Remote Sensing of Environment 90 (2004) 268 – 280
www.elsevier.com/locate/rse
Interannual variability of vegetation over the Indian sub-continent
and its relation to the different meteorological parameters
Sudipta Sarkar *, Menas Kafatos
Center for Earth Observing and Space Research, School of Computational Sciences, MS 5C3,
George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
Received 4 March 2003; received in revised form 7 November 2003; accepted 1 January 2004
Abstract
The dynamic nature of climate over Indian sub-continent is well known which influences Indian monsoon. Such dynamic variability of
climate factors can also have significant implications for the vegetation and agricultural productivity of this region. Using empirical
orthogonal function (EOF) and wavelet decomposition techniques, normalized difference vegetation index (NDVI) monthly data over Indian
sub-continent for 18 years from 1982 to 2000 have been used to study the variability of vegetation. The present study shows that the
monsoon precipitation and land surface temperature over the Indian sub-continent landmass have significant impact on the distribution of
vegetation. Tropospheric aerosols exert a strong influence too, albeit secondary to monsoon precipitation and prove to be a powerful
governing factor. Local climate anomaly is seen to be more effective in determining the vegetation change than any global teleconnection
effects. The study documents the dominating influence of monsoon precipitation and highlights the importance of aerosols on the vegetation
and necessitates the need for remedial measures. The present study and an earlier one point towards a possible global teleconnection pattern
of ENSO as it is seen to affect a particular mode of vegetation worldwide.
D 2004 Elsevier Inc. All rights reserved.
Keywords: Interannual variability; Vegetation over; Indian sub-continent
1. Introduction
Green biomass covers nearly three-fourths of the Earth’s
land surface and play a key role in global energy, hydrological and biogeochemical cycles. The Global Climate
Modeling community requires inputs from biophysical
variables like albedo, evapotranspiration, and surface resistance (Sellers et al., 1996), all of which are dependent
on soil type and vegetation cover. The variability of
vegetation can have variable impact on the potential
long-term entrapment of CO2 and thus global climate
change. Hence, it is very important to know and understand variability of vegetation cycles and the driving
mechanism of such cycles.
Climate variability can have a large impact on the
ecosystem and the vegetation pattern of different ecosystems as climate and terrestrial ecosystems are closely
coupled. Climate variability can be the result of natural
* Corresponding author. Tel.: +1-703-993-4694.
E-mail address: [email protected] (S. Sarkar).
0034-4257/$ - see front matter D 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2004.01.003
periodic or long-term changes, or the result of human
induced factors like greenhouse gases and land use change.
The effect of climate change on vegetation has been studied
by numerous workers (Anyamba & Eastman, 1996; Cihlar
et al., 1991; Eastman & Fulk, 1993; Gray & Tapley, 1985;
Li & Kafatos, 2000; Lu et al., 2001; Neilson, 1986). Thus,
an understanding of the climate induced variability of
vegetation can help us in understanding the long-term
behavior of the different ecosystems and biomes and also
help us in achieving a better synthesis of global ecosystem
simulation, biogeochemical and atmosphere – biosphere general circulation models.
A number of studies have shown that the normalized
difference vegetation index (NDVI) derived by dividing the
difference in infrared and red reflectance measurements by
their sum provides an effective measure of photosynthetically active biomass (Justice et al., 1985; Tucker & Sellers,
1986). NDVI is also found to be well correlated with
physical climate variables including rainfall, temperature
and evapotranspiration in a wide range of environmental
conditions (Cihlar et al., 1991; Gray & Tapley, 1985). The
interannual and inter-seasonal variability of NDVI have
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
269
Fig. 1. (a) The first five EOFs or spatial modes of NDVI over Indian sub-continent. (b) The corresponding temporal loadings (PCs). (c) The Eigen value
spectrum for the EOFs. The scale for the EOFs in (a) varies from red to blue with the red signifying positive variance whiles the blue showing negative
variance. The variances explained by each EOF are shown in Table 1.
