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Applying pruning residues in the lanes of olive groves has become a popular practice because it is economical and accrues benefits for soil and water management. This study presents an analysis of the impact of different rates of pruning residue on soil properties, in particular related with soil quality. Over 4 annual campaigns, chopped pruning residues used as a mulch were analyzed in terms of composition, coverage and moisture content to evaluate their effects on the amount of soil organic carbon (−10 cm and −20 cm) and CO2 emissions, temperature and moisture. The experiment was carried out in a super-intensive olive orchard in Cordoba (SE, Spain) and used four amounts of fresh pruning residue: 7.5 t ha⁻1(T1), 15.0 t ha⁻1 (T2) and 30.0 t ha⁻1 (T3), with a control T0 = 0.0 t ha1. Mulch mean leaf fraction was 46.0 ± 17.5% (±SD) and initial water content, 24.8 ± 8.6%. The mulching benefits for soil moisture were observed in amounts of pruning residue >7.5 t ha⁻1, which are only produced in super-intensive olive groves or in orchards with high tree densities. The low impact of the treatments on soil moisture was explained by the dramatic annual variations in residue moisture contents, caused by the regimes of high temperatures and rainfall-evapotranspiration deficits inherent to the Mediterranean Basin climate. Thus, the mulching capacity only resulted efficient when the residues were still humid in spring. In addition, 15.0 t ha⁻1 of pruning residues was the threshold to provide significant increases in soil organic carbon at depths of 0–20 cm. Thus, accumulating pruning residue in lanes at rates of over 15 t ha⁻1 (T2 and T3) is more convenient than a uniform distribution with lower amounts, due to the low mineralization rates occurring during warm seasons and the larger inputs of OM increasing the annual balance of SOC.
Encarnación V. Taguas; Víctor Marín-Moreno; Concepción M. Díez; Luciano Mateos; Diego Barranco; Francisco-Javier Mesas-Carrascosa; Rafael Pérez; Alfonso García-Ferrer; José L. Quero. Opportunities of super high-density olive orchard to improve soil quality: Management guidelines for application of pruning residues. Journal of Environmental Management 2021, 293, 112785 .
AMA StyleEncarnación V. Taguas, Víctor Marín-Moreno, Concepción M. Díez, Luciano Mateos, Diego Barranco, Francisco-Javier Mesas-Carrascosa, Rafael Pérez, Alfonso García-Ferrer, José L. Quero. Opportunities of super high-density olive orchard to improve soil quality: Management guidelines for application of pruning residues. Journal of Environmental Management. 2021; 293 ():112785.
Chicago/Turabian StyleEncarnación V. Taguas; Víctor Marín-Moreno; Concepción M. Díez; Luciano Mateos; Diego Barranco; Francisco-Javier Mesas-Carrascosa; Rafael Pérez; Alfonso García-Ferrer; José L. Quero. 2021. "Opportunities of super high-density olive orchard to improve soil quality: Management guidelines for application of pruning residues." Journal of Environmental Management 293, no. : 112785.
Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.
Fernando-Juan Pérez-Porras; Paula Triviño-Tarradas; Carmen Cima-Rodríguez; Jose-Emilio Meroño-De-Larriva; Alfonso García-Ferrer; Francisco-Javier Mesas-Carrascosa. Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires. Sensors 2021, 21, 3694 .
AMA StyleFernando-Juan Pérez-Porras, Paula Triviño-Tarradas, Carmen Cima-Rodríguez, Jose-Emilio Meroño-De-Larriva, Alfonso García-Ferrer, Francisco-Javier Mesas-Carrascosa. Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires. Sensors. 2021; 21 (11):3694.
Chicago/Turabian StyleFernando-Juan Pérez-Porras; Paula Triviño-Tarradas; Carmen Cima-Rodríguez; Jose-Emilio Meroño-De-Larriva; Alfonso García-Ferrer; Francisco-Javier Mesas-Carrascosa. 2021. "Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires." Sensors 21, no. 11: 3694.
Industrial heritage is linked to the cultural processes that human society sets through the traces from the past. The conservation and dissemination of this industrial–cultural heritage are crucial for sustainable urban development, and positively influences the transition to resilient and sustainable cities. The wine industry around Montilla has suffered as a result of a sharp reduction of the vineyard area in the last 25 years. Wineries, as one of the historic typologies of wine-making facilities in the Montilla-Moriles Protected Designation of Origin (PDO), as well as their materials and construction techniques, are a reference in the agricultural landscape of Montilla. Many historic wineries are the result of the abandonment and cessation of the wine industry. These buildings are linked to the agrarian activity in this area, mostly wine-making, although in some cases, they coexist with similar production processes, such as milling the fruit of the olive grove. This research characterises and analyses four historic wineries in the Montilla-Moriles PDO, which represent an example of architecture in the wine-making transformation during the 19th–20th centuries. This manuscript contributes to the attainment of some objectives set in one of the Sustainable Development Goals (SDGs), protecting and disseminating the industrial cultural heritage in Montilla-Moriles.
