102 results on '"Gómez-Candón, David"'
Search Results
2. Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors
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Gracia-Romero, Adrian, primary, Rufo, Rubén, additional, Gómez-Candón, David, additional, Soriano, José Miguel, additional, Bellvert, Joaquim, additional, Yannam, Venkata Rami Reddy, additional, Gulino, Davide, additional, and Lopes, Marta S., additional
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- 2023
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3. Field phenotyping of water stress at tree scale by UAV-sensed imagery: new insights for thermal acquisition and calibration
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Gómez-Candón, David, Virlet, Nicolas, Labbé, Sylvain, Jolivot, Audrey, and Regnard, Jean-Luc
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- 2016
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4. Unravelling the responses of different apple varieties to water constraints by continuous field thermal monitoring
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Gómez-Candón, David, primary, Mathieu, Vincent, additional, Martinez, Sébastien, additional, Labbé, Sylvain, additional, Delalande, Magalie, additional, and Regnard, Jean-Luc, additional
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- 2022
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5. Plant Breeding and Management Strategies to Minimize the Impact of Water Scarcity and Biotic Stress in Cereal Crops under Mediterranean Conditions
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Agencia Estatal de Investigación (España), Generalitat de Catalunya, Ministerio de Ciencia e Innovación (España), Pérez-Méndez, Néstor, Miguel-Rojas, Cristina, Jiménez-Berni, José A., Gómez-Candón, David, Pérez de Luque, Alejandro, Fereres Castiel, Elías, Villegas Tort, Dolors, Sillero, Josefina C., Agencia Estatal de Investigación (España), Generalitat de Catalunya, Ministerio de Ciencia e Innovación (España), Pérez-Méndez, Néstor, Miguel-Rojas, Cristina, Jiménez-Berni, José A., Gómez-Candón, David, Pérez de Luque, Alejandro, Fereres Castiel, Elías, Villegas Tort, Dolors, and Sillero, Josefina C.
- Abstract
Wheat and rice are two main staple food crops that may suffer from yield losses due to drought episodes that are increasingly impacted by climate change, in addition to new epidemic outbreaks. Sustainable intensification of production will rely on several strategies, such as efficient use of water and variety improvement. This review updates the latest findings regarding complementary approaches in agronomy, genetics, and phenomics to cope with climate change challenges. The agronomic approach focuses on a case study examining alternative rice water management practices, with their impact on greenhouse gas emissions and biodiversity for ecosystem services. The genetic approach reviews in depth the latest technologies to achieve fungal disease resistance, as well as the use of landraces to increase the genetic diversity of new varieties. The phenomics approach explores recent advances in high-throughput remote sensing technologies useful in detecting both biotic and abiotic stress effects on breeding programs. The complementary nature of all these technologies indicates that only interdisciplinary work will ensure significant steps towards a more sustainable agriculture under future climate change scenarios.
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- 2022
6. Understanding the errors in input prescription maps based on high spatial resolution remote sensing images
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Gómez-Candón, David, López-Granados, Francisca, Caballero-Novella, Juan J., Peña-Barragán, José M., and García-Torres, Luis
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- 2012
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7. Sectioning remote imagery for characterization of Avena sterilis infestations. Part A: Weed abundance
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Gómez-Candón, David, López-Granados, Francisca, Caballero-Novella, Juan J., García-Ferrer, Alfonso, Peña-Barragán, José M., Jurado-Expósito, Montserrat, and García-Torres, Luis
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- 2012
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8. Sectioning remote imagery for characterization of Avena sterilis infestations. Part B: Efficiency and economics of control
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Gómez-Candón, David, López-Granados, Francisca, Caballero-Novella, Juan J., García-Ferrer, Alfonso, Peña-Barragán, José M., Jurado-Expósito, Montserrat, and García-Torres, Luis
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- 2012
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9. Performance of the Two-Source Energy Balance (TSEB) Model as a Tool for Monitoring the Response of Durum Wheat to Drought by High-Throughput Field Phenotyping
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Gómez-Candón, David, primary, Bellvert, Joaquim, additional, and Royo, Conxita, additional
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- 2021
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10. Cork oak woodland land-cover types classification: a comparison between UAV sensed imagery and field survey
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Heuschmidt, Florence, primary, Gómez-Candón, David, additional, Soares, Cristina, additional, Cerasoli, Sofia, additional, and Silva, João M. N., additional
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- 2020
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11. Remote imagery to assess water stress variability within the orchard
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Gómez-Candón, David, primary
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- 2020
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12. Water stress assessment at tree scale: high-resolution thermal UAV imagery acquisition and processing
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Gómez-Candón, David, Torres-Sánchez, Jorge, Labbé, Sylvain, Jolivot, A., Martínez, S., Regnard, J. l., Gómez-Candón, David, Torres-Sánchez, Jorge, Labbé, Sylvain, Jolivot, A., Martínez, S., and Regnard, J. l.
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Water stress assessment can be performed by analyzing thermal images taken onboard Unmanned Aerial Vehicles (UAVs). This study focuses on the acquisition and data extraction of high-resolution UAV-sensed thermal images. The datasets obtained, through computation of spectral indices and image classification, allowed to assess the response to drought of an apple hybrid population submitted to different water regimes. Studies were performed in an experimental apple orchard located in southern France. Flights were planned at solar noon on four successive dates during summer 2013 (40 m altitude, 0.10 m spatial resolution) and at five successive hours of the day, once water stress was established. The high temporal and spatial resolution of images allowed acquiring data at canopy and intra-canopy scales, with a short revisit time. As the miniaturized uncooled thermal camera carried onboard the UAV needs careful correction of radiometry, this was performed by continuous reference to ground thermal targets, while an automatized image processing was carried out. Thanks to the high resolution of the remote images obtained, and the capacity to efficiently delineate each individual tree within the whole trial, it was possible to analyze inter- and intra-canopy thermal variations. Indices extracted from thermal images showed significantly higher canopy temperatures in water restricted trees compared to well-irrigated ones. These differences were related to the severity of water deficit. However, responses also varied significantly according to the genotype. The image-based variables in apple trees constitute a basis for a further finely tuned analysis of the differential response to water stress.
