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

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

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

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

[ES] 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[EN] This paper demonstrates the use and applications of Canopy Reflectance Models (CR) with airborne hyperspectral CASI data. SAIL and Kuusk canopy reflectance models are the link between the leaf and canopy levels: leaf-level relationships obtained between optical indices and stress bioindicators, such as chlorophyll content and chlorophyll fluorescence can be scaled-up to the canopy level using canopy reflectance models. The application of canopy reflectance models is demonstrated with the results obtained in the Bioindicators of Forest Sustainability Project. The work was carried out in 12 study areas of Acer saccharum M. in the Algoma Region, Ontario (Canada), where field measurements and hyperspectral CASI imagery have been collected in 1997, 1998 and 1999 deployments. Single leaf reflectance and transmittance, chlorophyll and carotenoid content, and chlorophyll fluorescence of broad leaves were measured. The physiological indices and derivative analysis indices extracted from leaf spectral reflectance were tested at canopy level using CASI data of 72 channels and 2 m spatial resolution.Peer reviewe

Detección de estrés hídrico en olivar mediante datos hiperespectrales y térmicos del sensor AHS

El sensor hiperespectral AHS (Airborrne Hyperspectal Scanner) fue utilizado para obtener imágenes de 2.5 m de resolución espacial en el espectro visible, infrarrojo cercano y térmico en una parcela de olivar en Córdoba (España) con el fin de estudiar la variabilidad espacial y temporal del estrés hídrico. Los datos térmicos del AHS permitieron obtener imágenes de temperatura de superficie de la parcela a las 7:30, 9:30 y 12:30 GMT el 25 de julio de 2004. EL diseño experimental en bloques aleatorios consistió en aplicar tres dosis diferentes de riego durante julio, agosto y septiembre, realizando medidas semanales de potencial hídrico, fotosíntesis y conductancia para estudiar los efectos del estrés hídrico en el cultivo. Los sensores de infrarrojo IRT permitieron la realización de medidas continuas de temperatura sobre las copas de los árboles, facilitando la validación de las imágenes térmicas. Los resultados de este estudio son presentados, destacando la aplicabilidad en la agricultura de precisión de la teledetección térmica e hiperespectral de alta resolución espacial para el estudio del suministro y la dosificación del riego.The Airborne Hyperspectral Scanner (AHS) was used to acquire images with 2.5 m spatiala resolution in the visible, near infrared and thermal spectral regions over an olive orchard in Cordoba (Spain) to study the spatial and temporal variability of water stress. The AHS thermal information enabled obtaining surface temperature images of the orchard at 7:30, 9:30 and 12:30 GMT in 25 july 2004. The experimental design consisted of applying three different irrigation treatments in randomly selected blocks during july, august and septemper, acquiring measurements of leaf water potential, stomatal conductance and photosynthesis to study the water stress effects on the trees. Infrared sensors IRT placed on top of the trees allowed to obtain continuously temperature measurements, providing validation data for the airborne thermal imagery. Results of this study are presented, suggesting that hyperspectral and high resolution remote sensing methods have important applicability in precision agriculture for management of controlled deficit irrigation method

Método de segmentación de cultivos

Se describe un método de distribución espacial continua de la calidad del fruto dentro de una parcela de cultivo o una finca comercial que permite la segmentación de dicha finca en sectores de distinta calidad, por lo tanto las zonas con mayor precio de mercado pueden ser recolectadas independientemente; permitiendo al agricultor obtener un mapa de calidad completo de sus cultivos previo a la organización de la recolección del fruto.Peer reviewedConsejo Superior de Investigaciones CientíficasA1 Solicitud de patente con informe sobre el estado de la técnic

Soil temperature determines the reaction of olive cultivars to verticillium dahliae pathotypes

Development of Verticillium wilt in olive, caused by the soil-borne fungus Verticillium dahliae, can be influenced by biotic and environmental factors. In this study we modeled i) the combined effects of biotic factors (i.e., pathotype virulence and cultivar susceptibility) and abiotic factors (i.e., soil temperature) on disease development and ii) the relationship between disease severity and several remote sensing parameters and plant stress indicators. Methodology: Plants of Arbequina and Picual olive cultivars inoculated with isolates of defoliating and non-defoliating V. dahliae pathotypes were grown in soil tanks with a range of soil temperatures from 16 to 32°C. Disease progression was correlated with plant stress parameters (i.e., leaf temperature, steady-state chlorophyll fluorescence, photochemical reflectance index, chlorophyll content, and ethylene production) and plant growth-related parameters (i.e., canopy length and dry weight). Findings: Disease development in plants infected with the defoliating pathotype was faster and more severe in Picual. Models estimated that infection with the defoliating pathotype was promoted by soil temperatures in a range of 16 to 24°C in cv. Picual and of 20 to 24°C in cv. Arbequina. In the non-defoliating pathotype, soil temperatures ranging from 16 to 20°C were estimated to be most favorable for infection. The relationship between stress-related parameters and disease severity determined by multinomial logistic regression and classification trees was able to detect the effects of V. dahliae infection and colonization on water flow that eventually cause water stress. Conclusions: Chlorophyll content, steady-state chlorophyll fluorescence, and leaf temperature were the best indicators for Verticillium wilt detection at early stages of disease development, while ethylene production and photochemical reflectance index were indicators for disease detection at advanced stages. These results provide a better understanding of the differential geographic distribution of V. dahliae pathotypes and to assess the potential effect of climate change on Verticillium wilt development.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 (JANC), and projects AGL-2012-37521 (JANC) and AGL2012-40053-C03-01 (PJZT) from the Spanish ‘‘Ministerio de Economia y Competitividad’’ and the European Social Fund. R. Calderón is a recipient of research fellowship BES-2010-035511 from the Spanish ‘‘Ministerio de Ciencia e Innovación’’ and C. Lucena was a recipient of a JAE-DOC postdoctoral contract from ‘‘Consejo Superior de Investigaciones Científicas’’ (CSIC) co-funded by the European Social Fund. TPeer Reviewe

Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

"© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.This work was supported in part by the Belgian Science Policy Office in the frame of the Stereo II program (Hypermix project-SR/00/141), in part by the project Chameleon of the Flemish Agency for Innovation by Science and Technology (IWT), and in part by the Spanish Ministry of Science and Education (MEC) for the projects AGL2012-40053-C03-01 and CONSOLIDER RIDECO (CSD2006-67). The European Facility for Airborne Research EUFAR (www.eufar.net) funded the flight campaign (Transnational Access Project 'Hyper-Stress'). The work of D. S. Intrigliolo was supported by the Spanish Ministry of Economy and Competitiveness program "Ramon y Cajal."Delalieux, S.; Zarco-Tejada, PJ.; Tits, L.; Jiménez Bello, MÁ.; Intrigliolo Molina, DS.; Somers, B. (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2571-2582. https://doi.org/10.1109/JSTARS.2014.2330352S257125827