Antonia Merino-Aranda; Isabel Castillejo-González; Almudena Velo-Gala; Francisco De Paula Montes-Tubío; Francisco-Javier Mesas-Carrascosa; Paula Triviño-Tarradas. Strengthening Efforts to Protect and Safeguard the Industrial Cultural Heritage in Montilla-Moriles (PDO). Characterisation of Historic Wineries. Sustainability 2021, 13, 5791 .
AMA StyleAntonia Merino-Aranda, Isabel Castillejo-González, Almudena Velo-Gala, Francisco De Paula Montes-Tubío, Francisco-Javier Mesas-Carrascosa, Paula Triviño-Tarradas. Strengthening Efforts to Protect and Safeguard the Industrial Cultural Heritage in Montilla-Moriles (PDO). Characterisation of Historic Wineries. Sustainability. 2021; 13 (11):5791.
Chicago/Turabian StyleAntonia Merino-Aranda; Isabel Castillejo-González; Almudena Velo-Gala; Francisco De Paula Montes-Tubío; Francisco-Javier Mesas-Carrascosa; Paula Triviño-Tarradas. 2021. "Strengthening Efforts to Protect and Safeguard the Industrial Cultural Heritage in Montilla-Moriles (PDO). Characterisation of Historic Wineries." Sustainability 13, no. 11: 5791.
Yield prediction is crucial for the management of harvest and scheduling wine production operations. Traditional yield prediction methods rely on manual sampling and are time-consuming, making it difficult to handle the intrinsic spatial variability of vineyards. There have been significant advances in automatic yield estimation in vineyards from on-ground imagery, but terrestrial platforms have some limitations since they can cause soil compaction and have problems on sloping and ploughed land. The analysis of photogrammetric point clouds generated with unmanned aerial vehicles (UAV) imagery has shown its potential in the characterization of woody crops, and the point color analysis has been used for the detection of flowers in almond trees. For these reasons, the main objective of this work was to develop an unsupervised and automated workflow for detection of grape clusters in red grapevine varieties using UAV photogrammetric point clouds and color indices. As leaf occlusion is recognized as a major challenge in fruit detection, the influence of partial leaf removal in the accuracy of the workflow was assessed. UAV flights were performed over two commercial vineyards with different grape varieties in 2019 and 2020, and the photogrammetric point clouds generated from these flights were analyzed using an automatic and unsupervised algorithm developed using free software. The proposed methodology achieved R2 values higher than 0.75 between the harvest weight and the projected area of the points classified as grapes in vines when partial two-sided removal treatment, and an R2 of 0.82 was achieved in one of the datasets for vines with untouched full canopy. The accuracy achieved in grape detection opens the door to yield prediction in red grape vineyards. This would allow the creation of yield estimation maps that will ease the implementation of precision viticulture practices. To the authors’ knowledge, this is the first time that UAV photogrammetric point clouds have been used for grape clusters detection.
Jorge Torres-Sánchez; Francisco Mesas-Carrascosa; Luis-Gonzaga Santesteban; Francisco Jiménez-Brenes; Oihane Oneka; Ana Villa-Llop; Maite Loidi; Francisca López-Granados. Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards. Sensors 2021, 21, 3083 .
AMA StyleJorge Torres-Sánchez, Francisco Mesas-Carrascosa, Luis-Gonzaga Santesteban, Francisco Jiménez-Brenes, Oihane Oneka, Ana Villa-Llop, Maite Loidi, Francisca López-Granados. Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards. Sensors. 2021; 21 (9):3083.
Chicago/Turabian StyleJorge Torres-Sánchez; Francisco Mesas-Carrascosa; Luis-Gonzaga Santesteban; Francisco Jiménez-Brenes; Oihane Oneka; Ana Villa-Llop; Maite Loidi; Francisca López-Granados. 2021. "Grape Cluster Detection Using UAV Photogrammetric Point Clouds as a Low-Cost Tool for Yield Forecasting in Vineyards." Sensors 21, no. 9: 3083.
Significant advances in weed mapping from unmanned aerial platforms have been achieved in recent years. The detection of weed location has made possible the generation of site specific weed treatments to reduce the use of herbicides according to weed cover maps. However, the characterization of weed infestations should not be limited to the location of weed stands, but should also be able to distinguish the types of weeds to allow the best possible choice of herbicide treatment to be applied. A first step in this direction should be the discrimination between broad-leaved (dicotyledonous) and grass (monocotyledonous) weeds. Considering the advances in weed detection based on images acquired by unmanned aerial vehicles, and the ability of neural networks to solve hard classification problems in remote sensing, these technologies have been merged in this study with the aim of exploring their potential for broadleaf and grass weed detection in wide-row herbaceous crops such as sunflower and cotton. Overall accuracies of around 80% were obtained in both crops, with user accuracy for broad-leaved and grass weeds around 75% and 65%, respectively. These results confirm the potential of the presented combination of technologies for improving the characterization of different weed infestations, which would allow the generation of timely and adequate herbicide treatment maps according to groups of weeds.
