M. A. Moreno

Energy analysis of irrigation delivery systems: monitoring and evaluation of proposed measures for improving energy efficiency

In Spain, the Ministry of Industry is implementing actions for analyzing the energy efficiency of Water User Associations (WUAs) by using energy indicators and proposing measures to improve the use of energy. The main objective of this work was to develop tools to improve energy efficiency in WUAs. These tools were validated by utilizing them in the energy analysis of 15 WUAs located in Castilla-La Mancha Region (Spain) during the 2007 irrigation season. These tools were also utilized for the proposal of measures to improve the use of energy. The proposed measures were monitored and evaluated in 7 of the 15 WUAs during the 2008 irrigation season. The developed tools were integrated into a Decision Support System for performing energy analysis and for proposing measures of energy efficiency improvement. In most of the study cases, an improvement of the energy efficiency after the implementation of the proposed measures was detected, with an average energy saving of the 10.2%.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part I: Description of image acquisition and processing

There are many aspects of crop management that might benefit from aerial observation. Unmanned aerial vehicle (UAV) platforms are evolving rapidly both technically and with regard to regulations. The purpose of this study was to acquire images with conventional RGB cameras using UAVs and process them to obtain geo-referenced ortho-images with the aim of characterizing the main plant growth parameters required in the management of irrigated crops under semi-arid conditions. The paper is in two parts, the first describes the image acquisition and processing procedures, and the second applies the proposed methodology to a case study. In the first part of the paper, the type of UAV utilized is described. It was a vertical take-off and landing quadracopter aircraft with a conventional RGB compact digital camera. Other types of on-board sensors are also described, such as near-infrared sensors and thermal sensors, and the problems of using these types of expensive sensor is discussed. In addition, software developed by the authors for photogrammetry processing, and information extraction from the geomatic products are described and analysed for agronomic applications. This software can also be used in other applications. To obtain agronomic parameters, different strategies were analysed, such as the use of computer vision for canopy cover extraction, as well as the use of vegetation indices derived from the visible spectrum, as a proper solution when very-high resolution imagery is available. The use of high-resolution images obtained with UAVs together with proper treatment might be considered a useful tool for precision in monitoring crop growth and development, advising farmers on water requirements, yield production, weed and insect infestations, among others. More studies, focusing on the calibration and validation of these relationships in other crops are required.

Evaluation of Irrigation Systems by Using Benchmarking Techniques

Water scarcity, which is typical of arid and semiarid regions such as Castilla-La Mancha (Spain), makes necessary the efficient use of water and energy resources. For this aim, there are several decision support system tools, including the benchmarking technique. The aim of this work is to compare two of the most extended irrigation systems (sprinkler and drip irrigation systems) in Castilla-La Mancha (Spain) by using performance indicators related to management of irrigated areas. The benchmarking technique was applied during three irrigation seasons (2006–2008) in six water users associations (WUAs) in Castilla-La Mancha Region (Spain). The indicators utilized in the benchmarking techniques were classified into two groups: descriptive and performance indicators. The information required to calculate the indicators was obtained from managers and farmers of each WUA, complemented with data obtained by using specialized equipment. To determine the grouping and differences between WUAs with different irriga...

Optimization of Underground Water Pumping

Nowadays, it is necessary to develop methodologies, tools, and actions that try to optimize the use of the energy resources. One of the main problems found was the improper dimensioning of the pumping for undergroundwater extractions that supply water to reservoirs. In this paper, a new methodology to obtain the minimum total cost ( investment+operation costs) by optimizing the characteristic and efficiency curves, together with the pumping pipe diameter, was developed. This methodology was based on the theoretical relations between the characteristic and efficiency curves and it considered different variables such as: hydrologic, topographic, hydraulic, and economic variables. In addition, software implemented in MATLAB environment was developed to facilitate the transference of this methodology to engineers and managers of irrigable areas. The results show that the steepness of the characteristic curve is mainly associated with the water table level variation throughout the year, and the pumping pipe di...

Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Leaf area index (LAI), green canopy cover (GCC), and canopy volume (V) are associated with grape vigor, quality, and yield. Thus, analyzing these parameters throughout the growing season may help optimize site-specific management of grape vineyards. Because direct measurements of LAI are destructive, tedious, and not repeatable on the same vine, developing and validating nondestructive methods to estimate LAI are essential. Canopy pattern is characterized by GCC and V, which can be measured using aerial observation. The purpose of this study was to characterize growth parameters, such as LAI, GCC, and V, of irrigated and rainfed Vitis vinifera L. under semiarid conditions on two different vineyards using aerial images from an unmanned aerial vehicle. The relationships between GCC versus LAI and V versus LAI were calculated and validated. Relationships between LAI and the other parameters depend on canopy management, training system, and pruning practices. Relationships between LAI and growing degree days (GDD) and V and GDD were also obtained to determine the canopy structure pattern during the growing season. Exponential polynomial and second-order polynomial models showed the best fit for describing the relationships between GCC and GDD and between V and GDD, respectively, for Airén variety.

Survey and Classification of Large Woody Debris (LWD) in Streams Using Generated Low-Cost Geomatic Products

Water authorities are required to have a survey of large woody debris (LWD) in river channels and to manage this aspect of the stream habitat, making decisions on removing, positioning or leaving LWD in a natural state. The main objective of this study is to develop a new methodology that assists in decision making for sustainable management of river channels by using generated low-cost, geomatic products to detect LWD. The use of low-cost photogrammetry based on the use of economical, conventional, non-metric digital cameras mounted on low-cost aircrafts, together with the use of the latest computational vision techniques and open-source geomatic tools, provides useful geomatic products. The proposed methodology, compared with conventional photogrammetry or other traditional methods, led to a cost savings of up to 45%. This work presents several contributions for the area of free and open source software related to Geographic Information System (FOSSGIS) applications to LWD management in streams, while developing a QGIS [1] plugin that characterizes the risk from the automatic calculation of geometrical parameters.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain

Leaf area index (LAI) is involved in biological, environmental and physiological processes, which are related to photosynthesis, transpiration, interception of radiation and energy balance. Thus, most crop models use LAI as a key feature to characterize the growth and development of crops. However, direct measures of LAI are destructive and tedious so that samplings can seldom be repeated in time and in space. Green canopy cover (GCC) is directly involved in crop growth and development. GCC estimation can benefit from aerial observation, as it can be measured by using image analysis or estimated by obtaining different vegetation indices. The main purpose of the second part of this paper was to study the relationships between GCC and LAI by using aerial images from UAVs in order to characterize crop growth. Also, the relationships between GCC and a vegetation index based on the visible spectrum was calibrated and validated. Relationships between LAI and GCC, growing degree days (GDD) and GCC and GDD and LAI were calibrated and validated for maize and onion crops with proper fitting. Visible atmospherically resistant index also appears to be a sensitive indicator to different growing stages and could generally be applied to any field crop. To apply this methodology, GCC and LAI relationships must be calibrated for many other crops in different irrigable areas. In addition, the cost of the UAV is expected to decrease while autonomy increases through improved battery life and reductions in the weight of on-board sensors.

Uncooled Thermal Camera Calibration and Optimization of the Photogrammetry Process for UAV Applications in Agriculture

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.

Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)

Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.

A non-destructive method for estimating onion leaf area

Abstract Leaf area is one of the most important parameters for characterizing crop growth and development, and its measurement is useful for examining the effects of agronomic management on crop production. It is related to interception of radiation, photosynthesis, biomass accumulation, transpiration and gas exchange in crop canopies. Several direct and indirect methods have been developed for determining leaf area. The aim of this study is to develop an indirect method, based on the use of a mathematical model, to compute leaf area in an onion crop using non-destructive measurements with the condition that the model must be practical and useful as a Decision Support System tool to improve crop management. A field experiment was conducted in a 4.75 ha commercial onion plot irrigated with a centre pivot system in Aguas Nuevas (Albacete, Spain), during the 2010 irrigation season. To determine onion crop leaf area in the laboratory, the crop was sampled on four occasions between 15 June and 15 September. At each sampling event, eight experimental plots of 1 m2 were used and the leaf area for individual leaves was computed using two indirect methods, one based on the use of an automated infrared imaging system, LI-COR-3100C, and the other using a digital scanner EPSON GT-8000, obtaining several images that were processed using Image J v 1.43 software. A total of 1146 leaves were used. Before measuring the leaf area, 25 parameters related to leaf length and width were determined for each leaf. The combined application of principal components analysis and cluster analysis for grouping leaf parameters was used to reduce the number of variables from 25 to 12. The parameter derived from the product of the total leaf length (L) and the leaf diameter at a distance of 25% of the total leaf length (A25) gave the best results for estimating leaf area using a simple linear regression model. The model obtained was useful for computing leaf area using a non-destructive method.