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- 2017
13. Acquisition d'images thermiques par drone : corrections radiométriques à partir de données terrain
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Jolivot, Audrey, primary, Gómez-Candón, David, additional, Labbé, Sylvain, additional, Virlet, Nicolas, additional, and Regnard, Jean-Luc, additional
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- 2017
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14. UAV thermal imagery contribution to high throughput field phenotyping of apple tree hybrid population and characterization of genotypic response to water stress
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Gómez-Candón, David, Virlet, Nicolas, Costes, Evelyne, Jolivot, Audrey, Martinez, Sébastien, Labbé, Sylvain, Regnard, Jean-Luc, Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Architecture et Fonctionnement des Espèces Fruitières [AGAP] (AFEF), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Département Environnements et Sociétés (Cirad-ES), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Federation of European Societies of Plant Biology (FESPB). INT., Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro), and Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)
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[SDV]Life Sciences [q-bio] - Abstract
High-throughput field phenotyping can be performed by using high resolution multispectral imagery. This presentation focuses on Unmanned Aerial Vehicle (UAV) sensed thermal imagery used to assess the response of an apple population to drought and analyze the genotypic variability of stomatal behavior. Studies were performed in an experimental apple orchard located in Southern France submitted to different water regimes. For remote image acquisition, UAV flights were performed at different dates, hours and altitudes with a thermal camera on board (0.10-0.27m spatial resolution). Temperature of different reference ground targets (hot, cold, wet and dry bare soil) was continuously measured by thermo-radiometers for image radiometric calibration. To assess the effect of image resolution and that of vegetation cover fraction, a sample of 18 apple trees was chosen and the mean canopy temperature (Ts) in tree central zone and its variability were measured. As distortions were revealed in Ts, it seemed advisable to separate mixed pixels (including shaded leaves and soil) from well-illuminated vegetation. Considering the effect of altitude, standard deviation of Ts increased according to the image resolution, and this was particularly true where resolution was close to leaf average size (0.10m2). Thanks to the ultra-high resolution of remote images obtained, and beyond capacity of the approach to delineate efficiently each individual tree within the whole trial, it was possible to analyze inter- and intra-canopy thermal variations. By using image-based vegetation and stress indices as phenotypic variables, genetic dissection of the traits is currently undertaken and first results will be presented.
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- 2014
15. Method for automatic standardization of multitemporal remote images on the basis of vegetative pseudo-invariant soil uses
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García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, and López Granados, Francisca
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[EN] Automatic method for the radiometric standardization of series of multitemporal remote images of one and the same geographical scene or area, on the basis of vegetative pseudo-invariant soil uses, which comprises: a) the capture of multispectral remote images corresponding to bands selected in the visible or hyperspectral spectrum, b) the digitization or georeferencing of an image, c) analysis of the images captured in a) and selection of parcels using one or two vegetative soil uses, d) the formation of parcels by means of the definition of regions of interest in an image and the transposition thereof to the rest of the image series, e) extraction/determination of the digital values of each spectral band of each image in the reference pseudo-invariant soil use, f) calculation of the correction factors of each band in each image in the series, g) linear conversion of each band of each image using the previously calculated CF, h) formation of the standardized image in its entirety by means of the composition of the bands previously converted linearly., [FR] La présente invention concerne un procédé automatique pour la normalisation radiométrique de séries d'images distantes multitemporelles d'une même zone ou d'un même lieu géographique, sur la base d'utilisations de sols végétaux pseudo-invariants qui comprend: a) la capture d'images distantes multispectrales correspondant à des bandes sélectionnées du spectre visible ou hyperspectrales; b) la numérisation ou géolocalisation d'une image; c) l'analyse des images prises en a) et la sélection de parcelles présentant une ou deux utilisations de sols végétaux; d) la configuration de parcelles par délimitation de région d'intérêt dans une image et leur transposition au reste de la série d'images; e) l'extraction/détermination des valeurs numériques de chaque bande spectrale de chaque image de l'utilisation du sol pseudo-invariant de référence; f) le calcul des facteurs de correction de chaque bande dans chaque image de la série; g) la transformation linéaire de chaque bande de chaque image en appliquant le calcul CF précédent; h) la configuration de l'image normalisée dans son ensemble au moyen de la composition des bandes ayant été auparavant transformées linéairement., [ES] Procedimiento automático para la normalización radiométrica de series de imágenes remotas multitemporales de una misma área o escena geográfica, en base a usos de suelo vegetales pseudo- invariantes que comprende: a) toma de imágenes remotas multiespectrales correspondientes a bandas que se seleccionan del espectro visible o hiperespectrales, b) digitalización o geo-referenciación de una imagen, c) análisis de las imágenes tomadas en a) y selección de parcelas con usos de uno o dos usos de suelo vegetales, d) conformación de parcelas mediante delimitación de regiones de interés en una imagen y su trasposición al resto de la serie de imágenes, e) extracción/determinación de los valores digitales de cada banda espectral de cada imagen en el uso de suelo pseudo-invariante de referencia, f) cálculo de los factores de corrección de cada banda en cada imagen de la serie, g) transformación lineal de cada banda de cada imagen aplicando el antes calculado CF, h) conformación de la imagen normalizada en su conjunto mediante la composición de las bandas antes transformadas linealmente., Consejo Superior de Investigaciones Científicas, A1 Solicitud de patente con informe sobre el estado de la técnica
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- 2013
16. Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology
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Consejo Superior de Investigaciones Científicas (España), Ministerio de Economía y Competitividad (España), García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, Consejo Superior de Investigaciones Científicas (España), Ministerio de Economía y Competitividad (España), García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, and Peña Barragán, José Manuel
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A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
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- 2015
17. Normalización automática de imágenes remotas multitemporales en base a usos de suelo vegetativos pseudo-invariantes
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Caballero Novella, Juan José, García Torres, Luis, and Gómez-Candón, David
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Trabajo presentado en la conferencia Esri España, celebrada en Madrid el 2 y 3 de octubre de 2013., 1) Las imágenes multitemporales requieren calibración/normalización. 2) Correcciones radiométricas absolutas (ARC) vs. Normalización radiométrica relativa (RRN). 3) ARIN® procedimiento & software. 4) ARIN® contribuye a la normalización de imágenes remotas relativas a entornos agrícolas. 5) Proyectos, publicaciones, registro y patente.
