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LiDAR (Light Detection And Ranging) laser scanners acquire 3D point clouds of real environments. The process consists in sampling the scene with laser beams rotating around an axis. By construction, the point density decreases with the distance to the scanner. This density heterogeneity is a major issue, in particular for mobile systems in the context of autonomous driving, as usually a single scan is processed simultaneously (instead of mapping applications that can integrate several scans, reducing the density heterogeneity). We propose a dartboard grid with cell size increasing radially in order to adapt the grid size to the point density. The effectiveness of this strategy is demonstrated by means of a ground detection task, a fundamental step in many workflows of analysis of 3D point clouds.

Point cloud datasets for perception tasks in the context of autonomous driving often rely on high resolution 64-layer Light Detection and Ranging (LIDAR) scanners. They are expensive to deploy on real-world autonomous driving sensor architectures which usually employ 16/32 layer LIDARs. We evaluate the effect of subsampling image based representations of dense point clouds on the accuracy of the road segmentation task. In our experiments the low resolution 16/32 layer LIDAR point clouds are simulated by subsampling the original 64 layer data, for subsequent transformation in to a feature map in the Bird-Eye-View(BEV) and Spherical-View (SV) representations of the point cloud. We introduce the usage of the local normal vector with the LIDAR’s spherical coordinates as an input channel to existing LoDNN architectures. We demonstrate that this local normal feature in conjunction with classical features not only improves performance for binary road segmentation on full resolution point clouds, but it also reduces the negative impact on the accuracy when subsampling dense point clouds as compared to the usage of classical features alone. We assess our method with several experiments on two datasets: KITTI Road-segmentation benchmark and the recently released Semantic KITTI dataset.

Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20 % of all cloudy pixels as multilayer (decreasing to about 13 % if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

In the last decades terrestrial ecosystems have reabsorbed on average more than one-quarter of anthropogenic emissions (Le Quéré et al., 2018). However, this large carbon sink is modulated by climate and is therefore highly variable in time and space. The magnitude and temporal changes in the sensitivity of terrestrial CO2 fluxes to climate drivers are key factors to determine future atmospheric CO2 concentration and climate trajectories. In the literature, there is so far a strong focus on the climatic controls of daily and long-term variability, while less is known about the key drivers at a seasonal timescale and about their variation over time (Wohlfahrt et al., 2008). This latter temporal scale is relevant to assess which climatic drivers dominate the seasonality of the fluxes and to understand which factors limit the CO2 exchange during the course of the year. Here, we investigate the global sensitivity of net terrestrial CO2 fluxes, derived from atmospheric inversion, to three key climate drivers (i.e. global radiation and temperature from WFDEI and soil water content from ERA-Interim) from weekly to seasonal temporal scales, in order to explore the short-term interdependence between climate and the terrestrial carbon budget. We observed that the CO2 exchange is controlled by temperature during the carbon uptake period over most of the land surface (from 55 % to 52 % of the total surface), while radiation is the most widespread dominant climate driver during the carbon release period (from 64 % to 70 % of the total surface). As expected, soil water content plays a key role in arid regions of the Southern Hemisphere during both the carbon uptake and the carbon release period. Looking at the decadal trend of these sensitivities (1985–2016) we observed that the importance of radiation as a driver is increasing over time, while we observed a decrease in sensitivity to temperature in Eurasia. Overall, we show that flux temporal variation due to a specific driver has been dominated by the temporal changes in ecosystem sensitivity (i.e. the response of ecosystem to climate) rather than to the temporal variability of the climate driver itself over the last decades. Ultimately, this analysis shows that the ecosystem response to climate is significantly changing both in space and in time, with potential repercussion on the future terrestrial CO2 sink and therefore on the role that land may play in climate trajectories.

