Aridity influence on vegetation patterns in the middle
Ebro Valley (Spain): evaluation by means of AVHRR
images and climate interpolation techniques
Sergio M. Vicente-Serrano1,2*, José M. Cuadrat-Prats3 and Alfredo Romo4
1
Instituto Pirenaico de Ecología, CSIC (Spanish Research Council), Campus de Aula Dei, P.O.
Box 202, Zaragoza 50080, Spain
2
Unit for Landscape Modelling, University of Cambridge, Sir William Hardy Building, Tennis
Court Road, Cambridge, CB2 1QB, UK
3
Departamento de Geografía. Universidad de Zaragoza. C/ Pedro Cerbuna 12. 50009. Zaragoza.
Spain.
4
Laboratorio de Teledetección. Departamento de Física Aplicada I. Facultad de Ciencias.
Universidad de Valladolid. 47071. Valladolid. Spain
* e-mail: svicen@ipe.csic.es
ABSTRACT. This paper analyses the role of climatic aridity on the spatial differences of
vegetation activity and its inter-annual variability in a semiarid region of the North East of
the Iberian Peninsula. The vegetation activity was quantified by means of a monthly
NDVI database from NOAA-AVHRR satellite images (1987-2000) at 1 km2 of spatial
resolution. Coefficients of variation (CoV) from temporal NDVI series were also
calculated. The greater temporal variability of NDVI was recorded in areas with low
vegetation cover (steppe and dry farming lands), whereas the lowest temporal variability
of NDVI was recorded in the irrigated lands and forests located in the most humid areas.
There is a strong and negative relationship between the CoV and the NDVI distribution,
but we recorded important differences among land uses. Using maps of annual
precipitation and temperature obtained by means of a regression-based method, we
studied the influence of aridity on spatial patterns of NDVI and CoV values. The results
show the high influence of aridity on the spatial distribution of vegetation activity in the
Middle Ebro Valley. Aridity causes a general decrease of NDVI and an increase of CoV.
Nevertheless, non-linear relationships between aridity and NDVI and CoV were recorded
for the whole of the study area and for the different land uses. The relationships between
aridity and vegetation patterns in the semiarid region studied are discussed in depth.
KEY WORDS. Remote Sensing, NDVI, Temporal variability of vegetation, regressionbased interpolation, land cover types, Mediterranean region.
1. Introduction
Arid and semiarid regions of the world are highly sensitive to human-induced climate and/or land
transformation (Nicholson and Farrar 1994; Nicholson et al. 1998; Evans and Geerken 2004). In
these regions, lack of water is the main constraint for vegetation development (Hadley and Szarek
1981; Le Houreou 1984). In the semiarid areas of the Mediterranean region there is high ecological
uncertainty related to the consequences of possible climatic change during the twenty-first century.
Some climatic models show an important decrease in water availability in this area (Houghton et al.
2001; Jones et al. 1996), which could cause dramatic consequences for natural ecosystems and dry
farming. The semiarid regions of southern Europe are ecotones where the impacts of climate change
can be identified early (Lavorel et al. 1998). Knowing the influence of climate on vegetation in
these regions is very important to determine the possible consequences of climatic change on
vegetation characteristics.
Remote sensing is an useful tool to analyse the vegetation dynamic on local (Ringrose and
Matheson 1991), regional (Nicholson et al. 1990), or global scales (Lucht et al. 2002; Kawabata et
al. 2001; Kogan 2001) and to determine the impact of climate on vegetation (Wang et al. 2001 and
2003). Several studies have shown that interannual differences in vegetation parameters are mainly
driven by water availability (Farrar et el. 1994; Santos and Negrin 1997; Weiss et al. 2004; Sannier
and Taylor 1998; Maseli et al. 1992; Groten 1993; Al-Bakri and Taylor 2003). Ichii et al. (2002)
analysed the relationships between precipitation and vegetation activity, quantified by means of
remote sensing, on a global scale and found the highest correlation in the arid and semiarid regions
of the world in South Africa, Australia and Central Asia. Moreover, other studies have shown by
means of remote sensing that interannual variability of vegetation activity in semiarid regions is
higher than in humid and sub-humid regions (e.g., Wang et al. 2003; Tucker et al. 1991). Also
deserts and herbaceous covers were found to have the highest variability (Fang et al. 2001, for
China).
Nevertheless, in the semiarid regions of southern Europe there are few studies on the relationships
between climatic conditions and vegetation cover quantified by means of remote sensing (e.g.,
González-Alonso et al. 1995 and 2001; Ascaso 1997).
This paper analyses the spatial patterns of vegetation activity and its temporal variability in the
Middle Ebro Valley, a semiarid Mediterranean region located in the North East of the Iberian
Peninsula, where the transformation of vegetation cover due to human activity has been intense
(Frutos, 1976). In the Middle Ebro Valley, there are agricultural areas with an important economic,
social, and environmental interest (Martí 1992; Austin et al 1998). Also, there are some natural
areas modified by humans (steppes) with great landscape diversity and biological richness (SuárezCardona et al. 1992; Pedrocchi 1998) and forests located in the boundaries of their climatic
limitations (Braun-Blanquet and Bolós, 1957).