270
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
Fig. 1 (continued).
been studied for different regions in the past. Significant
changes in NDVI pattern with ENSO and associated ‘‘teleconnection’’ effects have been found over Africa (Anyamba
& Eastman, 1996; Eastman & Fulk, 1993), whereas others
have found global associations of NDVI anomaly patterns,
over Africa, Australia and South America with sea surface
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
271
Fig. 1 (continued).
temperature anomaly (SSTA) over the tropical Pacific
(Myneni et al., 1996). Li and Kafatos (2000) have studied
the spatial patterns of NDVI and its relation to ENSO over
North America, while Lim and Kafatos (2002) performed a
statistical spatial analysis of NDVI for different ecoregions.
In the present paper, efforts have been made to understand
the effect of meteorological parameters on NDVI which is
very important in contributing to the agricultural productivity of the Indian sub-continent.
2. Data and analysis
thickness at 0.5 Am increases from 0 to 0.5. This value of
optical thickness of 0.5 is in the range of expected values for
stratospheric aerosols. The NDVI data is corrected for such
effects; the details of approach are discussed by Nemani et al.
(2003), the supporting material can be obtained from Science
online at http://www.sciencemag.org.
The data period covered four prominent El Niño events,
namely 1982 –1983, 1986 – 1987, 1991 –1992 and 1997–
1998, of which the El Niño events of 1982 – 1983 and
1997 – 1998 are found to be the most prominent ones of
this century. The data set used in this analysis is a subset of
the global coverage over the entire Indian sub-continent
from 0 – 40jN and 40jE– 100jE.
2.1. Vegetation and meteorological indices
The improved NDVI global monthly data (version
3) for the period January 1982– December 2000 with
0.5j by 0.5j resolution is downloaded from ftp://crsa.bu.
edu/pub/rmyneni/myneniproducts/AVHRR _DATASETS/
PATHFINDER. These data is corrected for any residual
noise due to orbital drift, inter-sensor variations and stratospheric aerosol effects following large scale volcanic eruptions like the eruption of Mount Pinatubo (June 1991). The
stratospheric aerosol layer is composed largely of small
particles (0.5 Am) located 20 km high in the atmosphere. A
sensitivity analysis carried out by Vermote et al. (1997) using
a radiative transfer code has shown a decrease in NDVI for a
typical vegetation cover from 0.5 to 0.4 when the optical
Table 1
The first five dominant modes for NDVI (1982 – 1993) with their variances
and forcing parameters
PC no.
Variance
(%)
Time
scale
Climatic
indicator
1
51
Annual
2
8
3
4
5.2
3.5
Annual,
3 – 5 years
?
?
Asian monsoon,
anthropogenic
effects
Asian winter
monsoon, ENSO
5
2.7
3 – 5 years
Anthropogenic
effects
ENSO
272
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
The Southern Oscillation Index (SOI) has been used in
our study as an indicator of the El Niño event and its
associated sea level pressure fluctuations between Darwin,
Australia (131jW, 12jS) and Papeete, Tahiti (149jW, 17jS)
(Allan et al., 1991; Ropelewski & Jones, 1987). This data set
is obtained from the Climate Research Unit of the University
of East Anglia (http://www.cru.uea.ac.uk/cru/data/soi.htm).
The SOI data is in the form of monthly average values from
1866 to 2001 from which our temporal subset from January
1982 to December 2000 have been extracted.
We have used the NINO3 index as another indicator of
the onset of El Niño condition. The NINO3 is derived from
monthly composited sea surface temperature anomaly in the
Central Pacific between 5jS– 5jN and 150jW –90jW. This
data was downloaded from the Climate Data Library maintained by the Lamont Doherty Earth Observatory (LDEO),
Fig. 2. The EOFs of NDVI from 1982 – 2000 and EOF1 of Aerosol optical depth. (a) EOF1 of NDVI, (b) EOF4 of NDVI and (c) EOF1 of aerosol optical depth
(AI). Note the bright red streak in (c) signifying high accumulation of suspended particulates and its correspondence with areas of low vegetation in EOF1 and
EOF4 as shown in (a) and (b).
