Analysis of floods and droughts events in the Bermejo River Basin: Contribution of microwave
remote sensing in monitoring and prediction
V. Barraza Bernadas F. Grinds, A. Carbajo, H. Karszenbaum
FUNCEME - COSPAR TRAINING AND CAPACITY BUILDING COURSE ON Earth Observation Understanding of the Water Cycle (Over Land and Ocean)
01 to November 12, 2010
Floods and droughts are the most important hydrological disturbances in intermittent
streams.
The concept of hydrological disturbance is strongly related to the ecosystem dynamics.
The hydrologic mechanisms underlying the climate-soil-vegetation dynamics control the most
basic ecologic patterns and processes.
Introduction
Eco-hydrology is the science that studies the mutual interaction between the
hydrological cycle and the ecosystems.
Remote sensing provides an useful approach toward measuring important
elements of the hydrological balance and vegetation monitoring.
Map the hydrological behavior of the Bermejo watershed using microwave
systems (AMSR-E, SMOS and SAC-D Aquarius) and optical systems (MODIS), with
special emphasis in the analysis of multitemporal series (2002-on).
Use optical images to obtain patterns of NDVI and enhanced vegetation index (EVI)
at regional level, from TERRA-AQUA MODIS time series that accounts for
ecosystem dynamics related to flood and drought events.
Thesis Objectives
Study Area
Bermejo River Basin
The Bermejo River is one of the main
tributaries of the Paraguay-Paraná River
system, and flows from south-west Bolivia to
north-east Argentina through a variety of
habitats from the Andean Chain to the plains
of the lower Chaco.
It is the only river that crosses completely the huge Chaco Plains, behaving as a corridor for the
connection of biotic elements of both the Andean mountains and the Chaco Plains.
It is one of the rivers plains with the largest solid discharge of the world.
Hydrological variables
Microwave passive systems have advantages over the optical ones related to
hydrological variables.
Frequency (1-30GHz) passive microwave studies can reveal large scale
inundation patterns even in the presence of cloud cover and dense vegetation.
Provides low resolution brightness temperature (Tb) data (~ hundreds of Km2),
characterized by a high temporal resolution (~ 2-3 days).
The measured brightness temperature of the terrain is related to soil dielectric
constant and ultimately, to its flood condition.
Introduction
AMSR-E
data from 2002-on.
microwave radiometer operating at six frequency bands: 6.925 GHz (C), 10.65 GHz (X),
18.7 GHz (Ku), 23.8 GHz, 36.5 GHz (Ka), and 89.0 GHz, and two polarizations (H y V).
The IFOV is dependent on frequency, going from 43 x 75 km at C Band to 8 X 14 km at Ka
band.
In order to compare among different frequencies, we are using products with the same
resolution (AMSR-E Res-1).
We have adopted a repetition time of 16 days, which corresponds to the exact repeat
orbit.
SAC-D Aquarius and SMOS will be providing data for L band (1.3 GHz).
Available data
Work in progress
Processing AMSR-E data of all frequencies from 2002-on
Estimating flooded area and mean water level
Basic model [Hamilton, 2002]
The model has three end-members, that represent the contributions of water, non-flooded
land, and inundated floodplain to the total observed polarization difference ΔTobs,
where ΔTobs is the ΔT observed by the radiometer, fw, fnf and ff are the fractional areas of open
water (rivers and lakes without emergent vegetation), non-flooded land, and seasonally
flooded land, respectively, and ΔTw, ΔTnf, and ΔTf are the ΔT values for open water, non-
flooded land, and seasonally flooded land.
Simultaneous solution of these equations yields the following equation for the fraction of
inundated floodplain (ff):
ffnfnfwwobs TfTfTfT
fnfw fff 1
nff
nfwnfwwobsf TT
TfTTfTf
fTwT
nfT
Vegetation dynamic.
Vegetation Index analysis is one of the techniques most frequently used in remote sensing
to estimate the type/quality/biomass of the vegetation.
There is huge experimental evidence that shows that the temporal variation of NDVI can
be used to evaluate vegetation phenological patterns and to monitor landuse/landcover
change.
Normalized Difference Vegetation Index
NDVI= (NIR – RED)/(NIR + RED)
NDVI is based on the reflection properties of green vegetation and is determined by the
ratio of the amount of absorption by chlorophyll in the red wavelength (600–700 nm) to
the reflectance of the near infrared (720–1300 nm) radiation.
Introduction
NDVI data from MODIS sensor, with 250 m spatial resolution and a revisit period of once
each 16 days from 2002-on.
Moderate temporal evolution of NDVI
Vegetation dynamics
2JDJDNDVI
The main advantage of this type of model is
that it is sufficiently complex to provide a
good fit, and simple enough to have an
ecological interpretation for each parameter.
2JDJDNDVI
Parameter Definition Biological meaning
α [NDVI] Initial value of NDVI pattern
amount of green biomass at the beginning of the observation period
β [NDVI/day] Linear slope of NDVI pattern
Initial growth rate of vegetation or biomass increased green.
γ [NDVI/day2] Concavity of NDVI pattern.
Largest absolute values mean shorter vegetation growing seasons.
Vegetation dynamics
F. Grings, V. Douna, V. Barraza, M. Salvia, H. Karszenbaum, N. Gasparri, P. Ferrazzoli y R.
Rahmoune. C band radiometric response to rainfall events in the subtropical Chaco forest.
(submitted).
M. Salvia, F. Grings, P. Ferrazzoli, V. Barraza, V. Douna, P. Perna y H. Karszenbaum. Estimating
flooded area and mean water level using active and passive microwaves: The example of
Paraná River Delta floodplain (submitted).
Project
La Plata Basin floods and droughts: Contribution of microwave remote sensing in monitoring
and prediction. SAC-D Aquarius AO- CONAE-NASA. PI: Haydee Karszenbaum
Publications
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