The microstructural record of fault rocks active at the brittle–ductile transition zone (BDTZ) may retain information on the rheological parameters driving the switch in deformation mode and on the role of stress and fluid pressure in controlling different fault slip behaviours. In this study we analysed the deformation microstructures of the strike-slip fault zone BFZ045 in Olkiluoto (SW Finland), located in the site of a deep geological repository for nuclear waste. We combined microstructural analysis, electron backscatter diffraction (EBSD), and mineral chemistry data to reconstruct the variations in pressure, temperature, fluid pressure, and differential stress that mediated deformation and strain localization along BFZ045 across the BDTZ. BFZ045 exhibits a mixed ductile–brittle deformation, with a narrow ( cm thick) brittle fault core with cataclasites and pseudotachylytes that overprint a wider (60–100 cm thick) quartz-rich mylonite. Mylonitic deformation took place at 400–500 ∘C and 3–4 kbar, typical of the greenschist facies metamorphism at the base of the seismogenic crust. We used the recrystallized grain size piezometry for quartz to document a progressive increase in differential stress, from ca. 50 to ca. 120 MPa, towards the shear zone centre during mylonitization and strain localization. Syn-kinematic quartz veins formed along the mylonitic foliation due to transiently high pore fluid pressure (up to lithostatic value). The overprint of the veins by dynamic recrystallization and mylonitic creep is further evidence of the occurrence of brittle events under overall ductile conditions. We propose a conceptual model in which the ductile–brittle deformation cycle was controlled by transient oscillations in fluid pressure and progressively higher differential stress, possibly occurring in a narrowing shear zone deforming towards the peak strength of the crust at the BDTZ.

The dynamic evolution of fault zones at the seismogenic brittle–ductile transition zone (BDTZ) expresses the delicate interplay between numerous physical and chemical processes. Deformation and fluid flow at the BDTZ are closely related and mutually dependent during repeating and transient cycles of frictional and viscous deformation. Despite numerous studies documenting in detail seismogenic faults exhumed from the BDTZ, uncertainties remain as to the exact role of fluids in facilitating broadly coeval brittle and ductile deformation at that structural level. We combine structural analysis, fluid inclusion, and mineral chemistry data from synkinematic and authigenic minerals to reconstruct the temporal variations in fluid pressure (Pf), temperature (T), and bulk composition (X) of the fluids that mediated deformation and steered strain localization along BFZ300, a strike–slip fault originally active at the BDTZ. BFZ300 deforms the Paleoproterozoic migmatitic basement of southwestern Finland and hosts in its core two laterally continuous quartz veins formed by two texturally distinct types of quartz – Qtz I and Qtz II, with Qtz I older than Qtz II. Veins within the damage zone are formed exclusively by Qtz I. Mesostructural and microstructural analysis combined with fluid compositional data indicate recurrent cycles of mutually overprinting brittle and ductile deformation triggered by oscillations of fluid pressure peaking at 210 MPa. Fluid inclusion microthermometry and mineral pair geothermometry indicate that the two documented quartz types precipitated from different fluid batches, with bulk salinities in the 1 wt % NaCleq–5 wt % NaCleq range for Qtz I and in the 6 wt % NaCleq–11 wt % NaCleq range for Qtz II. The temperature of the fluids involved with initial strain localization and later fault reactivation evolved through time from > 350 ∘C during Qtz I precipitation to ∘C at the time of Qtz II crystallization. The peak fluid pressure estimates constrain pore pressure oscillations between 80 and 210 MPa during the recorded faulting episodes. Our results suggest variability of the physico-chemical conditions of the fluids steering deformation (Pf, T, X), reflecting the ingress and effects of multiple batches of fluid in the fault zone. Initial fluid-mediated embrittlement generated a diffuse network of joints and/or hybrid–shear fractures in the damage zone; subsequent strain localization led to more localized deformation within the fault core. Localization was guided by cyclically increasing fluid pressure and transient embrittlement of a system that was otherwise under overall ductile conditions. Our analysis suggests that fluid overpressure at the BDTZ can play a key role in the initial embrittlement of the deforming rock and steer subsequent strain localization.

