Deriving Predictive Relationships of Carotenoid Content at the Canopy Level in a Conifer Forest Using Hyperspectral Imagery and Model Simulation

Recent studies have demonstrated that the R570/R515 index is highly sensitive to carotenoid (Cx + c) content in conifer forest canopies and is scarcely influenced by structural effects. However, validated methods for the prediction of leaf carotenoid content relationships in forest canopies are still needed to date. This paper focuses on the simultaneous retrieval of chlorophyll (Ca + b) and (Cx + c) pigments, which are critical bioindicators of plant physiological status. Radiative transfer theory and modeling assumptions were applied at both laboratory and field scales to develop methods for their concurrent estimation using high-resolution hyperspectral imagery. The proposed methodology was validated based on the biochemical pigment quantification. Canopy modeling methods based on infinite reflectance formulations and the discrete anisotropic radiative transfer (DART) model were evaluated in relation to the PROSPECT-5 leaf model for the scaling-up procedure. Simpler modeling methods yielded comparable results to more complex 3-D approximations due to the high spatial resolution images acquired, which enabled targeting pure crowns and reducing the effects of canopy architecture. The scaling-up methods based on the PROSPECT-5+DART model yielded a root-mean-square error (RMSE) and a relative RMSE of 1.48 μg/cm2 (17.45%) and 5.03 μg/cm2 (13.25%) for Cx+c and Ca+ b, respectively, while the simpler approach based on the PROSPECT-5+Hapke infinite reflectance model yielded 1.37 & mug/cm2 (17.46%) and 4.71 μg/cm2 (14.07%) for Cx + c and Ca+b, respectively. These predictive algorithms proved to be useful to estimate Ca + b and Cx + c from high-resolution hyperspectral imagery, providing a methodology for the monitoring of these photosynthetic pigments in conifer forest canopies. © 2013 IEEE.Peer Reviewe

Early detection and quantification of verticillium wilt in olive using hyperspectral and thermal imagery over large areas

© 2015 by the authors. Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.Financial support for this research was provided by Project P08-AGR-03528 from “Consejería de Economía, Innovación y Ciencia” of Junta de Andalucía and the European Social Fund, and projects AGL-2012-37521 and AGL2012-40053-C03-01 from the Spanish “Ministerio de Economía y Competitividad” and the European Social Fund. Rocio Calderón is a recipient of research fellowship BES-2010-035511 from the Spanish “Ministerio de Ciencia e Innovación”.We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI).Peer Reviewe

Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery

The operational monitoring of forest decline requires the development of remote sensing methods that are sensitive to the spatiotemporal variations of pigment degradation and canopy defoliation. In this context, the red-edge spectral region (RESR) was proposed in the past due to its combined sensitivity to chlorophyll content and leaf area variation. In this study, the temporal dimension of the RESR was evaluated as a function of forest decline using a radiative transfer method with the PROSPECT and 3D FLIGHT models. These models were used to generate synthetic pine stands simulating decline and recovery processes over time and explore the temporal rate of change of the red-edge chlorophyll index (CI) as compared to the trajectories obtained for the structure-related Normalized Difference Vegetation Index (NDVI). The temporal trend method proposed here consisted of using synthetic spectra to calculate the theoretical boundaries of the subspace for healthy and declining pine trees in the temporal domain, defined by CItime=n/CItime=n+1 vs. NDVItime=n/NDVItime=n+1. Within these boundaries, trees undergoing decline and recovery processes showed different trajectories through this subspace. The method was then validated using three high-resolution airborne hyperspectral images acquired at 40 cm resolution and 260 spectral bands of 6.5 nm full-width half-maximum (FWHM) over a forest with widespread tree decline, along with field-based monitoring of chlorosis and defoliation (i.e., ‘decline’ status) in 663 trees between the years 2015 and 2016. The temporal rate of change of chlorophyll vs. structural indices, based on reflectance spectra extracted from the hyperspectral images, was different for trees undergoing decline, and aligned towards the decline baseline established using the radiative transfer models. By contrast, healthy trees over time aligned towards the theoretically obtained healthy baseline. The applicability of this temporal trend method to the red-edge bands of the MultiSpectral Imager (MSI) instrument on board Sentinel-2a for operational forest status monitoring was also explored by comparing the temporal rate of change of the Sentinel-2-derived CI over areas with declining and healthy trees. Results demonstrated that the Sentinel-2a red-edge region was sensitive to the temporal dimension of forest condition, as the relationships obtained for pixels in healthy condition deviated from those of pixels undergoing decline.JRC.D.1-Bio-econom

