20 vs. 0.26 in Cohen's κ ) by adding FW features, we propose the use of such features in combination with features from multispectral data for this purpose. The SND decomposition is comparable to the Gaussian decomposition regarding the decomposition accuracy ( RMSE = 4.45 vs. RMSE = 3.50) and computational cost. Hence, we propose the default use of the SND decomposition, as the SND is a more flexible function, allowing for the modelling of normally distributed echoes, as well as the fitting of skewed echoes, while no limitations regarding the direction of the skewness are introduced. We attribute the difficulties in the tree species classification to the relatively wide ranges of the crown aggregated features within one species, which for some features results in a considerable overlap of the feature ranges across the species. [ABSTRACT FROM AUTHOR]

Abstract: The science of spectroscopy has existed for more than three centuries, and imaging spectroscopy for the Earth system for three decades. We first discuss the historical background of spectroscopy, followed by imaging spectroscopy, introducing a common definition for the latter. The relevance of imaging spectroscopy is then assessed using a comprehensive review of the cited literature. Instruments, technological advancements and (pre-)processing approaches are discussed to set the scene for application related advancements. We demonstrate these efforts using four examples that represent progress due to imaging spectroscopy, namely (i) bridging scaling gaps from molecules to ecosystems using coupled radiative transfer models (ii) assessing surface heterogeneity including clumping, (iii) physical based (inversion) modeling, and iv) assessing interaction of light with the Earth surface. Recent advances of imaging spectroscopy contributions to the Earth system sciences are discussed. We conclude by summarizing the achievements of thirty years of imaging spectroscopy and strongly recommend this community to increase its efforts to convince relevant stakeholders of the urgency to acquire the highest quality imaging spectrometer data for Earth observation from operational satellites capable of collecting consistent data for climatically-relevant periods of time. [Copyright &y& Elsevier]

Abstract: This paper reviews developments in the science of field spectroscopy, focusing on the last twenty years in particular. During this period field spectroscopy has become established as an important technique for characterising the reflectance of natural surfaces in situ, for supporting the vicarious calibration of airborne and satellite sensors, and for providing a means of scaling-up measurements from small areas (e.g. leaves, rocks) to composite scenes (e.g. vegetation canopies), and ultimately to pixels. This paper describes the physical basis of the subject and evaluates the different methods and instruments which have been employed across a range of studies. The development and use of field goniometers is described, and related to methods for estimating the bidirectional reflectance distribution function (BRDF) from directional reflectance measurements in the field. The paper also considers the practical aspects of field spectroscopy, and identifies a number of factors affecting the useability of field spectroradiometers, including the weight and cost of the instruments, limitations of some commonly used methodologies and practical issues such as the legibility of displays and limited battery life. The prospects for the future of field spectroscopy are considered in relation to the increasingly important contribution that field spectral data will make to EO-based global measurement and monitoring systems, specifically through their assimilation into numerical models. However, for this to be achieved it is essential that the data are of high quality, with stated levels of accuracy and uncertainty, and that common protocols are developed and maintained to ensure the long-term value of field spectroscopic data. The importance of employing a precise terminology for describing the geometric configuration of measurements is highlighted in relation to issues of repeatability and reproducibility. Through such refinements in methodology, field spectroscopy will establish its credentials as a reliable method of environmental measurement, underpinning quantitative Earth observation and its applications in the environmental and Earth sciences. [Copyright &y& Elsevier]