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
273
Table 2
The lag correlation values of PC2 of NDVI with SOI
SOI
PC2 of NDVI
January
February
March
April
May
June
July
August
September
October
November
December
January
February
March
April
May
June
July
August
September
October
November
December
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.39
0.38
0
0
0
0
0
0
0
0
0
0
0.4
0.4
0
0
0
The all India Rainfall index (Parthasarathy et al., 1995)
(AIR) represents an aerial average of 29 subdivisional
rainfalls (in millimeters) total for June, July, August, and
September. This data is taken from the Center for Ocean,
Land, Atmosphere Studies (COLA), http://grads.iges.org/
india/allindia.html for the periods January 1982 –December
2000.
Webster and Yang index (W –Y) is used as a further
indicator of the strength of the monsoon and is defined as
the vertical shear of zonal wind (Webster & Yang, 1992)
between 850 and 200 hPa:
Columbia University. The data cover a similar time span of
January 1982 to December 2000.
Aerosol optical depth (AOD) measured by the NASA
Total Ozone Monitoring Spectrometer (TOMS) mission
(toms.gsfc.nasa.gov) has been used to study the impact of
anthropogenic effects, in the form of an aerosol index (AI).
This is an index of the attenuation of radiation as it passes
through the atmosphere due to the presence of suspended
particles. Torres et al. (1998) have discussed the possible
errors associated with the TOMS aerosol index. For a
surface reflectivity uncertainty of F 0.01 the accuracy of
the retrieved optical depth is F 0.1 for non-absorbing and
weakly absorbing aerosols. As the aerosol becomes more
absorbing, the sensitivity to surface albedo decreases, and
the accuracy of the retrieval is better than F 0.05. The AOD
is overestimated when the prescribed aerosol layer height is
lower than the actual value. When the assumed aerosol
location is higher than the actual value, an AOD underestimate takes place. This AOD data has been obtained only
for the period of 1982 January to December of 1992 because
of a data gap after that time till 1996 and covers the spatial
domain from 0 – 40jN and 40jE – 100jE similar to the
NDVI data set.
W Y ¼ U850 U200
where U850 and U200 are the zonal wind at 850- and 200-hPa
levels, respectively, averaged over the area (0 –20jN and
40j and 110jE).
The land surface temperature data is taken from LDEO
climate data repository (http://iridl.ldeo.columbia.edu/
SOURCES/.NOAA/.NCDC/.GCPS/.MONTHLY/).
This data is available for selected ground stations
throughout the globe. For the present study the data have
been composited over the area (20jN–70jN and 60jE –
Table 3
The lag correlation values of PC2 of NDVI with NINO3
NINO3
January
February
March
April
May
June
July
August
September
October
November
December
PC2 of NDVI
January
February
March
April
May
June
July
August
September
October
November
December
0.42
0
0
0
0
0
0
0
0
0
0
0
0.37
0.34
0
0
0
0
0
0
0
0
0
0
0.31
0.27
0
0
0
0
0
0
0
0
0
0
0.28
0
0
0
0
0
0
0
0
0
0
0
0.27
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.29
0.38
0.28
0.44
0.3
0.3
0
0
0
0
0
0
0
0.37
0.36
0.37
0.41
0.43
0
0
0
0
0
0
0
0.30
0.33
0.44
0.41
0.41
274
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
Table 4
The lag correlation values of PC5 of NDVI with NINO3
NINO3
January
February
March
April
May
June
July
August
September
October
November
December
PC5 of NDVI
January
February
March
April
May
June
July
August
September
October
November
December
0.35
0.34
0
0
0
0
0
0
0
0
0
0
0.41
0.4
0.38
0.36
0
0
0
0
0
0
0
0
0.44
0.48
0.46
0.46
0.40
0
0
0
0
0
0
0
0.42
0.41
0.4
0.4
0.4
0
0
0
0
0
0
0
0.35
0.34
0.34
0.35
0.36
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
100jE) for 1982– 1995 and could not be extended to other
time scales for lack of data.
