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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.

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.

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%.

[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.

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.

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.

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.

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

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.

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

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.

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.

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.

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.

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.

Software was developed to spatially assess key crop characteristics from remotely sensed imagery. Sectioning and Assessment of Remote Images (SARI ®), written in IDL ® works as an add-on to ENVI ®, has been developed to implement precision agriculture strategies. SARI ® splits field plot images into grids of rectangular >micro-images> or >micro-plots>. The micro-plot length and width were defined as multiples of the image spatial resolution. SARI ® calculates different indicators for each micro-plot, including the integrated pixel digital values. Studies on weed patches were done with SARI ® using ground-truth data and remote images of two wheat plots infested with Avena sterilis at LaFloridaII and Navajas (Southern Spain). Patches of A. sterilis represented 47.5 and 19.2% of the field areas at the two locations, respectively; the infested areas were a combination of a few large and several small patches. At LaFloridaII, 2.1% of all patches were >500 m 2 and 55.0% of all patches were smaller than 10 m 2. Based on ground-truth weed abundance data, SARI ® output includes geo-referenced and visual herbicide prescription maps, which could be used with variable-rate application equipment. © 2011 Springer Science+Business Media, LLC.

Avena sterilis weed pressure categories can be discriminated in wheat through remote images taken at late stages of wheat senescence. Site-specific image processing was achieved with SARI ®, an add-on software program for ENVI ® developed to implement precision agriculture. Using the SARI software and crop-weed competition and economic models, the precision yield losses for each micro-plot can be estimated and herbicide prescription maps obtained. Simulation studies on control indicators and herbicide use efficiency were undertaken using real-time ground data and remote images of two wheat plots infested with Avena sterilis at LaFloridaII and Navajas (Southern Spain). The simulation indicated that precision application of herbicides would produce higher overall herbicide savings (OHS) compared to broadcast applications but would vary depending on the level of weed infestation, the decision making criteria (DMC) of applying herbicide above a weed infestation level and the size of the spray grid considered. For example, for areas with low levels of infestation (around 15%), the OHS was 20, 44, 81 and 90% for a DMC of 0, 10, 20 and 30%, respectively. SARI ® also estimates the overall herbicide application efficiency (OHAE), a key agro-environmental index to estimate the efficiency of herbicide applications in weedy areas and the lack of herbicide applications in weed-free areas. Ideally, the OHAE is equal to 1 if weed control is complete and herbicide applications in weed-free areas are not necessary. The OHAE index is influenced by the size of the micro-plot and decision-making herbicide application criteria (DMC). The OHAE values increased as the size of the micro-plot decreased, regardless of the intensity of the weed infestation. For example, micro-plots of 20 × 6 m, 5 × 3 m and 1.2 × 1.5 m had OHAE values of 0.27, 0.57 and 0.76, for areas of low infestation, when averaged over the DMC. Generally, the OHAE values increased as the size of the micro-plot decreased re

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.

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.

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.

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.

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.

The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2. 0® software. This works as an add-on of ENVI® for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5-1.5 m, and the UAV images showed a mean accuracy of 1.0-4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., <1.5 m pixel-1), accurately geo-referenced (errors <1-2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing r

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.

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.

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.