Jorge Torres-Sánchez; Francisco Mesas-Carrascosa; Francisco Jiménez-Brenes; Ana de Castro; Francisca López-Granados. Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery. Agronomy 2021, 11, 749 .
AMA StyleJorge Torres-Sánchez, Francisco Mesas-Carrascosa, Francisco Jiménez-Brenes, Ana de Castro, Francisca López-Granados. Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery. Agronomy. 2021; 11 (4):749.
Chicago/Turabian StyleJorge Torres-Sánchez; Francisco Mesas-Carrascosa; Francisco Jiménez-Brenes; Ana de Castro; Francisca López-Granados. 2021. "Early Detection of Broad-Leaved and Grass Weeds in Wide Row Crops Using Artificial Neural Networks and UAV Imagery." Agronomy 11, no. 4: 749.
The advances in Unmanned Aerial Vehicle (UAV) platforms and on-board sensors in the past few years have greatly increased our ability to monitor and map crops. The ability to register images at ultra-high spatial resolution at any moment has made remote sensing techniques increasingly useful in crop management. These technologies have revolutionized the way in which remote sensing is applied in precision agriculture, allowing for decision-making in a matter of days instead of weeks. However, it is still necessary to continue research to improve and maximize the potential of UAV remote sensing in agriculture. This Special Issue of Remote Sensing includes different applications of UAV remote sensing for crop management, covering RGB, multispectral, hyperspectral and LIght Detection and Ranging (LiDAR) sensor applications on-board (UAVs). The papers reveal innovative techniques involving image analysis and cloud points. It should, however, be emphasized that this Special Issue is a small sample of UAV applications in agriculture and that there is much more to investigate.
Francisco Mesas-Carrascosa. UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops. Remote Sensing 2020, 12, 3873 .
AMA StyleFrancisco Mesas-Carrascosa. UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops. Remote Sensing. 2020; 12 (23):3873.
Chicago/Turabian StyleFrancisco Mesas-Carrascosa. 2020. "UAS-Remote Sensing Methods for Mapping, Monitoring and Modeling Crops." Remote Sensing 12, no. 23: 3873.
Natural resource management requires reliable and timely information available at local, regional, national, and global scales. Geo-informatics, by remote sensing, global navigation satellite systems, geographical information systems, and related technologies, provides information for natural resource management, environmental protection, and support related to sustainable development. Geo-informatics has proven to be a powerful technology for studying and monitoring natural resources as well as in generating predictive models, making it an important decision-making tool. The manuscripts included in this Special Issue focus on disciplines that advance the field of resource management in geomatics. The manuscripts showcased here provide different examples of challenges in resource management.
Francisco Javier Mesas-Carrascosa. Geo-Informatics in Resource Management. ISPRS International Journal of Geo-Information 2020, 9, 628 .
AMA StyleFrancisco Javier Mesas-Carrascosa. Geo-Informatics in Resource Management. ISPRS International Journal of Geo-Information. 2020; 9 (11):628.
Chicago/Turabian StyleFrancisco Javier Mesas-Carrascosa. 2020. "Geo-Informatics in Resource Management." ISPRS International Journal of Geo-Information 9, no. 11: 628.
Identifying and mapping irrigated areas is essential for a variety of applications such as agricultural planning and water resource management. Irrigated plots are mainly identified using supervised classification of multispectral images from satellite or manned aerial platforms. Recently, hyperspectral sensors on-board Unmanned Aerial Vehicles (UAV) have proven to be useful analytical tools in agriculture due to their high spectral resolution. However, few efforts have been made to identify which wavelengths could be applied to provide relevant information in specific scenarios. In this study, hyperspectral reflectance data from UAV were used to compare the performance of several wavelength selection methods based on Partial Least Square (PLS) regression with the purpose of discriminating two systems of irrigation commonly used in olive orchards. The tested PLS methods include filter methods (Loading Weights, Regression Coefficient and Variable Importance in Projection); Wrapper methods (Genetic Algorithm-PLS, Uninformative Variable Elimination-PLS, Backward Variable Elimination-PLS, Sub-window Permutation Analysis-PLS, Iterative Predictive Weighting-PLS, Regularized Elimination Procedure-PLS, Backward Interval-PLS, Forward Interval-PLS and Competitive Adaptive Reweighted Sampling-PLS); and an Embedded method (Sparse-PLS). In addition, two non-PLS based methods, Lasso and Boruta, were also used. Linear Discriminant Analysis and nonlinear K-Nearest Neighbors techniques were established for identification and assessment. The results indicate that wavelength selection methods, commonly used in other disciplines, provide utility in remote sensing for agronomical purposes, the identification of irrigation techniques being one such example. In addition to the aforementioned, these PLS and non-PLS based methods can play an important role in multivariate analysis, which can be used for subsequent model analysis. Of all the methods evaluated, Genetic Algorithm-PLS and Boruta eliminated nearly 90% of the original spectral wavelengths acquired from a hyperspectral sensor onboard a UAV while increasing the identification accuracy of the classification.
