Application of Meteorological and Vegetation Indices for Evaluation of Drought Impact - A case Study for Rajasthan, India

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ORI GIN AL PA PER
Application of meteorological and vegetation indices
for evaluation of drought impact: a case study
for Rajasthan, India
Sanjay K. Jain • Ravish Keshri • Ajanta Goswami •
Archana Sarkar
Received: 5 February 2008 / Accepted: 20 December 2009
� Springer Science+Business Media B.V. 2010
Abstract Drought is a serious climatic condition that affects nearly all climatic zones
worldwide, with semi-arid regions being especially susceptible to drought conditions because
of their low annual precipitation and sensitivity to climate changes. Drought indices such as
the standardized precipitation index (SPI) using meteorological data and vegetation indices
from satellite data were developed for quantifying drought conditions. Remote sensing of
semi-arid vegetation can provide vegetation indices which can be used to link drought
conditions when correlated with various meteorological data based drought indices. The
present study was carried out for drought monitoring for three districts namely Bhilwara,
Kota and Udaipur of Rajasthan state in India using SPI, normalized difference vegetation
index (NDVI), water supply vegetation index (WSVI) and vegetation condition index (VCI)
derived from the Advanced Very High resolution Radiometer (AVHRR). The SPI was
computed at different time scales of 1, 2, 3, 6, 9 and 12 months using monthly rainfall data.
The NDVI and WSVI were correlated to the SPI and it was observed that for the three stations,
the correlation coefficient was high for different time scales. Bhilwara district having the best
correlation for the 9-month time scale shows late response while Kota district having the best
correlation for 1-month shows fast response. On the basis of the SPI analysis, it was found that
the area was worst affected by drought in the year 2002. This was validated on the basis of
NDVI, WSVI and VCI. The study clearly shows that integrated analysis of ground measured
data and satellite data has a great potential in drought monitoring.
Keywords Drought � Standardized precipitation index (SPI) � Normalized
difference vegetation index (NDVI) � Water supply vegetation index (WSVI) �
Vegetation condition index (VCI) � National oceanic atmospheric administration (NOAA)
S. K. Jain (&) � A. Sarkar
National Institute of Hydrology, Roorkee, India
e-mail: sjain@nih.ernet.in
R. Keshri
College of Technology and Engineering, Udaipur, India
A. Goswami
Indian Institute of Technology, Roorkee, India
123
Nat Hazards
DOI 10.1007/s11069-009-9493-x
1 Introduction
Drought is considered the most complex but least understood of all natural hazards,
affecting more people than any other hazard. Drought is a normal feature of climate and its
recurrence is inevitable (Mishra and Desai 2005). Variables which are used either alone or
in combination for defining drought are: rainfall, temperature, humidity, evaporation from
free water, transpiration from plants, soil moisture, wind, stream flow and wind condition.
Drought is difficult to detect and monitor for three reasons: (1) it develops slowly, and the
onset and end are indistinct; (2) it is not precisely and universally defined and (3) its impact
is non-structural and often spreads over very large areas (Wilhite 2000).
Like many countries, drought is common in India and was pronounced in the year 1972,
1979, 1987 and 2002 (Rainwaterharvesting 2006). Bhalme and Mooley (1980) have carried
out a study on large scale droughts/floods and monsoon circulation. They found that during
1871–1975, there were 14 events of drought and 13 events of flood. Out of these, 1918 was
the most affected by drought while 1961 was the most flooded year. Drought areas are
mainly confined to the peninsular and western parts of the country. Especially, the western
regions of India (Rajasthan and Gujarat provinces) have suffered with severe droughts at
many times in the past. The frequent occurrence of drought in these regions is due to poor
and untimely monsoon, abnormally high temperature especially in the summer and various
unfavorable meteorological conditions. In addition, there are few more drought prone
pockets in other parts of India. The National Commission on Agriculture in India defines
three types of droughts, namely, meteorological, agricultural and hydrological droughts.
