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Dr. Rocio Ballesteros
Castilla-La Mancha University

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0 Cereal
0 Precision Agriculture
0 Remote Sensing
0 crop production
0 Unmanned Aerial Vehicle

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Remote Sensing
Unmanned Aerial Vehicle
Precision Agriculture
Cereal
crop production

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Journal article
Published: 17 July 2021 in Computers and Electronics in Agriculture
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In a world where the availability of water is decreasing, its use must be thoroughly optimized. Irrigated agricultural systems, as the main user of the planet's fresh water, must improve its management and save as much of this scarce resource as possible. However, the heterogeneity of these complex systems that are frequently organized in water user associations makes the daily management of this resource difficult. The new information and communication technologies as well as artificial intelligence techniques help to understand the heterogeneity of these complex systems, making it possible to better manage them. However, the implementation of a tool with these characteristics requires a large and heterogeneous amount of data from different sources. Thus, in this work, a new tool for managers based on water demand forecasting at the field scale for the week ahead has been developed. This tool, WatergyForecaster, combines artificial intelligence techniques, satellite remote sensing (Sentinel 2) and open source climate data to automatically build a water forecasting model at the farm scale for a week in advance. WatergyForecaster, developed in Python, was applied to a real water user association (WUA), obtaining a set of optimum models with an accuracy that ranged from 17% to 19% and representativeness higher than 80%.

ACS Style

Rafael Gonzalez Perea; Rocío Ballesteros; José F. Ortega; Miguel Ángel Moreno. Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms. Computers and Electronics in Agriculture 2021, 188, 106327 .

AMA Style

Rafael Gonzalez Perea, Rocío Ballesteros, José F. Ortega, Miguel Ángel Moreno. Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms. Computers and Electronics in Agriculture. 2021; 188 ():106327.

Chicago/Turabian Style

Rafael Gonzalez Perea; Rocío Ballesteros; José F. Ortega; Miguel Ángel Moreno. 2021. "Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms." Computers and Electronics in Agriculture 188, no. : 106327.

Journal article
Published: 08 June 2021 in American Journal of Enology and Viticulture
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Multispectral and conventional cameras, RGB (red, green, blue) imager, onboard unmanned aerial vehicles (UAVs) provide very high spatial, temporal, and spectral resolution data. To evaluate the capacity of these techniques to assess vineyard water status, we carried out a study in a cv. Monastrell vineyard located in southeastern Spain in 2018 and 2019. Several irrigation strategies were applied, including different water quality and quantity regimes. Flights were performed using conventional and multispectral cameras mounted on the UAV throughout the growth cycle. Several visible and multispectral vegetation indices (VIs) were determined from the images with only vegetation (without soil and shadows, among others). Stem water potential was measured by pressure chamber and the water stress integral (Sψ) was obtained along the season. Simple linear regression models that used VIs and green cover canopy (GCC) to predict Sψ were tested. The results indicate that visible VIs best correlated with Sψ. The green leaf index (GLI), visible atmospherically resistance index (VARI), and GCC showed the best fits in 2018, with R2 = 0.8, 0.72, and 0.73, respectively. When the best model developed with the 2018 data was applied to the 2019 data set, the model fit poorly. This suggests that on-ground measurements of vine stress must be taken each growing season to redevelop a model that predicts water stress from UAV based imaging.

ACS Style

Patricia López-García; Diego S. Intrigliolo; Miguel A. Moreno; Alejandro Martínez-Moreno; Jose F. Ortega; Eva P. Pérez-Álvarez; Rocío Ballesteros. Assessment of Vineyard Water Status by Multispectral and RGB Imagery Obtained from an Unmanned Aerial Vehicle. American Journal of Enology and Viticulture 2021, 1 .

AMA Style

Patricia López-García, Diego S. Intrigliolo, Miguel A. Moreno, Alejandro Martínez-Moreno, Jose F. Ortega, Eva P. Pérez-Álvarez, Rocío Ballesteros. Assessment of Vineyard Water Status by Multispectral and RGB Imagery Obtained from an Unmanned Aerial Vehicle. American Journal of Enology and Viticulture. 2021; ():1.