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- 2013
18. ARIN ® procedure for the normalization of multitemporal remote images through vegetative pseudo-invariant features
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García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, and Ministerio de Economía y Competitividad (España)
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ARIN - Abstract
Trabajo presentado en el SPIE Remote Sensing, celebrado en Dresden (Alemania) del 23 al 26 de septiembre de 2013., An Automatic Relative Image Normalization (ARIN®) method was developed to normalize multitemporal remote images based in vegetative pseudo-invariant features (VPIFs), as following: 1) defining the same parcel for each selected VPIF in each multitemporal image; 2) extracting the VIPF spectral bands data for each image; 3) calculating the correction factor (CF) for each image band to fit it to the same expected values, normally for each band the average of the series; 4) obtaining the normalized images by transforming each original image band through the corresponding CF linear functions. ARIN® software was developed to automatically achieve the previously described procedure. We have validated ARIN using a series of six GeoEye-1 satellite images taken over the same Southern of Spain scene in 2010, from early April to October, at about 4 weeks interval. Three VPIFs were chosen: citrus orchards (CIT), riparian trees (POP) and Mediterranean forest trees (MFO). The VPIFs spectral band correction factors (CFs) to implement the ARIN linear normalization procedure largely varied among spectral bands for any given image and among images for any given spectral band. The correlation coefficients between the CFs among VPIFs for any spectral band and overall all bands are over 0.83 and significant at P=0.95 or higher. For the ARIN normalized images, the range and standard deviation of any spectral bands and vegetation indices values were considerably reduced as compared to the original images, regardless the VPIF or the combination of VPIFs selected for normalization, which proves the method efficacy. Moreover, ARIN method was easier and efficient than the absolute calibration QUAC method, and somehow similarly efficient as the highly tunable FLAASH, in which solar position and weather calibration parameters are required., Content: 1) Multitemporal images need calibration/ normalization. 2) Absolute radiometric corrections (ARC) vs. Relative radiometric normalization (RRN). 3) ARIN® procedure & software. 4) ARIN contribute to normalize remote images of agricultural scenes. 5) Projects, publications, registration and patent
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- 2013
19. In-season site-specific control of cruciferous weeds at broad-scale using quickbird imagery
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Castro, Ana Isabel de, López Granados, Francisca, Gómez-Candón, David, Peña Barragán, José Manuel, Caballero Novella, Juan José, and Jurado-Expósito, Montserrat
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Póster presentado en la 9th European Conference on Precision Agriculture ECPA, celebrada en Lleida del 7 al 11 de julio de 2013., This research explores the use of multi-spectral high-spatial resolution QuickBird satellite imagery to detect cruciferous weed patches in winter wheat fields and to develop in-season site-specific cruciferous treatment maps at broad-scale.
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- 2013
20. Accuracy and crop line misalignment over high resolution ortho-mosaics from Unmanned Aerial Vehicles
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Gómez-Candón, David, Peña Barragán, José Manuel, Castro, Ana Isabel de, and López Granados, Francisca
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UAV ,Image ortho-mosaics ,Prescription Maps - Abstract
Póster presentado en la 9th European Conference on Precision Agriculture ECPA, celebrada en Lleida del 7 al 11 de julio de 2013., The Unmanned Aerial Vehicles (UAVs) are able to take multiple overlapped images of the study site at a very high spatial and temporal resolution. These image series have to be ortho-mosaiced to cover the whole target area. The UAV ortho-mosaics are turning into an important tool for the development of precision agriculture strategies, in particular for accurate prescription maps for site specific weed management (SSWM) at early grow stages, These stages require to discriminate small plants (crop and weeds) that can not be detected using other remote platforms with coarse spatial resolution. Recent studies on crop-weed discrimination at early stages are focused on crop line detection as the first step for a further discrimination of crop and weeds emerged between the crop rows due to plants which are not located in the crop lin e can be assumed as weeds. However, due to the instability of UAV and image distortion, some of the single images which compose the ortho-mosaic do not fit perfectly which would cause no correspondence between crop row alignment of two overlapped images. This study investigated the geometric accuracy differences among ortho-mosaics created from a UAV image series taken at three different flight altitudes (30, 60 and 100 m). The effect of crop line misalignment of the final mosaics was also evaluated according to these flight altitudes.
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- 2013
21. Procedimiento ARIN para la normalización de imágenes remotas multitemporales mediante el uso de cultivos pseudo-invariantes
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Caballero Novella, Juan José, García Torres, Luis, Gómez-Candón, David, and Ministerio de Economía y Competitividad (España)
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ARIN - Abstract
Trabajo presentado en la Conferencia ESRI España 2013, celebrada en Madrid el 1 y 2 de octubre., Contiene: 1) Las imágenes multitemporales requieren calibración/normalización. 2) Correcciones radiométricas absolutas (ARC) vs. Normalización radiométrica relativa (RRN) 3) ARIN® procedimiento & software 4) ARIN® contribuye a la normalización de imágenes remotas relativas a entornos agrícolas. 5) Proyectos, publicaciones, registro y patente
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- 2013
22. Procedimiento CROPCLASS® de clasificación de cultivos en imágenes remotas a nivel parcela para su uso en el censo agrícola
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Caballero Novella, Juan José, García Torres, Luis, Gómez-Candón, David, and Ministerio de Economía y Competitividad (España)
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CROPCLASS - Abstract
