Full text: Proceedings, XXth congress (Part 7)

  
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. 
Inte 
In 
alli 
“en 
alli 
gra 
im] 
for 
in 
tyr 
lay 
wil 
sor 
em 
of 
Cy 
fan 
the 
19
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.