Understanding the influence of climate on the spatial distribution of vegetation cover and vegetation
activity is very important, mainly for the areas where climate change is predicted during the
Twenty-first Century. These studies may help determine possible structural and functional
modifications of vegetation, corresponding to the present climate models. The study presented here
has been done in a region highly humanised and prone to soil degradation (Lasanta, 2003), that may
be affected by some of the problems related to global change.
2. Study area
The Middle Ebro Valley is one of the most arid regions of the Iberian Peninsula (Figure 1). The
vegetation activity in this region is highly determined by the water availability (Braun-Blanquet and
Bolos 1957; Austin et al. 1998). The vegetation cover is low due to aridity, poor soils and frequent
droughts (Guerrero et al. 1999; Vicente-Serrano, 2004). Lithology is characterised by millstones
and gypsums (Peña et al. 2002) that contribute to aridity because soils poorly retain the water
(Navas and Machin 1998). Relief determines the climate since the Valley is surrounded by
mountainous chains that cause continental climatic characteristics and important dry conditions
(Cuadrat 1999). In the centre of the Valley the annual precipitation is less than 350 mm. Moreover,
throughout the study area there is a negative water balance (precipitation – evapotranspiration),
which is very important in the central areas (> 900 mm). Temperature has an important seasonality.
Frost periods are frequent in winter, which limit, noticeably, the vegetation development. In the
centre of the valley the winter average temperature is 10º, but minimum temperatures below –10 ºC
are frequent. In summer, the average temperature is 24 ºC, although maximum temperatures above
40 ºC are also frequent. Seasonal differences of precipitation are less important, although the dry
season is usually recorded during summer months.
The landscape has for centuries been highly determined by human land use, which has substantially
altered the natural vegetation (Frutos 1976; Pinilla 1995). During the second half of the twentieth
century, the transformations affected large areas due to the creation of irrigated lands (Frutos 1982)
and land abandonment caused by the extensification policies of the European Union (Errea and
Lasanta 1993). At present, the dominant land uses are steppes (mosaic of shrubs and pastures)
(25.7%), dry farming areas with herbaceous cultivations (21.4%), and the mosaic of both these
previous land uses (13.6%). Coniferous forests (mainly Pinus halepensis) cover 7.9% of the total
surface of the study area. Moreover, an important percentage of the study area has been transformed
to introduce irrigation (20.8%) (Figure 1).
3. Methods
3.1. Remote sensing capability to ecosystems monitoring
We used remote sensing to analyse the influence of climate on vegetation activity and its
interannual variability. The use of remote sensing to analyse vegetation activity is based on the
spectral properties of vegetation, which absorbs an important percentage of electromagnetic energy
in the visible region and reflects a high percentage in the near-infrared region (Knipling 1970). Due
to these properties, different vegetation indices based on combinations of red and infrared bands can
be calculated. The most widely used is the Normalized Difference Vegetation Index (NDVI)
(Tucker 1979). High NDVI values indicate high vegetation activity due to the strong relationship of
this index to radiation absorbed in photosynthetic processes (Gallo et al. 1985). But NDVI is also
highly related to net primary production, vegetation cover and leaf area index (Sellers 1985).
The NDVI presents some problems for vegetation analysis due to the fact that relationships between
NDVI and biomass or vegetation cover are often not linear (Choudhury et al. 1994; Gillies et al.
1997). Nicholson et al. (1990) indicated that the NDVI is a poor indicator of vegetation biomass in
areas with high vegetation density. The soil background may also affects NDVI in semiarid regions
because as the percentage of vegetation cover decreases the signal become increasingly
contaminated by the soil reflectance (Huete and Jackson 1987). Nevertheless, in arid and semiarid
areas in which the vegetation cover is not dense, several studies have shown the strong relationship
between NDVI and vegetation biomass, vegetation cover and vegetation activity (i.e., Tucker et al.
1981; Gutman 1991; Diallo et al. 1991; Fuller 1998).
The NDVI is obtained from different satellites, but due to the elevated temporal frequency and the
spatial resolution, the NOAA-AVHRR images are widely used in NDVI calculation (Gutman 1991;
Gutman et al. 1995; Ehrlich et al. 1994). The AVHRR sensor registers radiometric data in the red
and infrared spectral regions. The spatial resolution (1.1 km at nadir) reduces radiometric problems
caused by soil (Jasinski 1990) or topography (Burgess et al. 1995).
