| TAZ-TFM-2018-1083 |
MODELIZACIÓN EMPÍRICA DEL ÍNDICE DE ÁREA FOLIAR EN ECOSISTEMAS DE DEHESA: INTEGRACIÓN DE DATOS DE CAMPO, AEROPORTADOS Y DE SATÉLITE
Mestre Runge, Christian
Lloveria Montorio, Raquel (dir.)
Universidad de Zaragoza,
FFYL,
2018
Máster Universitario en Tecnologías de la Información Geográfica para la Ordenación del Territorio: Sistemas de Información Geográfica y Teledetección
Tipo de Trabajo Académico: Trabajo Fin de Master
Notas: The leaf area index is considered a bioindicator of the state of the real health of the plants and the gross primary productivity of the vegetation. Numerous studies have shown that models based on linear simple regression are optimal tools that have the ability to relate the LAI measured in the field with information derived from optical remote sensing. The objective of the present Master's Thesis is to develop a predictive model of LAI from of multispectral information of medium spatial resolution (Landsat) from the analysis and previous modeling of the relationships between hyper-spectral information at high spatial resolution and truth-ground LAI using the upcaling technique and, developed for heterogeneous environments such as dehesas. For this purpose, hyperspectral data derived from the CASI sensor and LAI data measured in the field provided by SynerTGE and a range of Vegetation indices derived from the Landsat TM and OLI products were used. A first analysis was based on establishing empirical relationships between pseudo-LAI and vegetation in-dices. To further evaluate the performance of the model, regression analysis (RLS) was applied to model the relationship between pseudo-LAI and vegetation indices. The results established that the proposed method varies depending on the models used. On the other hand, a model was developed to i) apply and model the predictive functions generated by the RLS analyzes and, ii) validate the products using the RMSE statistic. For this, multitemporal series derived from Landast-8 OLI and LAI total and LAI green samples distributed over 5 field days were used, in each plot (11), samples were taken over 3 quadrants (25x25cm), in addition, samples taken are considered, a priori, representative of different moments of the phenological dynamics. The obtained results establish that the predictive models yield better for spring-summer periods, when the grassland is in its period of maximum growth. In addition, the model developed on grass and holm oaks yields better than model A. If we individualize the cases, it is established that the predictive model as of June 28, 2015 obtained the best values RMSE = 0.196 and RMSE (%) = 6.73 to predict the bio-physical variable LAI green.
Notas: The leaf area index is considered a bioindicator of the state of the real health of the plants and the gross primary productivity of the vegetation. Numerous studies have shown that models based on linear simple regression are optimal tools that have the ability to relate the LAI measured in the field with information derived from optical remote sensing. The objective of the present Master's Thesis is to develop a predictive model of LAI from of multispectral information of medium spatial resolution (Landsat) from the analysis and previous modeling of the relationships between hyper-spectral information at high spatial resolution and truth-ground LAI using the upcaling technique and, developed for heterogeneous environments such as dehesas. For this purpose, hyperspectral data derived from the CASI sensor and LAI data measured in the field provided by SynerTGE and a range of Vegetation indices derived from the Landsat TM and OLI products were used. A first analysis was based on establishing empirical relationships between pseudo-LAI and vegetation in-dices. To further evaluate the performance of the model, regression analysis (RLS) was applied to model the relationship between pseudo-LAI and vegetation indices. The results established that the proposed method varies depending on the models used. On the other hand, a model was developed to i) apply and model the predictive functions generated by the RLS analyzes and, ii) validate the products using the RMSE statistic. For this, multitemporal series derived from Landast-8 OLI and LAI total and LAI green samples distributed over 5 field days were used, in each plot (11), samples were taken over 3 quadrants (25x25cm), in addition, samples taken are considered, a priori, representative of different moments of the phenological dynamics. The obtained results establish that the predictive models yield better for spring-summer periods, when the grassland is in its period of maximum growth. In addition, the model developed on grass and holm oaks yields better than model A. If we individualize the cases, it is established that the predictive model as of June 28, 2015 obtained the best values RMSE = 0.196 and RMSE (%) = 6.73 to predict the bio-physical variable LAI green.
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trabajos-academicos-universidad-zaragoza > centro > facultad-de-filosofia-y-letras
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