Antonio Santos-Rufo; Francisco-Javier Mesas-Carrascosa; Alfonso García-Ferrer; Jose Meroño-Larriva. Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards. Remote Sensing 2020, 12, 3426 .
AMA StyleAntonio Santos-Rufo, Francisco-Javier Mesas-Carrascosa, Alfonso García-Ferrer, Jose Meroño-Larriva. Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards. Remote Sensing. 2020; 12 (20):3426.
Chicago/Turabian StyleAntonio Santos-Rufo; Francisco-Javier Mesas-Carrascosa; Alfonso García-Ferrer; Jose Meroño-Larriva. 2020. "Wavelength Selection Method Based on Partial Least Square from Hyperspectral Unmanned Aerial Vehicle Orthomosaic of Irrigated Olive Orchards." Remote Sensing 12, no. 20: 3426.
The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies
Rafael M. Navarro Cerrillo; Guillermo Palacios Rodríguez; Inmaculada Clavero Rumbao; Miguel Ángel Lara; Francisco Javier Bonet; Francisco-Javier Mesas-Carrascosa. Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain). ISPRS International Journal of Geo-Information 2020, 9, 458 .
AMA StyleRafael M. Navarro Cerrillo, Guillermo Palacios Rodríguez, Inmaculada Clavero Rumbao, Miguel Ángel Lara, Francisco Javier Bonet, Francisco-Javier Mesas-Carrascosa. Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain). ISPRS International Journal of Geo-Information. 2020; 9 (7):458.
Chicago/Turabian StyleRafael M. Navarro Cerrillo; Guillermo Palacios Rodríguez; Inmaculada Clavero Rumbao; Miguel Ángel Lara; Francisco Javier Bonet; Francisco-Javier Mesas-Carrascosa. 2020. "Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain)." ISPRS International Journal of Geo-Information 9, no. 7: 458.
The disease caused by SARS-CoV-2 has affected many countries and regions. In order to contain the spread of infection, many countries have adopted lockdown measures. As a result, SARS-CoV-2 has negatively influenced economies on a global scale and has caused a significant impact on the environment. In this study, changes in the concentration of the pollutant Nitrogen Dioxide (NO2) within the lockdown period were examined as well as how these changes relate to the Spanish population. NO2 is one of the reactive nitrogen oxides gases resulting from both anthropogenic and natural processes. One major source in urban areas is the combustion of fossil fuels from vehicles and industrial plants, both of which significantly contribute to air pollution. The long-term exposure to NO2 can also cause severe health problems. Remote sensing is a useful tool to analyze spatial variability of air quality. For this purpose, Sentinel-5P images registered from January to April of 2019 and 2020 were used to analyze spatial distribution of NO2 and its evolution under the lockdown measures in Spain. The results indicate a significant correlation between the population’s activity level and the reduction of NO2 values.
Francisco-Javier Mesas-Carrascosa; Fernando Pérez Porras; Paula Triviño-Tarradas; Alfonso García-Ferrer; Jose Meroño-Larriva. Effect of Lockdown Measures on Atmospheric Nitrogen Dioxide during SARS-CoV-2 in Spain. Remote Sensing 2020, 12, 2210 .
AMA StyleFrancisco-Javier Mesas-Carrascosa, Fernando Pérez Porras, Paula Triviño-Tarradas, Alfonso García-Ferrer, Jose Meroño-Larriva. Effect of Lockdown Measures on Atmospheric Nitrogen Dioxide during SARS-CoV-2 in Spain. Remote Sensing. 2020; 12 (14):2210.
Chicago/Turabian StyleFrancisco-Javier Mesas-Carrascosa; Fernando Pérez Porras; Paula Triviño-Tarradas; Alfonso García-Ferrer; Jose Meroño-Larriva. 2020. "Effect of Lockdown Measures on Atmospheric Nitrogen Dioxide during SARS-CoV-2 in Spain." Remote Sensing 12, no. 14: 2210.