Observing functional diversity continuously in time and space using satellite imagery forms the basis for studying impact, interactions, and feedback of environmental change mechanisms on ecosystems and biodiversity globally. Functional diversity of plant traits links ecosystem functioning and biodiversity. This work presents an approach to map and quantify functional diversity of physiological forest traits derived from 20 m Sentinel-2 data in a temperate forest ecosystem. We used two complementary data sources, namely high-resolution, as well as spatially resampled airborne imaging spectroscopy data and Sentinel-2 data, to ensure our methods support consistently mapping functional diversity from space. We retrieved three physiological traits related to forest health, stress, and potential productivity, namely chlorophyll, carotenoid, and water content, from airborne imaging spectroscopy and Sentinel-2 data using corresponding spectral indices as proxies. We analyzed changes in two functional diversity metrics, namely functional richness and divergence, at different spatial resolutions. Both functional diversity metrics depend on the size and number of pixels to derive functional diversity as a function of distance, leading to different interpretations. When mapping functional diversity using Sentinel-2 data, small-scale patterns <1.1 ha were no longer visible, implying a minimum calculation area with 60 m radius recommended for retrieval of functional diversity metrics. The spectrally convolved and spatially resampled airborne spectroscopy data and the native Sentinel-2 data were correlated with r = 0.747 for functional richness and r = 0.709 for divergence in a 3.1 ha neighborhood. Functional richness was more affected by the differences in trait maps between the acquisitions resulting from effects in illumination and topography compared with functional divergence. Further differences could be explained by varying illumination/observation effects and phenological status of the vegetation at acquisition. Our approach demonstrates the importance of spatial and spectral resolution when scaling diversity assessments from regional to continental scales. • Mapping functional diversity using physiological forest traits from Sentinel-2 data. • Comparison of high spatial resolution trait information with Sentinel-2 data. • Spectral properties of Sentinel-2 data allow for physiological trait derivation. • Spatial resolution has a profound impact on diversity metrics. • Importance of resolution when scaling diversity from regional to continental scales. [ABSTRACT FROM AUTHOR]

Abstract An improved understanding of increased human influence on ecosystems is needed for predicting ecosystem processes and sustainable ecosystem management. We studied spatial variation of human influence on grassland ecosystems at two scales across the Qinghai-Tibetan Plateau (QTP), where increased human activities may have led to ecosystem degradation. At the 10 km scale, we mapped human-influenced spatial patterns based on a hypothesis that spatial patterns of biomass that could not be attributed to environmental variables were likely correlated to human activities. In part this hypothesis could be supported via a positive correlation between biomass unexplained by environmental variables and livestock density. At the 500 m scale, using distance to settlements within a radius of 8 km as a proxy of human-influence intensity, we found both negatively human-influenced areas where biomass decreased closer to settlements (regions with higher livestock density) and positively human-influenced areas where biomass increased closer to settlements (regions with lower livestock density). These results suggest complex relationships between livestock grazing and biomass, varying between spatial scales and regions. Grazing may boost biomass production across the whole QTP at the 10 km scale. However, overgrazing may reduce it near settlements at the 500 m scale. Our approach of mapping and understanding human influence on ecosystems at different scales could guide pasture management to protect grassland in vulnerable regions on the QTP and beyond. Graphical abstract Unlabelled Image Highlights • We studied spatial variation of human influence on grassland biomass at two spatial scales on the Qinghai-Tibetan Plateau. • At 10 km scale, human-influenced biomass is positively correlated with livestock density, suggesting grazing boost biomass. • At 500 m scale, biomass decreases near settlements in areas with high livestock density, indicating overgrazing. • These results suggest complex relationships between human influences and grassland biomass. [ABSTRACT FROM AUTHOR]

Abstract There is an increasing demand for clove products, mainly dried buds and essential oil on global markets. Consequently, the importance of clove trees as a provisioning service is increasing at the local level, particularly for smallholders cultivating clove trees as cash crops. Due to limited availability of data on local production, using remote sensing-based methods to quantify today's clove production is of key interest. We estimated the clove bud yield in a study site in northeastern Madagascar by detecting individual clove trees and determining relevant production systems, including pasture and clove, clove plantation and agroforestry systems. We implemented an individual tree detection method based on two machine learning approaches. Specifically, we proposed using a circular Hough transform (CHT) for the automated detection of individual clove trees. Subsequently, we implemented a tree species classification method using a random forests (RF) classifier based on a set of features extracted for relevant trees in the above production systems. Finally, we classified and mapped different production systems. Based on the number of detected clove trees growing in a clove production system, we estimated the production system-dependent clove bud yield. Our results show that 97.9% of all reference clove trees were detected using a CHT. Classifying clove and non-clove trees resulted in a producer accuracy of 70.7% and a user accuracy of 59.2% for clove trees. The classification of the clove production systems resulted in an overall accuracy of 77.9%. By averaging different clove tree yield estimates obtained from the literature, we estimated an average total yield of approximately 575 tons/year for our 25,600 ha study area. With this approach, we demonstrate a first step towards large-scale clove bud yield estimation using remote sensing data and methodologies. Highlights • Single clove tree detection based on very high resolution optical satellite data • A tree detection rate of 97.9% was achieved using Circular Hough Transform. • Clove trees can be distinguished from other trees using a Random Forest classifier. • Clove bud yield for three production systems was estimated using reference data. [ABSTRACT FROM AUTHOR]

