Search

Publisher Copyright: © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. In this study, the capability of Landsat-8 (L8), Sentinel-2 (S2), Sentinel-1 (S1), and their combination was investigated for estimating aboveground biomass (AGB). A pure stand of Fagus Orientalis located in the Hyrcanian forest of Iran was selected as the study area. The performance of a parametric approach, i.e., Multiple Linear Regression (MLR) model and non-parametric approaches, i.e., k-Nearest Neighbor (k-NN), Random Forest (RF), and Support Vector Regression (SVR), were also evaluated for AGB estimations. Our results indicated that among S2 metrics, the FAPAR canopy biophysical index and NDVI index based on the red-edge band (NIR-b8a) have the highest correlation coefficient (r) of 0.420 and 0.417, respectively. The results of AGB estimation showed that a combination of S2 and S1 datasets using the k-NN algorithm had the best accuracy (R 2 of 0.57 and rRMSE of 14.68%). The best rRMSE using L8, S2, and S1 datasets was 18.95, 16.99, and 19.17% using k-NN, k-NN, and MLR algorithms, respectively. The combination of L8 with S1 dataset also improved the rRMSE relative to L8 and S1 separately by 0.96 and 1.18%, respectively. We concluded that the combination of optical data (L8 or S2) with SAR data (S1) improves the broadleaved Hyrcanian AGB estimation.

The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land management. This results in strong temporal variation of spectral diversity, which cannot be accurately represented by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao's quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral diversity of a region (γSD) into additive alpha (αSD, within communities) and spatio-temporal beta (βSD; between communities) components, allowing the calculation of βSD from community mean spectral features, independent from αSD. Second, we illustrated our methodological approach with a case study in which βSD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in βSD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to βSD was assessed, and the results were validated against a dataset of in-situ measured β diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimilarities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.

Aquatic and terrestrial ecosystems are tightly connected via spatial flows of organisms and resources. Such land-water linkages integrate biodiversity across ecosystems and suggest a spatial association of aquatic and terrestrial biodiversity. However, knowledge about this spatial extent is limited. By combining satellite remote sensing (RS) and environmental DNA (eDNA) extraction from river water across a 740-km2 mountainous catchment, we identify a characteristic spatial land-water fingerprint. Specifically, we find a spatial association of riverine eDNA diversity with RS spectral diversity of terrestrial ecosystems upstream, peaking at a 400 m distance yet still detectable up to a 3.3 km radius. Our findings testify that biodiversity patterns in rivers can be linked to the functional diversity of surrounding terrestrial ecosystems and provide a dominant scale at which these linkages are strongest. Such spatially explicit information is necessary for a functional understanding of land-water linkages and provides a reference scale for adequate conservation and landscape management decisions.

Terrestrial laser scanning (TLS) has emerged as a reference for three-dimensional measurements of forest structure as well as forest reconstruction and modeling. Ground-based measurements can be complemented by new light-weight sensors on unmanned aerial vehicles (UAVs) or laser scans from canopy cranes or towers. However, it is still largely unknown how much of the forest canopy volume can be sampled and how occlusion is spatially distributed. We present an approach for highly detailed 3D structure measurements based on TLS on the ground and above canopy measurements from a canopy crane or UAV platform, and assess their spatial sampling in terms of occlusion. Comparing the application in a dense tropical and temperate forest, we demonstrate the ability to sample the complete canopy volume with

The measurement of leaf optical properties (LOP) using reflectance and scattering properties of light allows a continuous, time-resolved, and rapid characterization of many species traits including water status, chemical composition, and leaf structure. Variation in trait values expressed by individuals result from a combination of biological and environmental variations. Such species trait variations are increasingly recognized as drivers and responses of biodiversity and ecosystem properties. However, little has been done to comprehensively characterize or monitor such variation using leaf reflectance, where emphasis is more often on species average values. Furthermore, although a variety of platforms and protocols exist for the estimation of leaf reflectance, there is neither a standard method, nor a best practise of treating measurement uncertainty which has yet been collectively adopted. In this study, we investigate what level of uncertainty can be accepted when measuring leaf reflectance while ensuring the detection of species trait variation at several levels: within individuals, over time, between individuals, and between populations. As a study species, we use an economically and ecologically important dominant European tree species, namely Fagus sylvatica. We first use fabrics as standard material to quantify the measurement uncertainties associated with leaf clip (0.0001 to 0.4 reflectance units) and integrating sphere measurements (0.0001 to 0.01 reflectance units) via error propagation. We then quantify spectrally resolved variation in reflectance from F. sylvatica leaves. We show that the measurement uncertainty associated with leaf reflectance, estimated using a field spectroradiometer with attached leaf clip, represents on average a small portion of the spectral variation within a single individual sampled over time (2.7 ± 1.7%), or between individuals (1.5 ± 1.3% or 3.4 ± 1.7%, respectively) in a set of monitored F. sylvatica trees located in Swiss and French forests. In all forests, the spectral variation between individuals exceeded the spectral variation of a single individual measured within one week. However, measurements of variation within an individual at different canopy positions over time indicate that sampling design (e.g., standardized sampling, and sample size) strongly impacts our ability to measure between-individual variation. We suggest best practice approaches towards a standardized protocol to allow for rigorous quantification of species trait variation using leaf reflectance.HighlightsWe partition biological variation from measurement uncertainty for leaf spectra.Measurement uncertainty represents ca. 3% of spectral variation among beech trees.Biological variation within an individual increases by 80% as leaves mature.Maxima of uncertainty correspond to maxima of biological variation (water content).We recommend procedures to quantify biological variation in spectral measurements.