Meteorological drought is defined as a situation when there is significant decrease from
normal precipitation over an area (i.e., more than 25%). Agricultural drought occurs when
soil moisture and rainfall are inadequate during the growing season to support healthy crop
growth up to maturity and causes crop stress and wilting. Hydrological drought may be a
result of long-term meteorological droughts that results in the drying up of reservoirs,
lakes, streams, rivers and decline in groundwater level (Rathore 2004). Out of 3.28 million
km2 of geographical area in India, about 1.07 million km2 of land is subjected to different
degrees of water stress and drought conditions. In order to tackle the problem of drought at
the national level and to promote a unified and efficient drought strategy several disaster
risk management programmes are in progress. The first step toward mitigation of drought
disaster is to monitor it in near real time (Rathore 2004).
There is extensive literature on the quantification of drought by using various indices,
models and water balance simulations (Palmer 1965; Alley 1985; Karl et al. 1987; Sen
1998; Lana et al. 1998; Stahl and Demuth 1999). Drought indices are particularly useful for
monitoring the impact of climate variability on vegetation because the spatial and temporal
identification of drought episodes is extremely complex. Drought duration may also lead to
spatial differences in drought impact on vegetation (Ji and peters 2003; Wang et al. 2003),
while drought impact can vary as a function of the season of the year, since the water
requirements of the vegetation can change markedly over a 12-month period. Among the
meteorological indices, the palmer drought severity index (PDSI) and standardized pre-
cipitation index (SPI) are more commonly used. The SPI has certain advantages over
others such as use of rainfall data alone and also its variable time scale, which allows it to
describe drought conditions important for a range of meteorological, hydrological and
agricultural applications (McKee et al. 1993). Although it is quite a recent index, the SPI
was used in Turkey (Komuscu 1999), Argentina (Seiler et al. 2002) and Spain (Lana et al.
2003) for real time monitoring or retrospective analysis of droughts.
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Among various satellite-derived indices, the NDVI has evolved over a period of time as
a primary tool for monitoring vegetation changes and interpretation of the impact of
climatic/weather events on the biosphere. Extensive research has shown that NDVI can be
used not only for the accurate assessment of vegetation phenology and estimating net
primary production but also for effective monitoring of rainfall and drought situations
(Justice et al. 1989; Franklin and Hiernaux 1991; Srivastava et al. 1994; Thiruvengadachari
and Gopalkrishna 1994). Because of close relationship between vegetation vigor and
available soil moisture, the AVHRR derived NDVI was widely used to evaluate drought
condition by directly comparing it to precipitation or drought indices (Gutman 1990;
Herrieksen and Durkin 1986; Mohler et al. 1986; Tucker and Chaudhary 1987). The other
index that is important for drought monitoring is the water supply vegetation index (WSVI)
(Xiao et al. 1995). This index incorporates brightness temperature in addition to NDVI.
Kogan (1995) has developed the vegetation condition index (VCI) using the Advanced
Very High Resolution Radiometer (AVHRR) thermal bands.
The relationship between NDVI and rainfall is known to vary spatially, notably due to
the effects of variation in properties such as vegetation type and soil background (Li andTao 2002; Farrar et al. 1994). Consequently, a strong relationship, involving a brief time–
lag in the vegetation response to rainfall, would be expected between vegetation indices,
such as NDVI and rainfall (Li and Tao 2002). Many studies have focused on the rela-
tionship between the NDVI and rainfall. A study related to early detection of drought in
East Asia was done by Song et al. (2004). In this study, NDVI from NOAA/AVHRR was
used wherein standard NDVI and up-to-date NDVI were calculated from NOAA/HVRR to
derive difference NDVI images, in order to detect the intensity and agricultural area
damaged by drought. Anyamba and Tucker (2005) analyzed seasonal and inter annual
vegetation dynamics in Sahel using NOAA-AVHRR NDVI. The study concentrated only
on NDVI patterns in growing season, which were defined by examining the long-term
patterns of both rainfall and NDVI. The correlation between NDVI and rainfall anomaly
time series was found to be positive and significant, indicating the close coupling between
rainfall and land surface response patterns over the region. A study by Wang et al. (2003)
has concentrated on temporal responses of NDVI to precipitation and temperature in the
central Great Plains, USA. In this study, it was found that average growing season NDVI
values are highly correlated with precipitation received during the growing season and
seven preceding months.