Abstract Due to the large carbon dioxide (CO 2) fluxes between terrestrial ecosystems and the atmosphere, dynamics of photosynthesis can have significant effects on atmospheric CO 2 concentrations and lead to large uncertainties in ecosystem C budgets. Remote sensing approaches using sun-induced chlorophyll fluorescence (SIF) hold the potential to directly assess ecosystem photosynthesis. However, many challenges remain linked to using the SIF emission signal to estimate gross primary production (GPP). The goal of this study was to gain a better understanding of the relationships between GPP and SIF over different time scales (minutes to years) and under varying environmental conditions. Two different ecosystems were investigated, a cropland and a mixed forest, with continuous eddy covariance flux measurements. Continuous tower-based SIF retrievals were performed in 2015 and 2016 at both ecosystems. In both ecosystems, SIF was found to be more affected by environmental conditions than GPP. Annual cycles for GPP and SIF differed at the mixed forest due in part to the influence of the different footprint size of the two independent measurements. Diurnal cycles in GPP and SIF corresponded well under unstressed conditions and followed the incoming photosynthetic photon flux density (PPFD). However, depressions in SIF were found at both sites either at midday or in the afternoon during the growing season. At the cropland site, reductions in SIF occurred at high PPFD (PPFD > 1470 μmol m−2 s−1, R2 = 0.62) and high VPD (VPD > 1590 Pa, R2 = 0.35). Whereas at the forest site, reductions in SIF were linked to high VPD (VPD > 1250 Pa, R2 = 0.25), but not to high PPFD (R2 = 0.84). The depression in SIF was also associated with an increase in non-photochemical quenching, as indicated by the photochemical reflectance index (R2 = 0.78), thus showing the complementarity between SIF and non-photochemical quenching as different energy pathways. Our results show the importance of characterizing the influence of different environmental conditions on SIF-GPP relationships for specific ecosystems to reliably estimate GPP from remote sensing measurements. Highlights • A continuous time-series of tower-based sun-induced fluorescence was measured. • SIF and GPP had similar annual and diurnal patterns. • Depressions in SIF were found under high light and/or high VPD conditions. • Environmental conditions affect the ecosystem derived SIF-GPP relationships. [ABSTRACT FROM AUTHOR]

Spatially well-informed decisions are essential to sustain and regulate processes and ecosystem services (ES), and to maintain the capacity of ecosystems to supply services. However, spatially explicit ES information is often lacking in decision-making, or exists only as ES maps based on categorical land cover data. Remote sensing (RS) opens new pathways to map ES, in particular biophysical ES supply. We developed an observation-based concept for spatially explicit and continuous ES mapping at landscape scale following the biophysical part of the ES cascade. We used Earth observations in combination with in situ data to map ecosystem properties, functions, and biophysical ES supply. We applied this concept in a case study to map two ES: carbon dioxide regulation and food supply. Based on Earth observations and in situ data, we determined the ecosystem property Sun-Induced chlorophyll Fluorescence (SIF) to indicate ecosystem state and applied scaling models to estimate gross primary production (GPP) as indicator for ecosystem functioning and consequently carbon dioxide regulation and food supply as ES. Resulting ES maps showed heterogeneous patterns in ES supply within and among ecosystems, which were particularly evident within forests and grasslands. All investigated land cover classes were sources of CO 2 , with averages ranging from ‐66 to ‐748 g C m ‐2 yr ‐1 , after considering the harvest of total above ground biomass of crops and the storage organ, except for forest being a sink of CO 2 with an average of 105 g C m ‐2 yr ‐1 . Estimated annual GPP was related to food supply with a maize grain yield average of 9.5 t ha ‐1 yr ‐1 and a sugar beet root yield of 110 t ha ‐1 yr ‐1 . Validation with in situ measurements from flux towers and literature values revealed a good performance of our approach for food supply (relative RMSE of less than 23%), but also some over- and underestimations for carbon dioxide regulation. Our approach demonstrated how RS can contribute to spatially explicit and continuous ES cascade mapping and suggest that this information could be useful for environmental assessments and decision-making in spatial planning and conservation. [ABSTRACT FROM AUTHOR]