Shifts in phenology are important traces of climate change affecting mountainous ecosystems. We present an analysis of changes in spring phenology using a suite of Earth observation based parameters, i.e., start of season (SOS), snow cover extent and meteorological variables from 1991 up to 2012/2014 for the European Alps. Our results show that SOS tends to occur earlier throughout the Alps during this period and spring temperatures have increased in the Eastern Alps. Spring temperatures presented a predominant influence on SOS for both, grasslands and forests across elevations between 500–2,200 m asl, while this effect is particularly pronounced in the northeastern Alps. Snow cover duration and snow cover melting days showed secondary impact on SOS. Our research provides a comprehensive observation of spatiotemporal changes in alpine spring vegetation phenology and its driving factors. They improve our understanding of the sensitivity of the European Alps ecosystems to a changing climate.

Arid grassland ecosystems undergo degradation because of increasing environmental and human pressures. Degraded grasslands show vegetation cover reduction and soil-patch development, leading to grassland fragmentation and changes in spatial heterogeneity. Understanding grassland degradation that involves soil-patch development remains a challenge over large areas with limited accessibility such as the Qinghai-Tibetan Plateau. We hypothesized that vegetation cover, its spatial heterogeneity and changes thereof over time retrieved from satellite data can indicate grassland development and degradation levels. To test the hypothesis, we studied these indicators from 2000 to 2016 and related them to previously described degradation levels on the eastern Qinghai-Tibetan Plateau (QTP) in 2004. We further use these indicators to map the new grassland development and degradation levels in 2016. We found that lower vegetation cover does not always indicate a more severe degradation; instead, higher spatial heterogeneity is a better correlate of degradation. Combined temporal changes in grassland cover and its spatial heterogeneity are related to the literature-defined degradation levels. We found that grassland areas on the eastern QTP have moved into new degradation stages from 2000 to 2016 using changes in grassland cover and its spatial heterogeneity as indicators. The normalized difference vegetation index (NDVI) as a proxy for grassland cover declined over time in the literature-defined degraded areas but increased in the desert areas from 2000 to 2016. Spatial heterogeneity generally increased across different degradation levels from 2000 to 2016; however, this increase was less pronounced in severely degraded and slightly deserted areas. Our newly defined degradation levels in 2016 included degradation, desertification, and improving levels. Across our study area, 63% of all areas were classified as degraded and 2% were at risk of desertification. The remaining areas (35%) classified as improving and re-growing occurred in higher-elevation or previously severely degraded grassland. Our study demonstrates that a combination of changes in grassland cover and in its spatial heterogeneity can indicate grassland degradation levels and serve as an early-warning signal for desertification threats.

Snow accumulation and melt have multiple impacts on Land Surface Phenology (LSP) and greenness in Alpine grasslands. Our understanding of these impacts and their interactions with meteorological factors are still limited. In this study, we investigate this topic by analyzing LSP dynamics together with potential drivers, using satellite imagery and other data sources. LSP (start and end of season) and greenness metrics were extracted from time series of vegetation and leaf area index. As explanatory variables we used snow accumulation, snow cover melt date and meteorological factors. We tested for inter-annual co-variation of LSP and greenness metrics with seasonal snow and meteorological metrics across elevations and for four sub-regions of natural grasslands in the Swiss Alps over the period 2003–2014. We found strong positive correlations of snow cover melt date and snow accumulation with the start of season, especially at higher elevation. Autumn temperature was found to be important at the end of season below 2000 m above sea level (m asl), while autumn precipitation was relevant above 2000 m asl, indicating climatic growth limiting factors to be elevation dependent. The effects of snow and meteorological factors on greenness revealed that this metric tends to be influenced by temperatures at high elevations, and by snow melt date at low elevations. Given the high sensitivity of alpine grassland ecosystems, these results suggest that alpine grasslands may be particularly affected by future changes in seasonal snow, to varying degree depending on elevation.

Spatial monitoring of grassland management is crucial for ecosystem assessment and the establishment of sustainable agriculture. Switzerland is covered by large areas of small structured grassland parcels differing in management practices and use intensities, making the mapping of grassland management challenging. We present a monitoring tool to map grassland management, distinguishing between mowing- and grazing practice, and between different use intensities for Swiss agroecosystems. By analyzing pixelwise spectral time series of 2015, derived from satellite imagery of the Landsat archive, we estimated the number of management events and biomass productivity. Both estimates were used to map classes of dominant management practices and use intensities following a stepwise clustering approach. The grassland management (GM) classes were evaluated relative to established spectral and topographical patterns of grassland use intensity, and in terms of spatial conformity with available regional land use data. The GM classes were also analyzed with respect to management related vegetation plot data on species diversity, as well as on indicator values for nutrient supply and management tolerance. The stepwise clustering gave three use intensity classes for each dominant management practice of grazing (pasture) and mowing (meadow). Use intensity was higher for meadows than pastures with a distinct intensity gradient for each grassland practice. The GM classes reproduced established spectral and topographical patterns of grassland use intensity, indicated by increased standard deviations (SD) of spectral time series profiles (e.g. mean SD of 0.048 for pastures and 0.054 for meadows) and lower slopes (e.g. mean slopes of 10° for pastures and 7° for meadows). The averaged spatial conformity of the GM classes with a cantonal land use map was 82% for meadows and 97% for pastures. The GM classes spatially matched with land use patterns of three subregions, e.g. with an areal proportion of 73% pasture classes for a subregion dominated by grazing. Moreover, the GM classes reproduced established vegetation patterns of grassland use intensity along the GM intensity gradient, showing a mean decrease in species richness (33%), as well as a mean increase in indicator values for nutrient supply (5%), grazing tolerance (4%), and mowing tolerance (6%).

Crop species separation is essential for a wide range of agricultural applications&mdash, in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of &gt, 92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA &gt, 90%).

Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.