Chicago/Turabian Style

Patricia López-García; Diego S. Intrigliolo; Miguel A. Moreno; Alejandro Martínez-Moreno; Jose F. Ortega; Eva P. Pérez-Álvarez; Rocío Ballesteros. 2021. "Assessment of Vineyard Water Status by Multispectral and RGB Imagery Obtained from an Unmanned Aerial Vehicle." American Journal of Enology and Viticulture , no. : 1.

Journal article
Published: 10 May 2021 in Agronomy
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The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

ACS Style

Rocío Ballesteros; Miguel Moreno; Fellype Barroso; Laura González-Gómez; José Ortega. Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products. Agronomy 2021, 11, 940 .

AMA Style

Rocío Ballesteros, Miguel Moreno, Fellype Barroso, Laura González-Gómez, José Ortega. Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products. Agronomy. 2021; 11 (5):940.

Chicago/Turabian Style

Rocío Ballesteros; Miguel Moreno; Fellype Barroso; Laura González-Gómez; José Ortega. 2021. "Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products." Agronomy 11, no. 5: 940.

Journal article
Published: 12 January 2021 in Remote Sensing
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The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

ACS Style

Amal Chakhar; David Hernández-López; Rocío Ballesteros; Miguel A. Moreno. Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data. Remote Sensing 2021, 13, 243 .

AMA Style

Amal Chakhar, David Hernández-López, Rocío Ballesteros, Miguel A. Moreno. Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data. Remote Sensing. 2021; 13 (2):243.

Chicago/Turabian Style

Amal Chakhar; David Hernández-López; Rocío Ballesteros; Miguel A. Moreno. 2021. "Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data." Remote Sensing 13, no. 2: 243.

Journal article
Published: 28 May 2020 in Remote Sensing
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The launch of Sentinel-2A and B satellites has boosted the development of many applications that could benefit from the fine resolution of the supplied information, both in time and in space. Crop classification is a necessary task for efficient land management. We evaluated the benefits of combining Landsat-8 and Sentinel-2A information for irrigated crop classification. We also assessed the robustness and efficiency of 22 nonparametric classification algorithms for classifying irrigated crops in a semiarid region in the southeast of Spain. A parcel-based approach was proposed calculating the mean normalized difference vegetation index (NDVI) of each plot and the standard deviation to generate a calibration-testing set of data. More than 2000 visited plots for 12 different crops along the study site were utilized as ground truth. Ensemble classifiers were the most robust algorithms but not the most efficient because of their low prediction rate. Nearest neighbor methods and support vector machines have the best balance between robustness and efficiency as methods for classification. Although the F1 score is close to 90%, some misclassifications were found for spring crops (e.g., barley, wheat and peas). However, crops with quite similar cycles could be differentiated, such as purple garlic and white garlic, showing the powerfulness of the developed tool.

ACS Style

Amal Chakhar; Damián Ortega-Terol; David Hernández-López; Rocío Ballesteros; José F. Ortega; Miguel A. Moreno. Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data. Remote Sensing 2020, 12, 1735 .

AMA Style

Amal Chakhar, Damián Ortega-Terol, David Hernández-López, Rocío Ballesteros, José F. Ortega, Miguel A. Moreno. Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data. Remote Sensing. 2020; 12 (11):1735.

Chicago/Turabian Style

Amal Chakhar; Damián Ortega-Terol; David Hernández-López; Rocío Ballesteros; José F. Ortega; Miguel A. Moreno. 2020. "Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data." Remote Sensing 12, no. 11: 1735.

Journal article
Published: 18 April 2020 in Computers and Electronics in Agriculture
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ACS Style

R. González Perea; M.A. Moreno; J.F. Ortega; A. Del Castillo; R. Ballesteros. Dynamic Simulation Tool of fertigation in drip irrigation subunits. Computers and Electronics in Agriculture 2020, 173, 105434 .

AMA Style

R. González Perea, M.A. Moreno, J.F. Ortega, A. Del Castillo, R. Ballesteros. Dynamic Simulation Tool of fertigation in drip irrigation subunits. Computers and Electronics in Agriculture. 2020; 173 ():105434.

Chicago/Turabian Style

R. González Perea; M.A. Moreno; J.F. Ortega; A. Del Castillo; R. Ballesteros. 2020. "Dynamic Simulation Tool of fertigation in drip irrigation subunits." Computers and Electronics in Agriculture 173, no. : 105434.