The terrestrial carbon fluxes show the largest variability among the components of the global carbon cycle and drive most of the temporal variations in the growth rate of atmospheric CO2. Understanding the environmental controls and trends of the terrestrial carbon budget is therefore essential to predict the future trajectories of the CO2 airborne fraction and atmospheric concentrations. In the present work, patterns and controls of the inter-annual variability (IAV) of carbon net ecosystem exchange (NEE) have been analysed using three different data streams: ecosystem-level observations from the FLUXNET database (La Thuile and 2015 releases), the MPI-MTE (model tree ensemble) bottom–up product resulting from the global upscaling of site-level fluxes, and the Jena CarboScope Inversion, a top–down estimate of surface fluxes obtained from observed CO2 concentrations and an atmospheric transport model. Consistencies and discrepancies in the temporal and spatial patterns and in the climatic and physiological controls of IAV were investigated between the three data sources. Results show that the global average of IAV at FLUXNET sites, quantified as the standard deviation of annual NEE, peaks in arid ecosystems and amounts to ∼ 120 gC m−2 y−1, almost 6 times more than the values calculated from the two global products (15 and 20 gC m−2 y−1 for MPI-MTE and the Jena Inversion, respectively). Most of the temporal variability observed in the last three decades of the MPI-MTE and Jena Inversion products is due to yearly anomalies, whereas the temporal trends explain only about 15 and 20 % of the variability, respectively. Both at the site level and on a global scale, the IAV of NEE is driven by the gross primary productivity and in particular by the cumulative carbon flux during the months when land acts as a sink. Altogether these results offer a broad view on the magnitude, spatial patterns and environmental drivers of IAV from a variety of data sources that can be instrumental to improve our understanding of the terrestrial carbon budget and to validate the predictions of land surface models.

Accurate multi-point monitoring systems are required to derive atmospheric measurements of greenhouse gas concentrations both for the calculation of surface fluxes with inversion transport models and for the estimation of non-turbulent components of the mass balance equation (i.e. advection and storage fluxes) at eddy covariance sites. When a single analyser is used to monitor multiple sampling points, the deployment of buffer volumes (BVs) along sampling lines can reduce the uncertainty due to the discrete temporal sampling of the signal. In order to optimize the use of buffer volumes we explored various set-ups by simulating their effect on time series of high-frequency CO2 concentration collected at three Fluxnet sites. Besides, we proposed a novel scheme to calculate half-hourly weighted arithmetic means from discrete point samples, accounting for the probabilistic fraction of the signal generated in the averaging period. Results show that the use of BVs with the new averaging scheme reduces the mean absolute error (MAE) up to 80 % compared to a set-up without BVs and up to 60 % compared to the case with BVs and a standard, non-weighted averaging scheme. The MAE of CO2 concentration measurements was observed to depend on the variability of the concentration field and on the size of BVs, which therefore have to be carefully dimensioned. The optimal volume size depends on two main features of the instrumental set-up: the number of measurement points and the time needed to sample at one point (i.e. line purging plus sampling time). A linear and consistent relationship was observed at all sites between the sampling frequency, which summarizes the two features mentioned above, and the renewal frequency associated with the volume. Ultimately, this empirical relationship can be applied to estimate the optimal volume size according to the technical specifications of the sampling system.

Cloud thermodynamic phase (ice, liquid, undetermined) classification is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial errors in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance. For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm and CALIOP. A wholesale improvement is seen for C6 compared to C5.

As it is broadly understood, recording serves as a basis for the diagnosis, treatment and preservation of historic places and contributes to record our built cultural heritage for posterity. This work is not a stand-alone practice but a part of the overall conservation process of cultural heritage at imminent risk of irreversible damage. Recording of heritage places should be directly related to the needs, skills and the technology that are available to the end users that are responsible for the management and care of these sites. They should be selected in a way that the future managers of these sites can also access and use the data collected. This paper explains an innovative heritage recording approach applied by the Getty Conservation Institute (GCI) and Carleton Immersive Media Studio (CIMS) in the documentation of historic decorated surfaces at the Caïd Residence, located at Tighermt (Kasbah) Taourirt in Ouarzazate, Morocco; as part of a collaborative project between the GCI and the Centre de Conservation et Réhabilitation du Patrimoine Architectural des Zones Atlasiques et Sub-Atlasiques (CERKAS) to rehabilitate the entire architectural ensemble. The selected recording techniques were used for the rapid mapping of conditions of the decorated surfaces at the Caïd Residence using international standards. The resulting work is being used by GCI staff, consultants and CERKAS team to conduct emergency stabilization and protection measures for these important decorated surfaces.