The growing pace of environmental change has increased the need for large‐scale monitoring of biodiversity. Declining intraspecific genetic variation is likely a critical factor in biodiversity loss, but is especially difficult to monitor: assessments of genetic variation are commonly based on measuring allele pools, which requires sampling of individuals and extensive sample processing, limiting spatial coverage. Alternatively, imaging spectroscopy data from remote platforms may hold the potential to reveal genetic structure of populations. In this study, we investigated how differences detected in an airborne imaging spectroscopy time series correspond to genetic variation within a population of Fagus sylvatica under natural conditions.We used multi‐annual APEX (Airborne Prism Experiment) imaging spectrometer data from a temperate forest located in the Swiss midlands (Laegern, 47°28'N, 8°21'E), along with microsatellite data from F. sylvatica individuals collected at the site. We identified variation in foliar reflectance independent of annual and seasonal changes which we hypothesize is more likely to correspond to stable genetic differences. We established a direct connection between the spectroscopy and genetics data by using partial least squares (PLS) regression to predict the probability of belonging to a genetic cluster from spectral data.We achieved the best genetic structure prediction by using derivatives of reflectance and a subset of wavebands rather than full‐analyzed spectra. Our model indicates that spectral regions related to leaf water content, phenols, pigments, and wax composition contribute most to the ability of this approach to predict genetic structure of F. sylvatica population in natural conditions.This study advances the use of airborne imaging spectroscopy to assess tree genetic diversity at canopy level under natural conditions, which could overcome current spatiotemporal limitations on monitoring, understanding, and preventing genetic biodiversity loss imposed by requirements for extensive in situ sampling., Declining intraspecific genetic variation is likely a critical factor in biodiversity loss. In this study, we demonstrated the high potential of airborne imaging spectroscopy to access information about intraspecific genetic variation in a systematic and spatially continuous manner.

Land use change strongly affects soil organic carbon (SOC), which is a critical driving force for soil quality and soil services. However, the influence of detailed land use dynamics on SOC remains an active research field. We investigated the impact of long-term grass-/cropland conversion dynamics on SOC for the Swiss agroecosystem (8‵500 km2). In Switzerland multi-temporal SOC observations are sparse, while the spatio-temporal resolution of available land use data is inadequate to capture small structured and dynamic land use. Thus, we stratified one-time SOC observations of two periods (n1995–1999 = 1096; n2011–2015 = 502) into typical land use classes of grass-/cropland conversion regimes to analyze states and trends of SOC. First, based on spectral imagery and auxiliary variables, we established a Random Forest classifier to detect annual grass-/cropland distributions in a spatial resolution of 30 × 30 m for 2000–2015. Second, we used the annual land use maps to derive classes of typical land use dynamics based on a pattern description of conversion regimes. Third, we comparatively assessed the SOC covariate importance (IMP) and SOC sample representation of land use dynamics using terrain and climate covariates. Subsequently, we statistically analyzed SOC across land use dynamics and two periods. The land use classifier shows an Overall Accuracy of 86%, while the annual land use maps deviate by 6%–11% from census data. 46% of the farmland underlies frequent grass-/cropland conversions. The SOC covariate importance of land use dynamics is increased by at least 2.4 times compared to the SOC covariates. The SOC observations consistently represent land use dynamics across SOC covariates. In both periods, SOC increased as the grassland share within the land use regimes increased. The mean SOC for permanent grasslands is 35 g kg−1 and 17 g kg−1 for permanent croplands. Areas of land use conversions where grassland is dominant show increased SOC (29–30 g kg−1) compared to areas where cropland was dominant (23–25 g kg−1). The mean differences of SOC between the periods and classes are statistically not significant except for permanent cropland, for which the SOC is decreased by 5.2 g kg−1.

Understanding and monitoring pressures on ecosystems and their consequences for ecosystem services (ES) is essential for management decisions and verification of progress towards national and international policies (e.g. Aichi Biodiversity Targets, Sustainable Development Goals). Remote sensing (RS) offers a unique capability to assess ES systematically and regularly across spatial and temporal scales. We aim to evaluate the benefits of RS to monitor spatio-temporal variations of ES by assessing several ES in Switzerland between 2004 and 2014. We coupled mechanistic ES models and RS data to estimate time series of three regulating (i.e. carbon dioxide regulation (CO₂R), soil erosion prevention (SEP), and air quality regulation (AQR)) and one cultural ES (re- creational hiking (RH)). The resulting ES were used to assess spatial and temporal changes, trade-offs and sy- nergies of ES potential supply and flow in Switzerland between 2004 and 2014. Resulting ES trends showed diverse spatial patterns across Switzerland with largest changes in CO₂R and AQR. ES interactions revealed a scale and elevation dependency. We identified weak to strong synergies between all ES combinations except for trade-offs between CO₂R–AQR and AQR–RH at Swiss scale. Spatially, all ES interactions revealed a hetero- geneous mix of synergies and trade-offs within Switzerland. Our results demonstrate the strength of RS for systematic and regular spatio-temporal ES monitoring and contribute insights to the large potential of RS, which will be extended with future Earth observation missions. Derived spatially explicit ES information will facilitate decision-making in landscape planning and conservation and will allow examining progress towards environmental policies.

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 characterize 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 m2 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/m2, 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.

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.

Snow cover impacts alpine land surface phenology in various ways, but our knowledge about the effect of snow cover on alpine land surface phenology is still limited. We studied this relationship in the European Alps using satellite-derived metrics of snow cover phenology (SCP), namely, first snow fall, last snow day, and snow cover duration (SCD), in combination with land surface phenology (LSP), namely, start of season (SOS), end of season, and length of season (LOS) for the period of 2003–2014. We tested the dependency of interannual differences (Δ) of SCP and LSP metrics with altitude (up to 3000 m above sea level) for seven natural vegetation types, four main climatic subregions, and four terrain expositions. We found that 25.3% of all pixels showed significant (p < 0.05) correlation between ΔSCD and ΔSOS and 15.3% between ΔSCD and ΔLOS across the entire study area. Correlations between ΔSCD and ΔSOS as well as ΔSCD and ΔLOS are more pronounced in the northern subregions of the Alps, at high altitudes, and on north and west facing terrain—or more generally, in regions with longer SCD. We conclude that snow cover has a greater effect on alpine phenology at higher than at lower altitudes, which may be attributed to the coupled influence of snow cover with underground conditions and air temperature. Alpine ecosystems may therefore be particularly sensitive to future change of snow cover at high altitudes under climate warming scenarios.

