Tesis Doctorado
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Browsing Tesis Doctorado by Author "Lillo Saavedra, Mario"
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Item Agricultural drought in Chile From the assessment toward prediction using satellite data.(Universidad de Concepción., 2017) Zambrano Bigiarini, Francisco Javier; Lillo Saavedra, MarioClimate change is occurring and there is a scientific consensus that human being is playing a key role by pouring greenhouses gases to the atmosphere. Temperature has been increasing globally and the precipitation patterns are changing. Regionally, since the year 2010 Chile has been experiencing which has been called a mega drought, however, it has been seen mostly in meteorological terms by analyzing precipitation deficits. Further, the future projection for Chile indicates that the precipitation will decrease in Central-South Chile, this addded to the increase on temperature likely could increase drought frequency and intensity. Also, in this regard crop yield of corn and wheat decreases are forecasted by 2050 for Chile. The study on how climate variability and human activity impact agriculture has been known as agricultural drought. One of the main factors that trigger this drought conditions is precipitation deficit, thus is crucial to understand how this depletion relates to agriculture development. Although, since 2010 Chile has been facing water shortage mostly as results of the analysis of annual precipitation, but still there is a lack of knowledge about how this mega drought is affecting agriculture over Chile. Moreover, during the growing season 2007-2008 a large part of the country experienced decreases in crop yield for which these areas were declared under drought emergency by the government. However, by analyzing the total amount of annual precipitation these years are not seen as relevant drought years. This happens in part because for vegetation is more important the timing of the rainfall deficit rather than the cumulative over a year. Thus, the study and understanding of agricultural drought and methods that could help to anticipate it are challenging. The study of agricultural drought at regional and global scale brings the problem of having enough data that allow to analyze it spatially and temporally. Nonetheless, since the 70’s the use of remote sensing data obtained from satellite to monitor the environment at global and regional scale has been highly improved, and nowadays are a key data source to support climatic and environmental studies. In that regard, there is an important amount of satellite-derived data publicly available. One of this dataset that provided useful data for the monitoring of vegetation is provided for the National Aeronautics and Space Administration (NASA) and its sensor the Moderate-Resolution Imaging Spectroradiometer (MODIS) which is coupled to the TERRA and AQUA satellites. Further, multiple microwave and infrared satellites have allowed the development of precipitation estimates products at different temporal and spatial resolutions Between them, highlight the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) having data since 1998, and also has been derived long-term precipitation products such as the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANNCDR) with data since 1983, and the Climate Hazards Group InfraRed Precipitation with Station data version 2 (CHIRPS v2) providing estimates since 1981. These vegetation and precipitation satellite products are valuable data sources for agricultural drought studies, allowing to evaluate the interaction vegetationprecipitation at regional and global scale. Accordingly, in this thesis was studied the usefulness of satellite data for the assessment and prediction of agricultural drought over Chile. The main research question is: How well the satellite data of vegetation and precipitation together with climatic oscillation indices can be used to predict agricultural drought before the end of the growing season? To achieve this, the work was developed in three stages: 1) assessment of vegetation response to water shortage in the BioBío Region of Chile, 2) the evaluation of long-term satellite precipitation data over Chile for use in drought studies, and 3) prediction of agricultural drought in Chile from one to four month before the end of the growing season for 2000-2016. For the first stage, was usedItem Determinación de evotranspiración a escala regional mediante imágenes satelitales MODIS.(Universidad de Concepción., 2011) Flores Pacheco, Fabiola Alejandra; Lillo Saavedra, MarioDurante los últimos años se han desarrollado una gran variedad de modelos para estimar evapotranspiración (ET) espacialmente distribuida, pero muchos de ellos necesitan de una gran cantidad de datos proporcionados por estaciones meteorológicas distribuidas homogéneamente en el área de estudio, lo cual es bastante difícil de encontrar a escala regional. En la presente Tesis Doctoral se han implementado tres modelos de estimación de evapotranspiración en la Región del Bío-Bío, Chile, por medio de teledetección. Los modelos de ET implementados fueron: ET a partir del défi cit de presión de vapor (EDPV ), ET a partir de la temperatura de la Cubierta Vegetal (ETV CI) y Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC). Las imágenes satelitales utilizadas corresponden a las proporcionadas por el sensor MODIS (Moderate Resolution Imaging Spectroradiometer), el cual entrega las imágenes con una resolución espacial de 250, 500 y 1000 m, según el producto deseado, trabajado en específico a una resolución espacial de 1000 m. El modelo METRIC se tomó como base para realizar la evaluación del error de los modelos ETV CI y EDPV , ya que ha sido aplicado y calibrado con técnicas lisimétricas en diversos lugares, y además se ha comprobado su e ficiencia y exactitud para estimar flujos de ET:Item Estimación de evapotranspiración espacialmente distribuida mediante un enfoque GEOBIA (Estimation of spatially distributed evapotranspiration through of GEOBIA approach).(Universidad de Concepción., 2015) Fonseca Luengo, David Alexander; Lillo Saavedra, Mario; Lagos Roa, Luis; Gonzalo Martin, ConsueloEstimation 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. Actually in most cases the crop water requirement, represented by the Evapotranspiration (ET), is estimated through local 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 have 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. The main drawback of METRIC model is the low robustness in the selection of a pair of parameters called 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 to 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 pix 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 adaptation of SLIC algorithm proposed 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 Forest that was 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, entire set of scenes where the methodology was implemented showed a decrease in the variability of the result. Homogeneity analysis showed that ET objects with classes where crops can be found have low intra-object variability and high inter-object separability. These results are a good indicator of that segmentation process allowed to generate homogeneous segments without loss of spectral characteristics and correctly separated from one another. Validation of the proposed methodology comparing estimated with measured data showed that the proposed methodology generates a slight error greater than original model. This result suggest that some improvements to nal selection of anchor objects can be carried out, in this sense, could be considered di erent metrics to describe the segments, additional mapps, and to ex´plore other relations between segments considering spatial, temporal and hierarchical characteristics.