Ji and Peters (2003) undertook a study assessing vegetation response in the northern
Great Plains using vegetation and drought indices. The study aimed to determine the
response of vegetation to moisture availability through analysis of monthly AVHRR-NDVI
and SPI over grass and cropland cover types in the northern U.S. Great Plains. The study
focused on three major areas namely the relationship between NDVI and SPI at different
time scales, response of NDVI to SPI during different time periods within a growing
season and regional characteristics of the NDVI-SPI relationship. A study was carried out
by Chaudhari and Dadhwal (2004) to quantify the impact of drought on production of
kharif and rabi crops using SPI. SPI was computed at monthly (SPI 1), bimonthly (SPI 2)
and tri-monthly (SPI 3) time scales with the suggested Pearson Type III distribution. SPI
values were then classified into seven categories suggested by Hayes. Correlation coeffi-
cients were computed between state-wise production of major kharif crops (1980–2001)
and SPI values (SPI1, SPI2 and SPI3). Production forecasts using SPI3 have shown good
agreement with the statistics from state department of agriculture, thereby suggesting that
SPI at different time scales can be used as a predicator of regional crop production in India.
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The use of AVHRR derived vegetation indices has a number of advantages over
meteorological drought indices: (1) spatial density of data collection is very high (2) the
sensor covers very large areas; (3) data are available from lands with a low density of
weather stations and (4) the NDVI indicates the severity of vegetation stress resulting from
a water deficit. Therefore, the objective of this study is to determine the response of
vegetation vigor to the moisture availability through the analysis of AVHRR derived
NDVI, WSVI and VCI over the study area. Also the relationship between these indices and
SPI was studied to monitor the drought.
2 The study area and data used
The area selected for the study is the three districts of the Rajasthan state, India viz.
Bhilwara, Kota and Udaipur located in southern part of the state (Fig. 1). The districts of
Bhilawara and Udaipur fall in sub-humid southern plain zone. This region has a moderately
warm climate in summer with mild winters. Kota falls in the humid southeastern plain zone
with warm summers. For this study, meteorological data pertaining to monthly precipi-
tation data were collected from the College of Technology and Engineering, Udaipur, India
for a period of 24 years (1981–2004). The National Oceanic and Atmospheric Adminis-
tration (NOAA) of the USA operates the series of NOAA satellite that carry the AVHRR
sensor. AVHRR is an optical instrument, which provides multi-spectral imaging by sensing
reflected sunlight and thermal emissions. It is a five-channeled radiometer providing data in
the visible, near infrared and thermal infrared for a wide range of applications. The thermal
channels 4 and 5 are the windows in the range of 10.3–11.3 and 11.5–12.5 lm. AVHRR
has a spatial resolution of 1.1 km. Twenty-seven cloud free NOAA 15, 16 & 17 images
of the study area were acquired through ftp from the Satellite Active Archive site
(http://www.class.noaa.gov.in) for a period of 3 years ranging from 2002 to 2004. The time
of the satellite passes were between 8.30 am and 10.00 am local time. The dates of the
satellite data used in the study are given in Table 1.