Trabajo presentado en la ESRI EUROPEAN USER CONFERENCE, celebrada en Madrid del 2 al 3 de octubre de 2013, A methodology to isolate semi-automatically agrarian parcels from remotely sensed images and classifying their cropping systems and other land uses is described. This is achieved throughout CROPCLASS® software, which is written in IDL® and works as an “add-on” of ENVI®. Main steps are: a) parcels individualization, b) spectral band and vegetative indices calculation for each parcel; and c) cropping systems classification. We have validated this procedure using a series GeoEye-1 satellite images taken over Southern of Spain. The classification of cropping systems for each parcel was executed using CRT Decision Trees analysis. Traditionally, agricultural land use information is updated routinely in many cropland regions in USA and Europe through farmer communications or ground visit of administrative inspectors, which is tedious, time consuming, and therefore economically expensive. The patented CROPCLASS® procedure (in process) will get census agri-environmental administrative data through multitemporal remote images, therefore avoiding at large extend the traditional methodology., Contenido: 1) Importancia de clasificar los usos de suelo en agricultura (cultivos) 2) Método de clasificación y de diagnóstico de cultivos 3) CROPCLASS-1.0 ® procedimiento & software (+CROPCLASS-2.0 en desarrollo) 4) Futuro y universalización del procedimiento 5) Proyectos, publicaciones, registro y patente
- Published
- 2013
23. Position error of input prescription map delineated from remote images
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García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Gómez-Casero, M. Teresa, Jurado-Expósito, Montserrat, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
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AUGEO - Abstract
Trabajo presentado en la 11th International Conference on Precision Agriculture, celebrada en Indianapolis del 15 al 18 de julio de 2012, Content: 1) Position error of any satellite/airplane/uav images 2) Pro and cons of types of geo-referentiation 3) Input prescription map error from remote images 4) Final comments
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- 2012
24. Procedimiento para la normalización automática de imágenes remotas multitemporales en base a usos de suelo pseudo-invariantes vegetales
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García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
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education ,ARIN - Abstract
Procedimiento para la normalización automática de imágenes remotas multitemporales en base a usos de suelo pseudo-invariantes vegetales. Procedimiento automático para la normalización radiométrica de series de imágenes remotas multitemporales de una misma área o escena geográfica, en base a usos de suelo vegetales pseudo-invariantes que comprende: a) toma de imágenes remotas multiespectrales correspondientes a bandas que se seleccionan del espectro visible o hiperespectrales, b) digitalización o geo-referenciación de una imagen, c) análisis de las imágenes tomadas en a) y selección de parcelas con usos de uno o dos usos de suelo vegetales, d) conformación de parcelas mediante delimitación de regiones de interés en una imagen y su trasposición al resto de la serie de imágenes, e) extracción/determinación de los valores digitales de cada banda espectral de cada imagen en el uso de suelo pseudo-invariante de referencia, f) cálculo de los factores de corrección de cada banda en cada imagen de la serie, g) transformación lineal de cada banda de cada imagen aplicando el antes calculado CF, h) conformación de la imagen normalizada en su conjunto mediante la composición de las bandas antes transformadas linealmente., Consejo Superior de Investigaciones Científicas (España), A1 Solicitud de patente con informe sobre el estado de la técnica
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- 2012
25. Automatic remote image processing for agriculture uses though specific software
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García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Jurado-Expósito, Montserrat, Castro, Ana Isabel de, Peña Barragán, José Manuel, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
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SARI - Abstract
Trabajo presentado en la 11th International Conference on Precision Agriculture, celebrada en Indianapolis del 15 al 18 de julio de 2012, Content 1) Powerful image processing programme (ENVI, ERDAS, others) 2) Complementary/ specific modules are NEEDED for Agriculture/ Precision Agriculture (why?/ which?) 3) Specific ENVI modules (“add-on) developed by IAS-CSIC 3a. Orchards trees assessment (CLUAS®) 3b. Herbaceous crop assessment (SARI®) 3c. Cropping systems classification (CROPCLASS®) and parcel isolation (CROPCLASS++, under development) 3e. Automatic image georeferentiation/ co-registration AUGEO-2.0® 3f. Semi-automatic modules integration (SAMI, under development) 4) AIM: to contribute to the automatic designing of agricultural operations through remote images, ENVI and new specific “ENVI-add-on” 5) Projects, publications, registrations and patents (IAS/ CSIC)
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- 2012
26. Mapeo y cuantificación de las infestaciones de Orobanche crenata en guisantes mediante teledetección
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Gómez-Candón, David, García Torres, Luis, Caballero Novella, Juan José, Gómez-Casero, M. Teresa, Peña Barragán, José Manuel, Jurado-Expósito, Montserrat, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
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SARI - Abstract
Póster presentado en el XIII Congreso Nacional de Malherbología celebrado en La Laguna (Tenerife) en noviembre de 2011., Los jopos (Orobanche crenata Forsk.) son especies parásitas de cultivos leguminosos, muy extendidas en el área mediterránea (García-Torres et al., 1994). La agricultura de precisión trata de determinar y manejar la distribución espacial de factores bióticos, tales como malas hierbas y patógenos, y de factores abióticos y así fundamentar la aplicación de inputs a dosis variables, ajustados a las necesidades de pequeñas aéreas o sub-parcelas. El objetivo de este trabajo es describir brevemente la discriminación de rodales de jopos en el cultivo de guisante (Pisum sativum L.) mediante imágenes remotas multiespectrales y su manejo de precisión mediante el software SARI® (Sectioning and Assessment of Remote Images) un módulo complementario de ENVI® que divide y cuantifica la imagen de una parcela en sub-parcelas., Esta investigación se ha financiado en parte a través de los proyectos AGL2007-60926 (FEDER) y AGL2010-15506 (FEDER).
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- 2011
27. Caracterización espectral de crucíferas en cultivos de invierno aplicando redes neuronales
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Gómez-Casero, M. Teresa, López Granados, Francisca, Castro, Ana Isabel de, Peña Barragán, José Manuel, Gómez-Candón, David, Caballero Novella, Juan José, García Torres, Luis, and Jurado-Expósito, Montserrat
- Abstract
Póster presentado en el XIII Congreso Nacional de Malgerbología, celebrado en San Cristóbal de la Laguna del 22 al 24 de noviembre de 2011.