Journal article
Published: 12 October 2019 in Remote Sensing
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The 3D digital characterization of vegetation is a growing practice in the agronomy sector. Precision agriculture is sustained, among other methods, by variables that remote sensing techniques can digitize. At present, laser scanners make it possible to digitize three-dimensional crop geometry in the form of point clouds. In this work, we developed several methods for calculating the volume of vine wood, with the final intention of using these values as indicators of vegetative vigor on a thematic map. For this, we used a static terrestrial laser scanner (TLS), a mobile scanning system (MMS), and six algorithms that were implemented and adapted to the data captured and to the proposed objective. The results show that, with TLS equipment and the algorithm called convex hull cluster, the volumes of a vine trunk can be obtained with a relative error lower than 7%. Although the accuracy and detail of the cloud obtained with TLS are very high, the cost per unit for the scanned area limits the application of this system for large areas. In contrast to the inoperability of the TLS in large areas of terrain, the MMS and the algorithm based on the L1-medial skeleton and the modelling of cylinders of a certain height and diameter have solved the estimation of volumes with a relative error better than 3%. To conclude, the vigor map elaborated represents the estimated volume of each vine by this method.

ACS Style

Ana Del-Campo-Sanchez; Miguel Moreno; Rocio Ballesteros; David Hernandez-Lopez. Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems. Remote Sensing 2019, 11, 2365 .

AMA Style

Ana Del-Campo-Sanchez, Miguel Moreno, Rocio Ballesteros, David Hernandez-Lopez. Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems. Remote Sensing. 2019; 11 (20):2365.

Chicago/Turabian Style

Ana Del-Campo-Sanchez; Miguel Moreno; Rocio Ballesteros; David Hernandez-Lopez. 2019. "Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems." Remote Sensing 11, no. 20: 2365.

Research article
Published: 22 April 2019 in PLOS ONE
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With the increasing competitiveness in the vine market, coupled with the increasing need for sustainable use of resources, strategies for improving farm management are essential. One such effective strategy is the implementation of precision agriculture techniques. Using photogrammetric techniques, the digitalization of farms based on images acquired from unmanned aerial vehicles (UAVs) provides information that can assist in the improvement of farm management and decision-making processes. The objective of the present work is to quantify the impact of the pest Jacobiasca lybica on vineyards and to develop representative cartography of the severity of the infestation. To accomplish this work, computational vision algorithms based on an ANN (artificial neural network) combined with geometric techniques were applied to geomatic products using consumer-grade cameras in the visible spectra. The results showed that the combination of geometric and computational vision techniques with geomatic products generated from conventional RGB (red, green, blue) images improved image segmentation of the affected vegetation, healthy vegetation and ground. Thus, the proposed methodology using low-cost cameras is a more cost-effective application of UAVs compared with multispectral cameras. Moreover, the proposed method increases the accuracy of determining the impact of pests by eliminating the soil effects.

ACS Style

Ana Del-Campo-Sanchez; Rocio Ballesteros; David Hernandez-Lopez; J. Fernando Ortega; Miguel A. Moreno; on behalf of Agroforestry and Cartography Precision Research Group. Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques. PLOS ONE 2019, 14, e0215521 .

AMA Style

Ana Del-Campo-Sanchez, Rocio Ballesteros, David Hernandez-Lopez, J. Fernando Ortega, Miguel A. Moreno, on behalf of Agroforestry and Cartography Precision Research Group. Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques. PLOS ONE. 2019; 14 (4):e0215521.

Chicago/Turabian Style

Ana Del-Campo-Sanchez; Rocio Ballesteros; David Hernandez-Lopez; J. Fernando Ortega; Miguel A. Moreno; on behalf of Agroforestry and Cartography Precision Research Group. 2019. "Quantifying the effect of Jacobiasca lybica pest on vineyards with UAVs by combining geometric and computer vision techniques." PLOS ONE 14, no. 4: e0215521.