The NDVI data used in this paper were created in Spain by the LATUV (Remote Sensing
Laboratory of Valladolid University) at local area coverage resolution (LAC, 1 km 2 of grid cell
size). The images are received daily, calibrated (Kaufman and Holben 1993; NOAA 2003) and
atmospherically corrected based on a modification of the 5S code (Tanré et al. 1990) using standard
atmospheric measures for ozone absorption and molecular dispersion. For water vapour a method
based on brightness temperatures of channels 4 and 5 is used (Illera et al. 1997). Later, the NDVI is
obtained and geometrically corrected (Illera et al. 1996a). Residual atmospheric errors are reduced
with the creation of monthly indices using the Maximum Value Composite method (MVC) (Holben
1986). The method is based on the selection of the maximum monthly NDVI from the daily images
because the maximum values are recorded under optimum atmospheric and observation conditions:
near Nadir, low aerosols and free of cloud (Gutman, 1989). The NDVI data set used in this paper
has been extensively applied in the Iberian Peninsula for numerous purposes: identification of
droughts (González-Alonso et al. 2000 and 2001), yield productivity and natural vegetation
monitoring (Vázquez et al. 2001), or forest fire danger (Illera et al. 1996b).
We obtained one NDVI image per month from 1988 to 2000. For analysis, we selected the months
between April and September, a period when vegetation is active in the different land-uses. We
used the monthly NDVI data for each month, but also calculated annual values to summarise the
vegetation activity between April and September. For this purpose we used integrated NDVI values
during the season (ΣNDVI) because these values are highly related to net primary production
(Tucker et al. 1981; Tucker and Sellers 1986; Prince and Tucker 1986). Integrated values were
calculated following a trapezoidal approximation from monthly data (Justice and Hiernaux, 1986;
Samson, 1993). Each annual value is obtained by means of the sum of the monthly NDVI values
between April and September.
The average of monthly (NDVI) and annual (April to September) values (ΣNDVI) were obtained
for each pixel of 1 km2. Moreover, coefficients of variation (CoV) at monthly and annual time
scales were calculated to determine the temporal variability of NDVI following Weiss and Milich
(1997). Coefficient of variation is a simple statistic calculated from the average and the standard
deviation of the NDVI series in each pixel. This statistic has been widely used to determine the
spatial differences of temporal variability in the vegetation activity of different arid and semiarid
regions of the world (i.e., Tucker et al., 1991; Milich and Weiss, 1997; Fang et al. 2001).
The CORINE land cover map of the study area (CLC, 1989), reclassified to 12 categories, was used
to determine the role of different land uses in spatial patterns of NDVI and CoV. For this purpose,
we calculated the dominant land cover for each pixel of 1 km2, using a mode criterion (each pixel of
1 Km2 was assigned to the land cover type that represents the higher percentage of surface within
the pixel). The classes used in this study were: 1: Dry farming areas (herbaceous), 2: Dry farming
areas (permanent), 3: Dry farming areas (mosaic of herbaceous and permanent), 4: Irrigated lands,
5: Leafy forests, 6: Coniferous forests, 7: Steppes (shrubs and pastures) and 8: Mosaic of dry
farming areas and steppes.
3.2. Continuous aridity mapping
To determine the role of aridity on the spatial patterns of NDVI and CoV we used precise climatic
maps to take into account the climatic spatial differences caused by topographic and geographic
factors. For this purpose we used the mean monthly precipitation from 159 and the mean monthly
temperature from 72 weather stations. Random sampling was carried out on the original data set:
seventy-percent of the data was used for interpolations and the remaining was reserved for
subsequent test.
There are several methods to obtain continuous climatic surfaces over the territory (Isaaks and
Strivastava, 1989; Goovaerts 1997; Borrough and McDonnell, 1998). In this paper we have used
regression-based methods by means of independent geographic and topographic variables.
Regression methods are based on the creation of dependence models between climatic data and
geographic and topographic variables. The climatic data in a location where information is not
available is predicted according to:
z ( x)  b0  b1P1  b2 P2  ...  bn Pn
Where z is the predicted value in point (x), b0,...,bn are the regression coefficients and P1,...,Pn are
the values of the different independent variables in point x. The relationship between climatic data
and geographical and topographical variables has been widely analysed in the scientific literature
(Basist et al. 1994, Daly et al. 2002). These relationships may be used to create empirical models
that predict the climatic data from geographic and topographic variables. In this paper, to map
precipitation and temperature we have used an approach similar to that of Agnew and Palutikof
(2000), which mapped mean temperature and precipitation in the Mediterranean Basin. The
independent variables used in this paper can be consulted in Vicente-Serrano et al. (2003). They
were obtained from a digital elevation model of the study area at the same spatial resolution as the
NDVI images (1 km2).
Results obtained from regression models are inexact. The predicted values z(xi) do not coincide
with the climatic data obtained in the weather stations. There is a known error in the prediction
(residual). To improve the climatic maps and to correct this known error we interpolated the
residuals (Ninyerola et al. 2000; Agnew and Palutikof, 2000; Brown and Comrie, 2002) by means
of splines with tension (Mitasova and Mitas, 1993). The sum of prediction and residual maps
modifies the initial results of the model and real climatic values are obtained in the location of
weather stations. To validate the models and to know the final quality of the maps we used different
accuracy statistics (See Vicente-Serrano et al. 2003) by means of 30% of the weather stations that
were retained to validate the models.
To obtain a general measure of aridity we calculated an Aridity Index (AI) based on the ratio
between the mean precipitation and temperature maps on monthly and annual time scales by means
of Geographic Information Systems (GIS). This index has been widely used for climatic
classification (Köppen 1923; UNESCO 1979) and aridity quantification (De Martonne 1957;
Korzun et al. 1976; Botzan et al. 1998). Low values of the index indicate higher aridity.