The volume of the food produced across the world should be related to agricultural sustainability and is crucial for natural capital protection. Hence, sustainability assessment on farms and the identification of improvements is relevant. A mixed farm of vineyard and olive trees was chosen for sustainability assessment, based on the Best Management Practices (BMPs) that have been implemented. The aim of this research was to assess sustainability on a mixed vineyard and olive-grove farm and validate the INSPIA model for this kind of typology of a farm, which is very typical in the South of Spain. The sustainability assessment was monitored across 5-agricultural seasons based on the INSPIA methodology. INSPIA is based on the application of a set of BMPs, calculated on 31 basic indicators, providing a final composite index of sustainability. The greater the implementation of sustainable farming practices, the higher the value of the composite index. Enhanced soil, water, and air quality, improvement for biodiversity and for ecosystem services help towards sustainable agricultural productivity. Indicators’ results are shown during that period, depicting their relationship with the BMPs. The highest composite index was reached in the 4th year. This paper confirms the relevance of BMPs, such as groundcover establishment and minimum soil disturbance to upgrade sustainability on the permanent croplands in Southern Spain. The indicator-based sustainability assessment is considered a helpful tool in decision-making, which guides farmers towards BMPs performance.
Paula Triviño-Tarradas; Pilar Carranza-Cañadas; Francisco-Javier Mesas-Carrascosa; Emilio J. Gonzalez-Sanchez. Evaluation of Agricultural Sustainability on a Mixed Vineyard and Olive-Grove Farm in Southern Spain through the INSPIA Model. Sustainability 2020, 12, 1090 .
AMA StylePaula Triviño-Tarradas, Pilar Carranza-Cañadas, Francisco-Javier Mesas-Carrascosa, Emilio J. Gonzalez-Sanchez. Evaluation of Agricultural Sustainability on a Mixed Vineyard and Olive-Grove Farm in Southern Spain through the INSPIA Model. Sustainability. 2020; 12 (3):1090.
Chicago/Turabian StylePaula Triviño-Tarradas; Pilar Carranza-Cañadas; Francisco-Javier Mesas-Carrascosa; Emilio J. Gonzalez-Sanchez. 2020. "Evaluation of Agricultural Sustainability on a Mixed Vineyard and Olive-Grove Farm in Southern Spain through the INSPIA Model." Sustainability 12, no. 3: 1090.
Remote sensing applied in the digital transformation of agriculture and, more particularly, in precision viticulture offers methods to map field spatial variability to support site-specific management strategies; these can be based on crop canopy characteristics such as the row height or vegetation cover fraction, requiring accurate three-dimensional (3D) information. To derive canopy information, a set of dense 3D point clouds was generated using photogrammetric techniques on images acquired by an RGB sensor onboard an unmanned aerial vehicle (UAV) in two testing vineyards on two different dates. In addition to the geometry, each point also stores information from the RGB color model, which was used to discriminate between vegetation and bare soil. To the best of our knowledge, the new methodology herein presented consisting of linking point clouds with their spectral information had not previously been applied to automatically estimate vine height. Therefore, the novelty of this work is based on the application of color vegetation indices in point clouds for the automatic detection and classification of points representing vegetation and the later ability to determine the height of vines using as a reference the heights of the points classified as soil. Results from on-ground measurements of the heights of individual grapevines were compared with the estimated heights from the UAV point cloud, showing high determination coefficients (R² > 0.87) and low root-mean-square error (0.070 m). This methodology offers new capabilities for the use of RGB sensors onboard UAV platforms as a tool for precision viticulture and digitizing applications.
Francisco-Javier Mesas-Carrascosa; Ana I. De Castro; Jorge Torres-Sánchez; Paula Triviño-Tarradas; Francisco M. Jiménez-Brenes; Alfonso García-Ferrer; Francisca López-Granados. Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications. Remote Sensing 2020, 12, 317 .
AMA StyleFrancisco-Javier Mesas-Carrascosa, Ana I. De Castro, Jorge Torres-Sánchez, Paula Triviño-Tarradas, Francisco M. Jiménez-Brenes, Alfonso García-Ferrer, Francisca López-Granados. Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications. Remote Sensing. 2020; 12 (2):317.
Chicago/Turabian StyleFrancisco-Javier Mesas-Carrascosa; Ana I. De Castro; Jorge Torres-Sánchez; Paula Triviño-Tarradas; Francisco M. Jiménez-Brenes; Alfonso García-Ferrer; Francisca López-Granados. 2020. "Classification of 3D Point Clouds Using Color Vegetation Indices for Precision Viticulture and Digitizing Applications." Remote Sensing 12, no. 2: 317.
The development of unmanned aerial vehicle (UAV) technology and the miniaturization of sensors have changed the way remote sensing (RS) is used, popularizing this geoscientific discipline in other fields, such as precision agriculture. This makes it necessary to implement the use of these technologies in teaching RS alongside the classical platforms (satellite and manned aircraft). This manuscript describes how The Higher Technical School of Agricultural Engineering at the University of Córdoba (Spain) has introduced UAV RS into the academic program by way of project-based learning (PBL). It also presents the basic characteristics of PBL, the design of the subject, the description of the teacher-guided and self-directed activities, as well as the degree of student satisfaction. The teaching and learning objectives of the subject are to learn how to determine the vigor, temperature, and water stress of a crop through the use of RGB, multispectral, and thermographic sensors onboard a UAV platform. From the onset, students are motivated, actively participate in the tasks related to the realization of UAV flights, and subsequent processing and analysis of the registered images. Students report that PBL is more engaging and allows them to develop a better understanding of RS.