Journal article
Published: 12 February 2019 in Agricultural Systems
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Satellite imagery is the foremost source of information to analyze and monitor land covers in several time ranges, especially over large areas. However, it is not always either freely available or easily compatible for the final users due to the different resolutions offered by sensors onboard the satellite platforms. Crop classification is an important task to control and make decisions related to the agricultural practice and its regulation. However, it is not trivial, especially for extensive areas. Thus, this paper proposes a new approach for crop classification in large areas by a combined use of multi-temporal open-source remote sensing data from Sentinel-2 (S2) and Landsat-8 (L8) satellite platforms. Having to deal with different spatial and temporal resolutions, special spatial regions (called Tuplekeys) were created within a local nested grid to allow a proper integration between the data of both sensors. Temporal variation of the Normalized Difference Vegetation Index (NDVI) was the chosen input to classify crops. Moreover, due to the massive quantity of data collected, filters considering some agronomic and edaphic criteria were applied with the dual goal of decreasing redundancies and increasing the process efficiency. Out of three different machine learning classifiers analyzed, a plot-based approach was considered for the algorithms calibration while a pixel-based approach was used for the final classification process. The methodology was both tested and validated in the Duero river basin (Spain), 78,859 km2, for the 2017 spring and summer seasons. Finally, classification outputs were analyzed throughout their overall accuracy (OA), not only for the whole basin but also for each of the Tuplekeys so that the OA spatial distribution was evaluated as well. The Ensemble Bagged Trees (EBT) algorithm showed the maximum OA, 87% and 92%, when classifying crops individually (15 classes) and grouped (7 classes), respectively, proving both the accuracy and efficiency of the developed approach.

ACS Style

Laura Piedelobo; David Hernández-López; Rocío Ballesteros; Amal Chakhar; Susana Del Pozo; Diego González-Aguilera; Miguel A. Moreno. Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin. Agricultural Systems 2019, 171, 36 -50.

AMA Style

Laura Piedelobo, David Hernández-López, Rocío Ballesteros, Amal Chakhar, Susana Del Pozo, Diego González-Aguilera, Miguel A. Moreno. Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin. Agricultural Systems. 2019; 171 ():36-50.

Chicago/Turabian Style

Laura Piedelobo; David Hernández-López; Rocío Ballesteros; Amal Chakhar; Susana Del Pozo; Diego González-Aguilera; Miguel A. Moreno. 2019. "Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin." Agricultural Systems 171, no. : 36-50.

Journal article
Published: 01 October 2018 in International Journal of Applied Earth Observation and Geoinformation
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ACS Style

R. Ballesteros; J.F. Ortega; D. Hernandez; A. del Campo; M.A. Moreno. Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring. International Journal of Applied Earth Observation and Geoinformation 2018, 72, 66 -75.

AMA Style

R. Ballesteros, J.F. Ortega, D. Hernandez, A. del Campo, M.A. Moreno. Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring. International Journal of Applied Earth Observation and Geoinformation. 2018; 72 ():66-75.

Chicago/Turabian Style

R. Ballesteros; J.F. Ortega; D. Hernandez; A. del Campo; M.A. Moreno. 2018. "Combined use of agro-climatic and very high-resolution remote sensing information for crop monitoring." International Journal of Applied Earth Observation and Geoinformation 72, no. : 66-75.

Technical note
Published: 15 June 2018 in ISPRS International Journal of Geo-Information
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Proper control and planning of water resource use, especially in those catchments with large surface, climatic variability and intensive irrigation activity, is essential for a sustainable water management. Decision support systems based on useful tools involving main stakeholders and hydrological planning offices of the river basins play a key role. The free availability of Earth observation products with high temporal resolution, such as the European Sentinel-2B, has allowed us to combine remote sensing with cadastral and agronomic data. This paper introduces HidroMap to the scientific community, an open source tool as a geographic information system (GIS) organized in two different modules, desktop-GIS and web-GIS, with complementary functions and based on PostgreSQL/PostGIS database. Through an effective methodology HidroMap allows monitoring irrigation activity, managing unregulated irrigation, and optimizing available fluvial surveillance resources using satellite imagery. This is possible thanks to the automatic download, processing and storage of satellite products within field data provided by the River Surveillance Agency (RSA) and the Hydrological Planning Office (HPO). The tool was successfully validated in Duero Hydrographic Basin along the 2017 summer irrigation period. In conclusion, HidroMap comprised an important support tool for water management tasks and decision making tackled by Duero Hydrographic Confederation which can be adapted to any additional need and transferred to other river basin organizations.