The Dipole Mode Index (DMI) represents the dipole
nature of the SST anomaly over the western and eastern
half of the Indian Ocean (Saji et al., 1999). The DMI
describes the difference in SST anomaly between the
tropical western Indian Ocean (50jE – 70jE, 10jS– 10jN)
and the tropical south-eastern Indian Ocean (90jE – 110jE,
10jS-Equator). DMI has been computed using SST data
obtained from the NCEP/NCAR reanalysis project (http://
iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCEP-NCAR/
.CDAS-1/.MONTHLY/.Diagnostic/.surface/.temp/).
relevant data periods. The interannual variability of vegetation analysis has been carried out through wavelet
decomposition following the approach given by Li and
Kafatos (2000).
We have used empirical orthogonal function (EOF)/
Principal Component Analysis (PCA) for analyzing the
variability of a single field, i.e. a field of only one scalar
variable (like NDVI and AOD). The method (Preisendorfer,
1988) finds the spatial patterns of variability (EOF), their
time variation or Principal Components (PC), and gives a
measure of the importance of each pattern.
2.2. Time series analysis
3. Discussion and results
Time series data contain intra-annual seasonality that is
often much stronger than the interannual signal we wish to
study. The seasonality in NDVI time series is removed by
applying a seasonal decomposition method outlined in
Cleveland et al. (1990). The AOD and SOI time series
have been corrected in terms of their climatological values
or annual cycle of monthly means calculated for the
Fig. 1a shows the EOFs, the corresponding normalized
principal components (PC) are shown in Fig. 1b and the
Eigen value spectrum for the EOFs in Fig. 1c. First five
principal modes are chosen for further analysis based on
North’s criteria for significance (North et al., 1982). The
variance explained by each of these first five principal
modes is given in Table 1. The scale in Fig. 1 and in all
Table 5
The lag correlation values of PC5 of NDVI with SOI
SOI
January
February
March
April
May
June
July
August
September
October
November
December
PC5 of NDVI
January
February
March
April
May
June
July
August
September
October
November
December
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.32
0.32
0.30
0
0
0
0
0
0
0
0
0
0.29
0.31
0.30
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
subsequent figures varies from red (positive variance) to
blue (negative variance).
The EOF1 for the period 1982– 2000 accounts for more
than 50% of the variance and explains the major distribution
of vegetation over the Indian sub-continent. This distribution is principally explained by the summer monsoon
rainfall which takes place mainly from June to September
of each year under the influence of the monsoonal trade
winds blowing from the southwest and southeast, respectively. PC1 of NDVI shows a zero lag correlation of 0.57
with the AIR from 1982 to 2000 at 99% significance. As a
proof of further validation of the effect of monsoon rainfall
on the vegetation distribution over India, PC1 of NDVI is
compared with the W– Y index. The NDVI is seen to show a
lag of 2 –3 months with a lag correlation of 0.82 at 99%
significance. The lag correlation came out to be less
substantial but positive at 0.45 and at a lag of 4 months
for the PC1 of NDVI from 1982 to 1995 with the land
surface temperature, composited over the area (20jN – 70jN
and 60jE – 100jE). This correlation test could not be
extended for the entire data period for lack of land surface
temperature data after the year, 1995. All correlation values
quoted above are Pearson’s product moment values obtained
for a two tailed t-test statistic. Land ocean thermal contrast is
the primary driver of Asian monsoon (Li & Yanai, 1996).