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- 2011
28. Teledetección multiespectral de malas hierbas en fases tempranas: un desafío agronómico transversal
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López Granados, Francisca, Jurado-Expósito, Montserrat, Peña Barragán, José Manuel, Gómez-Casero, M. Teresa, Castro, Ana Isabel de, Caballero Novella, Juan José, Gómez-Candón, David, and García Torres, Luis
- Abstract
Ponencia presentada en el XIII Congreso de la Sociedad Española de Malherbología celebrado en San Cristobal de la Laguna (España) del 22 al 24 de noviembre 2011
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- 2011
29. Automatic image processing for agriculture through specific ENVI modules (add-on)
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García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
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SARI ,AUGEO ,CROPCLASS - Abstract
Trabajo presentado en la ESRI EUROPEAN USER CONFERENCE, celebrada en Madrid del 26 al 28 de octubre de 2011, Precision agriculture takes into account the spatial variability of biotic factors (weeds, pathogens) and of abiotic factors (nutrients, water content), and it uses diverse technologies to apply fertilisers, pesticides or other inputs at variable rates, fitted to the needs of each small-defined area (“micro-plot”). The economic and environmental benefits of precision agriculture are widely accepted. Remote sensing could become an important tool in precision agriculture applications only if specific modules are developed to automate image processing. The aim of this presentation is to outline the contribution of our research group in the development of ENVI® add-on for precision agriculture, as follows: 1) CLUAS®, for clustering and assessment orchards units; 2) SARI®, for sectioning and assessment images of annual crops; 3) AUGEO-2.0®, to increase the image georeferencing accuracy; and 4) CROPCLASS®, to isolate and assess individual agriculture plots before precision processing. These modules are free for research groups upon request., Content: 1) ENVI, a powerful image processing software 2) Complementary ENVI modules are needed for Agriculture/ Precision Agriculture (why?/ which?) 3) Specific ENVI modules (“add-on) developed by IAS-CSIC 3a. Orchards trees assessment (CLUAS®) 3b. Herbaceous crop assessment (SARI®) 3c. Cropping systems classification (CROPCLASS®) and parcel isolation (CROPCLASS++, under development) 3e. Automatic image geo-referencing/ co-registration AUGEO-2.0® 3f. Automatic modules integration (AMI, under development) 4)AIM: we intend the automatic designing of agricultural operations through remote images, ENVI and new specific “ENVI-add-on” 5) Publications, registrations and patents by IAS/ CSIC
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- 2011
30. Mapeo de las infestaciones de jopo (Orobanche crenata) en guisantes (Pisum sativum L) mediante teledetección
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García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Gómez-Casero, M. Teresa, Peña Barragán, José Manuel, Jurado-Expósito, Montserrat, and López Granados, Francisca
- Abstract
Póster presentado en el XIII Congreso Nacional de Malgerbología, "Plantas Invasoras Resistencias a Herbicidas y Detección de Malas Hierbas", celebrado en San Cristóbal de la Laguna del 22 al 24 de noviembre de 2011.
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- 2011
31. Discriminación de Malas Hierbas Crucíferas en Fase Avanzada del Cultivo con Imágenes Satélite de alta resolución espacial
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Castro, Ana Isabel de, Jurado-Expósito, Montserrat, Gómez-Casero, M. Teresa, Peña Barragán, José Manuel, Gómez-Candón, David, Caballero Novella, Juan José, García Torres, Luis, and López Granados, Francisca
- Subjects
Clasificación supervisada ,Manejo supervisado ,Agricultura de precisión ,Índices de vegetación - Abstract
Comunicación presentada en el XIII Congreso Nacional de Malgerbología, celebrado en San Cristóbal de la Laguna del 22 al 24 de noviembre de 2011., El objetivo de este trabajo fue estudiar la discriminación de malas hierbas crucíferas en fase avanzada del cultivo de trigo de invierno mediante imágenes de satélite de alta resolución espacial y técnicas de teledetección, y elaborar mapas de infestaciones de crucíferas. En primavera se tomó una imagen QuickBird en un área de la campiña de Córdoba con predominio de campos de trigo altamente infestados de crucíferas. Para discriminar los rodales de crucíferas en trigo se llevaron a cabo clasificaciones supervisadas utilizando bandas e índices de vegetación. Las bandas Verde, Rojo, NIR y los índices A/V, R/A permitieron discriminar las crucíferas con precisiones superiores al 85 % en la mayoría de los casos estudiados. Los resultados muestran la enorme potencialidad de estas imágenes para la discriminación y mapeo de malas hierbas crucíferas, así como para el diseño de estrategias de control de precisión en post-emergencia a escalas mayores de las los que permiten las imágenes aéreas., Investigación parcialmente financiada por MICINN (FEDER, AGL-2008-04670-CO3-03), y CSIC (FEDER, A.I. de Castro programa JAEPre; Peña-Barragán y Gómez-Casero programa JAEDoc).
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- 2011
32. Evaluation of aerial and QuickBird images for mapping cruciferous weeds
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Castro, Ana Isabel de, Jurado-Expósito, Montserrat, Gómez-Casero, M. Teresa, Gómez-Candón, David, Caballero Novella, Juan José, and López Granados, Francisca
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Site-specific weed management ,Aerial images ,Supervised classifications - Abstract
Ponencia presentada en la 8th European Conference on Precision Agriculture, celebrada en Praga del 11 al 14 de julio de 2011., Cruciferous weeds are very competitive broadleaf species and frequently infest cereal and legume crops. These weeds seriously impair crop development and cause high yield losses. Herbicides are commonly applied over an entire agricultural field although weeds are spatially distributed in patches. To reduce the herbicide use by applying them only where weeds patches occur, it is necessary to develop accurate weed maps., This research was partially funded by the Spanish Ministry of Science and Innovation (FEDER, R+D project AGL-2008-04670-CO3-03). The research of Ana Isabel de Castro was supported by CSIC-JAEPre-Predoctoral Program (also co-financed by FEDER funds).
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- 2011
33. How the spatial resolution can affect the quality of mosaics and assessment of optimum number of tie points necessary to obtain good quality images
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Gómez-Candón, David, Labbé, Sylvain, Jurado-Expósito, Montserrat, Peña Barragán, José Manuel, Rabatel, Gilles, and López Granados, Francisca
- Abstract
Ponencia presentada en la First International Workshop On Robotics And Associated High Technologies And Equipment For Agriculture, celebrado en Montpellier (Francia) el 9 de septiembre de 2011., Orthorectification and mosaicing are two important steps in the design of input application strategies for precision agriculture. Images with low spatial resolution and big georreferencing errors are not useful to obtain good quality maps. In this paper we tried to compare the usefulness between two different pieces of software for orthorectificacion and mosaicing of remote images. Furthermore, we studied the spatial resolution requirements and minimum number of tie points/GCPs needed to obtain good quality orthomosaics. These orthomosaics have to be ready to be used to detect weeds in crops and to obtain high precision prescription maps., The research leading to these results has received funding from the European Union’s Seventh Framework Programme [FP7/2007-2013] under Grant Agreement nº 245986.
