International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
climate of the study area can be classified as sub-desert, and it
is characterized fundamentally by a strong anticyclone
influence that governs the radiative, thermal and hydric
regimes. Nevertheless, the space variability of these regimes is
due to its physiographic state, composed by four unities, such as
the coastal strip, the cross-sectional valleys, the pre-mountain
range and the mountain range of the Andes. The region
fundamentally presents an important hydric deficit during the
months of summer, which is due mainly to an atmospheric
demand of elevated water, and low precipitations. These last
ones have an annual rank between 100 and 500 mm,
nevertheless present a noticeable space variability, growing in
the west and east directions. The combination of both previous
elements together with the incidence of a strong solar radiation
due to the presence of an atmosphere is transparent and with
very low cloudiness. The thermal regime is characterized by the
thermal amplitude caused by the high diurnal temperatures and
moderate nocturnal temperatures. The region presents a coastal
strip where the climate becomes considerably fresher as a result
of the oceanic regulation of the thermal regime. The sea is
something colder than what it should be in this latitude, due to
the presence of the cold current of Humboldt (Santibáfiez,
1986). The study area is dominated preferably by natural
prairies, which constitute the main resource to feed for the
cattle mass of the region. If the precipitations are sufficient,
there will be pastes from March to November, which gives an
appreciable inter-annual variability viewed by satellite images.
Nevertheless, unpredictable rains and the increase of the
degradation of the prairies, contribute to a diminution of the
availability of vegetation apt for pasturing. In addition, the fact
that the precipitations have diminished in the last century
between a 10% and 30 %, with a decreasing rate of
approximately 0.7 mm/year (Morales, 2003). This situation
could be due to cycles in the climate, or to a possible climatic
change. However, in any case a monitoring of the dynamics of
the vegetation would contribute as an antecedent of
management and decision making.
3. SPATIAL HOMOGENEITY INDEX
To analyze the vegetation spatial dynamics is necessary to
express its behaviour based on homogenous areas. Based on this
fact, we propose an index that expresses the inter-annual
variability of the vegetation as a relation between the average
values and their coefficient of variation, in the interior of a
series of time. In order to do this, we was used a series of time
of seven years of satellite images NOAA (National Oceanic and
Atmospheric Administration)-AVHRR(Advanced Very High
Resolution Radiometer). This series of images was acquired by
an antenna pertaining to the University of Chile, corresponding
to the period 1986-1992, and geo-referenced and corrected from
the atmospheric effects (Chávez, 1996). From them, we
calculated the NDVI using the relation where p,;, corresponds
to the reflectivity in the near infrared band and pq the
reflectivity in the red band. From this series, monthly average
images from weekly average images were calculated, because
they are divided in three daily images, from which the
maximum values were extracted, with the purpose of
eliminating the effect of the cloudiness. Later, the monthly
average values for every year were calculated, using the same
previous method. Considering that the amount and the
seasonality of the biomass production depend on the type and
state of the vegetation, an index was elaborated that combines
both aspects. For this, a matrix of two entrances was
constructed and combines the maximum NDVI considered for
each one of the seven years and the coefficient of variation
560
respectively. It was divided in three intervals and within each
one of them three states were considered (low, medium and
high), so that 9 combinations were generated that represent the
different behaviours of the vegetation (Figure 2).
9 8 7
4 5|6
1 2 3
Low NDVT- Lew Variahitity
Low NDVI Media Variability
Low NDVI. High Vaxishility
Medium NDVI - Low Variahility
Medium NDVI - Median Variability
Medium ND VI - High Variahility
Hihg ND VI - High Variability
High ND VI - Medtura Varialuluty
High ND VI - Lew Variability
50 (NDVI * 1)
CMI ew
30 40 60 60 90
Variation Cofficient (%)
Figure 2.- Proposed relation for the study area classification. It
is based on the variability among the time series, and its
absolute maximum values. This corresponds to the used
classification matrice.
In this sense, we refer by the NDVI to the biomass, because it
was found, for the zone of study, a linear relationship between
both variables (Morales, 1998). High biomass in combination
with a low variability indicates the presence of an ecosystem in
good conditions, with a participation discharge of shrubs or
arboreal perennial species. On the contrary, low biomasses,
with low variability indicates; that it is an ecosystem in
desertification end, incapable to respond as opposed to the
variations of the climate. As a general rule, increases in the
biomass means better quality of the ecosystem, and increases in
the variability is interpreted as an increase of the instability of
the ecosystem which is typical of annual vegetal covers with
little perennial vegetation. Figure 3 shows the space distribution
of the inter-annual variability of the vegetation, stratified in the
biological patrons defined by the SHI. This last is obtained
thanks to the cross of the information about average values and
the coefficient of variation in agreement with the matrix of
relation shown in Figure 2. The original image has been
reclassified to show a simpler vision at the time of interpreting
the vegetational zones. Figure 4 shows the temporary variability
of the NDVI for all the time series. However, Figure 5 shows
the maximum mean monthly values, both for each one of the
reclassified zones of SHI. In Figure 5, it is possible to observe
that the vegetation shows two important tips, associated to the
shrubs vegetation and the prairies. Nevertheless, classes 7 and 8
correspond to the zones of irrigated land, fundamentally grapes
for export, which is out of the present work analysis. Figure 6
shows the accumulated NDVI, that presents a logistic form
approximately and has the same structure of the curve of
biomass accumulation of a prairie during their interval of
growth.
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