ACS Style

Laura Piedelobo; Damián Ortega-Terol; Susana Del Pozo; David Hernández-López; Rocio Ballesteros; Miguel A. Moreno; José-Luis Molina; Diego González-Aguilera. HidroMap: A New Tool for Irrigation Monitoring and Management Using Free Satellite Imagery. ISPRS International Journal of Geo-Information 2018, 7, 220 .

AMA Style

Laura Piedelobo, Damián Ortega-Terol, Susana Del Pozo, David Hernández-López, Rocio Ballesteros, Miguel A. Moreno, José-Luis Molina, Diego González-Aguilera. HidroMap: A New Tool for Irrigation Monitoring and Management Using Free Satellite Imagery. ISPRS International Journal of Geo-Information. 2018; 7 (6):220.

Chicago/Turabian Style

Laura Piedelobo; Damián Ortega-Terol; Susana Del Pozo; David Hernández-López; Rocio Ballesteros; Miguel A. Moreno; José-Luis Molina; Diego González-Aguilera. 2018. "HidroMap: A New Tool for Irrigation Monitoring and Management Using Free Satellite Imagery." ISPRS International Journal of Geo-Information 7, no. 6: 220.

Article
Published: 15 February 2018 in Precision Agriculture
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Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (\({\text{R}}_{\text{adj}}^2\)) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.

ACS Style

Rocio Ballesteros; Jose Fernando Ortega; David Hernández-López; Miguel Angel Moreno. Onion biomass monitoring using UAV-based RGB imaging. Precision Agriculture 2018, 19, 840 -857.

AMA Style

Rocio Ballesteros, Jose Fernando Ortega, David Hernández-López, Miguel Angel Moreno. Onion biomass monitoring using UAV-based RGB imaging. Precision Agriculture. 2018; 19 (5):840-857.

Chicago/Turabian Style

Rocio Ballesteros; Jose Fernando Ortega; David Hernández-López; Miguel Angel Moreno. 2018. "Onion biomass monitoring using UAV-based RGB imaging." Precision Agriculture 19, no. 5: 840-857.

Journal article
Published: 31 October 2017 in Revista Brasileira de Agricultura Irrigada
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Resumo Fue realizado el seguimiento fenológico, análisis de crecimiento y desarrollo del cultivo del maíz durante las campañas de riego de los años 2015 y 2016 en parcelas ubicadas en una finca comercial en Tarazona de La Mancha, Albacete, España. Se realizaron vuelos con un vehículo aéreo no tripulado (VANT) equipado con una cámara RGB, durante todo el ciclo del cultivo en ambos años. A partir de las imágenes capturadas en estos vuelos se obtuvieron ortoimágenes de alta resolución espacial, que fueron utilizadas para el cálculo del grado de cobertura verde (GCV) a partir del software de análisis de imágenes LAIC. El GCV presenta una baja cobertura vegetal al inicio de su ciclo, aumentando a medida en que se desarrolla el cultivo, hasta alcanzar su máximo valor, que en el año 2015 se dio al inicio de la etapa media del maíz (91,8%), en cuanto que en el año 2016 los máximos valores de GCV fueron alcanzados al final de esta etapa (81%). Al relacionar los valores de GCV con el índice de área foliar (IAF) en los dos años de estudio, se observa que el crecimiento de la cobertura vegetal está directamente relacionado con la evolución del área foliar del cultivo. Se concluye que el GCV es un parámetro importante en el análisis del desarrollo del cultivo del maíz, puesto que el estudio de las relaciones existentes entre el GCV y el IAF permitió analizar en detalle la arquitectura del cultivo y su evolución a lo largo del tiempo.

ACS Style

Fellype Rodrigo Barroso Costa; José Fernando Ortega; Miguel Angel Moreno; Krishna Ribeiro Gomes; Rocío Ballesteros. CARACTERIZACIÓN DEL CRECIMIENTO DE UN CULTIVO DE MAÍZ REGADO EN UNA ZONA SEMIÁRIDA MEDIANTE EL EMPLEO DE IMÁGENES AÉREAS DE ALTA RESOLUCIÓN. Revista Brasileira de Agricultura Irrigada 2017, 11, 1763 -1771.