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- 2011
34. Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology
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García-Torres, Luis, primary, Caballero-Novella, Juan J., additional, Gómez-Candón, David, additional, and Peña, José Manuel, additional
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- 2015
- Full Text
- View/download PDF
35. Manejo de imágenes remotas en agricultura de precisión
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García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Peña Barragán, José Manuel, Jurado-Expósito, Montserrat, Castillejo González, Isabel L., García-Ferrer, Alfonso, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
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Georeferenciación de precisión ,SARI ,Seccionamiento de imágenes ,“Add-on” software ,CLUAS ,Manejo micro-parcelas ,Mapas de prescripción/ tratamientos - Abstract
Póster presentado en las III Jornadas de Agricultura de Precisión, celebradas en Évora, del 2 al 3 de Marzo 2010, Se indican los principales procesos de imágenes remotas en agricultura de precisión, así como la contribución de nuestro grupo de investigación a la necesaria automatización de dichos procesos mediante el desarrollo de programas informáticos específicos.
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- 2010
36. Method for automatic standardization of multitemporal remote images on the basis of vegetative pseudo-invariant soil uses
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García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, López Granados, Francisca, García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, and López Granados, Francisca
- Abstract
[EN] Automatic method for the radiometric standardization of series of multitemporal remote images of one and the same geographical scene or area, on the basis of vegetative pseudo-invariant soil uses, which comprises: a) the capture of multispectral remote images corresponding to bands selected in the visible or hyperspectral spectrum, b) the digitization or georeferencing of an image, c) analysis of the images captured in a) and selection of parcels using one or two vegetative soil uses, d) the formation of parcels by means of the definition of regions of interest in an image and the transposition thereof to the rest of the image series, e) extraction/determination of the digital values of each spectral band of each image in the reference pseudo-invariant soil use, f) calculation of the correction factors of each band in each image in the series, g) linear conversion of each band of each image using the previously calculated CF, h) formation of the standardized image in its entirety by means of the composition of the bands previously converted linearly., [FR] La présente invention concerne un procédé automatique pour la normalisation radiométrique de séries d'images distantes multitemporelles d'une même zone ou d'un même lieu géographique, sur la base d'utilisations de sols végétaux pseudo-invariants qui comprend: a) la capture d'images distantes multispectrales correspondant à des bandes sélectionnées du spectre visible ou hyperspectrales; b) la numérisation ou géolocalisation d'une image; c) l'analyse des images prises en a) et la sélection de parcelles présentant une ou deux utilisations de sols végétaux; d) la configuration de parcelles par délimitation de région d'intérêt dans une image et leur transposition au reste de la série d'images; e) l'extraction/détermination des valeurs numériques de chaque bande spectrale de chaque image de l'utilisation du sol pseudo-invariant de référence; f) le calcul des facteurs de correction de chaque bande dans chaque image de la série; g) la transformation linéaire de chaque bande de chaque image en appliquant le calcul CF précédent; h) la configuration de l'image normalisée dans son ensemble au moyen de la composition des bandes ayant été auparavant transformées linéairement., [ES] Procedimiento automático para la normalización radiométrica de series de imágenes remotas multitemporales de una misma área o escena geográfica, en base a usos de suelo vegetales pseudo- invariantes que comprende: a) toma de imágenes remotas multiespectrales correspondientes a bandas que se seleccionan del espectro visible o hiperespectrales, b) digitalización o geo-referenciación de una imagen, c) análisis de las imágenes tomadas en a) y selección de parcelas con usos de uno o dos usos de suelo vegetales, d) conformación de parcelas mediante delimitación de regiones de interés en una imagen y su trasposición al resto de la serie de imágenes, e) extracción/determinación de los valores digitales de cada banda espectral de cada imagen en el uso de suelo pseudo-invariante de referencia, f) cálculo de los factores de corrección de cada banda en cada imagen de la serie, g) transformación lineal de cada banda de cada imagen aplicando el antes calculado CF, h) conformación de la imagen normalizada en su conjunto mediante la composición de las bandas antes transformadas linealmente.
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- 2014
37. Procedimiento para la normalización automática de imágenes remotas multitemporales en base a usos de suelo pseudo-invariantes vegetales
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García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, López Granados, Francisca, García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, and López Granados, Francisca
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- 2014
38. Procedimiento para la normalización automática de imágenes remotas multitemporales en base a usos de suelo pseudo-invariantes vegetales
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Ministerio de Economía y Competitividad (España), García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, López Granados, Francisca, Ministerio de Economía y Competitividad (España), García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Peña Barragán, José Manuel, and López Granados, Francisca
- Abstract
Procedimiento para la normalización automática de imágenes remotas multitemporales en base a usos de suelo pseudo-invariantes vegetales. Procedimiento automático para la normalización radiométrica de series de imágenes remotas multitemporales de una misma área o escena geográfica, en base a usos de suelo vegetales pseudo-invariantes que comprende: a) toma de imágenes remotas multiespectrales correspondientes a bandas que se seleccionan del espectro visible o hiperespectrales, b) digitalización o geo-referenciación de una imagen, c) análisis de las imágenes tomadas en a) y selección de parcelas con usos de uno o dos usos de suelo vegetales, d) conformación de parcelas mediante delimitación de regiones de interés en una imagen y su trasposición al resto de la serie de imágenes, e) extracción/determinación de los valores digitales de cada banda espectral de cada imagen en el uso de suelo pseudo-invariante de referencia, f) cálculo de los factores de corrección de cada banda en cada imagen de la serie, g) transformación lineal de cada banda de cada imagen aplicando el antes calculado CF, h) conformación de la imagen normalizada en su conjunto mediante la composición de las bandas antes transformadas linealmente.
