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Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Remote sensing efforts made as part of European initiatives via POnTE, XF-ACTORS and the JRC, as well as through regional programs, have focused, among others, on the development of algorithms for the early detection of Xylella fastidiosa (Xf)-induced symptoms. Airborne campaigns carried out between 2016 and 2019 collected high-resolution hyperspectral and thermal images from infected areas in the Apulia region (Italy), in the province of Alicante and on the island of Mallorca (Spain). The remote sensing imagery collections were performed alongside field surveys and laboratory analyses to assess the presence of Xf, and the severity and incidence of disease in olive and almond trees. Radiative transfer models and machine learning algorithms were used to quantify spectral plant traits for each individual infected tree, assessing their importance as pre visual indicators of Xf-induced stress. These studies conducted across species have demonstrated that specific spectral plant traits successfully revealed Xf induced symptoms at early stages, i.e., before visual symptoms appear. The results show that spectral plant traits contribute differently to symptom detection across host species (olive vs. almond), and that abiotic-induced stress affects the performance of the algorithms used for detecting infected trees. Together, the different European initiatives studying the use of remote sensing to support the monitoring of landscapes for Xylella fastidiosa detection lead us to conclude that the early detection of Xf-induced symptoms is feasible when high-resolution hyperspectral imagery and physically-based plant trait retrievals are used, obtaining accuracies exceeding 92% (kappa>0.8). These results are essential to enable the implementation of effective control and management of plant diseases using airborne- droneand satellite-based remote sensing technologies. Moreover, these large-scale hyperspectral and thermal imaging methods greatly contribute to the future operational monitoring of infected areas at large scales, well beyond what is possible from field surveys and laboratory analyses alone.

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Two major threats affecting olives groves are the bacterium Xylella fastidiosa (Xf) and the soil borne fungus Verticillium dahliae (Vd) Kleb. Both pathogens block the water flow along the xylem vessels, triggering similar physiological symptoms which are also easily confounded with water-stress induced responses. Previous remote sensing studies have attempted to independently detect Xf and Vd infections using hyperspectral and thermal imagery, but the potential discrimination between both infections has not been addressed yet. In this study, a multistage machine learning (ML) methodology is proposed to detect and discriminate the spectral changes caused by Xf and Vd infection. The multi-stage ML approach assessed spectral plant traits retrieved by physical model inversion such as leaf pigment content (carotenoids, Cx+c, chlorophyll a+b, Ca+b and anthocyanins, Anth.), canopy structural parameters (leaf area index, LAI and the leaf inclination distribution function, LIDFa), solar-induced fluorescence (SIF@760), the thermal-based Crop Water Stress Index (CWSI) and narrow-band hyperspectral indices. On each stage, key indicators were used to i) detect infection by each of the two pathogens accounting for the importance of the spectral traits, ii) reduce the detection uncertainties due to the confounding physiological responses triggered by water stress, and iii) discriminate between Vd and Xf infections. Results showed that SIF@760, Anth. and the Normalized Phaeophytinization Index (NPQI) were the most relevant traits to identify trees showing symptoms confounded with water stress. Using a Vd+Xf cross-infected balanced dataset the multi-stage ML approach yielded overall accuracies that exceeded 92% (kappa=0.8). The most sensitive spectral plant-trait indicators to discriminate between both pathogen infections will be discussed, describing operational aspects for the large-scale monitoring of olive orchards in geographic regions potentially affected by both pathogens.

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021., Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees.