AMA Style

Fellype Rodrigo Barroso Costa, José Fernando Ortega, Miguel Angel Moreno, Krishna Ribeiro Gomes, Rocío Ballesteros. CARACTERIZACIÓN DEL CRECIMIENTO DE UN CULTIVO DE MAÍZ REGADO EN UNA ZONA SEMIÁRIDA MEDIANTE EL EMPLEO DE IMÁGENES AÉREAS DE ALTA RESOLUCIÓN. Revista Brasileira de Agricultura Irrigada. 2017; 11 (5):1763-1771.

Chicago/Turabian Style

Fellype Rodrigo Barroso Costa; José Fernando Ortega; Miguel Angel Moreno; Krishna Ribeiro Gomes; Rocío Ballesteros. 2017. "CARACTERIZACIÓN DEL CRECIMIENTO DE UN CULTIVO DE MAÍZ REGADO EN UNA ZONA SEMIÁRIDA MEDIANTE EL EMPLEO DE IMÁGENES AÉREAS DE ALTA RESOLUCIÓN." Revista Brasileira de Agricultura Irrigada 11, no. 5: 1763-1771.

Journal article
Published: 15 October 2017 in Sensors
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Last advances in sensors, photogrammetry and computer vision have led to high-automation levels of 3D reconstruction processes for generating dense models and multispectral orthoimages from Unmanned Aerial Vehicle (UAV) images. However, these cartographic products are sometimes blurred and degraded due to sun reflection effects which reduce the image contrast and colour fidelity in photogrammetry and the quality of radiometric values in remote sensing applications. This paper proposes an automatic approach for detecting sun reflections problems (hotspot and sun glint) in multispectral images acquired with an Unmanned Aerial Vehicle (UAV), based on a photogrammetric strategy included in a flight planning and control software developed by the authors. In particular, two main consequences are derived from the approach developed: (i) different areas of the images can be excluded since they contain sun reflection problems; (ii) the cartographic products obtained (e.g., digital terrain model, orthoimages) and the agronomical parameters computed (e.g., normalized vegetation index-NVDI) are improved since radiometric defects in pixels are not considered. Finally, an accuracy assessment was performed in order to analyse the error in the detection process, getting errors around 10 pixels for a ground sample distance (GSD) of 5 cm which is perfectly valid for agricultural applications. This error confirms that the precision in the detection of sun reflections can be guaranteed using this approach and the current low-cost UAV technology.

ACS Style

Damian Ortega-Terol; David Hernandez-Lopez; Rocio Ballesteros; Diego Gonzalez-Aguilera. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images. Sensors 2017, 17, 2352 .

AMA Style

Damian Ortega-Terol, David Hernandez-Lopez, Rocio Ballesteros, Diego Gonzalez-Aguilera. Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images. Sensors. 2017; 17 (10):2352.

Chicago/Turabian Style

Damian Ortega-Terol; David Hernandez-Lopez; Rocio Ballesteros; Diego Gonzalez-Aguilera. 2017. "Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images." Sensors 17, no. 10: 2352.

Journal article
Published: 26 September 2017 in Sensors
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The multiple protocols that have been developed to characterize river hydromorphology, partly in response to legislative drivers such as the European Union Water Framework Directive (EU WFD), make the comparison of results obtained in different countries challenging. Recent studies have analyzed the comparability of existing methods, with remote sensing based approaches being proposed as a potential means of harmonizing hydromorphological characterization protocols. However, the resolution achieved by remote sensing products may not be sufficient to assess some of the key hydromorphological features that are required to allow an accurate characterization. Methodologies based on high resolution aerial photography taken from Unmanned Aerial Vehicles (UAVs) have been proposed by several authors as potential approaches to overcome these limitations. Here, we explore the applicability of an existing UAV based framework for hydromorphological characterization to three different fluvial settings representing some of the distinct ecoregions defined by the WFD geographical intercalibration groups (GIGs). The framework is based on the automated recognition of hydromorphological features via tested and validated Artificial Neural Networks (ANNs). Results show that the framework is transferable to the Central-Baltic and Mediterranean GIGs with accuracies in feature identification above 70%. Accuracies of 50% are achieved when the framework is implemented in the Very Large Rivers GIG. The framework successfully identified vegetation, deep water, shallow water, riffles, side bars and shadows for the majority of the reaches. However, further algorithm development is required to ensure a wider range of features (e.g., chutes, structures and erosion) are accurately identified. This study also highlights the need to develop an objective and fit for purpose hydromorphological characterization framework to be adopted within all EU member states to facilitate comparison of results.