Accurate three-dimensional information on canopy structure contributes to better understanding of radiation fluxes within the canopy and the physiological processes associated with them. Small-footprint airborne laser scanning (ALS) data proved valuable for characterising the three-dimensional structure of forest canopies and the retrieval of biophysical parameters such as plant and leaf area index (PAI and LAI), fractional cover or canopy layering. Nevertheless, few studies analysed combined occluded and observed canopy elements in dense vegetation as a result of airborne laser scanning geometries. The occluded space contains a substantial amount of vegetation elements (i.e. leaf, needle and wood material), which are missing in the analysis of the three-dimensional canopy structure. Consequently, this will lead to erroneous retrieval of biophysical parameters. In this study, we introduce a voxel traversal algorithm to characterise ALS observation patterns inside a voxel grid. We analyse the dependence of occluded and unobserved canopy volume on pulse density, flight strip overlap and season of overflight in a temperate mixed forest. ALS measurements under leaf-on and leaf-off conditions were used. For cross-comparison purposes, terrestrial laser scanning (TLS) measurements on a 50 ×50 m 2 subplot under leaf-on conditions were used. TLS acquisitions were able to depict the three-dimensional structure of the forest plot in high detail, ranging up to the top-most canopy layer. Our results at 1 m voxel size show that even with the highest average pulse density of 11 pulses/m 2 , at least 25% of the forest canopy volume remains occluded in the ALS acquisition under leaf-on conditions. Comparison with TLS acquisitions further showed that roughly 28% of the vegetation elements detected by the TLS acquisitions were not detected by the ALS system due to occlusion effects. By combining leaf-on and leaf-off acquisitions, we were able to recover roughly 7% of the occluded vegetation elements from the leaf-on acquisition. We find that larger flight strip overlap can significantly increase the amount of observed canopy volume due to the added observation angles and increased pulse density. [ABSTRACT FROM AUTHOR]

Vegetation composition and water surface area are changing in many tundra regions due to climate warming, which is twice as strong in the Arctic as compared to the global mean. Such land cover changes feed back to climate and permafrost thaw through altering the surface energy budget. We quantified the influence of vegetation type, canopy characteristics, and patchiness on the tundra shortwave radiation components. We used in situ measurements and vegetation mapping to parametrise a 3D radiative transfer model (DART) for summer conditions at the Kytalyk test site in northeast Siberia. We analysed model results assessing the most important drivers of canopy albedo, transmittance, and absorptance of photosynthetically active radiation (PAR). Tundra albedo was strongly influenced by the fractional cover of water surfaces. Albedo decreased with increasing shrub cover. However, plant area index effects on albedo were not statistically significant. Canopy transmittance and PAR absorptance (f APAR ) were almost entirely controlled by plant area index at the landscape scale. Only about one half of the total plant area index consisted of green leaves, while wood and standing dead leaves contributed equally to the other half. While spatial patterns and patch sizes of vegetation types and open water did not significantly influence the radiation budget at the landscape scale, it contributed to the large variability at the local scale. Such local variability of shortwave radiation may impact evapotranspiration and primary productivity at a range of scales. Therefore, the variation of radiation fluxes within single vegetation types potentially affects larger scale energy, water, and carbon fluxes. [ABSTRACT FROM AUTHOR]

