Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification

In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data.     

Geomorphological factors predict water quality in boreal rivers

Water quality is the outcome of numerous landscape factors in the catchment. In addition to land use, soil deposits, bedrock and topography are central in different catchment processes and thus important in predicting water quality. In this study, we explored the influence of geomorphological factors at the catchment scale on water quality in 32 boreal rivers in Finland. Water quality was studied through total phosphorus, total nitrogen, pH and water colour, whereas geomorphological factors covered variables from topography, bedrock and surficial ground material (Quaternary soil deposits). Spearman's rank correlation test was used to study the correlations between variables. The relationship between water quality and geomorphology was analysed using novel multivariate methods by fitting of geomorphological vectors and smooth surfaces onto a non‐metric multidimensional scaling (NMDS) scattergram. Hierarchical partitioning (HP) was used to assess the relative importance of geomorphological variables on water quality. Quaternary soil deposits, especially the covers of clay‐silt and till soils, were important factors in relation to phosphorus and nitrogen based on both NMDS and HP analyses. For example, clay‐silt cover explained over 40% of the variation in these nutrients according to HP. The variation in river water pH was best explained by the covers of sand and open bedrock terrain as well as by catchment topography. Geomorphological variables differed in their effect and relative significance, and thus several geomorphological attributes need to be considered when examining variation in water quality. In conclusion, these results demonstrate that geomorphological factors can be used to predict physical–chemical water quality in a cost‐efficient manner in boreal rivers. NMDS was successfully applied in water quality analyses at the catchment scale. Copyright © 2014 John Wiley & Sons, Ltd.     

Large biases and inconsistent climate change signals in ENSEMBLES regional projections

In this paper we analyze some caveats found in the state-of-the-art ENSEMBLES regional projections dataset focusing on precipitation over Spain, and highlight the need of a task-oriented validation of the GCM-driven control runs. In particular, we compare the performance of the GCM-driven control runs (20C3M scenario) with the ERA40-driven ones (“perfect” boundary conditions) in a common period (1961–2000). Large deviations between the results indicate a large uncertainty/bias for the particular RCM-GCM combinations and, hence, a small confidence for the corresponding transient simulations due to the potential nonlinear amplification of biases. Specifically, we found large biases for some RCM-GCM combinations attributable to RCM in-house problems with the particular GCM coupling. These biases are shown to distort the corresponding climate change signal, or “delta”, in the last decades of the 21st century, considering the A1B scenario. Moreover, we analyze how to best combine the available RCMs to obtain more reliable projections.     

Footprint Analysis For Measurements Over A Heterogeneous Forest

The air flow and vertical distribution of sources/sinks inside aforest canopy have been taken into accountin the analysis of the contribution of sources/sinks to measured fluxes and concentrations above a forest. Thestochastic estimators for concentrations and fluxes are described and their evaluation is performed by simulationof an ensemble of fluid parcel trajectories. The influence of the forest canopy on the footprint is important forobservation levels up to a few times the forest height. The influence of along-wind turbulent diffusion, whichanalytical atmospheric surface layer (ASL) footprint models do not account for, is significant even at higherlevels. The footprint analysis has been performed to deduce the Douglas fir canopy carbon dioxide uptake from eddycovariance flux measurements above a mixed Douglas fir–beech forest during the pre-leaf periods of the beech.The scatter in the results indicates that such an analysis is limited, presumably due to horizontal inhomogenetiesin flow statistics, which were not included in trajectory simulation. The analysis, however, is useful for theestimation of the qualitative effect of the forest canopy on the footprint function.     

Quality analysis applied on eddy covariance measurements at complex forest sites using footprint modelling

Summary Measuring turbulent fluxes with the eddy covariance method has become a widely accepted and powerful tool for the determination of long term data sets for the exchange of momentum, sensible and latent heat, and trace gases such as CO₂ between the atmosphere and the underlying surface. Several flux networks developed continuous measurements above complex terrain, e.g. AmeriFlux and EUROFLUX, with a strong focus on the net exchange of CO₂ between the atmosphere and the underlying surface. Under many conditions basic assumptions for the eddy covariance method in its simplified form, such as stationarity of the flow, homogeneity of the surface and fully developed turbulence of the flow field, are not fulfilled. To deal with non-ideal conditions which are common at many FLUXNET sites, quality tests have been developed to check if these basic theoretical assumptions are valid.In the framework of the CARBOEUROFLUX project, we combined quality tests described by Foken and Wichura (1996) with the analytical footprint model of Schmid (1997). The aim was to identify suitable wind sectors and meteorological conditions for flux measurements. These tools were used on data of 18 participating sites. Quality tests were applied on the fluxes of momentum, sensible and latent heat, and on the CO₂-flux, respectively. The influence of the topography on the vertical wind component was also checked. At many sites the land use around the flux towers is not homogeneous or the fetch may not be large enough. So the relative contribution of the land use type intended to be measured was also investigated. Thus the developed tool allows comparative investigations of the measured turbulent fluxes at different sites if using the same technique and algorithms for the determination of the fluxes as well as analyses of potential problems caused by influences of the surrounding land use patterns.     

Modelling spatio-temporal soil moisture dynamics in mountain tundra

Soil moisture has a fundamental influence on the processes and functions of tundra ecosystems. Yet, the local dynamics of soil moisture are often ignored, due to the lack of fine resolution, spatially extensive data. In this study, we modelled soil moisture with two mechanistic models, SpaFHy (a catchment-scale hydrological model) and JSBACH (a global land surface model), and examined the results in comparison with extensive growing-season field measurements over a mountain tundra area in northwestern Finland. Our results show that soil moisture varies considerably in the study area and this variation creates a mosaic of moisture conditions, ranging from dry ridges (growing season average 12 VWC%, Volumetric Water Content) to water-logged mires (65 VWC%). The models, particularly SpaFHy, simulated temporal soil moisture dynamics reasonably well in parts of the landscape, but both underestimated the range of variation spatially and temporally. Soil properties and topography were important drivers of spatial variation in soil moisture dynamics. By testing the applicability of two mechanistic models to predict fine-scale spatial and temporal variability in soil moisture, this study paves the way towards understanding the functioning of tundra ecosystems under climate change.