Trabajo presentado en la European Conference on Xylella 2017 (Finding answers to a global problem), celebrada en Palma de Mallorca del 13 al 15 de noviembre de 2017., A remote sensing campaign carried out in Puglia in summer 2016 collected hyperspectral and thermal images of ca 200,000 olive trees at sub-meter resolution. In the 1200 ha study area within the X. fastidiosa infected zone, 3500 trees were simultaneously evaluated in the field for severity of X. fastidiosa symptoms. The hyperspectral sensor used in this experiment acquired data in the visible and near-infrared spectral region (400-885 nm) with 260 bands of 6.5 nm FWHM at 1.85 nm/pixel and 12-bit radiometric resolution. The sensor was radiometrically calibrated in the laboratory, and images atmospherically corrected to obtain surface reflectance using total incoming irradiance measured in the field. The high resolution hyperspectral and thermal imagery acquired over the orchards allowed the delineation of individual trees using object-based crown detection algorithms. Crown temperature and hyperspectral indices were calculated for each tree to classify disease severity levels using different machine learning algorithms, including linear discriminant analysis, support vector machines and neural networks. The success and applicability of these early detection methods to other areas will be discussed in the context of a new airborne campaign planned in July 2017 over X. fastidiosa infected zones in Mallorca. In this new airborne campaign the impact of X. fastidiosa symptoms on crown reflectance will also be evaluated in the 800-1700 nm spectral region., This work was supported by JRC and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 635646 “Pest Organisms Threatening Europe POnTE” and grant agreement N. 727987 “Xylella fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy XF-ACTORS”.

La infección temprana por Vertícíllium dahliae en olivar y la discriminación entre niveles de severidad de la Verticilosis es viable mediante la utilización de imágenes térmicas, multiespectrales e hiperespectrales adquiridas con vehículos aéreos no tripulados a escala de parcela o tripulados a escala de comarca. El indicador relacionado con la temperatura de copa (CWSI), rndices de reflectancia (B, BG1, BR1) y la fluorescencia clorofOica (FLD3) se identifican como buenos indicadores para detectar la Verticilosis en etapas tempranas del desarrollo de la enfermedad, mientras que NDVI, PRI515, RIG, Hl e índices de estimación de clorofila y carotenos demuestran ser buenos indicadores para la cuantificación de daño moderado o severo causada por Verticilosis, lo que puede ser de utilidad para el diseño de estrategias de control de la Verticilosis a escala de parcela y de comarca., Las investigaciones descritas en este artículo han sido financiadas por el proyecto POB-AGR-03528 de la Consejerfa de Economía, Innovación y Ciencia de la Junta de Andalucía y el Fondo Social Europeo, y los proyectos AGL-2012-37521 y AGL2012-40053-C03-01 del Ministerio de Economía y Competitividad y el Fondo Social Europeo.

