FluorMODgui V3.0: A Graphic User Interface for the Spectral Simulation of Leaf and Canopy Chlorophyll Fluorescence

The FluorMODgui Graphic User Interface (GUI) software package developed within the frame of the FluorMOD project Development of a Vegetation Fluorescence Canopy Model is presented in this manuscript. The FluorMOD project was launched in 2002 by the European Space Agency (ESA) to advance the science of vegetation fluorescence simulation through the development and integration of leaf and canopy fluorescence models based on physical methods. The design of airborne or space missions dedicated to the measurement of solar-induced chlorophyll fluorescence using remote-sensing instruments require physical methods for quantitative feasibility analysis and sensor specification studies. The FluorMODgui model developed as part of this project is designed to simulate the effects of chlorophyll fluorescence at leaf and canopy levels using atmospheric inputs, running the leaf model, FluorMODleaf, and the canopy model, FluorSAIL, independently, through a coupling scheme, and by a multiple iteration protocol to simulate changes in the viewing geometry and atmospheric characteristics. Inputs for the FluorMODleaf model are the number of leaf layers, chlorophyll a+b content, water equivalent thickness, dry matter content, fluorescence quantum efficiency, temperature, species type, and stoichiometry. Inputs for the FluorSAIL canopy model are a MODTRAN-4 6-parameter spectra or measured direct horizontal irradiance and diffuse irradiance spectra, a soil reflectance spectrum, leaf reflectance & transmittance spectra and a excitation-fluorescence response matrix in upward and downward directions (all from FluorMODleaf), 2 PAR-dependent coefficients for the fluorescence response to light level, relative azimuth angle and viewing zenith angle, canopy leaf area index, leaf inclination distribution function, and a hot spot parameter. Outputs available in the 400-1000 nm spectral range from the graphical user interface, FluorMODgui, are the leaf spectral reflectance and transmittance, and the canopy reflectance, with and without fluorescence effects. In addition, solar and sky irradiance on the ground, radiance with and without fluorescence on the ground, and top-of-atmosphere (TOA) radiances for bare soil and surroundings same as target are also produced. The models and documentation regarding the FluorMOD project can be downloaded at http://www.ias.csic.es/fluormod

Avances en teledetección para la prevención y detección temprana de Xylella fastidiosa en el marco de los proyectos H2020 POnTE y XF-ACTORS.

Informational article of the tasks carried out in WP6 of POnTE and WP3 of XF-ACTORS in a professional Spanish magazin

Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance

This study provides insight into the assessment of the spatio-temporal trends of chlorophyll fluorescence, narrow-band physiological indices, and structural indices acquired with a hyperspectral imager flown over a flux tower in a canopy characterized by small seasonal structural changes and a heterogeneous architecture. A total of seven flights between summer and autumn were conducted with a hyperspectral camera that captured 30 cm resolution imagery and 260 spectral bands in the 400-900 nm region. This enabled the identification of pure-vegetation tree crown pixels around an eddy covariance flux tower without shadow components or background effects. The hyperspectral imagery was used to study the temporal patterns of canopy fluorescence and reflectance indices related to physiology and canopy structure. The seasonal trends observed in the airborne indices and fluorescence and their relationship with gross primary production (GPP) demonstrated that vegetation indices mostly related to structure such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) yielded non-significant relationships (r2 = 0.17; p > 0.05) due to the small structural changes in the canopy through the season. By contrast, physiological indices related to chlorophyll content (TCARI/OSAVI), light use efficiency (PRI570), and canopy chlorophyll fluorescence calculated through the Fraunhofer Line Depth principle (FLD3) showed a similar seasonal trend to that of GPP measured at the same time of the flights (r2 in the range 0.75–0.84; p 0.05) to the physiological indicators such as TCARI/OSAVI, PRI570 and fluorescence. The spatial variability of the hyperspectral indices investigated through the coefficient of variation (CV) showed that fluorescence around the tower varied up to 17% at the time of the maximum stress (summer), while LAIp showed little variation during that time (CV = 1.8%). After the summer stress period, the CV for fluorescence and chlorophyll content decreased in autumn down to 9%. This study demonstrates that small physiological changes occurring in an evergreen canopy were still captured by remote sensing physiological indices and high-resolution airborne fluorescence. These indicators are required for GPP monitoring when the vegetation dynamics are not captured by remote sensing structural indices

Utilización de modelos de reflectancia como nexo entre muestras foliares y la cobertura forestal: aplicación a datos hiperespectrales

El presente trabajo demuestra la utilización de modelos de simulación de la cobertura forestal mediante su aplicación a datos hiperespectrales del sensor aerotransportado CASI. Los modelos SAIL y Kuusk permiten ser utilizados como nexo de unión entre los niveles de hoja y de cobertura: las relaciones a nivel de hoja obtenidas entre índices ópticos y bioindicadores de estrés, como contenido clorofílico o fluorescencia clorofílica, pueden ser transformadas a un nivel superior de cobertura mediante la utilización de dichos modelos. Finalmente se realiza una demostración de la utilización de modelos de cobertura a través de los resultados obtenidos en el proyecto Bioindicators of Forest Sustainability, desarrollado en 12 zonas de Acer saccharum M. localizadas en Ontario (Canadá) donde se obtuvieron medidas de campo de muestras foliares, así como datos hiperespectrales del sensor aerotransportado CASI en 1997, 1998 y 1999. Los indices ópticos desarrollados a nivel de hoja fueron aplicados, a través de modelos de cobertura, a los datos de reflectancia obtenidos por CASI de 2 m de resolución espacial y 72 bandas