Relationships between the AI, the NDVI and the CoV were measured by means of a non-parametric
correlation coefficient (Rho-Spearman) because it is more robust than parametric coefficients, it is
not affected by outliers and its use is convenient when relationships between variables are not linear
(Siegel and Castelan, 1988).
4. Results
4.1. Spatial distribution of aridity
The results of the regression models obtained for precipitation and temperature are shown in Table
1. In general, the models have a great quality, with high percentages of variance explained and low
errors in the predictions in relation to the range of spatial variability of precipitation and
temperature over the territory.
Table 2 shows the results of validation. In general the models have a high quality. The mean
absolute error is less than 6 mm for monthly precipitation maps and less than 0.8 ºC for maps of
monthly mean temperature. Annual maps (April to September) also show a high quality. The
agreement index (Willmott’s D), which scales with the magnitude of the variables retains mean
information and does not amplify outliers (Willmott, 1981), also shows values higher than 0.86 in
all cases, but in general the values are higher than 0.90. This indicates a high agreement between
measured and predicted data in the weather stations reserved for validation.
Figure 2 shows the spatial distribution of the averaged Aridity Index (AI) between April and
September. Important spatial differences are observed, mainly related to the relief distribution,
which determines the spatial distribution of precipitation and temperature. Aridity is more intense in
the centre and east of the study area, where low precipitation and high temperatures are recorded. In
the North, close to the Pyrenean range, the most humid conditions are shown as a consequence of
high precipitation and low temperatures. In the southeast, close to the Mediterranean Sea and also
corresponding to a Mountainous area, the conditions are also humid.
4.2. Spatial patterns of NDVI
Vegetation activity has an important seasonality. The highest vegetation activity is recorded in the
months of April and May. In spring the highest NDVI values are recorded in the north of the study
area, corresponding to forests, steppes and dry farming areas. In June, as a consequence of the start
of the summer dryness, vegetation activity decreases and the mean NDVI decreases dramatically in
dry farming areas and in steppes. However, spatial diversity is very important and it is caused by
the presence of irrigated lands and some forested areas.
Figure 3 shows the spatial distribution of the NDVI average from 1988 to 2000. The spatial
differences are very important. Large areas located in the centre of the Valley have low values that
indicate low vegetation activity and net primary production. Higher values are recorded in the
forests of the north and the south-east and the irrigated areas (NDVI > 2.8). There are important
differences in the NDVI values related to the spatial distribution of land-uses. The lowest NDVI
average is recorded in the dry farming areas (1.98), whereas the highest values are recorded in the
irrigated lands (2.74), leafy forests (2.75) and coniferous forests (2.33).
4.3. Spatial patterns of CoV
Monthly differences are important, but values are higher during the summer months. In spring, high
CoV values are recorded, mainly in the dry farming lands and steppes. During the summer the most
important pattern is the low CoV values recorded in the irrigated lands and forested areas. The
irrigated lands show lower CoV values than dry farming areas in all months. The forests also show
low CoV values, although leafy forests show a lower variability than coniferous forests, mainly in
spring. Moreover, independently of the monthly differences, the land uses characterised by a lower
vegetation cover such as the steppes and the dry farming areas are located in the driest regions of
the centre of the Valley, which correspond to the highest CoV values.
The areas with the highest interannual variability of the NDVI values are the dry farming lands
and the steppes (Figure 4). Also some areas modified by irrigation in the 1990s show a high
variability. These areas have been eliminated for subsequent analysis to avoid perturbations caused
by abrupt changes in land uses. Irrigated lands show a low interannual variability and this is also
observed in the forests located in the north and the south of the study area. Also the small patches of
coniferous forests located in the centre of the Valley have lower CoV values than surrounding dryfarming lands and steppes. In relation to the averages in the different land-uses, the dry farming
lands show the highest CoV averages (0.10), whereas the irrigated lands (0.074), leafy forests
(0.070) and coniferous forests (0.078), show lower values. The steppes show medium CoV values
(0.085).
4.4. Relationships between NDVI and CoV
Figure 5 shows the relationship between the spatial distribution of NDVI and CoV for the whole
study area. Irrigated lands are not shown because irrigation alters importantly the relationship.
There is a clear negative relationship between both variables, which indicates that areas of high
NDVI values have lower CoV values than areas of low NDVI. Nevertheless, the relationship
between these two parameters is not linear. A negative exponential relationship is recorded, which
indicates that areas with low NDVI values coincide with spaces in which a high interannual
variability of NDVI is recorded (CoV values higher than 0.15). Between NDVI values of 1.5 and
2.5 there is a high CoV range. Over 2.5 this range is reduced but there is not a decrease of the CoV
values proportional to the NDVI increase. Over NDVI values higher than 2.5 the CoV is not
sensitive to NDVI increase. The relationship between NDVI and CoV is negative and
statistically significant (Rho = -0.59, p < 0.001).