ACS Style

Monica Rivas Casado; Rocio Ballesteros Gonzalez; José Fernando Ortega; Paul Leinster; Ros Wright. Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization. Sensors 2017, 17, 2210 .

AMA Style

Monica Rivas Casado, Rocio Ballesteros Gonzalez, José Fernando Ortega, Paul Leinster, Ros Wright. Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization. Sensors. 2017; 17 (10):2210.

Chicago/Turabian Style

Monica Rivas Casado; Rocio Ballesteros Gonzalez; José Fernando Ortega; Paul Leinster; Ros Wright. 2017. "Towards a Transferable UAV-Based Framework for River Hydromorphological Characterization." Sensors 17, no. 10: 2210.

Journal article
Published: 23 September 2017 in Sensors
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The acquisition, processing, and interpretation of thermal images from unmanned aerial vehicles (UAVs) is becoming a useful source of information for agronomic applications because of the higher temporal and spatial resolution of these products compared with those obtained from satellites. However, due to the low load capacity of the UAV they need to mount light, uncooled thermal cameras, where the microbolometer is not stabilized to a constant temperature. This makes the camera precision low for many applications. Additionally, the low contrast of the thermal images makes the photogrammetry process inaccurate, which result in large errors in the generation of orthoimages. In this research, we propose the use of new calibration algorithms, based on neural networks, which consider the sensor temperature and the digital response of the microbolometer as input data. In addition, we evaluate the use of the Wallis filter for improving the quality of the photogrammetry process using structure from motion software. With the proposed calibration algorithm, the measurement accuracy increased from 3.55 °C with the original camera configuration to 1.37 °C. The implementation of the Wallis filter increases the number of tie-point from 58,000 to 110,000 and decreases the total positing error from 7.1 m to 1.3 m.

ACS Style

Krishna Ribeiro-Gomes; David Hernández-López; José F. Ortega; Rocío Ballesteros; Tomás Poblete; Miguel A. Moreno. Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors 2017, 17, 2173 .

AMA Style

Krishna Ribeiro-Gomes, David Hernández-López, José F. Ortega, Rocío Ballesteros, Tomás Poblete, Miguel A. Moreno. Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture. Sensors. 2017; 17 (10):2173.

Chicago/Turabian Style

Krishna Ribeiro-Gomes; David Hernández-López; José F. Ortega; Rocío Ballesteros; Tomás Poblete; Miguel A. Moreno. 2017. "Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture." Sensors 17, no. 10: 2173.

Journal article
Published: 01 November 2016 in Biosystems Engineering
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ACS Style

Miguel A. Moreno; Amaro Del Castillo; Jesus Montero; Jose M. Tarjuelo; Rocio Ballesteros. Optimisation of the design of pressurised irrigation systems for irregular shaped plots. Biosystems Engineering 2016, 151, 361 -373.

AMA Style

Miguel A. Moreno, Amaro Del Castillo, Jesus Montero, Jose M. Tarjuelo, Rocio Ballesteros. Optimisation of the design of pressurised irrigation systems for irregular shaped plots. Biosystems Engineering. 2016; 151 ():361-373.

Chicago/Turabian Style

Miguel A. Moreno; Amaro Del Castillo; Jesus Montero; Jose M. Tarjuelo; Rocio Ballesteros. 2016. "Optimisation of the design of pressurised irrigation systems for irregular shaped plots." Biosystems Engineering 151, no. : 361-373.

Journal article
Published: 10 August 2016 in Remote Sensing
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Existing regulatory frameworks aiming to improve the quality of rivers place hydromorphology as a key factor in the assessment of hydrology, morphology and river continuity. The majority of available methods for hydromorphological characterisation rely on the identification of homogeneous areas (i.e., features) of flow, vegetation and substrate. For that purpose, aerial imagery is used to identify existing features through either visual observation or automated classification techniques. There is evidence to believe that the success in feature identification relies on the resolution of the imagery used. However, little effort has yet been made to quantify the uncertainty in feature identification associated with the resolution of the aerial imagery. This paper contributes to address this gap in knowledge by contrasting results in automated hydromorphological feature identification from unmanned aerial vehicles (UAV) aerial imagery captured at three resolutions (2.5 cm, 5 cm and 10 cm) along a 1.4 km river reach. The results show that resolution plays a key role in the accuracy and variety of features identified, with larger identification errors observed for riffles and side bars. This in turn has an impact on the ecological characterisation of the river reach. The research shows that UAV technology could be essential for unbiased hydromorphological assessment.

