Non-Invasive Measurement of Frog Skin Reflectivity in High Spatial Resolution Using a Dual Hyperspectral Approach

Background:Most spectral data for the amphibian integument are limited to the visible spectrum of light and have been collected using point measurements with low spatial resolution. In the present study a dual camera setup consisting of two push broom hyperspectral imaging systems was employed, which produces reflectance images between 400 and 2500 nm with high spectral and spatial resolution and a high dynamic range.Methodology/Principal Findings:We briefly introduce the system and document the high efficiency of this technique analyzing exemplarily the spectral reflectivity of the integument of three arboreal anuran species (Litoria caerulea, Agalychnis callidryas and Hyla arborea), all of which appear green to the human eye. The imaging setup generates a high number of spectral bands within seconds and allows non-invasive characterization of spectral characteristics with relatively high working distance. Despite the comparatively uniform coloration, spectral reflectivity between 700 and 1100 nm differed markedly among the species. In contrast to H. arborea, L. caerulea and A. callidryas showed reflection in this range. For all three species, reflectivity above 1100 nm is primarily defined by water absorption. Furthermore, the high resolution allowed examining even small structures such as fingers and toes, which in A. callidryas showed an increased reflectivity in the near infrared part of the spectrum.Conclusion/Significance:Hyperspectral imaging was found to be a very useful alternative technique combining the spectral resolution of spectrometric measurements with a higher spatial resolution. In addition, we used Digital Infrared/Red-Edge Photography as new simple method to roughly determine the near infrared reflectivity of frog specimens in field, where hyperspectral imaging is typically difficult. © 2013 Pinto et al

Elucidating the photosynthetic responses in chlorophyll-deficient soybean (Glycine max, L.) leaf

Chlorophyll (Chl)-deficient plants can potentially increase global surface albedo of mono-cropping systems, and simultaneously maintain a similar photosynthetic efficiency by increasing light canopy penetration and thus lowering investment in pigments. However, some previous studies have shown that pale mutants might reduce productivity in field conditions. Such lower yields were suspected to be due to loss of photosynthetic efficiency at leaf level during light fluctuations as a consequence of reduced capacity and slower relaxation of non-photochemical quenching (NPQ) of Chl fluorescence. In this paper, we tested this hypothesis by comparing, CO2 assimilation (A), photosystem II (PSII) efficiency (ΦPSII), photochemical quenching and NPQ, electron transport rate (ETR) and fluorescence yield (Fyield) in a green soybean (Glycine max L.) cultivar (Eiko) and in a Chl-deficient (MinnGold) mutant under dynamically fluctuating light conditions. MinnGold had significantly slower induction of ETR and lower A and ETR than Eiko, but there was little difference in ΦPSII between the two genotypes, suggesting that the lower photosynthesis of MinnGold was mainly due to lower light energy absorption by a Chl-deficient leaf. The NPQ capacity was also smaller in MinnGold than in Eiko. As for the kinetics of the rapidly inducible component of NPQ, MinnGold showed slower induction, not relaxation, than Eiko. The combination of the effect of Chl-deficiency on lower photosynthesis, NPQ capacity and slower NPQ induction may explain the lower biomass accumulation of MinnGold in the field. Our physiological observations, combined with fluorescence kinetics, can serve as a basis to parameterize Chl content in modelling radiative transfer and photosynthesis for upscaling measures of plant and ecosystem productivity by a big leaf model

Assessing the contribution of understory sun-induced chlorophyll fluorescence through 3-D radiative transfer modelling and field data

