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Descripción
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| Estimation of the crop water requirement is critical in the optimization of agricultural production process, due yield and costs are directly a ected by this estimation. Important bene?ts of a correct estimation of water requirement are the increasing irrigated area, and a high production due a better root condition (Millar, 1993; Baruch and Fisher, 1991; Ferreyra et al., 1985). Actually in most cases the crop water requirement, represented by the Evapotranspiration (ET), is estimated through punctual approaches. Some of these approaches use classical and novel techniques, that can estimate ET with high accurate but precluding the spatial crop representation of ET. In this sense, remote sensing appears as a way to ET estimation considering spatial and temporal variability. ET models using satellite images has been developed in the last decades, using in most cases the surface energy balance which has generated good ET representation in di erent study sites. One of these models is METRIC (Mapping Evapotranspiration at high Resolution using Internalized Calibration) that using a simple and physical-empirical basis solving the surface energy balance to estimate ET as residual. The main problematic of METRIC model is the low robustness in the selection of a pair of parameters (anchor pixels), that in the original version of METRIC were selected by an operator, but aiming to standardize this selection and avoid the e ect of di erent operator criteria an automation was proposed. Although this automation standardizes the model response, this requires a selection of an area where ?nd these anchor pixels, moreover a high sensibility to di erent anchor pixel candidates still can be observed. In addition, where this automation is implemented selecting di erent areas where anchor pixels are found, important di erences in the ET estimation are generated. In this dissertation an object based image analysis (GEOBIA) is implemented to anchor objects (changing the approach from pixel to object) identi?cation. With the GEOBIA approach, segmentation and classi?cation processes are used to a correct selection of segments in the image that ful?ll the requirements for anchor objects, considering spectral and contextual information. The implementation of this approach aiming to increase the model robustness regarding to anchor objects selection. Image segmentation was done using an adapted SLIC algorithm to carry out a hierarchical segmentation, in order to identify the optimal scale segmentation for each parent segment. While, classi?cation was divided in two processes: a primary classi?cation using Random LIST OF TABLES 13 Forest that were trained considering seasonal and humidity conditions; and a post-classi?cation step considering contextual and statistical information. Results showed that the proposed GEOBIA approach allows to improve the robustness in the selection of anchor objects, which was validated through comparison with original selection of anchor pixels in two scenes, moreover all set of scenes where the methodology was implemented showed a low standard deviation in the results. About the validation of results of the proposed methodology comparing with measured data, this showed that the proposed methodology generates a slight error greater than original model. Homogeneity analysis showed that 0.7 mm day?1 was the standard deviation for a full scene, considering all landcover. This value is a good indicator of that segmentation process allowed to generate homogeneous segments without loss spectral characteristics. | |
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Internacional
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No |
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ISBN
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Tipo de Tesis
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Doctoral |
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Calificación
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Apto cum laude |
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Fecha
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26/08/2015 |