More specifically, correlation of Indian monsoon with the
temperature regime over the Tibetan Plateau has been the
focus of much work (Prell & Kutzbach, 1992; Yanai & Li,
1994; Zhisheng et al., 2001). The positive lag between land
surface temperature and NDVI is significant. The increasing
sequence of lag as seen above, between NDVI and AIR,
W –Y index and land surface temperature is indicative of the
sequence of events that takes place leading up to the
monsoon. The increased heating of the Asian landmass
(Tibetan Plateau to be precise) drives and reverses the
direction of trade winds resulting in intense vertical shear
of the trades causing the summer monsoon rainfall over the
Indian sub-continent.
The distribution of EOF1 of NDVI also shows a good
relation with potential anthropogenic effects as seen from
the EOF analysis of the AI. Fig. 2 shows good correlation of
the EOF1 and EOF4 of NDVI from 1982 to 2000 with the
EOF1 of AI, despite the limited data span of the AI. The
PC1 and PC4 of NDVI show substantial negative correlation coefficients of 0.53 and 0.7 (with p-values of
0.0175 and 0.043, carried out through a two tailed t-test with
a test size of 0.05), respectively, with the PC1 of AI. Fig. 3a
shows the spatial correlation map of the PC1 of AI with
NDVI over the Indian sub-continent. Substantial negative
correlation with NDVI is apparent over the entire southern
edge of the Himalayas and most part of the Indo-Gangetic
(IG) plains and parts of southern peninsular India. The
aerosol over this region is rich in sulfates, nitrates, organic
and black carbon, and fly ash. Regions of low overall
correlation of AI with NDVI are areas having sparse
vegetation cover, like the Kathiawar Peninsula in India,
275
south-eastern tip of the Arabian Peninsula and large areas of
neighboring Pakistan. The IG plain receives most of the
rainfall as the monsoon winds are obstructed from flowing
beyond by the Himalayan range. Despite this, the vegetation
growth is not high in IG plains principally because of high
aerosol which is due to large population growth and
industrialization. Parts of southern and southeastern India
and Southeast Asia showing low negative correlation are
less affected due to significantly low aerosol concentration
over this region (Fig. 2c). In general, climatic factors being
the same over the Indian sub-continent and Southeast Asia,
the differences in vegetation density between these two
areas as shown above can only be attributed to anthropogenic influences. The mechanism by which such aerosol
Fig. 3. (a) The Spatial correlation map between the PC1 of aerosol against
the time series of deseasonalized NDVI. Note the areas of substantial
negative correlation in the Indo-Gangetic plain and towards the easternsoutheastern coastal region of India. Areas marked in red are also areas of
sparse vegetation growth where correlation does not carry much meaning.
(b) Physiographic map of Indian sub-continent showing the key areas
discussed in the text.
276
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
Fig. 4. The EOF5 of NDVI (upper left) and EOF2 of NDVI (upper right) for 1982 – 2000 with ENSO derived precipitation patterns (bottom) as derived by
Ropelewski and Halpert (1987). The two circles in the figure in the bottom represent the areas marked as ‘IND’ and ‘MSL’ by the original authors. Each having
its own characteristic precipitation pattern as has been described in the text.
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
buildup can affect vegetation growth can be argued from a
consideration of cloud microphysics. The aerosol clouds are
made up of smaller water droplets which are less likely to
fall and thus result in reduced rainfall (Boucher et al., 1994;
Jones et al., 1994). Moreover, such toxic buildup of clouds
because of aerosols can slash the incoming solar radiation
by 10 – 15% thus cutting off vital energy source for plant
growth. There is also a direct effect of the buildup of
tropospheric pollutants on plant growth and development.
The increase in pollutants like SO2, NO2 and tropospheric
ozone can harm the plants and crops in more than one way
and can have impacts on the yield, pest infestations,
nutritional quality and disease of different crops and on
forests around cities and point sources (Emberson et al.,
277
2001; Kuylenstierna & Hicks, 2003). In India, a number of
field studies (Emberson et al., 2001) have been carried out
on wheat around industrial sources of pollution, and clearly
show large reductions in yield close to the source. The yield
reductions are the result not only of SO2 but are also
associated with NO2 and particulates that accompany SO2
concentrations in the field.