4.5. Aridity influence on the spatial patterns of NDVI
Figure 6 shows the relationship between the spatial distribution of mean NDVI and the Aridity
Index (AI). Grey points indicate irrigated lands, which are mainly located in sectors with high
aridity (AI < 2.6) but, in general, show high NDVI values. The irrigation modifies the relationship
between aridity and vegetation productivity. For this reason higher NDVI values than expected by
climatic conditions are recorded in irrigated lands. In the other land-uses the relationship between
the spatial distribution of the AI and the NDVI is clearly positive (Rho = 0.74, p <0.001) but not
linear. Between 1.5 and 3 NDVI values the relationship between both variables is positive and
linear. Over 3, although the magnitude of the AI increases, the NDVI values remain constant. The
relationship indicates that the spatial distribution of the NDVI is highly determined by aridity,
mainly in areas with low water availability.
Figure 7 shows the average NDVI and AI for the different land-uses. The results indicate that the
spatial distribution of the different land-uses is highly related to the spatial distribution of aridity,
with the exception of irrigated areas. The mean values of AI correlate the mean NDVI values
corresponding to different land-uses (R2 = 0.96).
These results are confirmed when relationships between aridity and NDVI values are analysed
independently in the different land-uses. Important differences are found (Figure 8). The positive
and significant relationships between the AI and the NDVI are dominant and significant in the
different land-uses. The exceptions are the irrigated lands, where correlation is negative (Rho = -
0.40). In the dry farming areas, the relationship between NDVI and AI is more linear than in the
other land-uses. In this land-use the AI range is low (from 1.5 to 4). In the most humid areas (AI
values higher than 4), however, other land-uses are located, such as leafy and coniferous forests.
The non-linear relationship between NDVI and AI found throughout the study area is caused by
the NDVI values in some forested regions where the AI is higher than 4. Over this threshold the
NDVI is not sensitive to the AI increase.
These patterns are also recognised analysing the NDVI-AI relationships on a monthly time scale.
The monthly relationships between NDVI and the AI are positive and significant with Rho
coefficients higher than 0.52 in all cases, with a maximum of 0.75 in May.
Table 3 shows the monthly Rho-values between the NDVI and the AI in different land-uses. Results
are similar to those found on an annual scale, the positive and significant correlations prevailing in
the different land-uses with the exception of the irrigated lands, where correlations are negative.
This indicates that independently of the monthly phenological differences in vegetation activity,
aridity is a critical factor to explain the spatial differences in the NDVI values each month in the
land-uses of the middle Ebro Valley.
4.6. Aridity influence on the spatial patterns of CoV
Figure 9 shows the relationship between the spatial distribution of the AI and the annual CoV
values. Irrigated lands have been indicated separately as in NDVI analysis because these areas
have small CoV values corresponding to high aridity conditions. In the other land-uses there is a
negative and significant correlation between the spatial distribution of the AI and the CoV values
(Rho = -0.40, p <0.001). However, in this case the relationship is not linear either. In the most
humid areas, only low CoV values are recorded, and in the driest areas, the range of CoV values is
high but the highest CoV values (> 0.10) are only recorded in the most arid regions.
Figure 10 shows mean values of CoV and AI for the different land-uses. The relationship is
negative, the opposite of what is observed for the relationship between NDVI and AI. More
favourable moisture conditions favour the low interannual variability of the NDVI. The spatial
distribution of land-uses and their associated CoV values is also highly determined by aridity (R2 =
0.74). Only the irrigated lands show a different behaviour (low CoV values corresponding to high
aridity), which provides evidence that irrigation reduces the inter-annual uncertainty in the net
primary production in the most arid regions.
Figure 11 shows the relationships between the AI and the annual CoV values for different landuses. In general, the clearest relationships between these two parameters are recorded in natural
vegetation areas such as leafy and coniferous forests and in the steppe areas covered by pastures and
shrubs. In these land-uses, there is a negative exponential relationship between the spatial
distribution of the AI and the CoV values, which indicates that in the most humid areas the CoV is
low, whilst the highest values of CoV are recorded exclusively in the driest areas. The dry farming
areas show a higher dispersion that indicates a high uncertainty in the CoV values as a function of
the AI, although the relationship is negative and statistically significant. In irrigated lands the
relationship is positive and also statistically significant.
On a monthly time scale the relationships between the AI and the CoV is similar to that shown
annually. The monthly correlations in the different land-uses show a similar behaviour than
annually: positive correlations between AI and CoV in the irrigated lands and negative in the rest of
land-uses. Likewise, the correlations are higher in natural vegetated areas (forests and steppes)
(Table 4).
5. Discussion and conclusions
Our data show the influence of aridity on the spatial distribution of monthly vegetation activity and
of annual vegetation productivity and its interannual variability in the central Ebro Valley (NE
Spain). Evidence has been provided about how the use of AVHRR images has a high potential for
spatial and temporal analysis of vegetation activity by means of NDVI, allowing us to have
continuous spatial data, which can then be included into GIS to establish relationships with other
climatic and geographic variables. Moreover, the GIS have also been very useful to estimate
continuous climatic data from point measurements so as to provide greater knowledge about the
spatial variability of the climatic elements on monthly and annual time scales.
The spatial differences in the NDVI and CoV values in the central Ebro Valley are very important.
In general, the areas of high vegetation activity correspond to forests and irrigated lands, whereas
the dry farming lands and the steppes located in the centre of the Valley show low NDVI values.
The poor substrate (gypsums and milestones), the high aridity and the scarce vegetation cover of the
central areas explain these low values. The CoV distribution shows the opposite pattern because the
temporal variability is high in the dry-farming areas and the steppes and low in forests and irrigated
lands.
There is a negative and significant relationship between the spatial distribution of the NDVI and
CoV values both in the whole study area and in the different land-uses. This indicates that areas of
low NDVI values, which imply low vegetation cover and activity, also have the problem of a high
interannual uncertainty in vegetation activity and net primary production. Therefore, the availability
of grazing resources for livestock and crop productions vary extraordinarily among years, which
cause important economic problems in these areas (Frutos, 1976; Martí, 1992; Austin et al. 1998).
Moreover, the high interannual variability of the NDVI identified in the areas characterised by a
low vegetation cover could be indicative of possible degradation problems. The high interannual
variability of the vegetation cover might be indicative of non-adaptations to climatic characteristics,
which would be caused by the modification of the primitive landscape by humans (Puigdefábregas
1995). When human activity alters the natural vegetation cover, the system may become more
vulnerable to climatic variability, and the frequent drought periods that affect this region (VicenteSerrano and Beguería, 2003) could set off degradation processes (Hill and Peters 1996; Pickup
1998).
This paper has also provided evidence about the fact that in the Middle Ebro Valley the spatial
distribution of NDVI is mainly determined by the spatial distribution of aridity. This has also been
observed in other semiarid regions of the world, where the main limiting factor for vegetation
activity is soil moisture availability (Le Houerou, 1984; Bonifacio et al., 1993). Nevertheless, we
must indicate that the middle Ebro Valley is a region in which vegetation cover has been highly
altered by humans for centuries. Despite the important centuries-old human-induced land cover
change (deforestation and land-intensification), the NDVI values have experienced few changes
regarding the potential climatic characteristics. The single exceptions are irrigated lands, where
transformation causes a significant increase of NDVI values regarding the climatic characteristics.
This fact provides evidence that aridity is the main limiting factor to explain the present distribution
of the different land-uses in the middle Ebro Valley.
Climatic control of the spatial patterns of NDVI has been observed in other semiarid regions (Malo
and Nicholson, 1990; Farrar et al. 1994; Liu and Kogan, 1996; Weiss et al. 2004). Nicholson et al.
(1990) have indicated that in the Sahel and in East Africa spatial patterns of NDVI are highly
related to the spatial distribution of annual precipitation. These authors have also shown a high
relationship between the spatial distribution of NDVI and precipitation in regions in which annual
precipitation oscillates between 200 and 1200 mm. Over 1200 mm, they indicated that NDVI is not
sensitive to precipitation increase. Some other authors have also shown that the NDVI is not
sensitive to high precipitation values. This causes relationships between NDVI and precipitation to
be not linear but log-linear (Poccard and Richard, 1996; Santos and Negrín, 1997). This has also
been observed in the middle Ebro Valley monthly and annually. Over a threshold of 3.5 in the AI
the NDVI is non-sensitive to the increase in moisture availability. This could be explained by the
differences in the water-use efficiency by the distinct vegetation covers (Le Houerou, 1984), but
also by the spatial differences in precipitation values. In the most humid areas, the high
precipitation cannot be absorbed entirety by soil. High precipitation causes an increase of the aridity
index, but this does not necessarily involve an increase of the water availability because in the areas
that receive the highest precipitation there is a significant superficial runoff, which is not used by
vegetation to increase activity and net primary production.
Moreover, in forested regions and areas with high vegetation cover, the relationship among
vegetation parameters such as the leaf area index and the net primary production and the NDVI is,
on occasion, not linear (Gillies et al., 1997; Choudhury et al., 1994) because the NDVI saturates
before the maximum biomass is reached (Carlson et al., 1990). The NDVI can be a poor indicator of
vegetation biomass when a high density of the vegetation cover is recorded. When the surface is
totally foliated saturation can be reached, which causes absorbed radiation and NDVI to remain
constant while the leaf area index increases in subsurface vegetation layers. In such cases, the
surface and low leaf reflectivity is attenuated when the surface is totally darkened by the superficial
leaves (Carlson and Ripley, 1997), which causes the relationship between the NDVI and the net
primary production in dense forested areas to be not linear but log-linear (Spanner et al., 1990).
This could imply that in the most humid regions the high humidity favours vegetation activity, but
this is not well shown by the NDVI.
Nevertheless, and independently of the NDVI saturation over certain AI thresholds, the results
obtained in steppes and dry farming areas indicate that in non-forested regions the main constraint
to explain the spatial and temporal variability of the vegetation activity is aridity, independently of
monthly phenological differences. The land-uses located in the central regions of the Ebro Valley
are near their boundaries of ecological distribution due to the important aridity that characterises the
climate and the frequent and intense droughts (Vicente-Serrano and Beguería, 2003). In general,
vegetation located near its boundaries of ecological distribution is more vulnerable to climatic
variability (Fritts, 1976). Different studies have shown that in areas affected by extreme climatic
conditions the response of vegetation to interannual climatic variability is very high (Milich and
Weiss, 2000; Salinas-Zavala et al. 2002). The dominant herbaceous formations and shrubs with
little developed root systems cannot muffle the soil water decrease during dry periods. For this
reason, these areas will have important interannual differences in NDVI values (Milich and Weiss,
1997), which are highly determined by the soil water availability (Reed, 1993).
The creation of irrigated lands in the middle Ebro Valley generates some environmental and socioeconomic problems related to soil degradation and salinisation and social conflicts related to the
water availability (Lasanta et al. 2001; Herrero and Aragüés, 1988; Embid, 2003). However,
irrigation importantly increases the final crop productions and also reduces significantly the
interannual uncertainty in the vegetation productivity. The NDVI values in irrigated lands increase
noticeably in relation to the potentiality, depending on aridity conditions. Irrigation makes the
relationships between AI, NDVI and CoV the opposite of what is observed in the other land-uses. In
the most arid areas, higher temperatures are recorded, and these favour vegetation activity when
there are no restrictions of water availability. Few studies on the productivity in irrigated lands
using remote sensing have been carried out, but Millington et al. (1994) and Srivastava et al. (1997)
in Pakistan and India, respectively, have indicated that irrigation reduces the interannual variability
of NDVI in comparison to non-irrigated lands due to less dependence on precipitation variability.
The results obtained in this paper, where the important role of aridity on vegetation has been shown,
provide evidence about the high sensitivity to the climatic change processes exhibited by the
semiarid Mediterranean regions. In the Mediterranean area, the present climatic models predict a
precipitation decrease greater than 15% and a temperature increase higher than 3 degrees in the
second half of the twentieth century (Houghton et al. 2001). Moreover, an increase in the temporal
climatic variability is also predicted (Katz and Brown, 1992). This would cause a very important
increase of aridity conditions, a decrease of the vegetation activity and the net primary production
as well as an increment of the temporal variability and uncertainty about vegetation productivity.
Moreover, soil erosion and degradation processes could be reinforced by climatic change and even
the spatial distribution of land-uses could be affected.
Acknowledgements
This work has been supported by the projects: “Caracterización espacio-temporal de las sequías en
el valle medio del Ebro e identificación de sus impactos” (BSO2002-02743), “Variabilidad
climática y dinámica forestal en ecosistemas de ecotono” (REN2003-07453), “El papel de las
sequías en los procesos de modificación ambiental en el semiárido aragonés: evaluación mediante
imágenes de satélite de alta resolución espacial y Sistemas de Información Geográfica” (CGL200504508/BOS) financed by the Spanish Comission of Science and Technology (CICYT) and FEDER,
and “Programa de grupos de investigación consolidados” (grupo Clima, Cambio Global y Sistemas
Naturales, BOA 48 of 20-04-2005), financed by the Aragón Government. Research of the first
author was supported by postdoctoral fellowship by the Ministerio de Educación, Cultura y Deporte
(Spain). We would like to thank to Dr. D.A. Ravetta and the two anonymous reviewers for their
helpful comments.
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FIGURE CAPTIONS:
Figure 1. Location, topography and spatial distribution of land cover types in the study area. 1:
Elevation, 2: Land cover.
Figure 2: Spatial distribution of the monthly average aridity index (AI) (April-September)
Figure 3. Spatial distribution of mean NDVI values in the middle Ebro Valley
Figure 4. Spatial distribution of annual CoV values.
Figure 5. Relationship between the NDVI and annual CoV. Irrigated lands are not shown.
Figure 6. Relationship between the annual aridity index (AI) (April-September) and the mean
NDVI values. Grey points indicate irrigated lands.
Figure 7. Average values of the NDVI and AI for the different land-uses
Figure 8. Monthly relationships between the spatial distribution of AI and the NDVI in the
different land-uses. 1: Dry farming areas (herbaceous), 2: Dry farming areas (permanent) , 3: Dry
farming areas (mosaic of herbaceous and permanent), 4: Irrigated lands, 5: Leafy forests, 6:
Coniferous forests, 7: Steppes (shrubs and pastures) and 8: Mosaic of dry farming areas and
steppes. All correlations are statistically significant (< 0.01).
Figure 9. Relationship between the spatial distribution of the annual AI and the annual CoV values.
The irrigated lands are indicated independently (in grey colour).
Figure 10. Average values of CoV and AI in the different land-uses.
Figure 11. Monthly relationships between the spatial distribution of AI and the CoV in the different
land-uses. See codes in figure 8.
Table 1. Results of regression models for monthly precipitation and temperature
PRECIPITATION
April
May
June
July
August
September
R2
0.70
0.58
0.62
0.42
0.67
0.66
Corrected R2
0.69
0.56
0.61
0.39
0.65
0.64
Error St.
7.38
8.08
7.36
4.00
6.32
8.01
TEMPERATURE
R2
0.77
0.84
0.78
0.59
0.53
0.56
Corrected R2
0.76
0.83
0.76
0.57
0.52
0.54
Error St.
0.65
0.57
0.67
0.90
0.92
0.88
Table 2: Accuracy measurements for temperature and precipitation maps. RMSE: Root Mean
Standard Error, MAE: Mean absolute error.
April
May
June
July
August
September
Annual
RMSE
6.14
5.92
7.33
3.00
4.41
6.75
20.4
Precipitation
MAE
Willmott’s D
4.02
0.94
4.32
0.92
5.86
0.86
2.26
0.90
3.46
0.94
5.07
0.93
17.15
0.96
RMSE
0.60
0.69
0.73
0.93
0.87
0.71
0.67
Temperature
MAE
Willmott’s D
0.47
0.94
0.57
0.94
0.56
0.92
0.76
0.87
0.70
0.87
0.62
0.90
0.55
0.92
Table 3: Correlation (Rho-Spearman) between AI and NDVI values in the different land uses. See
codes in figure 8. ** Significant correlation (99 %), * Significant correlation (95 %).
April
May
June
July
August
September
1
2
3
4
5
6
7
0.65** 0.36** 0.54** -0.12** 0.51** 0.82** 0.59**
0.65** -0.15 0.55** -0.21** 0.72** 0.82** 0.73**
0.64** 0.04 0.37** -0.41** 0.79** 0.84** 0.65**
0.41** 0.22** 0.18** -0.18** 0.76** 0.65** 0.51**
0.42** 0.21* 0.40** -0.43** 0.82** 0.77** 0.60**
0.44** -0.32 0.49** -0.43** 0.83** 0.80** 0.62**
8
0.71**
0.79**
0.67**
0.40**
0.53**
0.52**
Table 4. Correlation (Rho-Spearman) between AI and CoV values in the different land uses. See
codes in figure 8. ** Significant correlation (99 %), * Significant correlation (95 %).
April
May
June
July
August
September
1
-0.33**
-0.16**
0.12**
0.04
-0.21**
-0.25**
2
-0.10
0.24**
0.27**
0.64**
0.23**
-0.03
3
-0.24**
-0.20**
-0.05
0.18
-0.16**
-0.31**
4
0.37**
0.52**
0.53**
0.47**
0.27**
0.00
5
-0.30**
-0.70**
-0.44**
-0.32**
-0.64**
-0.49**
6
-0.41**
-0.39**
0.03
-0.11**
-0.45**
-0.47**
7
-0.47**
-0.43**
-0.15**
-0.19**
-0.49**
-0.55**
8
-0.33**
-0.25**
0.05*
0.15**
-0.28**
-0.29**
Figure 1. Location, topography and spatial distribution of land cover types in the study area. 1:
Elevation, 2: Land cover.
Figure 2: Spatial distribution of the monthly average aridity index (AI) (April-September)
1.6
1.6
2.0
2.0
2.4
2.4
2.8
2.8
3.2
3.2
3.6
3.6
Figure 3. Spatial distribution of mean NDVI values in the middle Ebro Valley
Figure 4. Spatial distribution of annual CoV values.
Figure 5. Relationship between the NDVI and annual CoV. Irrigated lands are not shown.
Figure 6. Relationship between the annual aridity index (AI) (April-September) and the mean
NDVI values. Grey points indicate irrigated lands.
2.8
Leafy forests
Irrigated lands
Average NDVI
2.6
2.4
2.2
Coniferous forests
Dry farming (herbaceous)
Steppes
Mosaic of steppes and dry farming
2.0
Dry farming (permanent)
Dry farming (mosaic)
R2 = 0.96
1.8
1.5
2.0
2.5
3.0
3.5
Average AI
Figure 7. Average values of the NDVI and AI for the different land-uses
4.0
Figure 8. Monthly relationships between the spatial distribution of AI and the NDVI in the
different land-uses. 1: Dry farming areas (herbaceous), 2: Dry farming areas (permanent) , 3: Dry
farming areas (mosaic of herbaceous and permanent), 4: Irrigated lands, 5: Leafy forests, 6:
Coniferous forests, 7: Steppes (shrubs and pastures) and 8: Mosaic of dry farming areas and
steppes. All correlations are statistically significant (< 0.01).
Figure 9. Relationship between the spatial distribution of the annual AI and the annual CoV values.
The irrigated lands are indicated independently (in grey colour).
0.11
Dry farming (herbaceous)
Average CoV
0.10
Dry farming (mosaic)
0.09
Mosaic of steppes and dry farming
Dry farming (permanent)
Steppes
0.08
Coniferous forests
Irrigated lands
Leafy forests
0.07
R2 = 0.74
0.06
1.5
2.0
2.5
3.0
3.5
Average AI
Figure 10. Average values of CoV and AI in the different land-uses.
4.0
Figure 11. Monthly relationships between the spatial distribution of AI and the CoV in the different
land-uses. See codes in figure 8.