Fig. 1 Location of the study area
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http://www.class.noaa.gov.in
3 Methodology
3.1 Drought index calculation
There are a number of indices to quantify drought using meteorological data, however, the
SPI is most widely used index. The SPI was formulated by Tom McKee, Nolan Doesken
and John Kleist of the Colarado Climatic Centre in 1993 (McKee et al. 1993). SPI can be
calculated at different time scales and hence can quantify water deficits of different
duration. SPI was designed to show that it is possible to simultaneously experience wet
conditions on one or more time scales, and dry conditions at other time scale. SPI is
computed by fitting historical precipitation data to a Gamma probability distribution
function for a specific time period and location, and transforming the Gamma distribution
to normal distribution with a mean of zero and a standard deviation of one. SPI for given
rainfall amount is then given by the precipitation deviation from the mean of an equivalent
normally distributed function with a zero mean and a standard deviation of one (McKee
Table 1 Details of NOAA-AV-
HRR satellite data
Year Date of satellite pass
2002 25 April 2002
24 May 2002
21 October 2002
27 November 2002
2003 14 February 2003
01 March 2003
16 March 2003
15 May 2003
15 October 2003
30 October 2003
16 November 2003
2004 15 February 2004
01 March 2004
15 March 2004
04 April 2004
23 April 2004
31 May 2004
14 October 2004
29 October 2004
28 November 2004
2005 12 February 2005
31 March 2005
28 May 2005
01 October 2005
17 October 2005
30 October 2005
26 November 2005
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et al. 1993; Hayes and Svoboda 1999). Since the SPI is equal to the Z value of normal
distribution, McKee et al. (1993) proposed a seven category classification for the SPI:
extremely wet (z [ 2.0), very wet (1.5 to 1.99), moderately wet (1.0 to 1.49), near normal
(-0.99 to 0.99), moderately dry (-1.49 to -1.0), severely dry (-1.99 to -1.5) and
extremely dry (\-2.0). The precipitation total of the current month and previous i months
(i = 1, 2, 3 …) is used to compute (i ? 1) month scale of the SPI. For example the
1-month SPI of May utilizes only the precipitation of May; 3-month SPI of May uses the
precipitation total of March, April and May.
3.2 Remote sensing data
3.2.1 Generation of NDVI images
A number of vegetation indices based on remote sensing data have been used to monitor
vegetation, with the most widely adopted being the normalized difference vegetation index
(NDVI) (Tucker 1979). The data used in compiling the NDVIs are closely related to the
radiation absorbed and reflected by vegetation in the photosynthetic processes. Green and
healthy vegetation reflects much less solar radiation in the visible (channel 1) compared to
those in the near-infrared (channel 2). More importantly when vegetation is under stress,
the channel 1 value may increase and the channel 2 values may decrease. The NDVI were
generated using the radiation measured in red and near infrared spectral channels as
defined by Rouseet al. (1974) as follows:
NDVI ¼ Channel 2� Channel 1
Channel 2þ Channel 1
ð1Þ
where Channel 1: Radiation measured in channel 1 (red spectral channel), Channel 2:
Radiation measured in channel 2 (near-infrared spectral channel).
Using the above relationship, the NDVI for different dates were generated. The values
obtained were multiplied by 100 to show more variation. Now the NDVI for each station
were computed. The NDVI were then extracted from the images as the mean value for
3 9 3 pixel windows, centered at each station and compared to the SPI. For this study,
three stations viz. Bhilwara, Kota and Udaipur were selected.
3.2.2 Generation of WSVI images
The water supplying vegetation index (WSVI) is based on the fact that, in drought con-
ditions, the NDVI values derived from satellite data will fall below normal. At the same
time, the crop canopy temperature as seen by the same satellite will rise above normal.
Both effects are related to available water supply and by combining both effects in one
index, a sensitive measure of drought conditions can be obtained. WSVI, an index that can
combine both aspects are given by Xiao et al. (1995), is defined as:
WSVI ¼ NDVI
Ts
ð2Þ
where, Ts is Fourth channel brightness temperature of NOAA AVHR.
To retrieve brightness temperature, the AVHRR thermal channels have to be calibrated
first. The detailed procedure for calibration of the NOAA-AVHRR thermal channels is
given in the web site (NOAA 2006). The procedure suggested in the web site was followed
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123
in the present study to calibrate the AVHRR thermal channels. Brightness temperature was
retrieved from the thermal bands of Channel 4 and 5 of NOAA AVHRR images. Starting
from NOAA-15 satellites, National Environmental Satellite Data and Information Service
(NESDIS) incorporates the non-linear radiance corrections for AVHRR thermal channels 4
and 5. Hence, the radiance measured by the sensor was computed as a non-linear function
of the input data values as follows (NCDC 2003):
Ei ¼ A0 þ A1Ci þ A2C2
i ð3Þ
where, A0, A1, A2 (for channels 4 and 5) are constants and Ci is the input data value (Digital
Number) for Channels 4 and 5 (i = 4, 5).
