Uso di dati telerivelati multispettrali ed iperspettrali per lo studio delle condizioni di stress e le simulazioni degli scambi di carbonio in ecosistemi terrestri
Author(s)
Tramontana, Gianluca
Date Issued
April 13, 2012
Type
Doctoral Thesis
Abstract
The main sources of uncertainty in CO2 and H2O fluxes up-scaling using empirical methods can be
summarized in 1) degree of correlation between inputs and target variables, 2) uncertainty in inputs
values and 3) method used in the extrapolation. In this work a fluxes up-scaling dedicated algorithm
will be presented. It uses only remotely sensed input in order to estimate GPP fluxes in spatially
explicit way.
The study has been conducted at canopy level, ecosystem level and global scale. One of the most
important factor that affect carbon dioxide fluxes is water stress. Primary, at canopy level
correlation between multispectral spectrometer data and vegetation water status variables have been
evaluated. Preliminary hypothesis has been carried out by simulations using a radiative transfer
model (PROSAIL). A water stress experiment has been also carried out, inducing water status in
plants and acquiring spectrometer data to check the degree of correlation between multispectral
vegetation indices and water status variables. A library of satellite available vegetation indices has
been evaluated. In addition, all available multispectral bands in the range 350-2500 nm at bandpass
of 10 nm, have been used to calculate all available vegetation indices and correlation with
vegetation water status variables in order to evaluate other band combination not acquired currently
by satellite.
At ecosystem scale, relationships between eddy-covariance tower latent heat (LE) and gross
primary production (GPP) to MODIS satellite multispectral indices has been evaluated. Degrees of
correlation have been analysed in order to evaluate the if different fitting can be put in relation to
plant functional type (PFT). The effects of meteorological gradient and sites specifics characteristics
on degree of correlation between fluxes and vegetation indices haves been also evaluated.
In order to define the climatic conditions where relationships between fluxes and remotely sensed
indices in water stress situations works better, period with high water stress level (no precipitation
for at least 24 days) have been selected and the shape of the relation studied. These studies are
useful in order to elaborate empirical model for GPP up-scaling. We choose a model tree algorithm
in order to up-scale GPP fluxes. The reason was related to capability of model tree to generalize
relationships for datasets characterized by high variability and interaction. Model tree is able to
stratify dataset in homogeneous subset in order to maximize degree of fitting of multiple regression
function to a target variable. Model was been evaluated by leave one out method. Only remotely
sensed input are used reducing the level of uncertainty due to errors in the input dataset. The degree
of accuracy was similar to other remotely sensed methods that, in addition use ancillary interpolated
and modeled input (es. MOD 17). This results must be encourage the use of empirica methods
based only on measured data (like radiations) that have the advantage of a reduction of others
sources of uncertainty.
Additional information
Dottorato di ricerca in Ecologia forestale
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