Functional diversity is a critical component driving ecosystem functioning. Spatially explicit data of plant functional traits and diversity are essential for understanding biodiversity effects on ecosystem functioning. Here we retrieved three morphological traits (95th quantile height, leaf area index, foliage height diversity) and three physiological traits (chlorophyll a + b content, specific leaf area, equivalent water thickness) from airborne laser scanning and multispectral Sentinel-2 data, respectively. We found airborne LiDAR-derived parameters correlated well with in-situ plot-level morphological data (R2 ≥ 0.67). For satellite-derived physiological traits, partial least squares regression (PLSR) obtained higher prediction accuracy (R2 = 0.26–0.43, cross-validation with in-situ community-weighted mean (CWM) leaf physiological trait data) than a vegetation index (VI) approach. The remotely-sensed traits were used as input to estimate multi-trait functional diversity (FD) indices in a species-rich subtropical mountainous forest. Finally, we investigated the influence of single-trait CWMs, multi-trait FD indices and environmental variables on remotely-derived aboveground ecosystem carbon stocks (aboveground biomass, AGB) and primary productivity (kernel normalized difference vegetation index, kNDVI). CWMs of all functional traits were significant predictors of AGB and kNDVI, as suggested by the mass-ratio hypothesis. Morphological FD indices were also important predictors of AGB and kNDVI, indicating effects of complementarity in crown architectures. In best-fit multivariate models, the first principal component CWM of morphological traits and that of physiological traits were the most important predictors of AGB and kNDVI, respectively. The FD index of morphological richness was additionally selected in the best-fit models for AGB and kNDVI at ecosystem and landscape scales. Our work highlights the potential of using remotely-sensed functional traits to assess the relationship between trait diversity and ecosystem functioning across large, contiguous areas. • Community-weighted means (CWMs) of functional traits are assessed by remote sensing. • Single-trait CWMs derived from LiDAR and Sentinel-2 correlate well with in-situ data. • Remotely-sensed morphological richness–productivity relationships are hump-shaped. • Single-trait CWMs predict productivity better than multi-trait functional diversity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Remote sensing enhances large-scale biodiversity monitoring by overcoming temporal and spatial limitations of ground-based measurements and allows assessment of multiple plant traits simultaneously. The total set of traits and their variation over time is specific for each individual and can reveal information about the genetic composition of forest communities. Measuring trait variation among individuals of one species continuously across space and time is a key component in monitoring genetic diversity but difficult to achieve with ground-based methods. Remote sensing approaches using imaging spectroscopy can provide high spectral, spatial, and temporal coverage to advance the monitoring of genetic diversity, if sufficient relation between spectral and genetic information can be established. We assessed reflectance spectra from individual Fagus sylvatica L. (European beech) trees acquired across eleven years from 69 flights of the Airborne Prism Experiment (APEX) above the same temperate forest in Switzerland. We derived reflectance spectra of 68 canopy trees and correlated differences in these spectra with genetic differences derived from microsatellite markers among the 68 individuals. We calculated these correlations for different points in time, wavelength regions and relative differences between wavelength regions. High correlations indicate high spectral-genetic similarities. We then tested the influence of environmental variables obtained at temporal scales from days to years on spectral-genetic similarities. We performed an uncertainty propagation of radiance measurements to provide a quality indicator for these correlations. We observed that genetically similar individuals had more similar reflectance spectra, but this varied between wavelength regions and across environmental variables. The short-wave infrared regions of the spectrum, influenced by water absorption, seemed to provide information on the population genetic structure at high temperatures, whereas the visible part of the spectrum, and the near-infrared region affected by scattering properties of tree canopies, showed more consistent patterns with genetic structure across longer time scales. Correlations of genetic similarity with reflectance spectra similarity were easier to detect when investigating relative differences between spectral bands (maximum correlation: 0.40) than reflectance data (maximum correlation: 0.33). Incorporating uncertainties of spectral measurements yielded improvements of spectral-genetic similarities of 36% and 20% for analyses based on single spectral bands, and relative differences between spectral bands, respectively. This study highlights the potential of dense multi-temporal airborne imaging spectroscopy data to detect the genetic structure of forest communities. We suggest that the observed temporal trajectories of reflectance spectra indicate physiological and possibly genetic constraints on plant responses to environmental change. • Dense multi-temporal airborne imaging spectroscopy data linked to genetic structure. • Spectral-genetic similarities depend on wavelength region and vary with environment. • Spectral-genetic similarities suggest genetically constrained trait expression. • Accounting for uncertainty improves assessment of spectral-genetic similarities. • Multi-temporal spectral data hold potential for genetic diversity monitoring. [ABSTRACT FROM AUTHOR]