Following Planck’s equation, for the central wavelength VCi for channels 4 and 5, an
effective brightness temperature (TEi
* ) was calculated for each channel. Finally, the
brightness temperature for each Channel was calculated using the (TEi
* ) as follows:
TEi ¼ B0 þ B1T�Ei ð4Þ
where B0 and B1 (for channel 4 and 5) are constants.
The values of A0, A1, A2, B0 and B1 (for channels 4 and 5) can be found in NOAA-KLM
header of the HRPT (or Level 1b) images. The value of the central wave number of each
channel can be also found there, as well as the satellite height (and other parameters) that
are needed to correct AVHRR panoramic distortion.
3.2.3 Vegetation condition index
VCI is an indicator of the status of vegetation cover as a function of NDVI minima and
maxima encountered for a given ecosystem over many years. It normalizes NDVI (or any
other vegetation index) and allows for a comparison of different ecosystems. It is an
attempt to separate the short-term climate signal from the long-term ecological signal and
in this sense it is a better indicator of water stress condition than NDVI (Kogan and
Sullivan 1993). The significance of VCI is strongly related to the relation between the
vegetation index and the vitality of the vegetation cover under investigation. It is defined
as:
VCI ¼ 100� ðNDVI� NDVIminÞ
ðNDVImax � NDVIminÞ
ð5Þ
where NDVI, NDVImax and NDVImin are monthly NDVI, multi-year maximum NDVI and
multi-year minimum NDVI, respectively for each grid cell. VCI changes from 0 to100
corresponding to changes in vegetation condition from extremely unfavorable to optimal.
In the case of an extremely dry month, the vegetation condition is poor and VCI is close or
equal to zero. A VCI of 50 reflects fair vegetation conditions. At optimal condition of
vegetation, VCI is close to 100.
3.2.4 Correlation analysis
Toward understanding the interlink of NDVI-SPI and WSVI-SPI at multiple time scales,
correlation analysis were conducted for the NDVI/WSVI and SPI at 1, 2, 3, 6, 9 and
12 month time scale using these three parameters for year 2002–2004. The correlation
coefficients were obtained for three districts.
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4 Results and discussions
The calculated NDVI, WSVI and SPI at multiple time scales are presented in the Table 2.
The relationship between moisture availability was obtained by analyzing the covariation
of NDVI / WSVI and SPI time series with the scatter plots. During a year, there is
considerable year to year variation in precipitation and NDVI. NDVI has responded more
rapidly to precipitation during the drought years. The scatter plots between NDVI-SPI and
WSVI-SPI at 1, 2, 3, 6, 9 and 12 month time scales are presented in the Figs. 2 and 3.
It can be noted from scatter plots between SPI and NDVI/WSVI that the correlation
coefficients vary by SPI time scale. For example, in some of the years, there are less
significant correlations between NDVI and SPI. In one study carried out by Lei et al.
(2003), it was found that the correlation coefficient was even less than zero. High positive
correlation between NDVI and SPI are noted for Bhilwara and Udaipur districts. Among
all plots, correlation coefficient of NDVI vs. SPI is highest for Bhilwara (9-month time
scale), while lowest for Kota (1-month time scale). From these results, it is observed that at
Bhilwara, the correlation between NDVI / WSVI and SPI at the time scale of the 6, 9 and
12 months is quite high. The coefficient for SPI-9 is 0.691 and 0.697 with respect to NDVI
and WSVI, respectively. For Kota, the correlation between NDVI-SPI and WSVI-SPI is
not significant; however, it shows the highest correlation in the SPI at 1-month time scale
(0.337 and 0.365) with NDVI and WSVI, respectively. For Udaipur, there is good cor-
relation between NDVI / WSVI with SPI-3 and SPI-9, which shows the correlation
coefficient as 0.568 and 0.503, respectively, with NDVI and 0.576 and 0.528, respectively,
with WSVI.
This indicates that the impact of precipitation on vegetation does not occur instanta-
neously, but is cumulative. In most cases, precipitation occurring in 1 month does not
strongly affect vegetation in that month, but the response in notable over periods longer
than 1 month. The calculated SPI value for all the three stations shows that SPI for
9-month time scale is having good correlation with the NDVI and WSVI for Bhilwara,
3-month time scale for Udaipur and 1-month time scale for Kota. One probable reason for
low time scale for Kota is that the Chambal River flows through the area providing
adequate soil moisture.
