@article{MestreRunge:76161,
author = "Mestre Runge, Christian and Lloveria Montorio, Raquel",
title = "{MODELIZACIÓN EMPÍRICA DEL ÍNDICE DE ÁREA FOLIAR EN
ECOSISTEMAS DE DEHESA: INTEGRACIÓN DE DATOS DE CAMPO,
AEROPORTADOS Y DE SATÉLITE}",
year = "2018",
note = "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.",
}