The annual cycle of vegetation growth may be altered in response to climate changes affecting ecosystem dynamics. However, our understanding of vegetation seasonality is mostly limited to the mechanisms and attributes of phenological events, such as spring emergence and fall senescence. Here we have investigated the seasonal evolution of vegetation growth from winter dormancy to summer maturity of four forest types in the Northern Hemisphere (NH) temperate and boreal forests for 1982–2011. The present study assesses large-scale variations in the vegetation green-up rate ( VG rate ) and its connection to temperature variability using remotely sensed normalized difference vegetation index (NDVI) and surface air temperature. The average of the VG rate of the analysis period increases with latitude, which indicates that the canopy develops more rapidly from dormancy to maturity for vegetation in higher-latitude or colder climate zones. VG rate and precedent temperature also show a positive correlation (r) over temperate and boreal forests (67% of the forest area in the NH), indicating that increased temperatures lead to faster canopy development within the same climate zone or latitude band. Responsiveness of VG rate to temperature variability shows that despite the same magnitude of local temperature variability during extremely cold and warm years, the magnitude of VG rate acceleration in warm years (0.07 (15-day) − 1 ) is larger than the VG rate deceleration in cold years (− 0.03 (15-day) − 1 ), suggesting that the response of VG rate to temperature variability is nonlinear. Among the four forest types examined in this study, the nonlinear responses are most clearly observed in deciduous broadleaf forests indicating that forest composition may regulate the large-scale response of canopy development to temperature variability. Overall, our results suggest that anomalous seasonal warming will significantly affect canopy developments over wide deciduous forest areas. [ABSTRACT FROM AUTHOR]

In recent years, multi-temporal topographic measurements from airborne laser scanning (ALS) have been increasingly used as a source of spatially explicit and accurate information to calculate geodetic glacier mass balances. Simultaneous to collecting topographic data, most ALS instruments record the backscattered intensity for each laser emission and therefore provide additional information on the reflectance characteristics of the surveyed surface. Along with air temperature, the surface albedo of snow and ice was identified as a major driving factor of glacier melt. Consequently, better knowledge on the spatial distribution of the glacier albedo could substantially improve energy balance based glacier melt modeling. In this study, we collected on-glacier spectro-radiometric and albedometer measurements to serve as a ground reference to radiometrically calibrate high resolution ALS intensity data into a distributed albedo proxy map. This method resulted in an albedo proxy with values between 0.6 on the glacier tongue and 0.9 on fresh snow in high altitudes. 99.6% of all values fell within the albedo boundary conditions, i.e. values between 0 and 1. Corrected near-infrared ALS intensity data provided a distributed product that allows simulating albedo in glacier energy and mass balance models more realistically. Remaining challenges are (i) a different surface albedo response in the visual part of the electro-magnetic spectrum, (ii) the low radiometric resolution of the ALS system for higher intensity values, and (iii) an insufficient correction of the snow bi-directional reflectance distribution function (BRDF). [ABSTRACT FROM AUTHOR]

Abstract: The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is recognized as an essential climate variable (ECVs), playing a critical role in the estimation of the global energy and carbon balance. With multiple space-borne remote sensing FAPAR global products available from several sources the need for continual comparison and validation has become imperative. In this study, the performance of three global FAPAR algorithms (JRC-TIP, ESA/JRC MGVI and Boston University FAPAR) was evaluated over Europe for the year 2011. Results show an overall agreement among FAPAR products on sites having high and low FAPAR values, except for the north-eastern region of Europe characterized by boreal forest and the transition region with tundra biomes, where the Boston product exceeds values in other products by up to 0.5. Differences in FAPAR estimates over forest biomes suggest that assumptions on structure and optical properties of land surfaces in the different radiative transfer models play an important role in remote-sensing-derived FAPAR products. Uncertainty assessments were carried out using both quality indicators as proposed by the individual product teams as well as independent theoretical uncertainty estimates obtained with the triple collocation error model. The former revealed consistent spatial patterns but large differences in magnitudes (up to 0.1) with systematically lower uncertainties for the Boston product. The latter instead suggests similar uncertainty ranges among the three products. Finally, a comparison with ground estimates for the 2009–2011 period over four European flux tower sites showed consistent, plausible seasonal variations of remote-sensing-derived FAPAR products. Findings suggest that differences in absolute values and inconsistency in uncertainty representation among FAPAR products are still considerable. Standardization frameworks quantifying the impact of different radiative transfer formulations on the estimation of biophysical variables, independent uncertainty estimation methods and well-defined ground measurement protocols need to be put in place before FAPAR products can be reliably fed into existing biogeochemical process models. [Copyright &y& Elsevier]