EOF2 shows good vegetation over Indian sub-continent
during the Asian winter monsoon which is characterized by
cold trades emanating from the Siberian high. These trades
are largely obstructed by the Himalayas, as a result Indian
sub-continent experiences a warmer winter while the area
beyond the Himalayas in the central Asia experiences dry
and very cold winter. Hence, India remains relatively well
Fig. 5. (a) The plot of PC2 with NINO3 (upper panel) and PC5 with NINO3 (lower panel). Both the principal modes show significant anticorrelation with the
temperature anomaly over the Pacific. (b) Wavelet power spectrum plot of PC2 after the removal of all interannual scale signals. The high power concentration
between 4 and 8 years suggests impact of ENSO.
278
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
vegetated as opposed to little vegetation growth in Central
Asia and beyond.
Both, EOF2 and EOF5 show signatures of ENSO. Tables
2 –5 show the lag correlation values of PC2 and PC5 with
SOI and NINO3, respectively; in each case, the respective
PC time series are indicated on the horizontal while the SOI/
NINO3 time series forms the vertical. Only correlations
values significant above 95% have been shown for clarity.
The 95% significant test size estimates comes from the
effective degree of freedom of the respective time series
samples (Chiu & Newell, 1983). The correlations are based
on an actual sample size of 33(PC2) and 24(PC5) based on
decorrelation times of f 7(PC2) and f 10(PC5), respectively. PC2 shows a lag relation with SOI and NINO3 of
about 3 –4 months, whereas PC5 is seen to have a zero to
one lag response to ENSO and hence better captures the
effect of ENSO over Indian sub-continent. Fig. 4 shows the
EOF2 and EOF5 with the monsoonal precipitation patterns
associated with ENSO (Ropelewski & Halpert, 1987, 1989).
The spatial pattern for the two EOFs is very similar to the
ENSO related precipitation pattern obtained by Ropelewski
and Halpert (1987, 1989), for the period of 1982 – 2000.
However, EOF5 is found to show better similarity with the
pattern outlined by Ropelewski and Halpert. It shows major
differences in vegetation pattern between peninsular India
and Southern India, Sri Lanka, Minicoy (marked as IND and
MSL, respectively, in Fig. 4, lower panel) during the El
Niño periods. Ropelewski and Halpert found ENSO related
summer monsoon deficiency in northern, central and peninsular India (IND) and enhanced winter monsoon precipitation in extreme southern India, Sri Lanka and Minicoy
region (MSL). PC2 and PC5 with their interannual signal
removed have been plotted with the NINO3 index in Fig.
5a. A Morlet wavelet decomposition of PC2 is carried out to
bring out the inherent signal power. Morlet wavelet is
preferred in time series analysis as it is non-orthogonal
and complex and hence is able to capture the continuous and
oscillatory behavior of any time series. The power spectrum
of the wavelet transforms (Fig. 5b) shows 3 – 8-year periodicity which matches with the ENSO cycles; similar periodicity is also seen for PC5 (not shown).
In an effort to verify our observations regarding the effect
of tropospheric aerosol on ground vegetation and to dismiss
any chance of it being just an artifact on NDVI values, we
repeated our analysis for a shorter period. Our improved
NDVI data shows a marked decrease in NDVI during 1982
while the period of 1991 – 1994 has been found to be
affected by volcanic aerosols from the Pinatubo eruption.
We re-computed the EOFs for a shorter duration, ignoring
1982 and 1991– 1994 and estimated the correlation values
between the relevant PCs with AI and ENSO indices. Our
result shows that for AI, the correlation values obtained with
the original NDVI data and the NDVI data after ignoring
1982 and 1991 – 1994 are different by about 0.05. The
differences in correlation values with ENSO indices is even
smaller and is between f 0.02 and 0.04.