El olivo (Olea europaea L.) es el cultivo leñoso no tropical que ocupa mayorsuperficie en todo el mundo, con el 95% de la producción mundial localizada enla cuenca Mediterránea. España es el país con mayor superficie de olivar del mundo con2.5 MHa y aproximadamente el 39% de la producción mundial. Durante las últimasdécadas, la Verticilosis, causada por el hongo de suelo Verticillium dahliae Kleb., haocasionado severas pérdidas de rendimiento en el olivar, convirtiéndose en la enfermedadmás limitante causada por patógenos de suelo de este cultivo a nivel mundial. Estepatógeno coloniza el sistema vascular de la planta, bloqueando el flujo del agua yfinalmente induciendo estrés hídrico. El desarrollo de la Verticilosis en el olivo puedeestar influenciado por factores bióticos y abióticos, sin embargo, poco se sabe sobre lainfluencia del medio físico en él. Actualmente, ninguna medida de control aplicadaindividualmente es completamente efectiva para el tratamiento de la Verticilosis del olivo,no obstante, una estrategia de control integrado es la mejor forma de manejar laenfermedad, combinando el uso de medidas de control previas y posteriores a laplantación. Las medidas de control posteriores a la plantación serían más efectivas si laszonas del terreno con árboles afectados por Verticilosis fueran identificadas en etapastempranas del desarrollo de la enfermedad con el objetivo de disminuir la expansión delpatógeno y sucesivas infecciones a árboles o plantaciones vecinas. Sin embargo, lainspección visual en campo de síntomas de la enfermedad en estadios tempranos de sudesarrollo es costosa en tiempo y recursos. Por lo tanto, la teledetección puede ser unaherramienta muy útil para detectar el estrés hídrico inducido por la infección de V.dahliae en olivos en etapas tempranas del desarrollo de la enfermedad.Los principales objetivos de la presente Tesis Doctoral fueron: (i) evaluar el efectode la temperatura del suelo en el desarrollo de la Verticilosis teniendo en cuanta diferentespatotipos de V. dahliae y cultivares de olivo; (ii) valorar el uso de la teledetección térmicae hiperespectral de alta resolución como herramienta para detectar la infección yseveridad por Verticilosis en parcelas de olivar y áreas de mayor extensión, evaluando latemperatura e índices fisiológicos desde escala foliar a escala de cubierta.El primer objetivo se llevó a cabo con plantas de olivo de los cultivares (cv.)Arbequina y Picual que crecieron en suelo infestado con los patotipos defoliante (D) y nodefoliante (ND) de V. dahliae bajo condiciones climáticas controladas en tanques de suelocon temperaturas de 16 a 32ºC. El desarrollo de la Verticilosis en plantas infectadas por elpatotipo D fue más rápido y severo en cv. Picual que en cv. Arbequina. La temperatura desuelo óptima para el desarrollo de la infección del patotipo D fue de 16 a 24ºC para cv.Picual y de 20 a 24ºC para cv. Arbequina. Para el patotipo ND el rango de temperaturamás favorable para la infección por V. dahliae fue de 16 a 20ºC. Estos resultados permiten..., Olive (Olea europaea L.) is the most cultivated non-tropical fruit tree in theworld, with 95% of the world production located in the Mediterranean Basin.Spain is the leading olive-producing country with 2.5 MHa and nearly 39% of the worldproduction. During the last few decades, Verticillium wilt, caused by the soil-bornefungus Verticillium dahliae Kleb., has caused severe olive yield losses, becoming themost limiting soil-borne disease of this crop worldwide. This pathogen colonizes thevascular system of plants, blocking water flow and eventually inducing water stress.Development of Verticillium wilt in olive can be influenced by biotic and abiotic factors,nevertheless, little is known about the influence of the physical environment on it.Currently, no control measure applied singly is fully effective for the management ofVerticillium wilt of olive; therefore an integrated disease management strategy is neededto manage the disease, combining the use of pre-planting and post-planting controlmeasures. Post-planting control measures would be more efficient if Verticillium wiltaffectedtrees patches within fields are identified at early stages of disease development inorder to mitigate the spread of the pathogen and successive infections to neighboringtrees. However, visual inspection of disease symptoms at early stages of development inthe field is time-consuming and expensive. Thus, remote sensing is thought to be a usefultool to detect water stress induced by V. dahliae infection in olive trees at early stages ofdisease development.The main objectives of this PhD Thesis were: (i) to assess the effect of soiltemperature on Verticillium wilt development taking into account different V. dahliaepathotypes and olive cultivars; and (ii) to evaluate the use of high-resolution thermal andhyperspectral remote sensing imagery as a tool to detect Verticillium wilt infection andseverity in olive orchards and larger areas, assessing temperature and physiologicalindices from leaf to canopy scale.The first objective was conducted with olive plants of cultivar (cv.) Arbequina andcv. Picual grown in soil infested with the defoliating (D) or non-defoliating (ND)pathotype of V. dahliae under controlled climatic conditions in soil tanks with a range ofsoil temperatures from 16 to 32ºC. Verticillium wilt development in plants infected by theD pathotype was faster and more severe on cv. Picual than on cv. Arbequina. Modelsestimated that infection by the D pathotype was promoted by soil temperature in a rangeof 16 to 24°C for cv. Picual and of 20 to 24ºC for cv. Arbequina. For the ND pathotype arange of 16 to 20ºC was estimated as the most favorable for infection. These resultsprovide a better understanding of the differential geographic distribution of V. dahliaepathotypes and assess the potential effect of climate change on Verticillium wilt...development...

Ponencia presentada en el XV Congreso de la Asociación Española de Teledetección celebrado en Torrejón de Ardóz (Madrid) del 22 al 24 de octubre de 2013, This paper assesses the use of indicators calculated from high-resolution thermal and hyperspectral imagery which are based on structural and physiological indices related with xanthophyll, chlorophyll a+b, carotenoids concentration and chlorophyll fluorescence for the early detection of stress caused by Verticillium dahliae in olive trees. This study was conducted in two commercial olive orchards where foliar and crown measurements were obtained by punctual sensors and time series of airborne thermal and hyperspectral imagery were acquired using Unmanned Aerial Vehicles (UAV) in three consecutive years and related to Verticillium wilt severity at the time of the flights.