The feasibility of detecting trees affected by the Pine Wood Nematode using remote sensing

On request of DG SANTE , the Joint Research Centre has conducted between November 2014 and April 2015 a pilot study to establish the feasibility of remote sensing based detection of trees affected by Pine Wood Nematode (PWN) in the 2.2 Mha buffer zone established along the Portuguese and Spanish border. JRC collected multiple types of remote sensing data, from both aircraft and satellites, and a range of sensors and resolutions over a 7000 ha study site in Spain in the winter of 2014-2015. The images were evaluated for their ability to distinguish a) between pine trees that appeared to have a healthy canopy, and those showing decline, and b) between different levels of canopy decline, in terms of defoliation, decolouration and die-off. Detailed analysis of the imagery showed that when properly processed, remote sensing observations, particularly at high spatial and spectral resolution from aircraft, do permit the identification of pine trees showing canopy decline. The ability to detect individual tree crowns, and varying levels of canopy decline, varied with the image resolution, the type of sensor used to acquire the data, and the level of processing of the data. Based on the findings of this study the report spells out a set of technical recommendations for the operational monitoring of tree canopy health over large areas in the context of tree pest oubreaks.JRC.H.3-Forest Resources and Climat

Leaf Pigment retrievals from DAISEX data for crops at BARRAX: Effects of sun-angle and view-angle on inversion results

In Proceedings of the First International Sysmposium on Recent Advances in Quantitative Remote Sensing, Valencia, Spain, 16-20 September, 2002.The use of combined leaf and canopy models to retrieve biophysical crop variables are increasingly thought to provide an effective means of providing quantitative input needed to determine stress condition and improve crop yield predictions based on physiological condition. Nevertheless, the sensitivity of such retrieval results to changes in view and sun angle are needed if efficient single-view optical image data are to attain operational agriculture use. Although some studies have been carried out using synthetic model data, similar studies using real data have been very limited due to the unavailability of such data sets. In this research the focus is on the retrieval of leaf pigment (chlorophyll a+b). Some recent studies have demonstrated modelbased retrievals of leaf chlorophyll with RMSEs <5 mg/cm2 by comparison with field sampling and subsequent laboratory chemical analysis. The research reported here uses the extensive DAISEX data set acquired at Barrax, Spain in 1999 and 2000. Airborne data collection strategies provided DAIS, ROSIS and HyMap hyperspectral data in which various field study plots have been observed under widely varying view angles and also at significantly different solar zenith angle. Nearly simultaneously, a comprehensive field data set was acquired on specific crop plots which provided measurements of the following relevant crop variables among others: LAI, percent vegetation cover, leaf chlorophyll content, biomass, leaf and canopy water content, and soil reflectance. We use a combined modeling and indices-based approach, which predicts the leaf chlorophyll content while minimizing LAI influence and underlying soil effects. The sensitivity of leaf chlorophyll predictions with changes in view and sun angle are reported and analyzed through modeling studies for a range of plots in the DAISEX data set.Peer reviewe

GAEC workshop 2012 technical report

The report describes the main technical findings and results of the Good Agricultural and Environmental Condition (GAEC) workshop 2012 organised by the Joint Research Centre (JRC). The workshop was held at the JRC in Ispra from 8th-10th October 2012. 110 delegates attended the workshop representing 24 European Union Member States, two candidate countries (Croatia and Iceland) and Commission services. The workshop focussed on implementation and control issues related to the identification and measure of landscape features and buffer strips, as well as on scientific references for definition and mapping of soil related issues (e. g. soil erosion or soil organic matter). Participants also showed much interest on technical aspects related to the implementation of the future CAP with particular reference to landscape features in the framework of the so-called Ecological Focus Area. The workshop allows setting up and fine-tuning future main JRC activities taking into account DG AGRI and Member States inputs.JRC.H.4-Monitoring Agricultural Resource

Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery

Producción CientíficaPrevious studies have demonstrated the link between leaf chlorophyll fluorescence and photosynthesis, mainly at the leaf level and under controlled laboratory conditions. The present study makes progress in demonstrating the relationship between steady-state fluorescence and net photosynthesis measured under natural light field conditions both at the leaf and image levels. Ground measurements and airborne campaigns were conducted over two summers to acquire hyperspectral imagery at 40cm resolution and 260 spectral bands in the 400-885nm spectral region. This enabled the identification of pure vegetation pixels to extract their radiance spectra. The datasets were collected in August 2010 and 2011 in the western part of the area included in the Ribera del Duero Designation of Origin (Denominación de Origen), in northern Spain. The experiments were conducted in twelve full production vineyards where two study plots per field were selected to ensure adequate variability in leaf biochemistry and physiological condition. The vineyard fields were selected on the basis of their gradient in leaf nutrition and plant water status and showed variability in leaf pigment values and stomatal conductance. Leaves were collected for destructive sampling and biochemical determination of chlorophyll a+b, carotenoids and anthocyanins in the laboratory. Leaf steady-state and dark-adapted fluorescence parameters, net photosynthesis (Pn) and stomatal conductance (Gs) were measured in the field under natural light conditions. Such data were used as a validation dataset to assess fluorescence-photosynthesis relationships both at the leaf and the image level. The Fraunhofer Line Depth (FLD) principle based on three spectral bands (FLD3) was the method used to quantify fluorescence emission from radiance spectra extracted from pure vegetation pixels identified in the hyperspectral imagery. Fluorescence retrievals conducted using the FLD3 method yielded significant results when compared to ground-measured steady-state Fs (r2=0.48; p<0.01) and Fv'/Fm' (r2=0.53; p<0.01). The two-year assessment yielded consistent results on the relationship between Pn and Fs both at the leaf level and based on the airborne hyperspectral imagery. At the leaf level, significant relationships were found between leaf Fs and Pn (r2=0.55; p<0.001 for 2010; r2=0.59; p<0.001 for 2011). At the hyperspectral image level, the agreement between leaf Pn and airborne F was consistent for both years separately, yielding significant relationships at p<0.01 for 2010 (r2=0.54) and 2011 (r2=0.41) and a significant relationship at p<0.001 for the aggregated years (r2=0.52). Results show the link between net photosynthesis and steady-state fluorescence obtained under natural sunlight conditions at both leaf and airborne hyperspectral imagery levels. © 2013 Elsevier Inc.Este trabajo forma parte de un proyecto de investigación detro del PLAN NACIONAL 2008/2011. LIA DE PROYECTOS DE I+D+I

3D model validation to estimate intercepted radiation using high spatial resolution imagery in row-tree canopies

En este trabajo se llevó a cabo la validación del modelo 3D de transferencia radiativa FLIGHT para la estimación de la fracción de radiación fotosintéticamente activa interceptada (fIPAR) en cubiertas heterogéneas. El modelo permite simular cubiertas de tipo discontinuo evaluando la relación entre la energía reflejada y absorbida en función de distintos parámetros como la estructura de la plantación, geometría de visión o las propiedades espectrales del suelo y la vegetación. El estudio fue llevado a cabo en cultivos de melocotón y naranjo, pertenecientes a fincas comerciales situadas en las provincias de Córdoba y Sevilla. De cada plantación, se tomaron imágenes multiespectrales de alta resolución mediante un vehículo aéreo no tripulado (UAV) en zonas de estudio con un amplio rango de heterogeneidad estructural, donde se realizaron medidas ópticas foliares, estructurales y de interceptación de radiación. El sensor utilizado para la toma de imágenes fue una cámara multiespectral de 6 bandas y 10 nm FWHM, obteniendo los datos de radiación interceptada para validación de fIPAR mediante ceptómetro en el momento del vuelo del UAV. Los errores obtenidos en la estimación de fIPAR usando el modelo FLIGHT fueron de 10% RMSE, permitiendo parametrizar la relación NDVI vs fIPARA study was conducted to evaluate the 3D radiative transfer model FLIGHT to estimate fraction of Intercepted Photosyntetically Active Radiation (fIPAR) in heterogeneous canopies. The FLIGHT 3D canopy model enables simulation of the effects of different input parameters on fIPAR, such as the orchard architecture, planting grid, solar geometry and background artifacts. The study was conducted over two commercial peach and orange orchards located in Cordoba and Seville, where study areas showing a gradient in heterogeneous structure were selected. High resolution multispectral imagery was acquired by an unmanned aerial vehicle (UAV). The multispectral sensor used in this study was a 6-band multispectral camera with 10nm FWHM bands, using a ceptometer for ground truth data of intercepted radiation. Estimates for radiation interception using a modeling approach yielded errors bellow 10% RMS

