Effects of soil and canopy characteristics on microwave backscattering of vegetation

A frequency modulated continuous wave C-band (4.8 GHz) scatterometer was mounted on an aerial lift truck and backscatter coefficients of corn were acquired as functions of polarizations, view angles, and row directions. As phytomass and green leaf area index increased, the backscatter also increased. Near anthesis when the canopies were fully developed, the major scattering elements were located in the upper 1 m of the 2.8 m tall canopy and little backscatter was measured below that level. C-band backscatter data could provide information to monitor vegetation at large view zenith angles

Effect of the green-emitting CaF2:Ce3+,Tb3+ phosphor particles’ size on color rendering index and color quality scale of the in-cup packaging multichip white LEDs

In this paper, we investigate the effect of the green-emitting CaF2:Ce (3+), Tb (3+) phosphor particle's size on the color rendering index (CRI) and the color quality scale (CQS) of the in-cup packaging multichip white LEDs (MCW-LEDs). For this purpose, 7000K and 8500K in-cup packaging MCW-LEDs is simulated by the commercial software Light Tools. Moreover, scattering process in the phosphor layers is investigated by using Mie Theory with Mat Lab software. Finally, the research results show that the green-emitting CaF2: Ce (3+), Tb (3+) phosphor's size crucially influences on the CRI and CQS. From that point of view, CaF2: Ce (3+), Tb (3+) can be proposed as a potential practical direction for manufacturing the in-cup packaging phosphor WLEDs.Web of Science13235134

A Mixed Data-Based Deep Neural Network to Estimate Leaf Area Index in Wheat Breeding Trials

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model

Data analysis techniques: a tool for cumulative exposure assessment.

International audienceEveryone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise, etc.or with positive effects (e.g. green space). Studies considering such complex environmental settings in a global manner are rare. We propose to use statistical factor and cluster analyses to create a composite exposure index with a data-driven approach, in view to assess the environmental burden experienced by populations. We illustrate this approach in a large French metropolitan area. The study was carried out in the Great Lyon area (France, 1.2 M inhabitants) at the census Block Group (BG) scale. We used as environmental indicators ambient air NO2 annual concentrations, noise levels and proximity to green spaces, to industrial plants, to polluted sites and to road traffic. They were synthesized using Multiple Factor Analysis (MFA), a data-driven technique without a priori modeling, followed by a Hierarchical Clustering to create BG classes. The first components of the MFA explained, respectively, 30, 14, 11 and 9% of the total variance. Clustering in five classes group: (1) a particular type of large BGs without population; (2) BGs of green residential areas, with less negative exposures than average; (3) BGs of residential areas near midtown; (4) BGs close to industries; and (5) midtown urban BGs, with higher negative exposures than average and less green spaces. Other numbers of classes were tested in order to assess a variety of clustering. We present an approach using statistical factor and cluster analyses techniques, which seem overlooked to assess cumulative exposure in complex environmental settings. Although it cannot be applied directly for risk or health effect assessment, the resulting index can help to identify hot spots of cumulative exposure, to prioritize urban policies or to compare the environmental burden across study areas in an epidemiological framework.Journal of Exposure Science and Environmental Epidemiology advance online publication, 24 September 2014; doi:10.1038/jes.2014.66

Tracking the Seasonal Dynamics of Boreal Forest Photosynthesis Using EO-1 Hyperion Reflectance: Sensitivity to Structural and Illumination Effects

During the growing season, the photosynthesis and growth of boreal forests are regulated by physiological responses to environmental factors. Physiological variations affect the spectral properties of leaves. Linking canopy-level spectral reflectance to leaf-level processes for monitoring forest seasonal physiology using satellite images is hindered by view and illumination effects and variations in canopy structure. To better understand the connection between the two structural levels, we used nine narrow-band vegetation indices (VIs) derived from Hyperion imagery to track the seasonal dynamics of boreal forest stands: the photochemical reflectance indices (PRI and PRI 515 ) related to the xanthophyll cycle, the red edge (RE) index, the Maccioni (Macc) and the green normalized difference vegetation index related to chlorophyll concentration (Ca + b), the carotenoid simple ratio and Gitelson carotenoid concentration index related to carotenoid concentration (Cx + c), the normalized difference vegetation index related to fractional cover, and the plant senescence reflectance index related to the Cx + c/Ca + b ratio. As ground truth, we used measurements of exposed pine shoot light use efficiency (LUE) and photosynthesis. Over the study period (May to August), LUE and photosynthesis were best correlated with the chlorophyll VIs Macc and RE. Both indices also exhibited the lowest coefficient of variation in association with forest structure. PRI, on the other hand, was affected by canopy structure and observation geometry and was uncoupled from LUE during the growing season. Our findings demonstrate that the photosynthesis and productivity of boreal forests in the growing season are best tracked using VIs related to total pigment concentration (i.e., chlorophyll)

Evaluating performance in three-dimensional fluorescence microscopy

In biological fluorescence microscopy, image contrast is often degraded by a high background arising from out of focus regions of the specimen. This background can be greatly reduced or eliminated by several modes of thick specimen microscopy, including techniques such as 3-D deconvolution and confocal. There has been a great deal of interest and some confusion about which of these methods is ‘better’, in principle or in practice. The motivation for the experiments reported here is to establish some rough guidelines for choosing the most appropriate method of microscopy for a given biological specimen. The approach is to compare the efficiency of photon collection, the image contrast and the signal-to-noise ratio achieved by the different methods at equivalent illumination, using a specimen in which the amount of out of focus background is adjustable over the range encountered with biological samples. We compared spot scanning confocal, spinning disk confocal and wide-field/deconvolution (WFD) microscopes and find that the ratio of out of focus background to in-focus signal can be used to predict which method of microscopy will provide the most useful image. We also find that the precision of measurements of net fluorescence yield is very much lower than expected for all modes of microscopy. Our analysis enabled a clear, quantitative delineation of the appropriate use of different imaging modes relative to the ratio of out-of-focus background to in-focus signal, and defines an upper limit to the useful range of the three most common modes of imaging

Detection of irrigation inhomogeneities in an olive grove using the NDRE vegetation index obtained from UAV images

We have developed a simple photogrammetric method to identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an unmanned aerial vehicle (UAV). By comparing NDVI, GNDVI, SAVI, and NDRE vegetation indices, we find that the latter shows irrigation irregularities in an olive grove not discernible with the other indices. This may render the NDRE as particularly useful to identify growth inhomogeneities in crops. Given the fact that few satellite detectors are sensible in the red-edge (RE) band and none with the spatial resolution offered by UAVs, this finding has the potential of turning UAVs into a local farmer’s favourite aid tool.Peer ReviewedPostprint (published version

Effect of gain and phase errors on SKA1-low imaging quality from 50-600 MHz

Simulations of SKA1-low were performed to estimate the noise level in images produced by the telescope over a frequency range 50-600 MHz, which extends the 50-350 MHz range of the current baseline design. The root-mean-square (RMS) deviation between images produced by an ideal, error-free SKA1-low and those produced by SKA1-low with varying levels of uncorrelated gain and phase errors was simulated. The residual in-field and sidelobe noise levels were assessed. It was found that the RMS deviations decreased as the frequency increased. The residual sidelobe noise decreased by a factor of ~5 from 50 to 100 MHz, and continued to decrease at higher frequencies, attributable to wider strong sidelobes and brighter sources at lower frequencies. The thermal noise limit is found to range between ~10 - 0.3 $\mu$Jy and is reached after ~100-100 000 hrs integration, depending on observation frequency, with the shortest integration time required at ~100 MHz.Comment: 23 pages, 11 figures Typo correcte