Agroecosystems play an important role in providing economic and ecosystem services, which directly impact society. Inappropriate land use and unsustainable agricultural management with associated nutrient cycles can jeopardize important soil functions such as food production, livestock feeding, and conservation of biodiversity. The objective of this study was to integrate remotely sensed land cover information into a regional land management model (LMM) to improve the assessment of spatially explicit nutrient balances for agroecosystems. Remotely sensed data and an optimized parameter set contributed to an improved LMM output, allowing for a better land allocation within the model. The best input parameter combination was based on two different land cover classifications with overall accuracies of 98%, improving the land allocation performance compared with using nonspatially explicit input. We conclude that the combined use of remote sensing data and the LMM has the potential to provide valuable guidance for farm practices. It further helps to generate a spatial description of farm-level nutrient balance, a crucial ability when choosing policy options related to sustainable management of agricultural soils.

Remote sensing data allowlarge 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-bandMEMPHIS 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 0.2–0.3mrelative shifts. Both InSAR and photogrammetry techniques yielded points predominantly located in the upper levels of the forest vegetation, while ALS provided points fromthe top of the canopy down to the understory and forest floor. The canopy height models agreed very well with each other, with R² 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 R² value of 0.63. The estimated crown diameter agreed on an average sense, however with a relatively low R² value of 0.19. Very high success rates (N90%) were obtained when matching the trees detected fromthe InSAR-datawith those detected fromthe ALS- and photogrammetry-data. There, InSAR tree heightswere in themean 1–1.5mlower,with high R² 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.

Imaging spectroscopy is frequently used to assess traits and functioning of vegetated ecosystems. Applied reflectance- and radiance-based approaches critically rely on accurate estimates of surface irradiance. Accurate retrievals of surface irradiance are, however, nontrivial and often error-prone, thus causing inaccurate estimates of vegetation information. We analyze the irradiance field surrounding an isolated tree using the 3-D radiative transfer model DART in high spatial (25 cm) and spectral (1 nm, 350–2500 nm) resolution. We validate modeled irradiance with in situ measurements and quantify the impact of erroneous surface irradiance estimates on the retrieval of vegetation indices. We observe the irradiance gradients in the cast shadows of

The separation of crop types is essential for many agricultural applications, particularly when within-season information is required. Generally, remote sensing may provide timely information with varying accuracy over the growing season, but in small structured agricultural areas, a very high spatial resolution may be needed that exceeds current satellite capabilities. This paper presents an experiment using spectral and textural features of NIR-red-green-blue (NIR-RGB) bands data sets acquired with an unmanned aerial vehicle (UAV). The study area is located in the Swiss Plateau, which has highly fragmented and small structured agricultural fields. The observations took place between May 5 and September 29, 2015 over 11 days. The analyses are based on a random forest (RF) approach, predicting crop separation metrics of all analyzed crops. Three temporal windows of observations based on accumulated growing degree days (AGDD) were identified: an early temporal window (515–1232 AGDD, 5 May–17 June 2015) with an average accuracy (AA) of 70–75%; a mid-season window (1362–2016 AGDD, 25 June–22 July 2015) with an AA of around 80%; and a late window (2626–3238 AGDD, 21 August–29 September 2015) with an AA of

© 2019, The Author(s). Healthy vegetation function supports diverse biological communities and ecosystem processes, and provides crops, forest products, forage, and countless other benefits. Vegetation function can be assessed by examining dynamic processes and by evaluating plant traits, which themselves are dynamic. Using both trait-based and process-based approaches, spectroscopy can assess vegetation function at multiple scales using a variety of sensors and platforms ranging from proximal to airborne and satellite measurements. Since spectroscopic data are defined by the instruments and platforms available, along with their corresponding spatial, temporal and spectral scales, and since these scales may not always match those of the function of interest, consideration of scale is a necessary focus. For a full understanding of vegetation processes, combined (multi-scale) sampling methods using empirical and theoretical approaches are required, along with improved informatics. ispartof: Surveys in Geophysics vol:40 issue:3 pages:489-513 ispartof: location:SWITZERLAND, Int Space Sci Inst, Bern status: published

In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.

Bare soil surfaces vary spatially and temporally as a result of – among other - meteorological conditions and land management practices. Literature shows that most of the existing soil surface corrections are dependent on laboratory measurements. These measurements are time consuming and therefore unsuitable for large amounts of airborne or satellite spectroscopy data. Soil moisture and soil surface roughness present main challenges for in-field and remote sensing application of soil spectroscopy and soil moisture has substantial influence on surface reflectance. Here, we present a straightforward method for soil moisture reduction applicable to large amounts of airborne or satellite spectroscopy data. Multi-temporal airborne imaging spectrometer data was collected with the Airborne Prism Experiment (APEX) in September 2013, May 2014 and May 2015 on the same pilot site. A new soil moisture index was developed to quantify the soil moisture of the airborne imaging spectroscopy data (R2 of 0.59). The index appeared to be closely related to the existing NINSOL index. A, laboratory based, independent soil moisture spectral dataset was used to quantify the correction factor per wavelength based on the wavelength dependent relative differences. After minimizing the effect of soil moisture variability, the airborne spectrometer data better approximate the reference dry-soil spectra (up to 21.3%). Then, the corrected data were tested for soil properties prediction (soil organic matter (SOM), sand silt and clay). The soil moisture corrected spectra performed marginally better for SOM (RMSE from 3.13 to 3.10) and silt (RMSE from 5.74 to 5.72), although not for clay (RMSE from 9.14 to 9.33) and sand (RMSE from 5.98 to 6.31). The methodology shows that field spectra can be corrected to better match laboratory conditions, focusing on soil moisture as main varying factor. Next step should include the various types of soil surface variation and their interactions. A better understanding of these interactions is needed and can be achieved by field-scale experiments and by modelling the different processes.