Francisco Javier Mesas-Carrascosa; Fernando Pérez Porras; Paula Triviño-Tarradas; Jose Emilio Meroño De Larriva; Alfonso García-Ferrer. Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing. Remote Sensing 2019, 11, 2413 .
AMA StyleFrancisco Javier Mesas-Carrascosa, Fernando Pérez Porras, Paula Triviño-Tarradas, Jose Emilio Meroño De Larriva, Alfonso García-Ferrer. Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing. Remote Sensing. 2019; 11 (20):2413.
Chicago/Turabian StyleFrancisco Javier Mesas-Carrascosa; Fernando Pérez Porras; Paula Triviño-Tarradas; Jose Emilio Meroño De Larriva; Alfonso García-Ferrer. 2019. "Project-Based Learning Applied to Unmanned Aerial Systems and Remote Sensing." Remote Sensing 11, no. 20: 2413.
This study used Landsat temporal series to describe defoliation levels due to the Pine Processionary Moth (PPM) in Pinus forests of southeastern Andalusia (Spain), utilizing Google Earth Engine. A combination of remotely sensed data and field survey data was used to detect the defoliation levels of different Pinus spp. and the main environmental drivers of the defoliation due to the PPM. Four vegetation indexes were also calculated for remote sensing defoliation assessment, both inside the stand and in a 60-m buffer area. In the area of study, all Pinus species are affected by defoliation due to the PPM, with a cyclic behavior that has been increasing in frequency in recent years. Defoliation levels were practically equal for all species, with a high increase in defoliation levels 2 and 3 since 2014. The Moisture Stress Index (MSI) and Normalized Difference Infrared Index (NDII) exhibited similar overall (P < 0.001) accuracy in the assessment of defoliation due to the PPM. The synchronization of NDII-defoliation data had a similar pattern for all together and individual Pinus species, showing the ability of this index to adjust the model parameters based on the characteristics of specific defoliation levels. Using Landsat-based NDII-defoliation maps and interpolated environmental data, we have shown that the PPM defoliation in southeastern Spain is driven by the minimum temperature in February and the precipitation in June, March, September, and October. Therefore, the NDII-defoliation assessment seems to be a general index that can be applied to forests in other areas. The trends of NDII-defoliation related to environmental variables showed the importance of summer drought stress in the expansion of the PPM on Mediterranean Pinus species. Our results confirm the potential of Landsat time-series data in the assessment of PPM defoliation and the spatiotemporal patterns of the PPM; hence, these data are a powerful tool that can be used to develop a fully operational system for the monitoring of insect damage.
Javier Pérez-Romero; Rafael María Navarro-Cerrillo; Guillermo Palacios-Rodriguez; Cristina Acosta; Francisco Javier Mesas-Carrascosa. Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea Pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain. Remote Sensing 2019, 11, 1736 .
AMA StyleJavier Pérez-Romero, Rafael María Navarro-Cerrillo, Guillermo Palacios-Rodriguez, Cristina Acosta, Francisco Javier Mesas-Carrascosa. Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea Pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain. Remote Sensing. 2019; 11 (14):1736.
Chicago/Turabian StyleJavier Pérez-Romero; Rafael María Navarro-Cerrillo; Guillermo Palacios-Rodriguez; Cristina Acosta; Francisco Javier Mesas-Carrascosa. 2019. "Improvement of Remote Sensing-Based Assessment of Defoliation of Pinus spp. Caused by Thaumetopoea Pityocampa Denis and Schiffermüller and Related Environmental Drivers in Southeastern Spain." Remote Sensing 11, no. 14: 1736.
Bathing water quality has been monitored in the west coast of Tangier, Morocco due to increased urban and industrial discharge through the Boukhalef river, using in-situ bacteriological measurements which demand high economical and temporal costs. In this study, Landsat 8 Thermal Infrared Sensor (TIRS) images were used as an alternative to the classical method, for determining bathing water quality to help decision makers obtain up-to-date and cost-effective information for coastal environment protection. For this purpose, during spring and summer 2017, seven sampling points were examined in terms of bacteriological parameters: Total Coliforms (TC), Faecal Coliforms (FC), Intestinal Enterococci (IE) and Escherichia coli (E. coli). Also, a spatial-temporal analysis was performed in this temporal window to detect temperature anomalies and their spatial distribution along the coastal bathing area. In addition, a relationship between in-situ bacteriological parameter measurements and temperature from satellite images was analyzed. The results of the water temperature distribution showed the highest values next to the Boukhalef river mouth, as well as the poorest water quality according to in-situ measurements, while lower values and better water quality status were observed moving away from the Boukhalef river mouth. The relationship between water temperature and bacterial concentration showed a high correlation coefficient (R2 = 0.85). Consequently, the model development approaches used may be useful in estimating bacterial concentration in coastal bathing areas and can serve to create a monitoring system to support decision makers in the protection actions of the coast.