A major international effort has been made to monitor sun-induced chlorophyll fluorescence (SIF) from space as a proxy for the photosynthetic activity of terrestrial vegetation. However, the effect of spatial heterogeneity on the SIF retrievals from canopy radiance derived from images with medium and low spatial resolution remains uncharacterised. In images from forest and agricultural landscapes, the background comprises a mixture of soil and understory and can generate confounding effects that limit the interpretation of the SIF at the canopy level. This paper aims to improve the understanding of SIF from coarse spatial resolutions in heterogeneous canopies by considering the separated contribution of tree crowns, understory and background components, using a modified version of the FluorFLIGHT radiative transfer model (RTM). The new model is compared with others through the RAMI model intercomparison framework and is validated with airborne data. The airborne campaign includes high-resolution data collected over a tree-grass ecosystem with the HyPlant imaging spectrometer within the FLuorescence EXplorer (FLEX) preparatory missions. Field data measurements were collected from plots with a varying fraction of tree and understory vegetation cover. The relationship between airborne SIF calculated from pure tree crowns and aggregated pixels shows the effect of the understory at different resolutions. For a pixel size smaller than the mean crown size, the impact of the background was low (R2 > 0.99; NRMSE 0.2). This study demonstrates that using a 3D RTM model improves the calculation of SIF significantly (R2 = 0.83, RMSE = 0.03 mW m−2 sr−1 nm−1) when the specific contribution of the soil and understory layers are accounted for, in comparison with the SIF calculated from mixed pixels that considers only one layer as background (R2 = 0.4, RMSE = 0.28 mW m−2 sr−1 nm−1). These results demonstrate the need to account for the contribution of SIF emitted by the understory in the quantification of SIF within tree crowns and within the canopy from aggregated pixels in heterogeneous forest canopies

Assessing vegetation function with imaging spectroscopy

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

Airborne based spectroscopy of red and far-red sun-induced chlorophyll fluorescence: Implications for improved estimates of gross primary productivity

Remote sensing (RS) approaches commonly applied to constrain estimates of gross primary production (GPP) employ greenness-based vegetation indices derived from surface reflectance data. Such approaches cannot capture dynamic changes of photosynthesis rates as caused by environmental stress. Further, applied vegetation indices are often affected by background reflectance or saturation effects. Sun. induced chlorophyll fluorescence (F) provides the most direct measure of photosynthesis and has been recently proposed as a new RS approach to improve estimates of GPP and tracing plant stress reactions. This work aims to provide further evidence on the complementary information content of F and its relation to changes in photosynthetic activity compared to traditional RS approaches. We use the airborne imaging spectrometer HyPlant to obtain several F products including red fluorescence (F687), far-red fluorescence (F760), F760 yield (F760yield) and the ration between F687 and F760 (Fratio). We calculate several vegetation indices indicative for vegetation greenness. We apply a recently proposed F-based semi-mechanistic approach to improve the forward modeling of GPP using F760 and compare this approach with a traditional one based on vegetation greenness and ground measurements of GPP derived from chamber measurements. In addition, we assess the sensitivity of F760yield and Fratio for environmental stress. Our results show an improved predictive capability of GPP when using F760 compared to greenness-based vegetation indices. F760yield and Fratio show a strong variability in time and between different crop types suffering from different levels of water shortage, indicating a strong sensitivity of F products for plant stress reactions. We conclude that the new RS approach of F provides complements to the set of commonly applies RS: The use of F760 improves constraining estimates of GPP while the ratio of red and far-red F shows large potential for tracking spatio-temporal plant adaptation in response to environmental stress conditions

ON THE DERIVATION OF CROP HEIGHTS FROM MULTITEMPORAL UAV BASED IMAGERY

In this paper, we investigate the usage of unmanned aerial vehicles (UAV) to assess the crop geometry with special focus on the crop height extraction. Crop height is classified as a reliable trait in crop phenotyping and recognized as a good indicator for biomass, expected yield, lodging or crop stress. The current industrial standard for crop height measurement is a manual procedure using a ruler, but this method is considered as time consuming, labour intensive and subjective. This study investigates methods for reliable and rapid deriving of the crop height from high spatial, spectral and time resolution UAV data considering the influences of the reference surface and the selected crop height generation method to the final calculation. To do this, we performed UAV missions during two winter wheat growing seasons and generate point clouds from areal images using photogrammetric methods. For the accuracy assessment we compare UAV based crop height with ruler based crop height as current industrial standard and terrestrial laser scanner (TLS) based crop height as a reliable validation method. The high correlation between UAV based and ruler based crop height and especially the correlation with TLS data shows that the UAV based crop height extraction method can provide reliable winter wheat height information in a non-invasive and rapid way. Along with crop height as a single value per area of interest, 3D UAV crop data should provide some additional information like lodging area, which can also be of interest in the plant breeding community