Results show that in Bhilwara district, NDVI varies between 0.044 and 0.199 and WSVI
varies between 1.425 and 6.58 in the post-monsoon (after September) for the years 2002
and 2004, respectively. In Kota district, NDVI and WSVI varies between 0.014 to 0.196
and 0.424 to 6.485 in May and October 2003, respectively. Udaipur district shows the
NDVI variation as 0.013–0.279 and WSVI variation 0.788–9.426 in May, 2003 and
October, 2004, respectively. It is observed that during post-monsoon of the year 2002, all
the three stations depicts a low value of NDVI and WSVI that shows a considerable stress
on vegetation. It can be inferred that the year 2002 is the worst affected by drought, while
year 2004 is the least drought affected year. From the indices in all the three stations, it can
be seen that Udaipur shows the maximum NDVI and WSVI value in all the 3 years
followed by Bhilwara, while Kota shows the maximum value in the year 2004.
VCI was successfully used in recent years to detect drought and vegetation stress due to
excessive wetness. The drought and vegetation stress are mapped when VCI is less than 35.
They can be used for both localized/short term and wide spread/longterm droughts (Kogan
1995, 1997). VCI confirm that in the year 2002 almost all the parts of the study area were
affected by drought condition. The area covered having values of VCI less than 35 were
demarcated. In the year 2005, most of the area having VCI values higher than 35 shows the
normal condition. The VCI values range from 19.5 in the year 2002 to 70.0 in the year. It is
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Nat Hazards
123
Fig. 2 Scatter plot and correlation coefficient of NDVI and multiscale SPIs
Nat Hazards
123
Fig. 3 Scatter plot and correlation coefficient of WSVI and multiscale SPIs
Nat Hazards
123
revealed that VCI values are lower than 35 in the year 2002 and thus there were drought
conditions.
The results obtained by meteorological indicator i.e., SPI- and satellite-based indicator
i.e., NDVI and WSVI are almost comparable. However for one station i.e., Kota, the
results are not in close proximity as for other districts. The reason for this may be the effect
of moisture because of Chambal River flowing through the area. On the basis of SPI
analysis, it was observed that the year 2002 is having maximum drought. The pattern of
NDVI and WSVI for different years were studied and found that these values shows
downward trend during pre monsoon of 2003 (Jain et al. 2009). It suggests that there was
vegetation stress during post-monsoon in the year 2002. It confirms that the year 2002 was
having maximum drought like situation.
5 Conclusions
SPI is one of the drought indices which is more widely used for monitoring drought. For
assessing spatial behavior of the drought, NDVI, WSVI and VCI images using NOAA/
AVHRR are very useful. SPI at different time interval and NDVI/WSVI/VCI were
developed using 4 years 2002–2004.
The multi scale SPIs from 1 to 12 months were correlated with NDVI, and it is observed
that NDVI has highest correlation with 1-month SPI for Kota, 3-month SPI for Udaipur
and 9-months SPI for Bhilwara. It means that the effect of rain is slow in Bhilwara while it
is fast in Kota. Also Chambal River passes through Kota showing good correlation for
early time scale i.e., 1 month. So it is suggested that the 1 to 9-month SPI is best for
determining drought severity and duration in vegetation cover for the three different areas.
The results of plot of NDVI and WSVI have shown that these values were lowest in pre-
monsoon season of 2003. It is found that VCI values are lower than 35 in the year 2002.
These results indicate that year 2002 was having vegetation stress and thereby drought
year. It is also concluded that using NDVI/WSVI spatial distribution of drought vulnerable
area can be obtained.
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	Application of meteorological and vegetation indices �for evaluation of drought impact: a case study �for Rajasthan, India
	Abstract
	Introduction
	The study area and data used
	Methodology
	Drought index calculation
	Remote sensing data
	Generation of NDVI images
	Generation of WSVI images
	Vegetation condition index
	Correlation analysis
	Results and discussions
	Conclusions
	References
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