Imaging spectroscopy of vegetation requires methods for scaling and generalizing optical signals that are reflected, transmitted and emitted in the solar wavelength domain from single leaves and observed at the level of canopies by proximal sensing, airborne and satellite spectroradiometers. The upscaling embedded in imaging spectroscopy retrievals and validations of plant biochemical and structural traits is challenged by natural variability and measurement uncertainties. Sources of the leaf-to-canopy upscaling variability and uncertainties are reviewed with respect to: (1) implementation of retrieval algorithms and (2) their parameterization and validation of quantitative products through in situ field measurements. The challenges are outlined and discussed for empirical and physical leaf and canopy radiative transfer modelling components, considering both forward and inverse modes. Discussion on optical remote sensing validation schemes includes also description of a multiscale validation concept and its advantages. Impacts of intraspecific and interspecific variability on collected field and laboratory measurements of leaf biochemical traits and optical properties are demonstrated for selected plant species, and field measurement uncertainty sources are listed and discussed specifically for foliar pigments and canopy leaf area index. The review concludes with the main findings and suggestions as how to reduce uncertainties and include variability in scaling vegetation imaging spectroscopy signals and functional traits of single leaves up to observations of whole canopies.

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.

openaire: EC/H2020/771049/EU//FREEDLES Understanding the reflectance anisotropy of forests and the underlying scattering mechanisms is needed to improve the accuracy of retrievals of fundamental forest characteristics from optical remote sensing data. In this paper, we developed a laboratory measurement set-up for a large goniometer (LAGOS) and measured multi-angular spectra (350–2500 nm) of 18 small trees, composed of three common European tree species: Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst), sessile oak (Quercus petraea (Matt.) Liebl.). For all trees, we measured tree spectra in 47 view angles in the upper hemisphere. To our knowledge, this is the first study reporting multi-angular reflectance spectra of single trees. We also measured the reflectance and transmittance spectra of needles and leaves, as well as reflectance spectra of bark of the sample trees. We analyzed the spectro-directional characteristics of the trees, and the inter- and intraspecific variations of these characteristics. The anisotropy oftrees was shown to be strongly asymmetrical and characteristic to species: while pine and spruce exhibited strong hotspot effects, oak showed a strong specular component. Our results indicate that simultaneous measurements of both spectral and directional characteristics of trees may enhance the discrimination of species and thus, support the retrieval of information of their biophysical properties.

The three-dimensional (3D) distribution of light within forest ecosystems is a major driver for species competition, coexistence, forest ecosystem functioning, productivity, and diversity. However, accurate knowledge about the 3D distribution of light within the canopy is difficult to obtain. Recent advances in 3D forest reconstruction as well as the use of radiative transfer modelling provide new insights into spatio-temporal variations of light distribution within a forest canopy. We used high resolution laser scanning data coupled with in-situ leaf optical properties (LOP) measurements to parameterize the DART radiative transfer model for a temperate deciduous forest on the Laegern mountain, Switzerland, and for a tropical rain forest located in the Lambir Hills national park, Borneo, Malaysia. Combining terrestrial and unmanned aerial vehicle (UAV) laser scanning acquisitions allowed a high detailed, 3D reconstruction of forest canopies. We analyse the impact of the two contrasting forest canopies, both in terms of structure as well as optical properties, on the 3D extinction of photosynthetic active radiation (PAR, 400 nm - 700 nm) for a whole diurnal cycle. We show that PAR extinction is mainly driven by the canopy structure, resulting in an exponential light extinction profile for the temperate and a more linear extinction profile in the tropical site. The larger 3D heterogeneity in canopy structure for the tropical site also resulted in larger variability in light extinction throughout the whole canopy. We found that contrasting LOPs between the two forests had a minor influence on light extinction. However, approximating light extinction profiles with layered Beer-Lambert or Big-Leaf models only poorly represented the 3D heterogeneity of light extinction within the canopy, illustrating the need for more detailed 3D modelling of light distribution within forest ecosystems. This can give us important insights into light-related mechanisms driving species coexistence, competition and diversity in complex forest ecosystems.

Spatial and temporal patterns of forest background (understory) reflectance are crucial for retrieving biophysical parameters of forest canopies (overstory) and subsequently for ecosystem modeling. In this communication, we retrieved seasonal courses of understory normalized difference vegetation index (NDVI) from multiangular Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (MODIS BRDF)/albedo data. We compared satellite-based seasonal courses of understory NDVI to understory NDVI values measured in different types of forests distributed along a wide latitudinal gradient (65.12°N–31.35°N). Our results indicated that the retrieval method performs well particularly over open forests of different types. We also demonstrated the limitations of the method for closed canopies, where the understory signal retrieval is much attenuated.

Monitoring land surface phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP provides the potential to fill the gap between traditional phenological (field) observations and the large-scale view of global models. In this study, we review and evaluate the variability and evolution of satellite-derived growing season length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982-2012. We tested GSL, start- and end-of-season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. Our results confirmed an average lengthening of the growing season globally during 1982-2012 - averaging 0.22-0.34 days yr(-1), but with spatially heterogeneous trends. About 13-19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past three decades. Within this biome, the 'Cold and Mesic' environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large-scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models.

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.

Canopy layers form essential structural components, affecting stand productivity and wildlife habitats. Airborne laser scanning (ALS) provides horizontal and vertical information on canopy structure simultaneously. Existing approaches to assess canopy layering often require prior information about stand characteristics or rely on pre-defined height thresholds. We developed a multi-scale method using ALS data with point densities &gt, 10 pts/m2 to determine the number and vertical extent of canopy layers (canopylayer, canopylength), seasonal variations in the topmost canopy layer (canopytype), as well as small-scale heterogeneities in the canopy (canopyheterogeneity). We first tested and developed the method on a small forest patch (800 ha) and afterwards tested transferability and robustness of the method on a larger patch (180,000 ha). We validated the approach using an extensive set of ground data, achieving overall accuracies &gt, 77% for canopytype and canopyheterogeneity, and &gt, 62% for canopylayer and canopylength. We conclude that our method provides a robust characterization of canopy layering supporting automated canopy structure monitoring.