ACS Style

Monica Rivas Casado; Rocio Ballesteros Gonzalez; Ros Wright; Pat Bellamy. Quantifying the Effect of Aerial Imagery Resolution in Automated Hydromorphological River Characterisation. Remote Sensing 2016, 8, 650 .

AMA Style

Monica Rivas Casado, Rocio Ballesteros Gonzalez, Ros Wright, Pat Bellamy. Quantifying the Effect of Aerial Imagery Resolution in Automated Hydromorphological River Characterisation. Remote Sensing. 2016; 8 (8):650.

Chicago/Turabian Style

Monica Rivas Casado; Rocio Ballesteros Gonzalez; Ros Wright; Pat Bellamy. 2016. "Quantifying the Effect of Aerial Imagery Resolution in Automated Hydromorphological River Characterisation." Remote Sensing 8, no. 8: 650.

Journal article
Published: 01 July 2016 in Biosystems Engineering
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Optimal reservoir sizing in on-demand irrigation networks with a minimum cost was obtained, taking into account the variability of pressure and flow rate demanded by the network during the irrigation season. With this aim, a model called DRODIN (Design of Reservoirs of regulation in On-Demand Irrigation Networks) was developed under a holistic approach. That obtain the optimal design and management (minimum total annual cost, CT) of the water abstraction systems in an integrated manner to include the aquifer along with the pumping station, reservoir and pumping and distribution pipes in a collective irrigation network. This tool has been applied to an on-demand irrigation network located in Spain with 171 ha of drip irrigation in vineyard and olive crops. The optimal reservoir volume is approximately 5000 m3, and the CT for water lift (WL) = 100 m (the most common case for this aquifer) is 325 € ha−1 yr−1. The energy cost is the primary component of CT, both in the abstraction and the water supply to the irrigation network, representing between 57% and 80%. The operation of the pumping station determines the size of the reservoir and the annual costs of the water supply to the network for a given water supply guarantee. The CT increases linearly with the WL, primarily because of the increase in energy costs (Ce), although there is a clear relation between the investment costs (Ca), Ce and the reservoir size, which is only possible to analyse with tools such as DRODIN.

ACS Style

Argenis Izquiel; Rocio Ballesteros; Jose M. Tarjuelo; Miguel A. Moreno. Optimal reservoir sizing in on-demand irrigation networks: Application to a collective drip irrigation network in Spain. Biosystems Engineering 2016, 147, 67 -80.

AMA Style

Argenis Izquiel, Rocio Ballesteros, Jose M. Tarjuelo, Miguel A. Moreno. Optimal reservoir sizing in on-demand irrigation networks: Application to a collective drip irrigation network in Spain. Biosystems Engineering. 2016; 147 ():67-80.

Chicago/Turabian Style

Argenis Izquiel; Rocio Ballesteros; Jose M. Tarjuelo; Miguel A. Moreno. 2016. "Optimal reservoir sizing in on-demand irrigation networks: Application to a collective drip irrigation network in Spain." Biosystems Engineering 147, no. : 67-80.

Journal article
Published: 04 November 2015 in Sensors
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European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

ACS Style

Monica Rivas Casado; Rocio Ballesteros Gonzalez; Thomas Kriechbaumer; Amanda Veal Veal. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery. Sensors 2015, 15, 27969 -27989.

AMA Style

Monica Rivas Casado, Rocio Ballesteros Gonzalez, Thomas Kriechbaumer, Amanda Veal Veal. Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery. Sensors. 2015; 15 (11):27969-27989.

Chicago/Turabian Style

Monica Rivas Casado; Rocio Ballesteros Gonzalez; Thomas Kriechbaumer; Amanda Veal Veal. 2015. "Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery." Sensors 15, no. 11: 27969-27989.