Polarization-entangled photon pair sources based on spontaneous four wave mixing assisted by polarization mode dispersion

Photonic-based qubits and integrated photonic circuits have enabled demonstrations of quantum information processing (QIP) that promises to transform the way in which we compute and communicate. To that end, sources of polarization-entangled photon pair states are an important enabling technology, especially for polarization-based protocols. However, such states are difficult to prepare in an integrated photonic circuit. Scalable semiconductor sources typically rely on nonlinear optical effects where polarization mode dispersion (PMD) degrades entanglement. Here, we directly generate polarization-entangled states in an AlGaAs waveguide, aided by the PMD and without any compensation steps. We perform quantum state tomography and report a raw concurrence as high as 0.91$\pm$0.01 observed in the 1100-nm-wide waveguide. The scheme allows direct Bell state generation with an observed maximum fidelity of 0.90$\pm$0.01 from the 800-nm-wide waveguide. Our demonstration paves the way for sources that allow for the implementation of polarization-encoded protocols in large-scale quantum photonic circuits

New estimates of leaf angle distribution from terrestrial LiDAR: Comparison with measured and modelled estimates from nine broadleaf tree species Author links open overlay panel

Leaf angle distribution (LAD) is an important property which influences the spectral reflectance and radiation transmission properties of vegetation canopies, and hence interception, absorption and photosynthesis. It is a fundamental parameter of radiative transfer models of vegetation at all scales. Yet, the difficulty in measuring LAD causes it to be also one of the most poorly characterized parameters, and is typically either assumed to be random, or to follow one of a very small number of parametric ‘archetype’ functions. Terrestrial LiDAR scanning (TLS) is increasingly being used to measure canopy structure, but LAD estimation from TLS has been limited thus far. We introduce a fast and simple method for detection of LAD information from terrestrial LiDAR scanning (TLS) point clouds. Here, it is shown that LAD information can be obtained by simply accumulating all valid planes fitted to points in a leaf point cloud. As points alone do not have any normal vector, subsets of points around each point are used to calculate the normal vectors. Importantly, for the first time we demonstrate the effect of distance on the reliable LAD information retrieval with TLS data. We test, validate, and compare the TLS-based method with established leveled digital photography (LDP) approach. We do this using data from both real trees covering the full range of existing leaf angle distribution type, but also from 3D Monte Carlo ray tracing. Crucially, this latter approach allows us to simulate both images and TLS point clouds from the same trees, for which the LAD is known explicitly a priori. This avoids the difficulty of assessing LAD manually, which being a difficult and potentially error-prone process, is an additional source of error in assessing the accuracy of LAD extraction methods from TLS or photography. We show that compared to the LDP measurement technique, TLS is not limited by leaf curvature, and depending on the distance of the TLS from the target, is potentially capable of retrieving leaf angle information from more complex, non-flat leaf surfaces. We demonstrate the possible limitation of TLS measurement techniques for the retrieval of LAD information for more distant canopies, or for taller trees (h > 20 m)

Validating canopy clumping retrieval methods using hemispherical photography in a simulated Eucalypt forest

The so-called clumping factor (Ω) quantifies deviation from a random 3D distribution of material in a vegetation canopy and therefore characterises the spatial distribution of gaps within a canopy. Ω is essential to convert effective Plant or Leaf Area Index into actual LAI or PAI, which has previously been shown to have a significant impact on biophysical parameter retrieval using optical remote sensing techniques in forests, woodlands, and savannas. Here, a simulation framework was applied to assess the performance of existing in situ clumping retrieval methods in a 3D virtual forest canopy, which has a high degree of architectural realism. The virtual canopy was reconstructed using empirical data from a Box Ironbark Eucalypt forest in Eastern Australia. Hemispherical photography (HP) was assessed due to its ubiquity for indirect LAI and structure retrieval. Angular clumping retrieval method performance was evaluated using a range of structural configurations based on varying stem distribution and LAI. The CLX clumping retrieval method (Leblanc et al., 2005) with a segment size of 15° was the best performing clumping method, matching the reference values to within 0.05 Ω on average near zenith. Clumping error increased linearly with zenith angle to > 0.3 Ω (equivalent to a 30% PAI error) at 75° for all structural configurations. At larger zenith angles, PAI errors were found to be around 25–30% on average when derived from the 55–60° zenith angle. Therefore, careful consideration of zenith angle range utilised from HP is recommended. We suggest that plot or site clumping factors should be accompanied by the zenith angle used to derive them from gap size and gap size distribution methods. Furthermore, larger errors and biases were found for HPs captured within 1 m of unrepresentative large tree stems, so these situations should be avoided in practice if possible

Exploring the potential of DSCOVR EPIC data to retrieve clumping index in Australian terrestrial ecosystem research network observing sites

Vegetation foliage clumping significantly alters the radiation environment and affects vegetation growth as well as water, carbon cycles. The clumping index (CI) is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index. Previously generated CI maps using a diverse set of Earth Observation multi-angle datasets across a wide range of scales have all relied on the single approach of using the normalized difference hotspot and darkspot (NDHD) method. We explore an alternative approach to estimate CI from space using the unique observing configuration of the Deep Space Climate Observatory Earth Polychromatic Imaging Camera (DSCOVR EPIC) and associated products at 10 km resolution. The performance was evaluated with in situ measurements in five sites of the Australian Terrestrial Ecosystem Research Network comprising a diverse range of canopy structure from short and sparse to dense and tall forest. The DSCOVR EPIC data can provide meaningful CI retrievals at the given spatial resolution. Independent but comparable CI retrievals obtained with a completely different sensor and new approach were encouraging for the general validity and compatibility of the foliage clumping information retrievals from space. We also assessed the spatial representativeness of the five TERN sites with respect to a particular point in time (field campaigns) for satellite retrieval validation. Our results improve our understanding of product uncertainty both in terms of the representativeness of the field data collected over the TERN sites and its relationship to Earth Observation data at different spatial resolutions.Published versio

A proposed methodology for the correction of the Leaf Area Index measured with a ceptometer for pinus and eucalyptus forests = Proposta de uma methodologia para a correcao do indice de area foliar medido pelo ceptometro em provoamentos de pinus e eucalyptus

Leaf area index (LAI) is an important parameter controlling many biological and physiological processes associated with vegetation on the Earth's surface, such as photosynthesis, respiration, transpiration, carbon and nutrient cycle and rainfall interception. LAI can be measured indirectly by sunfleck ceptometers in an easy and non-destructive way but this practical methodology tends to underestimated when measured by these instruments. Trying to correct this underestimation, some previous studies heave proposed the multiplication of the observed LAI value by a constant correction factor. The assumption of this work is LAI obtained from the allometric equations are not so problematic and can be used as a reference LAI to develop a new methodology to correct the ceptometer one. This new methodology indicates that the bias (the difference between the ceptometer and the reference LAI) is estimated as a function of the basal area per unit ground area and that bias is summed to the measured value. This study has proved that while the measured Pinus LAI needs a correction, there is no need for that correction for the Eucalyptus LAI. However, even for this last specie the proposed methodology gives closer estimations to the real LAI values

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives