NAO influence on NDVI trends in the Iberian Peninsula (1982-2000)
S.M. VICENTE-SERRANO* and A. HEREDIA-LACLAUSTRA
Departamento de Geografía. Universidad de Zaragoza. Pedro
Cerbuna 12. 50009. Zaragoza. Spain.
*Corresponding author; e-mail: svicen@posta.unizar.es
Abstract. The present letter describes our analysis of the trends of
NDVI in the Iberian Peninsula from 1982 to 2000. The results were
compared with the influence of the North Atlantic Oscillation
(NAO) index. Significant spatial differences emerge in vegetation
trend analysis, identifying a positive trend in the northern part and
stability or negative trends in the South. Such a spatial pattern is
significantly related to NAO influence on vegetation, which is
linked to precipitation distribution in the Iberian Peninsula and
greatly influenced by this atmospheric pattern. The relationships
between vegetation trends and NAO are discussed in detail.
1. Introduction
Climatic global change processes such as thermal increment are related to an
increase in vegetation production, as has been recorded for wide areas of the Northern
Hemisphere (Slayback et al. 2003). The increase can be identified using remote sensing,
although most research focuses on the highest latitudes of the Northern Hemisphere
(Lucht et al. 2002, Myneni et al. 1997). Nevertheless, transition climatic areas such the
Mediterranean, where climatic change impacts are expected to be higher (Houghton et
al. 2001), have received less attention. Thus, more detailed studies of vegetation
production trends and their relation to climatic change and variability are required.
The main part of interannual variability in the atmospheric circulation of the
Western European areas is summarised by means of the North Atlantic Oscillation
(NAO), which is associated with changes in the surface westerlies across the North
Atlantic onto Europe (Hurrell 1995). NAO has several definitions, but it is always
associated with a north-south oriented bipolar structure in the pressure over the Atlantic
Ocean that can be summarised by different methods and that is usually defined as the
normalised pressure difference between a station in the Azores and one in Iceland
(Jones et al. 1997).
NAO patterns are mainly identified during boreal winter (Trigo and Palutikof
2001). Positive NAO years are related to a decrease in moisture conditions and drought
episodes in Southern Europe and in the Mediterranean areas. In negative phases, humid
conditions are recorded in these areas (Hurrell and Van Loon 1997). Through its control
over regional temperature and precipitation variability, the NAO directly affects
agricultural yields, water management activities and fish inventories, among other
things (Hurrell 2003).
Changes in the NAO index can affect the transport and convergence of
atmospheric moisture in Western European areas. The index can, therefore, be directly
linked to changes in regional precipitation and moisture availability. In the final decades
of the twentieth century, the NAO index showed a positive trend, having a different
spatial response on climatic elements and ecosystem evolution (Hurrell 1995). The main
consequences in the Mediterranean areas have been linked to a decrease in precipitation,
and more persistent droughts during the decade of the 1990s had important impacts on
agriculture, vegetation activity and forest fire frequency (González-Alonso et al. 2003).
Using pressure patterns is preferable to using climatic elements for monitoring
climate change effects on vegetation because changes in climate are first identified from
atmospheric patterns (Houghton et al. 2001). Moreover, despite terrestrial weather
records (precipitation or temperature) having long been compared to satellite sensor
data, they are limited spatially (mainly in the Mediterranean areas where precipitation is
highly variable in space), and this restricts an accurate characterisation of ecosystem
sensitivity to climatic variability and change.
Several studies have been carried out on the impact of El Niño/Southern
Oscillation (ENSO) on vegetation in different parts of the Southern Hemisphere (Kogan
2000), since this phenomenon is the most outstanding source of atmospheric variability
in this area. There are fewer studies for the Northern Hemisphere regarding atmospheric
variability impacts on vegetation. However, vegetation production and its trends in the
Northern Hemisphere have been shown to be explained by the atmospheric
teleconnection indices (Gong and Shi 2003, Wang 2003).
In the Iberian Peninsula the spatial differences of winter NAO influence on
climate are significant (Rodríguez-Puebla et al. 1998) and knowledge of the spatial
differences on the impact of the winter NAO on vegetation production are especially
interesting. This is important for agricultural management and for the monitoring of
global change impacts, mainly the processes related to degradation, desertification and
vegetation production, since the main moisture charge is produced in winter. In this
letter, we analyse vegetation production trends in the Iberian Peninsula and their
relationships to NAO index. This knowledge allows us to understand and predict local
vegetation production variations in response to global climate change.
2. Methodology
Remote sensing has been used widely for monitoring vegetation dynamics
because it permits analysis of large areas with a high temporal frequency (Gutman
1991). Different indices have been developed for monitoring and measuring vegetation
status using spectral data (Bannari et al. 1995). The most widely used is the Normalized
Difference Vegetation Index (NDVI, Tucker 1979) which is calculated via the
expression [NDVI = (Near Infrared - Red)/(Near Infrared + Red)]. There are some
shortcomings in the use of NDVI for monitoring vegetation status because the
relationships among vegetation parameters (leaf area index, vegetation cover, greenbiomass and NDVI) are often non-linear (Choudhury et al. 1994) due to the fact that the
NDVI signal saturates before the maximum biomass is reached (Carlson et al. 1990).
Nevertheless, in spite of these shortcomings, numerous authors have pointed out the
close relationship between NDVI and several ecological parameters. The NDVI exhibits
a large correlation with vegetation production (Gutman 1991) or fractional vegetation
cover (Duncan et al. 1993).
To analyse trends in vegetation dynamics the NDVI-Pathfinder data set was used
(James and Kalluri 1994) for the dates between 1982 and 2000. The spatial resolution is
0.1º and the temporal frequency is 1 month. The data set covers the whole of the Iberian
Peninsula. The quality of this database has been tested by numerous authors (Smith et
al. 1997), providing assurance of the calibration quality (Kaufman et al. 2000).
Shabanov et al. (2002) indicate that the temporal trends detected using this data set are
not caused by errors in data processing.
Tucker et al. (1983), Paruelo and Lauenroth (1998), among others, indicate a
close relationship (r2 > 0.7) between integrated monthly NDVI values thorough the year
and vegetation production. The annual integrated NDVI values (NDVI) produce a
result associated with the whole accumulation of vegetation production thorough the
year (Tucker et al. 1981). In our case, due to the monthly availability of the data,
integrated NDVI values were calculated using a trapezoidal approach (Samson 1993).
From the NDVI-Pathfinder data set, a series of NDVI was obtained for every pixel of
0.1º in the Iberian Peninsula.
The trends showed by NDVI data were calculated using the Spearman-Rho test
(p<0.1), a statistics tool widely used to analyse trends in climatic data (Lanzante 1996).
The relationship between NDVI, precipitation and winter NAO was analysed for every
pixel using the Pearson’s correlation coefficient (R), in order to identify where NAO
influence is greatest on vegetation activity and to determine the links with precipitation
patterns. We used an analysis of variance (one factor ANOVA) to determine whether
there are significant differences in the relationships between NDVI and NAO (r-values),
according to the presence of areas with significant and non-significant trends.
3. Results
3.1. Spatial patterns of NDVI trends
The temporal evolution of the NDVI data for the whole of the Iberian Peninsula
between 1982 and 2000 is shown in figure 1. A high interannual variability is revealed,
although a positive and significant trend was obtained (Rho = 0.49, p<0.1, n = 19).
Nevertheless, the spatial differences in the trends are significant and the general
evolution is not representative of some areas, as shown in figure 2. The positive trends
are mainly recorded in the N-NE areas, whereas in the South, non-significant trends are
dominant. Negative trends occur in a few cases. The centre and the east of the Iberian
Peninsula play a transition role, showing great spatial heterogeneity, with the
coexistence of areas with positive and non-significant trends.
3.2. NAO spatial influence on NDVI trends
Figure 3 shows the correlation between the winter NAO index and the NDVI in
every pixel between 1982 and 2000. The spatial patterns are clear. Negative correlation
is dominant in the Southwest, where it seems that years with negative winter-NAO
index tend to be related to the increase of NDVI values. However, in the East, the
correlation is positive. On the other hand, in the northern part, the correlation values are
non-significant.
Table 1 shows the results of the variance analysis, in which the correlation
between winter NAO and NDVI is the quantitative variable and the categories of the
trends is the group factor. Two possible cases were considered, first the areas with
positive trends and, second, the group of negative and non-significant areas. Due to the
scarcity, areas with negative trends, were included with the non-significant trend areas
in the same group. The results show that there are significant differences in R-values
between the two groups. In areas of negative or non-significant trend, the mean R-value
is –0.14, but in areas of positive trends the mean R-value is zero. Moreover, spatial
variability of NAO influence is lower in areas of significant NDVI trends. This fact
indicates that areas with closer relationships between NDVI and NAO have
dominantly shown vegetation stability, whereas the areas in which the NAO influence is
lower have experienced a general positive trend in the NDVI evolution.
These results can be explained by NAO influence on climatic conditions in the
Iberian Peninsula. Figure 4 shows the correlation between winter precipitation in the
Iberian Peninsula and NAO index. Thus, areas with non-significant or negative trends
coincide with those in which NAO influence on precipitation is greater.
In regard to the above subject, a coincidence exists between the areas in which
NAO influence on precipitation or on NDVI is greater and the Rho values are lower.
Figure 5 shows the relationship between the trend values (Rho) of NDVI and Rcorrelation values (NAO- precipitation, NAO- NDVI) from 100 random sampling
points in the Iberian Peninsula. This demonstrates that the trend towards the vegetation
production increase is lower in the areas most influenced by NAO while, on the other
hand, the areas less influenced by NAO generally show higher Rho values.
The Iberian Peninsula underwent an increase in temperatures during the years
analysed (figure 6). This agrees with the situation observed in the Northern Hemisphere
as a consequence of an increase in the effect of greenhouse gases (Jones and Moberg
2003). This thermal increase implies longer annual vegetation cycles, higher rates of
transpiration and, therefore, greater increase in vegetation production.
Nevertheless, this increase in vegetation production will only happen when there
is enough water to allow vegetation activity and photosynthetic processes. Otherwise,
thermal increase will have negative consequences on vegetation production due to more
stressful water conditions. Therefore, in areas where the correlation between
precipitation or NDVI and NAO is higher, there is no increase in NDVI. This fact
can be derived from the great increase experienced by NAO during the last four decades
of the twenty century (Hurrell, 1995), which has caused a fall in the water availability in
the areas whose precipitation is more determined by NAO. This fact has undoubtedly
limited the vegetation growth possibilities.
4. Discussion and conclusions
We observed a significant and positive trend in vegetation production in the
Iberian Peninsula, summarised by means of NDVI. However, the evolution is not
spatially homogeneous, although clear spatial patterns are recognised. Significant
relationships have been found between spatial patterns of vegetation production trends
and winter NAO.
The areas in the Iberian Peninsula where stable or negative trends in the NDVI
values have been detected coincide with the areas located in the South, where the NAO
influence is higher (Rodríguez Puebla et al. 1998). On the other hand, the areas which
show a greater increase in the NDVI (located in the North) are the areas in which the
NAO influence on vegetation dynamics is minor. This produces a coincidence in two
kinds of relationships: NAO-NDVI and between NAO and precipitation in the Iberian
Peninsula (Martín-Vide and Fernández 2001). Winter NAO mainly determines the
precipitation in the Southwest regions, whereas the East or the North are more
dependent upon other teleconnection patterns, such as the Polar (González Hidalgo et
al. 2003) or the Scandinavian Pattern (Rodríguez-Puebla et al. 1998). This fact could
explain the minor correlation between vegetation production and NAO in these areas.
During the two last decades of the twentieth century, the NAO recorded a
predominance of positive values (Hurrell 1995) which are especially outstanding in the
areas where the climatic elements are most related to this atmospheric pattern, and show
the least precipitation and the most intense and prolonged droughts. This presents an
important limitation for vegetation growth. In the areas most highly affected by NAO,
water stress conditions will be more frequent, limiting vegetation increase. The areas
least influenced by NAO do not suffer so greatly from this water shortage. Due to
higher water availability, the vegetation would benefit from thermal increase detected
on the global scale, increasing its coverage and its biomass production.
Further analysis is required to study in depth the relationships between NAO and
vegetation production trends at other temporal scales (monthly and seasonal). Such
analysis would help to determine the influence of other atmospheric teleconnections on
vegetation trends.
Acknowledgements
This research was funded by the projects BSO2002-02743, REN2002-01023-CLI
and REN2003-07453 (Financed by Ministerio de Ciencia y Tecnología, Spain and
FEDER), and “Programa de grupos de investigación consolidados” (grupo Clima,
Cambio Global y Sistemas Naturales, BOA 147 of 18-12-2002), financed by Aragón
Government. Data used by the authors in this study include data produced through
funding from the Earth Observing System Pathfinder Program of NASA's Mission to
Planet Earth in cooperation with the National Oceanic and Atmospheric Administration.
The data were provided by the Earth Observing System Data and Information System
(EOSDIS), Distributed Active Archive Center at Goddard Space Flight Center which
archives, manages and distributes this data set. We also thank specially Mr. R. Rycroft
for his linguistic revision, and we acknowledge the comments of the reviewers, which
have contributed to the improvement of this letter.
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Sum of Squares
df Mean Square
Between Groups
14.87
1
14.87
Within Groups
189.75 4065
4.668E-02
Total
204.62 4066
Table 1: Results of the analysis of variance.
F Sig.
318.59 .000
-2000)
Figure 2. NDVI trends in the Iberian Peninsula (1982-2000).
Figure 3. Correlation (R) between winter NAO index and the NDVI values. A low
pass filter (3 x 3) is applied to the image.
Figure 4. Spatial distribution of correlation coefficients (R) between NAO and winter
precipitation in the Iberian Peninsula.
Figure 5. NAO vs NDVI and NAO vs precipitation relationships: Links with NDVI
trends (Rho-Spearman values).
Figure 6. Annual mean temperature evolution for the Iberian Peninsula (1982-2000) in
standardized values. Data obtained from Mitchell et al. (2002).
Figure 1.
-2000)
Figure 2. NDVI trends in the Iberian Peninsula (1982-2000).
Figure 3. Correlation (R) between winter NAO index and the NDVI values. A low
pass filter (3 x 3) is applied to the image.
Figure 4: Spatial distribution of correlation coefficients (R) between NAO and winter
precipitation in the Iberian Peninsula.
Figure 5. NAO vs NDVI and NAO vs precipitation relationships: Links with NDVI
trends (Rho-Spearman values).
Figura 6. Annual mean temperature evolution for the Iberian Peninsula (1982-2000) in
standardized values. Data obtained from Mitchell et al. (2002).