El Khalil Cherif; Farida Salmoun; Francisco Javier Mesas-Carrascosa. Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results. Remote Sensing 2019, 11, 972 .
AMA StyleEl Khalil Cherif, Farida Salmoun, Francisco Javier Mesas-Carrascosa. Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results. Remote Sensing. 2019; 11 (8):972.
Chicago/Turabian StyleEl Khalil Cherif; Farida Salmoun; Francisco Javier Mesas-Carrascosa. 2019. "Determination of Bathing Water Quality Using Thermal Images Landsat 8 on the West Coast of Tangier: Preliminary Results." Remote Sensing 11, no. 8: 972.
Civil engineering uses digital elevation models (DEMs) and orthophotos as basic material to be able to design and execute any project. UAV photogrammetry has made it possible to obtain this type of information in an economic and practical way. However, it is necessary to know the accuracy of the data and that it is within the admissible limits. There are many factors that affect the accuracy of products resulting from UAV photogrammetry. Of all of these, the effect of the number of ground control points (GCPs) and their distribution in the study area are especially significant. Different distributions of GCPs have been studied to try to optimize the products obtained by UAV photogrammetry. Of all the distributions tested, the best results were obtained with edge distribution and stratified distribution. Therefore, it is necessary to place GCPs around the edge of the study area to minimize planimetry errors. In addition, it is advisable to create a stratified distribution inside the study area with a density of around 0.5–1 GCP × ha−1 to minimize altimetry errors. The combination of these two distributions minimizes the total error obtained.
Patricio Jesús Martínez Carricondo; Francisco Agüera-Vega; Fernando Carvajal-Ramírez; Francisco-Javier Mesas-Carrascosa; Alfonso García-Ferrer; Fernando-Juan Pérez-Porras. Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International Journal of Applied Earth Observation and Geoinformation 2018, 72, 1 -10.
AMA StylePatricio Jesús Martínez Carricondo, Francisco Agüera-Vega, Fernando Carvajal-Ramírez, Francisco-Javier Mesas-Carrascosa, Alfonso García-Ferrer, Fernando-Juan Pérez-Porras. Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points. International Journal of Applied Earth Observation and Geoinformation. 2018; 72 ():1-10.
Chicago/Turabian StylePatricio Jesús Martínez Carricondo; Francisco Agüera-Vega; Fernando Carvajal-Ramírez; Francisco-Javier Mesas-Carrascosa; Alfonso García-Ferrer; Fernando-Juan Pérez-Porras. 2018. "Assessment of UAV-photogrammetric mapping accuracy based on variation of ground control points." International Journal of Applied Earth Observation and Geoinformation 72, no. : 1-10.
El objetivo de este trabajo es determinar la calidad posicional de las soluciones ofrecidas por la Red de Andaluza de Posicionamiento (RAP). Red geodésica activa situada en la Comunidad Autónoma de Andalucía, en el sur de España. Se realizaron diferentes pruebas a los servicios ofrecidos por esta red activa, especialmente los que utilizan técnicas de posicionamiento en tiempo real (RTK). Se analizaron diferentes parámetros: precisión, exactitud y tiempo de resolución de ambigüedades. Posteriormente, se analizó el sistema desde el punto de vista del levantamiento topográfico, realizándose varios levantamientos de pequeña extensión, utilizados para proyectos de minería e ingeniería civil, con el objetivo de comprobar si una red geodésica activa puede utilizarse con un rendimiento similar a las obtenidos utilizando RTK convencional.
Enrique Cano-Jódar; Manuel Sánchez-De-La-Orden; Javier Mesas-Carrascosa. Active geodetic network: application in topography. DYNA 2018, 85, 114 -120.
AMA StyleEnrique Cano-Jódar, Manuel Sánchez-De-La-Orden, Javier Mesas-Carrascosa. Active geodetic network: application in topography. DYNA. 2018; 85 (206):114-120.
Chicago/Turabian StyleEnrique Cano-Jódar; Manuel Sánchez-De-La-Orden; Javier Mesas-Carrascosa. 2018. "Active geodetic network: application in topography." DYNA 85, no. 206: 114-120.
Francisco Agüera-Vega; Fernando Carvajal-Ramírez; Patricio Jesús Martínez Carricondo; Julián Sánchez-Hermosilla López; Francisco Javier Mesas-Carrascosa; Alfonso García-Ferrer; Fernando Juan Pérez-Porras. Reconstruction of extreme topography from UAV structure from motion photogrammetry. Measurement 2018, 121, 127 -138.