High-throughput field phenotyping reveals genetic variation in photosynthetic traits in durum wheat under drought

Chlorophyll fluorescence (ChlF) is a powerful non-invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high-throughput field phenotyping capabilities. The light-induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ((Formula presented.)) and the kinetics of electron transport measured by reoxidation rates ((Formula presented.) and (Formula presented.)). Short- and long-term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, (Formula presented.) and (Formula presented.) were little affected, while (Formula presented.) was consistently accelerated in water-limited compared to well-watered plants, increasingly so with rising vapour pressure deficit. This high-throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis

Dynamics of sun-induced chlorophyll fluorescence and reflectance to detect stress-induced variations in canopy photosynthesis

Passive measurement of sun-induced chlorophyll fluorescence (F) represents the most promising tool to quantify changes in photosynthetic functioning on a large scale. However, the complex relationship between this signal and other photosynthesis-related processes restricts its interpretation under stress conditions. To address this issue, we conducted a field campaign by combining daily airborne and ground-based measurements of F (normalized to photosynthetically active radiation), reflectance and surface temperature and related the observed changes to stress-induced variations in photosynthesis. A lawn carpet was sprayed with different doses of the herbicide Dicuran. Canopy-level measurements of gross primary productivity indicated dosage-dependent inhibition of photosynthesis by the herbicide. Dosage-dependent changes in normalized F were also detected. After spraying, we first observed a rapid increase in normalized F and in the Photochemical Reflectance Index, possibly due to the blockage of electron transport by Dicuran and the resultant impairment of xanthophyll-mediated non-photochemical quenching. This initial increase was followed by a gradual decrease in both signals, which coincided with a decline in pigment-related reflectance indices. In parallel, we also detected a canopy temperature increase after the treatment. These results demonstrate the potential of using F coupled with relevant reflectance indices to estimate stress-induced changes in canopy photosynthesis

Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions

This is the final version. Available from MDPI via the DOI in this record. Compact multi-spectral sensors that can be mounted on lightweight drones are now widely available and applied within the geo- and environmental sciences. However; the spatial consistency and radiometric quality of data from such sensors is relatively poorly explored beyond the lab; in operational settings and against other sensors. This study explores the extent to which accurate hemispherical-conical reflectance factors (HCRF) and vegetation indices (specifically: normalised difference vegetation index (NDVI) and chlorophyll red-edge index (CHL)) can be derived from a low-cost multispectral drone-mounted sensor (Parrot Sequoia). The drone datasets were assessed using reference panels and a high quality 1 m resolution reference dataset collected near-simultaneously by an airborne imaging spectrometer (HyPlant). Relative errors relating to the radiometric calibration to HCRF values were in the 4 to 15% range whereas deviations assessed for a maize field case study were larger (5 to 28%). Drone-derived vegetation indices showed relatively good agreement for NDVI with both HyPlant and Sentinel 2 products (R2 = 0.91). The HCRF; NDVI and CHL products from the Sequoia showed bias for high and low reflective surfaces. The spatial consistency of the products was high with minimal view angle effects in visible bands. In summary; compact multi-spectral sensors such as the Parrot Sequoia show good potential for use in index-based vegetation monitoring studies across scales but care must be taken when assuming derived HCRF to represent the true optical properties of the imaged surface.European Space Agency (ESA)European Union’s Horizon 202