Abstract: Vegetation variables such as leaf area index (LAI) and leaf chlorophyll content (Cab) are important inputs for vegetation growth models. LAI and Cab can be estimated from remote sensing data using either empirical or physically-based approaches. The latter are more generally applicable because they can easily be adapted to different sensors, acquisition geometries, and vegetation types. They estimate vegetation variables through inversion of radiative transfer models. Such inversions are ill-posed but can be regularized by coupling models, by using a priori information, and spatial and/or temporal constraints. Striving to improve the accuracy of LAI and Cab estimates from single remote sensing images, this contribution proposes a Bayesian object-based approach to invert at-sensor radiance data, combining the strengths of regularization by model coupling, as well as using a priori data and object-level spatial constraints. The approach was applied to a study area consisting of homogeneous agricultural fields, which were used as objects for applying the spatial constraints. LAI and Cab were estimated from at-sensor radiance data of the Airborne Prism EXperiment (APEX) imaging spectrometer by inverting the coupled SLC-MODTRAN4 canopy–atmosphere model. The estimation was implemented in two steps. In the first step, up to six variables were estimated for each object using a Bayesian optimization algorithm. In the second step, a look-up-table (LUT) was built for each object with only LAI and Cab as free variables, constraining the values of all other variables to the values obtained in the first step. The results indicated that the Bayesian object-based approach estimated LAI more accurately (R2 =0.45 and RMSE=1.0) than a LUT with a Bayesian cost function (LUT–BCF) approach (R2 =0.22 and RMSE=2.1), and Cab with a smaller absolute bias (−9 versus −23μg/cm2). The results of this study are an important contribution to further improve the regularization of ill-posed RT model inversions. The proposed approach allows reducing uncertainties of estimated vegetation variables, which is essential to support various environmental applications. The definition of objects and a priori data in cases where less extensive ground data are available, as well as the definition of the observation covariance matrix, are critical issues which require further research. [Copyright &y& Elsevier]

Abstract: We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (C ab) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new C ab sensitive index located between 650 and 720nm and termed ANCB650–720. The performance of ANCB650–720 was validated against ground-measured C ab of ten spruce crowns and compared with C ab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710nm (ND925&710), simple reflectance ratio between 750 and 710nm (SR750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable C ab spatial patterns, the ground validation revealed that the ANCB650–720 and ANN retrievals are more accurate than the other three chlorophyll indices (R 2 =0.72 for both methods). ANCB650–720 estimated C ab with an RMSE=2.27μgcm−2 (relative RRMSE=4.35%) and ANN with an RMSE=2.18μgcm−2 (RRMSE=4.18%), while SR750/710 with an RMSE=4.16μgcm−2 (RRMSE=7.97%), ND925&710 with an RMSE=9.07μgcm−2 (RRMSE=17.38%) and TCARI/OSAVI with an RMSE=12.30μgcm−2 (RRMSE=23.56%). Also the systematic RMSES was lower than the unsystematic one only for the ANCB650–720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for C ab values below 30μgcm−2, which are slightly overestimated (RMSE=2.42μgcm−2). The computationally efficient ANCB650–720 retrieval provides accurate high spatial resolution airborne C ab maps, considerable as a suitable reference data for validating satellite-based C ab products. [Copyright &y& Elsevier]

Abstract: Hyperspectral remote sensing is the definitive optical tool for increasing knowledge and understanding of the Earth''s surface. Contiguous high-resolution spectrometry provides a new dimension in mapping capability because of the potential for quantitative measurement of surface biogeochemistry. Alexander Goetz provided the vision and leadership that has produced nearly all critical developments in this field. He was among the first to recognize that spectrometry would change optical remote sensing from qualitative observations to quantitative physical measurements. His significant accomplishments over the last 25 years include development of critical image processing and atmospheric correction software, spectrometers that made it possible to move research out of the lab and into the field environment, and the development of NASA''s airborne imaging spectrometer program. This special issue is dedicated to Dr. Goetz and his accomplishments. [Copyright &y& Elsevier]

Abstract: The Sentinel-1, -2, and -3 satellite missions can meet various observational needs for spatially explicit physical, biogeophysical, and biological variables of the ocean, cryosphere, and land research activities. The currently known observational requirements were extracted from documents produced by major international scientific projects and programs. The summarized observational needs were then cross-referenced with the capabilities of the planned sensors aboard of the first three Sentinels. A comparative analysis, also incorporating scientific challenges of the ESA Living Planet Programme and the Essential Climate Variables (ECVs), resulted in a preliminary scientific priority assessment of the reviewed environmental variables. Results of these activities, discussed and consolidated in March 2011 at the Sentinels for Science (SEN4SCI) scientific workshop, demonstrate the high potential of the Sentinel-1, -2, and -3 missions for systematic, long-term observations of the Earth system. [Copyright &y& Elsevier]