Soil fragmentation study applying different tillage systems

Runoff generation depends on rainfall, infiltration, interception, and surface depressional storage. Surface depressional storage depends on surface microtopography, usually quantified trough soil surface roughness (SSR). SSR is subject to spatial and temporal changes that create a high variability. In an agricultural environment, tillage operations produce abrupt changes in roughness. Subsequent rainfall gradually decreases roughness. Beside it, local variation in soil properties and hydrology cause its SSR to vary spatially at different scales. The methods commonly used to measure it involve collecting point elevations in regular grids using laser profilers or scanners, digital close range stereo-photogrammetry and terrestrial laser scanning or LIDAR systems. In this case, a laser-scanning instrument was used to obtain representative digital elevation models (DEMs) at a grid resolution of 7.2x7.2mm that cover an area of 0.9x0.9m. The DEMs were obtained from two study sites with different soils. The first study site was an experimental field on which five conventional tillage methods were applied. The second study site was a large olive orchard with trees planted at 7.5x5.0m and bare soils between rows. Here, three tillage treatments were applied. In this work we have evaluated the spatial variability of SSR at several scales studying differences in height calculated from points separated by incremental distances h were raised to power values q (from 0 to 4 in steps of 0.1). The q = 2 data were studied as a semivariogram model. The logarithm of average differences plotted vs. log h were characterized by their slope, ?(q). Structure functions [?(q) vs. q] were fitted showing that data had nonlinear structure functions typical of multiscale phenomena. Comparisson of the two types of soil in their respective structure functions are shown

Assessing the effects of forest health on sun-induced chlorophyll fluorescence using the FluorFLIGHT 3-D radiative transfer model to account for forest structure

Sun-induced fluorescence (SIF) has been proven to serve as a proxy of photosynthesis activity and therefore, as an early indicator of physiological alterations for global monitoring of vegetation. However, the interpretation of SIF over different spatial resolutions is critical to bridge the existing gap between local and global scales. This study provides insight into the influence of scene components, and forest structure and composition on the quantification of the red and far-red fluorescence signal as an early indicator of forest decline. The experiments were conducted over an oak forest (Quercus ilex) affected by water stress and Phytophthora infection in the southwest of Spain. SIF retrievals through the Fraunhofer Line Depth (FLD) principle with three spectral bands F (FLD3) was assessed using high resolution (60 cm) hyperspectral imagery extracting sunlit crown, full crown and aggregated pixels. Results showed the link between F (FLD3) extracted from sunlit crown pixels and the tree physiological condition in this context of disease infection, yielding significant relationships (r2=0.57, p0.05). These results demonstrate the need for methods to accurately retrieve crown SIF from aggregated pixels in heterogeneous forest canopies with large physiological variability among individual trees. This aspect is critical where structural canopy variations and the direct influence of background and shadows affect the SIF amplitude masking the natural variations caused by physiological condition. FluorFLIGHT, a modified version of the three dimensional (3-D) radiative transfer model FLIGHT was developed for this work, enabling the simulation of canopy radiance and reflectance including fluorescence effects from different spatial resolutions and percentage cover levels. The 3-D modelling approach proposed here significantly improved the relationship between Fs and F (FLD3) extracted from aggregated pixels (r2=0.70, p<0.001), performing better than when aggregation effects were not considered (r2=0.42, p<0.01). The FluorFLIGHT model used in this study improved the retrieval of SIF from aggregated pixels as a function of fractional cover, leaf area index and chlorophyll content yielding significant relationships between Fs ground-data measurements and fluorescence quantum yield estimated with FluorFLIGHT at p<0.01 (r2=0.79). The methodology presented here using FluorFLIGHT also demonstrated its capabilities for mapping SIF at the tree level for single tree assessment of forest physiological condition in the context of early disease detection.JRC.D.1-Bio-econom