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- 2014
39. CROPCLASS-2.0 software for census parcel cropping systems classification from multitemporal remote imagery
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Ministerio de Economía y Competitividad (España), García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Peña Barragán, José Manuel, López Granados, Francisca, Jurado-Expósito, Montserrat, Ministerio de Economía y Competitividad (España), García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Peña Barragán, José Manuel, López Granados, Francisca, and Jurado-Expósito, Montserrat
- Abstract
A research group of the Institute for Sustainable Agriculture (CSIC, Cordoba, Spain) has developed an original procedure to classified crops from multitemporal remote sensing images, named CROPCLASS, to be used in agricultural and forestry scenes. The procedure is governed by registered CROPCLASS-2.0 software (1), which executes it semi-automatically. CROPCLASS procedure was patented (2) and it is described in a recent publication (3). Before developing CROPCLASS, the methodological approaches for cropping systems classification from remote sensing images widely varied among authors and required tremendous effort in image processing., For each geographical area CROPCLASS consists of: 1) a definition of census parcels through vector files in the images; 2) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; 3) the conformation of a matrix data (MD) of the extracted information; 4) the classification of MD through decision trees (DT), which provide a Structured Query Language (SQL) crop predictive model. The procedure is also based on preliminary land-use ground-truth work in a reduced number of parcels at least the first year of study. Crop SQL predictive models can be used to classify unidentified parcels land uses from the same area where the images were taken to generate the model., CROPCLASS procedure meets additional advantages as follows. First, the census parcel is the unit for most administrative actions and CROPCLASS provide record for each census parcel. Second, administrations require a defined crop classification method, almost fully relying on remote sensed images automatically or semi-automatically executed, consistently reducing the ground-visit work as much as possible. Third, the predictive models for each crop/cropping system are likely to be used for the same area in subsequent years if the images were taken on about the same dates. This use is based on the true assumption that in each geographical area, the diversity of the crops and the crop calendar remain about the same throughout the years. The phenology or crop growth stages will approximately coincide, as the images were taken at about the same time in different years; therefore, the predictive models that were determined for one year with similar timings could tentatively be used in subsequent years., Implementing the CROPCLASS procedure through conventional image processing is time consuming and requires computer language skills. The software CROPCLASS-2.0® can be implemented for any agricultural region semi-automatically, in an economically feasible manner. CROPCLASS-2.0 software is available at the digital repository (http://dx.doi.org/10.5061/dryad.j958j) only for research and academic purposes; furthermore its authorship should be mentioned with bold characters.
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- 2014
40. Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features
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Ministerio de Economía y Competitividad (España), García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, Castro, Ana Isabel de, Ministerio de Economía y Competitividad (España), García Torres, Luis, Caballero Novella, Juan José, Gómez-Candón, David, and Castro, Ana Isabel de
- Abstract
A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified. © 2014 Garcia-Torres et al.
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- 2014
41. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat
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Ministerio de Economía y Competitividad (España), European Commission, Consejo Superior de Investigaciones Científicas (España), Gómez-Candón, David, Castro, Ana Isabel de, López Granados, Francisca, Ministerio de Economía y Competitividad (España), European Commission, Consejo Superior de Investigaciones Científicas (España), Gómez-Candón, David, Castro, Ana Isabel de, and López Granados, Francisca
- Abstract
High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution re
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- 2014
42. Caracterización cuantitativa del olivar mediante teledetección
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Gómez-Candón, David, López Granados, Francisca, Caballero Novella, Juan José, Jurado-Expósito, Montserrat, García Torres, Luis, and Ministerio de Economía y Competitividad (España)
- Subjects
CLUAS - Abstract
Póster presentado en las XIV SIMPOSIUM CIENTÍFICO-TÉCNICO DEL ACEITE DE OLIVA EXPOLIVA, celebrado en Jaén, en Mayo de 2009, Las características agronómicas y ambientales del olivar pueden ser medidas automáticamente mediante el empleo de imágenes remotas utilizando un software denominado Clustering Assessment (CLUAS®). El objetivo de esta comunicación es describir el procedimiento operacional de CLUAS y mostrar ejemplos de la información que proporciona del olivar.
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- 2009
43. SARI®, computer software for sectioning and assessment remote images for precision agriculture strategies
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García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Peña Barragán, José Manuel, Jurado-Expósito, Montserrat, Castillejo González, Isabel L., García-Ferrer, Alfonso, López Granados, Francisca, and Ministerio de Economía y Competitividad (España)
- Subjects
SARI - Abstract
Póster presentado en el 8th EUROPEAN CONGRESS ON PRECISION AGRICULTURE celebrado en Wageningen (Netherlands) en julio de 2009., The software named Sectioning and Assessment of Remote Images® (SARI®) has been developed to implement precision agriculture strategies through remote sensing imagery. The aim of this work is to briefly describe the accomplishment of SARI® software in the site-specific management of a peas (Pisum sativum L.) field partly infested by Orobanche crenata Forsk. through remotely sensed imagery.
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- 2009
44. CLUAS® : A software for managing remotely sensed imagery of orchard plantations for precision agriculture
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García Torres, Luis, Peña Barragán, José Manuel, Gómez-Candón, David, López Granados, Francisca, Jurado-Expósito, Montserrat, and Ministerio de Economía y Competitividad (España)
- Subjects
CLUAS - Abstract
Trabajo presentado en la 9th International Conference on Precision Agriculture celebrada en julio de 2008 en Denver, Colorado
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- 2008
45. Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features
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Garcia-Torres, Luis, primary, Caballero-Novella, Juan J., additional, Gómez-Candón, David, additional, and De-Castro, Ana Isabel, additional
- Published
- 2014
- Full Text
- View/download PDF
46. Procedimiento ARIN para la normalización de imágenes remotas multitemporales mediante el uso de cultivos pseudo-invariantes
- Author
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Ministerio de Economía y Competitividad (España), Caballero Novella, Juan José, García Torres, Luis, Gómez-Candón, David, Ministerio de Economía y Competitividad (España), Caballero Novella, Juan José, García Torres, Luis, and Gómez-Candón, David
- Published
- 2013
47. Procedimiento CROPCLASS® de clasificación de cultivos en imágenes remotas a nivel parcela para su uso en el censo agrícola
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Ministerio de Economía y Competitividad (España), Caballero Novella, Juan José, García Torres, Luis, Gómez-Candón, David, Ministerio de Economía y Competitividad (España), Caballero Novella, Juan José, García Torres, Luis, and Gómez-Candón, David
- Abstract
A methodology to isolate semi-automatically agrarian parcels from remotely sensed images and classifying their cropping systems and other land uses is described. This is achieved throughout CROPCLASS® software, which is written in IDL® and works as an “add-on” of ENVI®. Main steps are: a) parcels individualization, b) spectral band and vegetative indices calculation for each parcel; and c) cropping systems classification. We have validated this procedure using a series GeoEye-1 satellite images taken over Southern of Spain. The classification of cropping systems for each parcel was executed using CRT Decision Trees analysis. Traditionally, agricultural land use information is updated routinely in many cropland regions in USA and Europe through farmer communications or ground visit of administrative inspectors, which is tedious, time consuming, and therefore economically expensive. The patented CROPCLASS® procedure (in process) will get census agri-environmental administrative data through multitemporal remote images, therefore avoiding at large extend the traditional methodology.