Knowledge of the spatial distribution of tree species is important for efficiently managing and monitoring forested ecosystems, especially in mixed forests of the temperate zone. In this study, we fused imaging spectroscopy (IS) data with leaf-on and off small-footprint airborne laser scanning (ALS) data, for tree species identification in a dense temperate forest in Switzerland. In addition to the spectral reflectance of the sunlit part of the tree crowns, structural features computed based on the height, intensity and point distribution of ALS data in both the vertical and horizontal dimensions are used as features. Features were extracted using a pixel-based (1 m × 1 m) and an individual tree crown approach. In addition, applying a floating forward feature selection approach revealed that the ALS-derived features provided relevant structural information for species identification, while IS-derived features added complementary biochemical information. Comparing the accuracies of three different combinations of ALS and IS data, shows the highest classification accuracy (kappa = 90.3%) was obtained by fusing a selected set of features at individual tree crowns (ITC), while the best kappa accuracies resulting from IS or ALS data alone were 74.7% and 75.1%, respectively. Inclusion of the ITC information improved the classification results for all datasets, however, this improvement is significantly higher for ALS derived datasets (+31%). Our results show that accurate ITC information drastically improves classification accuracy of tree species in dense forests and that multi-seasonal ALS structural attributes play a major part in species discrimination.

Timing and accumulation of snow are among the most important phenomena influencing land surface phenology in mountainous ecosystems. However, our knowledge on their influence on alpine land surface phenology is still limited, and much remains unclear as to which snow metrics are most relevant for studying this interaction. In this study, we analyzed five snow and phenology metrics, namely, timing (snow cover duration (SCD) and last snow day), accumulation of snow (mean snow water equivalent, SWEm), and mountain land surface phenology (start of season and length of season) in the Swiss Alps during the period 2003–2014. We examined elevational and regional variations in the relationships between snow and alpine land surface phenology metrics using multiple linear regression and relative weight analyses and subsequently identified the snow metrics that showed strongest associations with variations in alpine land surface phenology of natural vegetation types.We found that the relationships between snow and phenology metrics were pronounced in high-elevational regions and alpine natural grassland and sparsely vegetated areas. Start of season was influenced primarily by SCD, secondarily by SWEm, while length of season was equally affected by SCD and SWEm across different elevational bands. We conclude that SCD plays the most significant role compared to other snow metrics. Future variations of snow cover and accumulation are likely to influence alpine ecosystems, for instance, their species composition due to changes in the potential growing season. Also, their spatial distribution may change as a response to the new environmental conditions if these prove persistent.

Land surface phenology (LSP), the study of seasonal dynamics of vegetated land surfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood at global scale. Understanding the climatic constraints that underlie LSP is crucial for explaining climate change effects on global vegetation phenology. We used a combination of modelled and remotely-sensed vegetation activity records to quantify the interplay of three climatic constraints on land surface phenology (namely minimum temperature, moisture availability, and photoperiod), as well as the dynamic nature of these constraints. Our study examined trends and the relative importance of the three constrains at the start and the end of the growing season over eight global environmental zones, for the past three decades. Our analysis revealed widespread shifts in the relative importance of climatic constraints in the temperate and boreal biomes during the 1982–2011 period. These changes in the relative importance of the three climatic constraints, which ranged up to 8% since 1982 levels, varied with latitude and between start and end of the growing season. We found a reduced influence of minimum temperature on start and end of season in all environmental zones considered, with a biome-dependent effect on moisture and photoperiod constraints. For the end of season, we report that the influence of moisture has on average increased for both the temperate and boreal biomes over 8.99 million km2. A shifting relative importance of climatic constraints on LSP has implications both for understanding changes and for improving how they may be modelled at large scales.

Many soil remote sensing applications rely on narrow-band observations to exploit molecular absorption features. However, broadband sensors are invaluable for soil surveying, agriculture, land management and mineral exploration, amongst others. These sensors provide denser time series compared to high-resolution airborne imaging spectrometers and hold the potential of increasing the observable bare-soil area at the cost of spectral detail. The wealth of data coming along with these applications can be handled using cloud-based processing platforms such as Earth Engine. We present a method for identifying the least-vegetated observation, or so called barest pixel, in a dense time series between January 1985 and March 2017, based on Landsat 5, 7 and 8 observations. We derived a Barest Pixel Composite and Bare Soil Composite for the agricultural area of the Swiss Plateau. We analysed the available data over time and concluded that about five years of Landsat data were needed for a full-coverage composite (90% of the maximum bare soil area). Using the Swiss harmonised soil data, we derived soil properties (sand, silt, clay, and soil organic matter percentages) and discuss the contribution of these soil property maps to existing conventional and digital soil maps. Both products demonstrate the substantial potential of Landsat time series for digital soil mapping, as well as for land management applications and policy making.

Efficient methods to monitor forested areas help us to better understand their processes. To date, only a few studies have assessed the usability of multitemporal synthetic aperture radar (SAR) datasets in this context. Here we present an analysis of an unprecedented set of C-band observations of mixed temperate forests. We demonstrate the potential of using multitemporal C-band VV and VH polarisation data for monitoring phenology and classifying forests in northern Switzerland. Each SAR acquisition was first radiometrically terrain corrected using digital elevation model-based image simulations of the local illuminated area. The flattened backscatter values and the local area values were input to a temporal compositing process integrating backscatter values from ascending and descending tracks. The process used local resolution weighting of each input, producing composite backscatter values that strongly mitigated terrain-induced distortions. Several descriptors were calculated to show the seasonal variation of European beech (Fagus sylvatica), oak (Quercus robur, Quercus petraea) and Norway spruce (Picea abies) in C-band data. Using their distinct seasonal signatures, the timing of leaf emergence and leaf fall of the deciduous species were estimated and compared to available ground observations. Furthermore, classifications for the forest types ‘deciduous’ and ‘coniferous’ and the investigated species were implemented using random forest classifiers. The deciduous species backscatter was about 1 dB higher than spruce throughout the year in both polarisations. The forest types showed opposing seasonal backscatter behaviours. At VH, deciduous species showed higher backscatter in winter than in summer, whereas spruce showed higher backscatter in summer than in winter. In VV, this pattern was similar for spruce, while no distinct seasonal behaviour was apparent for the deciduous species. The time differences between the estimations and the ground observations of the phenological events were approximately within the error margin ( ± 12 days) of the temporal resolution. The classification performances were promising, with higher accuracies achieved for the forest types (OA of 86% and κ = 0.73) than for individual species (OA of 72% and κ = 0.58). These results show that multitemporal C-band backscatter data have significant potential to supplement optical remote sensing data for ecological studies and mapping of mixed temperate forests.