To investigate the possible dominance of local effects
over regional climatic factors in determining the vegetation
patterns, all the modes of NDVI are compared to the DMI
over the Indian Ocean. The first principal mode shows
correlation of 0.67 with DMI at a lag of 2 to 3 months
while the second principal mode shows correlation of 0.58
at similar lags. This shows the dominance of Indian Ocean
in dictating the vegetation pattern over the Indian subcontinent rather than any regional or global climate impact.
Next the NDVI time series is broken down into two
time spans of 1982– 93 and 1994 – 2000 to account for any
possible change in El Niño behavior with respect to India
(Kinter et al., 2002; Kumar et al., 1999). The radical
change in spatial pattern of EOF5 (Fig. 6) during 1982–
1993 and 1994– 2000 is clearly seen. During the 12-year
period from 1982 to 1993, ENSO seems to favor overall
low vegetation over the Indian region which becomes high
during 1994– 2000.
Fig. 6. Distribution of EOF5 for two different time scales of (a) 1982 – 1993
and (b) 1994 – 2000. An almost radical change in vegetation pattern is
observed between the earlier and later El Niño years, suggesting a lack of
coherent precipitation pattern over this region as outlined in the text.
S. Sarkar, M. Kafatos / Remote Sensing of Environment 90 (2004) 268–280
The variable impact of ENSO on the Indian monsoon has
been documented in the last two decades. Angell (1981),
Rasmusson and Carpenter (1983) and Ropelewski and
Halpert (1987, 1989) have found less precipitation when
the SOI was negative. During the period from 1900 to 1981
Shukla (1987) found below average rainfall over 14 out of
34 summer seasons which coincide with the warm events in
Pacific Ocean as a result of El Niño events. Such were the
case for 1982 –1993 involving three El Niño periods of
1982 –1983, 1986 – 1987 and 1991 – 1992. However, during
1997 –1998 El Niño, the strongest El Niño of the decade,
the Indian monsoon rainfall was slightly beyond normal
( + 2% of the long-term mean) (Kumar et al., 1999).
4. Conclusions
This paper discusses the interannual variability of vegetation over Indian sub-continent with regard to some key
parameters that govern the climate pattern of this area.
Spatial analyses of 19 years of monthly NDVI deviations
over the Indian sub-continent have been carried out. Five
principal modes, which explain about 70% of the total
variance, have been identified. Highest concentration of
vegetation is found to be clustered over specific regions
around the southern parts of the Himalayas, the western
coast of Bay of Bengal and southeastern Asia. The vegetation is found to be affected more by the local effects
prevailing over the Indian sub-continent and the Indian
Ocean, with the first two principal modes of vegetation
showing high covariablity with AIR and DMI. ENSO and
other teleconnection events fail to explain large variance of
vegetation over Indian sub-continent. The lack of coherency
in the ENSO related vegetation pattern supports earlier
observations (Shukla, 1987) regarding the unpredictable
nature of India when comes to ENSO related predictability.
The strong effect of tropospheric aerosols on improved
NDVI data suggests serious consequences of pollution on
vegetation that is endorsed by some recent studies (Emberson et al., 2001; Kuylenstierna & Hicks, 2003). The effect of
aerosol is clearly local and restricted to areas of industrial
belts and is clearly a result of growing urbanization and
industrialization. Such effects of pollutants on vegetation
and agricultural productivity need to be taken into account
as the severity has not been fully recognized by many
national and international agencies. In particular, the rapid
increase in pollutant emissions predicted over the coming
decades in many developing countries may have large
impacts on agriculture. Our results will be useful in taking
corrective measure to prevent further deterioration.
Acknowledgements
The authors are grateful to the three anonymous referees
for their comments and suggestions. The first author is
279
grateful to Dr. Ramesh P. Singh for his valuable suggestions
and corrections.
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