The study investigates the potential of a commercially available proximal sensing system – based on a 16-band multispectral sensor – for monitoring mean midday gross ecosystem production (GEPm) in a subalpine grassland of the Italian Alps equipped with an eddy covariance flux tower. Reflectance observations were collected for 5 consecutive years, characterized by different climatic conditions, together with turbulent carbon dioxide fluxes and their meteorological drivers. Different models based on linear regression (vegetation indices approach) and on multiple regression (reflectance approach) were tested to estimateGEPm from optical data. The overall performance of this relatively low-cost system was positive. Chlorophyll-related indices including the red-edge part of the spectrum in their formulation (red-edge normalized difference vegetation index, NDVIred-edge; chlorophyll index, CIred-edge) were the best predictors of GEPm, explaining most of its variability during the observation period. The use of the reflectance approach did not lead to considerably improved results in estimating GEPm: the adjusted R2 (adjR2) of the model based on linear regression – including all the 5 years – was 0.74, while the adjR2 for the multiple regression model was 0.79. Incorporating mean midday photosynthetically active radiation (PARm) into the model resulted in a general decrease in the accuracy of estimates, highlighting the complexity of the GEPm response to incident radiation. In fact, significantly higher photosynthesis rates were observed under diffuse as regards direct radiation conditions. The models which were observed to perform best were then used to test the potential of optical data for GEPm gap filling. Artificial gaps of three different lengths (1, 3 and 5 observation days) were introduced in the GEPm time series. The values of adjR2 for the three gap-filling scenarios showed that the accuracy of the gap filling slightly decreased with gap length. However, on average, the GEPm gaps were filled with an accuracy of 73% with the model fed with NDVIred-edge, and of 76% with the model using reflectance at 681, 720 and 781 nm and PARm data.

The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface reconstruction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.

Winter CO2 fluxes represent an important component of the annual carbon budget in northern ecosystems. Understanding winter respiration processes and their responses to climate change is also central to our ability to assess terrestrial carbon cycle and climate feedbacks in the future. However, the factors influencing the spatial and temporal patterns of winter ecosystem respiration (Reco) of northern ecosystems are poorly understood. For this reason, we analyzed eddy covariance flux data from 57 ecosystem sites ranging from ~35° N to ~70° N. Deciduous forests were characterized by the highest winter Reco rates (0.90 ± 0.39 g C m−2 d−1), when winter is defined as the period during which daily air temperature remains below 0 °C. By contrast, arctic wetlands had the lowest winter Reco rates (0.02 ± 0.02 g C m−2 d−1). Mixed forests, evergreen needle-leaved forests, grasslands, croplands and boreal wetlands were characterized by intermediate winter Reco rates (g C m−2 d−1) of 0.70(±0.33), 0.60(±0.38), 0.62(±0.43), 0.49(±0.22) and 0.27(±0.08), respectively. Our cross site analysis showed that winter air (Tair) and soil (Tsoil) temperature played a dominating role in determining the spatial patterns of winter Reco in both forest and managed ecosystems (grasslands and croplands). Besides temperature, the seasonal amplitude of the leaf area index (LAI), inferred from satellite observation, or growing season gross primary productivity, which we use here as a proxy for the amount of recent carbon available for Reco in the subsequent winter, played a marginal role in winter CO2 emissions from forest ecosystems. We found that winter Reco sensitivity to temperature variation across space (QS) was higher than the one over time (interannual, QT). This can be expected because QS not only accounts for climate gradients across sites but also for (positively correlated) the spatial variability of substrate quantity. Thus, if the models estimate future warming impacts on Reco based on QS rather than QT, this could overestimate the impact of temperature changes.

The global spatial and diurnal distribution of cloud properties is a key issue for understanding the hydrological cycle, and critical for advancing efforts to improve numerical weather models and general circulation models. Satellite data provides the best way of gaining insight into global cloud properties. In particular, the determination of cloud thermodynamic phase is a critical first step in the process of inferring cloud optical and microphysical properties from satellite measurements. It is important that cloud phase be derived together with an estimate of the confidence of this determination, so that this information can be included with subsequent retrievals (optical thickness, effective particle radius, and ice/liquid water content). In this study, we combine three different and well documented approaches for inferring cloud phase into a single algorithm. The algorithm is applied to data obtained by the MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER3 (Polarization and Directionality of the Earth Reflectance) instruments. It is shown that this synergistic algorithm can be used routinely to derive cloud phase along with an index that helps to discriminate ambiguous phase from confident phase cases. The resulting product provides a semi-continuous index ranging from confident liquid to confident ice instead of the usual discrete classification of liquid phase, ice phase, mixed phase (potential combination of ice and liquid particles), or simply unknown phase clouds. The index value provides simultaneously information on the phase and the associated confidence. This approach is expected to be useful for cloud assimilation and modeling efforts while providing more insight into the global cloud properties derived from satellite data.