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 (VGrate) and its connection to temperature variability using remotely sensed normalized difference vegetation index (NDVI) and surface air temperature. The average of the VGrate 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. VGrate 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 VGrate to temperature variability shows that despite the same magnitude of local temperature variability during extremely cold and warm years, the magnitude of VGrate acceleration in warm years (0.07 (15-day)! 1) is larger than the VGrate deceleration in cold years (!0.03 (15-day)!1), suggesting that the response of VGrate 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.

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).

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 R2 = 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., Remote Sensing of Environment, 158, ISSN:0034-4257

Surface albedo partitions the amount of energy received by glacier surfaces from shortwave fluxes and modulates the energy available for melt processes. The ice-albedo feedback, influenced by the contamination of bare-ice surfaces with light-absorbing impurities, plays a major role in the melting of mountain glaciers in a warming climate. However, little is known about the spatial and temporal distribution and variability of bare-ice glacier surface albedo under changing conditions. In this study, we focus on two mountain glaciers located in the western Swiss Alps and perform a cross-comparison of different albedo products. We take advantage of high spectral and spatial resolution (284 bands, 2 m) imaging spectrometer data from the Airborne Prism Experiment (APEX) and investigate the applicability and potential of Sentinel-2 and Landsat 8 data to derive broadband albedo products. The performance of shortwave broadband albedo retrievals is tested and we assess the reliability of published narrow-to-broadband conversion algorithms. The resulting albedo products from the three sensors and different algorithms are further cross-compared. Moreover, the impact of the anisotropy correction is analysed depending on different surface types. While degradation of the spectral resolution impacted glacier-wide mean albedo by about 5%, reducing the spatial resolution resulted in changes of less than 1%. However, in any case, coarser spatial resolution was no longer able to represent small-scale variability of albedo on glacier surfaces. We discuss the implications when using Sentinel-2 and Landsat 8 to map dynamic glaciological processes and to monitor glacier surface albedo on larger spatial and more frequent temporal scales., Remote Sensing, 9 (2), ISSN:2072-4292

Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity–ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology., As remote sensing technology improves, it is now possible to map fine-scale variation in plant functional traits. Schneider et al. remotely sense tree functional diversity, validate with field data, and reveal patterns of plant adaptation to the environment previously not retrievable from plot data

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.

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.

We outline an approach combining airborne laser scanning (ALS) and imaging spectroscopy (IS) to quantify and assess patterns of tree density (TD) and forest productivity (FP) in a protected heterogeneous alpine forest in the Swiss National Park (SNP). We use ALS data and a local maxima (LM) approach to predict TD, as well as IS data (Airborne Prism Experiment—APEX) and an empirical model to estimate FP. We investigate the dependency of TD and FP on site related factors, in particular on surface exposition and elevation. Based on reference data (i.e., 1598 trees measured in 35 field plots), we observed an underestimation of ALS-based TD estimates of 40%. Our results suggest a limited sensitivity of the ALS approach to small trees as well as a dependency of TD estimates on canopy heterogeneity, structure, and species composition. We found a weak to moderate relationship between surface elevation and TD (R2 = 0.18–0.69) and a less pronounced trend with FP (R2 = 0.0–0.56), suggesting that both variables depend on gradients of resource availability. Further to the limitations faced in the sensitivity of the applied approaches, we conclude that the combined application of ALS and IS data was convenient for estimating tree density and mapping FP in north-facing forested areas, however, the accuracy was lower in south-facing forested areas covered with multi-stemmed trees.

Vegetation activity may be changed in response to climate variability by affecting seasonality and phenological events. Monitoring of land surface phenological changes play a key role in understanding feedback of ecosystem dynamics. This study focuses on the analysis of trends in land surface phenology derived parameters using normalized difference vegetation index time series based on Global Inventory Monitoring and Mapping Studies data in the Hyrcanian forests of Iran covering the period 1981–2012. First, we applied interpolation for data reconstruction in order to remove outliers and cloud contamination in time series. Phenological parameters were retrieved by using the midpoint approach, whereas trends were estimated using the Theil–Sen approach. Correlation coefficients were evaluated from multiple linear regression between phenological parameters against temperature and precipitation time series. Significant Mann–Kendall test analysis indicate average start of season (SOS) and end of season (EOS) increased by −0.16 and +0.14 days per year, respectively. Results of significant trend analysis showed that later EOS was associated with increasing temperature trends and we found strongest relationships between temperature and phenological parameters in the west of the Hyrcanian forests, where precipitation was abundant. Moreover, SOS correlated strongly with total precipitation and mean temperature. This study allows us to better estimate the drivers affecting the vegetation dynamics in the Hyrcanian forests of Iran.