AMA StyleFrancisco Agüera-Vega, Fernando Carvajal-Ramírez, Patricio Jesús Martínez Carricondo, Julián Sánchez-Hermosilla López, Francisco Javier Mesas-Carrascosa, Alfonso García-Ferrer, Fernando Juan Pérez-Porras. Reconstruction of extreme topography from UAV structure from motion photogrammetry. Measurement. 2018; 121 ():127-138.
Chicago/Turabian StyleFrancisco Agüera-Vega; Fernando Carvajal-Ramírez; Patricio Jesús Martínez Carricondo; Julián Sánchez-Hermosilla López; Francisco Javier Mesas-Carrascosa; Alfonso García-Ferrer; Fernando Juan Pérez-Porras. 2018. "Reconstruction of extreme topography from UAV structure from motion photogrammetry." Measurement 121, no. : 127-138.
The development of lightweight sensors compatible with mini unmanned aerial vehicles (UAVs) has expanded the agronomical applications of remote sensing. Of particular interest in this paper are thermal sensors based on lightweight microbolometer technology. These are mainly used to assess crop water stress with thermal images where an accuracy greater than 1 °C is necessary. However, these sensors lack precise temperature control, resulting in thermal drift during image acquisition that requires correction. Currently, there are several strategies to manage thermal drift effect. However, these strategies reduce useful flight time over crops due to the additional in-flight calibration operations. This study presents a drift correction methodology for microbolometer sensors based on redundant information from multiple overlapping images. An empirical study was performed in an orchard of high-density hedgerow olive trees with flights at different times of the day. Six mathematical drift correction models were developed and assessed to explain and correct drift effect on thermal images. Using the proposed methodology, the resulting thermally corrected orthomosaics yielded a rate of error lower than 1° C compared to those where no drift correction was applied.
Francisco-Javier Mesas-Carrascosa; Fernando Pérez-Porras; Jose Emilio Meroño De Larriva; Carlos Mena Frau; Francisco Agüera-Vega; Fernando Carvajal-Ramírez; Patricio Martínez-Carricondo; Alfonso García-Ferrer. Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles. Remote Sensing 2018, 10, 615 .
AMA StyleFrancisco-Javier Mesas-Carrascosa, Fernando Pérez-Porras, Jose Emilio Meroño De Larriva, Carlos Mena Frau, Francisco Agüera-Vega, Fernando Carvajal-Ramírez, Patricio Martínez-Carricondo, Alfonso García-Ferrer. Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles. Remote Sensing. 2018; 10 (4):615.
Chicago/Turabian StyleFrancisco-Javier Mesas-Carrascosa; Fernando Pérez-Porras; Jose Emilio Meroño De Larriva; Carlos Mena Frau; Francisco Agüera-Vega; Fernando Carvajal-Ramírez; Patricio Martínez-Carricondo; Alfonso García-Ferrer. 2018. "Drift Correction of Lightweight Microbolometer Thermal Sensors On-Board Unmanned Aerial Vehicles." Remote Sensing 10, no. 4: 615.
Concentrated solar power (CSP) plants are increasingly gaining interest as a source of renewable energy. These plants face several technical problems and the inspection of components such as absorber tubes in parabolic trough concentrators (PTC), which are widely deployed, is necessary to guarantee plant efficiency. This article presents a system for real-time industrial inspection of CSP plants using low-cost, open-source components in conjunction with a thermographic sensor and an unmanned aerial vehicle (UAV). The system, available in open-source hardware and software, is designed to be employed independently of the type of device used for inspection (laptop, smartphone, tablet or smartglasses) and its operating system. Several UAV flight missions were programmed as follows: flight altitudes at 20, 40, 60, 80, 100 and 120 m above ground level; and three cruising speeds: 5, 7 and 10 m/s. These settings were chosen and analyzed in order to optimize inspection time. The results indicate that it is possible to perform inspections by an UAV in real time at CSP plants as a means of detecting anomalous absorber tubes and improving the effectiveness of methodologies currently being utilized. Moreover, aside from thermographic sensors, this contribution can be applied to other sensors and can be used in a broad range of applications where real-time georeferenced data visualization is necessary.
Francisco Javier Mesas-Carrascosa; Daniel Verdú Santano; Fernando Pérez Porras; José Emilio Meroño-Larriva; Alfonso García-Ferrer. The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants. Sensors 2017, 17, 1329 .
AMA StyleFrancisco Javier Mesas-Carrascosa, Daniel Verdú Santano, Fernando Pérez Porras, José Emilio Meroño-Larriva, Alfonso García-Ferrer. The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants. Sensors. 2017; 17 (6):1329.
Chicago/Turabian StyleFrancisco Javier Mesas-Carrascosa; Daniel Verdú Santano; Fernando Pérez Porras; José Emilio Meroño-Larriva; Alfonso García-Ferrer. 2017. "The Development of an Open Hardware and Software System Onboard Unmanned Aerial Vehicles to Monitor Concentrated Solar Power Plants." Sensors 17, no. 6: 1329.