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- 2013
48. ARIN software para la normalización automática de imágenes remotas multi-temporales en base a usos de suelo vegetales pseudo-invariantes
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Ministerio de Economía y Competitividad (España), García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Jurado-Expósito, Montserrat, Peña Barragán, José Manuel, López Granados, Francisca, Ministerio de Economía y Competitividad (España), García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Jurado-Expósito, Montserrat, Peña Barragán, José Manuel, and López Granados, Francisca
- Abstract
A research group of the Institute for Sustainable Agriculture (CSIC, Cordoba, Spain) has developed an original radiometric normalization procedure for multitemporal remote images, named ARIN, to be used in agricultural and forestry scenes. ARIN is governed by registered ARIN software, which executes it semi-automatically, at short-time, in an economically feasible manner. ARIN procedure was patented and it is described in a recent publication. The original problem to overcome is that remote sensing observations are usually instantaneous and are affected by many factors, such as atmospheric conditions, sun angle, and viewing angle, dynamic changes in the soil and plant–atmosphere system, and changes in the sensor calibration over time. So, the goal of radiometric corrections is to remove or compensate for all of the above effects. Absolute radiometric corrections (ARC) make it possible to relate the digital counts in satellite image data to radiance at the surface of the Earth. Relative radiometric normalization (RRN) based on the radiometric information intrinsic to the images themselves is an alternative whenever absolute surface radiances are not required. In remote sensing multitemporal images are required for most agricultural, forestry and environmental parameters assessment such as cover change detection, mosaicking and tracking vegetation indices over time, supervised and unsupervised land cover classification, crop nutrient status level, weed or disease patches, water stress, among many others. Furthermore, the key point is that parameters assessment from multitemporal images require first the image calibration or normalization to get contrastable/ comparable results. ARIN is much easier to be implemented than the absolute calibration methods and normalization procedures currently available, which uses physical parameters derived from the solar position and/ or weather conditions at the time of image taking. For example, ARIN was slightly more efficient than
- Published
- 2013
49. ARIN ® procedure for the normalization of multitemporal remote images through vegetative pseudo-invariant features
- Author
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Ministerio de Economía y Competitividad (España), García Torres, Luis, Gómez-Candón, David, Caballero Novella, Juan José, Ministerio de Economía y Competitividad (España), García Torres, Luis, Gómez-Candón, David, and Caballero Novella, Juan José
- Abstract
An Automatic Relative Image Normalization (ARIN®) method was developed to normalize multitemporal remote images based in vegetative pseudo-invariant features (VPIFs), as following: 1) defining the same parcel for each selected VPIF in each multitemporal image; 2) extracting the VIPF spectral bands data for each image; 3) calculating the correction factor (CF) for each image band to fit it to the same expected values, normally for each band the average of the series; 4) obtaining the normalized images by transforming each original image band through the corresponding CF linear functions. ARIN® software was developed to automatically achieve the previously described procedure. We have validated ARIN using a series of six GeoEye-1 satellite images taken over the same Southern of Spain scene in 2010, from early April to October, at about 4 weeks interval. Three VPIFs were chosen: citrus orchards (CIT), riparian trees (POP) and Mediterranean forest trees (MFO). The VPIFs spectral band correction factors (CFs) to implement the ARIN linear normalization procedure largely varied among spectral bands for any given image and among images for any given spectral band. The correlation coefficients between the CFs among VPIFs for any spectral band and overall all bands are over 0.83 and significant at P=0.95 or higher. For the ARIN normalized images, the range and standard deviation of any spectral bands and vegetation indices values were considerably reduced as compared to the original images, regardless the VPIF or the combination of VPIFs selected for normalization, which proves the method efficacy. Moreover, ARIN method was easier and efficient than the absolute calibration QUAC method, and somehow similarly efficient as the highly tunable FLAASH, in which solar position and weather calibration parameters are required.
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- 2013
50. Semiautomatic detection of artificial terrestrial targets for remotely sensed image georeferencing
- Author
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Ministerio de Economía y Competitividad (España), Gómez-Candón, David, López Granados, Francisca, Caballero Novella, Juan José, Peña Barragán, José Manuel, Gómez-Casero, M. Teresa, Jurado-Expósito, Montserrat, García Torres, Luis, Ministerio de Economía y Competitividad (España), Gómez-Candón, David, López Granados, Francisca, Caballero Novella, Juan José, Peña Barragán, José Manuel, Gómez-Casero, M. Teresa, Jurado-Expósito, Montserrat, and García Torres, Luis
- Abstract
Georeferencing of remote imagery with high spatial resolution can be achieved using the semiAUtomatic GEOreferencing (AUGEO) system which is based on artificial terrestrial targets (ATTs) and software AUGEO-2.0 for location and georeferencing. The aim of this letter is to describe the system and validate it. The ATTs consist of colored hexagonal tarps 0.25–1.0 m in diameter, placed on the ground and georeferenced. The proposed software works as an add-on of Environment for Visualizing Images and was able to locate the ATTs (isolated or disposed in associated couples) in remote images based on its spectral band specificity. To validate the AUGEO system, ATTs were placed on the ground, and remote images were taken from airplanes and unmanned aerial vehicles several times throughout the year at two locations in Southern Spain in 2008. Three variables were considered to study ATT detection accuracy: 1) ATT size; 2) ATT color; and 3) distance between ATTs when they were coupled in pairs. The averaged accuracy for the coupled 1-m red ATTs (separated by 2.5 m) was 95.9%. As the ATT size decreased, the accuracy generally decreased, regardless of the color of the ATTs. Results from coupled analysis show that ATT detection increased as the distance between the ATTs decreased. The proposed system required less time than conventional georeferencing work and allowed the georeferencing of images that do not contain recognizable ground control points. This also contributed to the site-specific management of agricultural plots through remote sensing, which required high-spatial-resolution and accurate georeferenced images.
- Published
- 2013
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