Remote sensing offers the potential to study forest ecosystems by providing spatially and temporally distributed information on key biophysical and biochemical variables. The estimation of biochemical constituents of leaves from remotely sensed data is of high interest revealing insight on photosynthetic processes, plant health, plant functional types, and species composition. However, upscaling leaf level observations to canopy level is not a trivial task, in particular due to the inherent structural complexity of forests. A common solution for scaling spectral information is the use of physically-based radiative transfer models. We parameterize the Discrete Anisotropic Radiative Transfer (DART) model based on airborne and in situ measurements. At-sensor radiances were simulated and compared with measurements of the Airborne Prism Experiment (APEX) imaging spectrometer. The study was performed on the Laegern site (47°28′43.0 N, 8°21′53.2 E, Switzerland), a temperate mixed forest characterized by steep slopes, a heterogeneous spectral background, and a high species diversity. Particularly the accurate 3D modeling of the complex canopy architecture is crucial to understand the interaction of photons with the vegetation canopy and its background. Two turbid medium based forest reconstruction approaches were developed and compared; namely based on a voxel grid and based on individual tree detection. Our study shows that the voxel grid based reconstruction yields better results. When using a pixel-wise comparison with the imaging spectrometer data, the voxel grid approach performed better (R 2 = 0.48, λ780 nm) than the individual tree approach (R 2 = 0.34, λ780 nm). Spatial patterns as compared to APEX data were similar, whereas absolute radiance values differed slightly, depending on wavelength. We provide a successful representation of a 3D radiative regime of a temperate mixed forest, suitable to simulate most spectral and spatial features of imaging spectrometer data. Limitations of the approach include the high spectral variability of leaf optical properties between and within species, which will be further addressed. The results also reveal the need of more accurate parameterizations of small-scale structures, such as needle clumping at shoot level as well as leaf angle.

Aims Imaging spectroscopy enables measurement of vegetation optical properties to predict vegetation characteristics that are important for a wide range of ecological applications. Our aim was to predict fresh above-ground biomass of heterogeneous alpine grasslands in two areas and at two ecological scales. We assessed model plausibility for an intensively studied alpine grassland site (plant community scale) having distinct biomass and ungulate grazing patterns. Location Alpine grasslands in the Swiss National Park. Methods Biomass data were collected in 51 plots and combined with imaging spectroscopy data to establish simple ratio models. We analysed the predictive power and transferability of models developed in two areas (Val Trupchun, Il Fuorn) and at two ecological scales (regional, local). In a next step, we compared our results to the broadband normalized difference vegetation index (NDVI). Finally, we assessed the correlations between model predictions and plant biomass distribution at the plant community scale. Results The best local simple ratio models yielded a model fit of R2 = 0.60 and R2 = 0.30, respectively, the best regional model a fit of R2 = 0.44. NDVI model performance was weaker for the regional and one local area, but slightly better for the other local area. However, at the plant community scale only the local model showed a significant positive correlation (RS = 0.39) with the known biomass distribution. Further, predictive power decreased when models were transferred from one local area to another or from one ecological scale to another. Conclusions Our study demonstrated that imaging spectroscopy is generally useful to predict above-ground plant biomass in alpine grasslands with distinct grazing patterns. Site-specific local models based on simple ratio indices performed better than the NDVI or regional models, suggesting that standardized approaches might not be adequate, particularly in heterogeneous grasslands inhabited by large ungulates. We emphasize the importance of collecting ground reference data covering the expected range of productivity and plant species composition. Moreover, plant community-scale data from a previous study proved to be extremely valuable to test model plausibility.

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. Uncertainty assessments were carried out using both, quantitative quality indicators as proposed by 25 the individual product teams as well as independent theoretical uncertainty estimates obtained with the triple collocation error model. Multitemporal analysis for the 2009-2011 period was further performed over four European flux tower sites. Results show an overall agreement among FAPAR products on sites having high and low FAPAR values, except for the north-eastern region of Europe where the Boston product overestimates values by other products by up to 0.5. Overall we observe a systematic negative bias for JRC-TIP and MGVI products as 32 compared to the Boston product across all land cover types and particularly over forest biomes. These findings suggest that differences between the assumptions and approximations implicitly made by using different radiative transfer models translate in differences in estimated fluxes. In particular assumptions on the optical properties of land surfaces, i.e., the absorbing/scattering efficiency of leaves and background, showed important in remote sensing derived FAPAR products. The uncertainty analysis using product-based information for JRC-TIP and Boston datasets revealed consistent spatial patterns but large differences in magnitudes (up to 0.1) with systematic lower uncertainties for the Boston product. Independent uncertainty estimates suggest instead similar uncertainty ranges among the three products. Investigations at the four flux tower sites highlighted good agreement of seasonal variations with higher FAPAR values in the photosynthetically active period and lower values during the dormant period. 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., JRC.H.7-Climate Risk Management

Climate, soil type, and management practices have been reported as primary limiting factors of gross primary production (GPP). However, the extent to which these factors predict GPP response varies according to scales and land cover classes. Nitrogen (N) deposition has been highlighted as an important driver of primary production in N-limited ecosystems that also have an impact on biodiversity in alpine grasslands. However, the effect of N deposition on GPP response in alpine grasslands hasn&rsquo, t been studied much at a large scale. These remote areas are characterized by complex topography and extensive management practices with high species richness. Remotely sensed GPP products, weather datasets, and available N deposition maps bring along the opportunity of analyzing how those factors predict GPP in alpine grasslands and compare these results with those obtained in other land cover classes with intensive and mixed management practices. This study aims at (i) analyzing the impact of N deposition and climatic variables (precipitation, sunshine, and temperature) on carbon (C) fixation response in alpine grasslands and (ii) comparing the results obtained in alpine grasslands with those from other land cover classes with different management practices. We stratified the analysis using three land cover classes: Grasslands, croplands, and croplands/natural vegetation mosaic and built multiple linear regression models. In addition, we analyzed the soil characteristics, such as aptitude for croplands, stone content, and water and nutrient storage capacity for each class to interpret the results. In alpine grasslands, explanatory variables explained up to 80% of the GPP response. However, the explanatory performance of the covariates decreased to maximums of 47% in croplands and 19% in croplands/natural vegetation mosaic. Further information will improve our understanding of how N deposition affects GPP response in ecosystems with high and mixed intensity of use management practices, and high species richness. Nevertheless, this study helps to characterize large patterns of GPP response in regions affected by local climatic conditions and different land management patterns. Finally, we highlight the importance of including N deposition in C budget models, while accounting for N dynamics.