Detection of oak decline using radiative transfer modelling and machine learning from multispectral and thermal RPAS imagery

Oak trees are declining at an unprecedented rate due to the interaction of many factors, such as pests, diseases, droughts, pollution and flooding. Such abiotic- and biotic-induced stress produces anomalies in plant physiological and functional traits (PTs) that may be spectrally detected, serving to quantify trees’ health status and condition. Previous studies have demonstrated that PTs’ dynamic response can be tracked with hyperspectral and thermal images acquired via aerial platforms. However, the ability to detect the decline at different stages of severity among distinct oak species by using high-resolution multispectral images acquired via miniaturised cameras located aboard unpiloted airborne platforms is still unknown. This cost-effective approach offers improved operability to perform missions with greater continuity and replicability, which is critical to assess the decline progression. In this work, we evaluated the use of airborne multispectral and thermal imagery coupled with a 3-D radiative transfer modelling and machine learning approach for detecting Phytophthora-infected holm oak and cork oak trees. The field study included 2299 trees classified into disease severity classes with a gradient in levels of disease incidence located in Portugal (Ourique and Avis) and Spain (Huelva and Alcuéscar). The classification model achieved an overall accuracy of 76 % (kappa = 0.51) in detecting decline for both species, successfully identifying up to 34 % of declining trees that were not initially detected by visual inspection and confirmed in a reevaluation six months later. When compared against airborne hyperspectral imagery, results yielded comparable accuracy, with a relative decrease of ca. 4 % in overall accuracy and an average Cohen’s kappa decrease of 7 %. The results further showed that classification using only hyperspectral imagery is slightly lower but equivalent to using combined multispectral and thermal data, and those derived from these sensors independently are not adequate to classify the different severity stages. The proposed model has enabled us to effectively discern various stages of decline in cork and holm oak forests across diverse geographical areas. Our study, therefore, demonstrates that the tandem use of multispectral and thermal sensors onboard a remotely piloted aircraft platform, together with a radiative transfer modelling and machine learning approach, helps us to predict the impact of this particularly damaging disease on oak trees. This capability facilitates the detection and swift mapping of disease progression, ensuring a proactive approach to forest management

Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)

Chlorophyll a+b (Ca+b) and carotenoids (Cx+c) are leaf pigments associated with photosynthesis, participation in light harvesting and energy transfer, quenching and photoprotection. This manuscript makes progress on developing methods for leaf carotenoid content estimation, using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV). Imagery was acquired over 3 years using two different UAV platforms, a 6-band multispectral camera and a micro-hyperspectral imager flown with 260 bands at 1.85nm/pixel and 12-bit radiometric resolution, yielding 40cm pixel size and a FWHM of 6.4nm with a 25-οm slit in the 400-885nm spectral region. Field data collections were conducted in August 2009-2011 in the western area of Ribera del Duero Appellation d'Origine, northern Spain. A total of twelve full production vineyards and two study plots per field were selected to ensure appropriate variability in leaf biochemistry and vine physiological conditions. Leaves were collected for destructive sampling and biochemical determination of chlorophyll a+b and carotenoids conducted in the laboratory. In addition to leaf sampling and biochemical determination, canopy structural parameters, such as grid size, number of vines within each plot, trunk height, plant height and width, and row orientation, were measured on each 10m×10m plot. The R515/R570 index recently proposed for carotenoid estimation in conifer forest canopies was explored for vineyards in this study. The PROSPECT-5 leaf radiative transfer model, which simulates the carotenoid and chlorophyll content effects on leaf reflectance and transmittance, was linked to the SAILH and FLIGHT canopy-level radiative transfer models, as well as to simpler approximations based on infinite reflectance R∞ formulations. The objective was to simulate the pure vine reflectance without soil and shadow effects due to the high resolution hyperspectral imagery acquired from the UAV, which enabled targeting pure vines. The simulation results obtained with synthetic spectra demonstrated the effects due to Ca+b content on leaf Cx+c estimation when the R515/R570 index was used. Therefore, scaling up methods were proposed for leaf carotenoid content estimation based on the combined R515/R570 (sensitive to Cx+c) and TCARI/OSAVI (sensitive to Ca+b) narrow-band indices. Results demonstrated the feasibility of mapping leaf carotenoid concentration at the pure-vine level from high resolution hyperspectral imagery, yielding a root mean square error (RMSE) below 1.3οg/cm2 and a relative RMSE (R-RMSE) of 14.4% (FLIGHT) and 12.9% (SAILH) for the 2 years of hyperspectral imagery. The simpler formulation based on the infinite reflectance model by Yamada and Fujimura yielded lower errors (RMSE=0.87οg/cm2; R-RMSE<9.7%), although the slope deviated more from the 1:1 line. Maps showing the spatial variability of leaf carotenoid content were estimated using this methodology, which targeted pure vines without shadow and background effects. © 2013 Elsevier B.V.Financial support from the Spanish Ministry of Science and Education (MEC) for the AGL2009-13105 project and FEDER funding are gratefully acknowledged.Peer Reviewe