studies on raifall and ndvi in selected regions of southern ethiopia
DESCRIPTION
STUDIES ON RAIFALL AND NDVI IN SELECTED REGIONS OF SOUTHERN ETHIOPIATRANSCRIPT
BY Mekonnen Daba
PRACTICAL ATTACHMENT COURSE FOR PARATIAL FULFILLMENT OF
B.SC DEGREE SUBMITTED TO
DEPARTMENT OF METEOROLOGY ARBAMINCH UNIVERSITY
FACULTY OF WATER TECHNOLOGY
ARBAMINCH, ETHIOPIA
JULY, 2009
2
SUBMITED BY: MEKONNEN DABA Under the guidance of: Advisor : Dr. Santosh Kunnummal M Co-advisor: Mr. Y ite
JUNE 2009
3
Acknowledgement First of all we would like to thank God for helping me in all our day
today activities, next we would like to thank our parents and family members for their
material, financial and moral support during our university level and elementary level
of education. Also we would like to thank to our polite advisors Dr.Santosh.K.M. And
Ato. Yetie for their considerable constructive comments in all our work. Finally we
would like to thank all Administrators, Head of the Dept., Teachers, staff members of
Meteorology Department and our friends in Arbaminch University who helped us for
conducting our project work. We would also like to express our gratitude to Ato Bisrat
for his valuable help in procuring data.
4
TABLE OF CONTENTS
CHAPTER ONE
1.1. Introduction ……………………………………………………………………. 1
1.1.1. Background…………………………………………………………………… 1
1.1.2. Objectives of the study…....………………………………………………… 2
CHAPTER TWO
2.1. Literature review……………………………………………………………….3
CHAPTER THREE
3.1. Materials and methods………………………………………………………….5
3.1.1. Description of the study area……………………………………………….5
3.2. Methodology……………………………………………………………………6
3.2.1 Remotely sensed Normalized Difference Vegetation Index… 6
3.2.2. Rainfall Data………………………………………………………………... 6
3.2.3. Statistical analysis of data………………………………………………….7
CHAPTER FOUR
4.1. Result and discussion…………………………………………………………..8
4.1.1. Gamogofa zone……………………………………………………………....8
4.1.2. Welayata zone……………………………………………………………… 14
4.1.3. Gurage zone………………………………………………………………….20
4.1.4. Konso zone……………………………………………………………………26
CHAPTER FIVE
5.1 Conclusion and recommendation…………………………………………….32
5.2 References……………………………………………………………………….33
5.3. Data Tables…………………………………………………………………….36
5
Abstract
This project present the rainfall and NDVI along southern Ethiopia the variability in
seasonal and annual rain fall and NDVI during the period of 1995-2004 for selected
zones along southern part of Ethiopia representing different region and climate zones of
the area was analyzed. The onset of rainfall is highly variable from year to year specially
over areas which are, zonal agro climatically also double cropping .the year to year
variability in amount of rain fall observed typically rang from low to high of long term
average in different part of located station. Monthly and yearly data of few years are
examined to determine the consequence of rainfall variability on the NDVI of vegetation
cover. Different principal analysis was used to examine deviation from the general
relation between rainfall and NDVI. Areas of low and high NDVI values for a given
input rainfall were identified at the station scale. Study gave an identification of areas low
and high production potential and possible degradation ecosystem.
6
Chapter 1 1.1 INTRODUCTION 1.11 Background
Southern part of Ethiopia, which encompass Arbaminch, Wolita sodo, Gurage, Konso,
Burji, Amarokelle which locates latitude of 5.3o N and 37.4 o E longitude to5.8 o N and
37.9 o
Seasonal dynamics of NDVI and rainfall is found to be apparent with some classes have
been used to access spatial distribution of NDVI for the period of 1995 -2004. Lee.et.al
(2000) has been used time series of NDVI at the field and regional scales for monitoring
E and from 220M above sea level. The most part of Ethiopia has a tropical climate
moderated by altitude which a marked wet season. South western and Western Ethiopia
experience ten months of rain in a year while central Ethiopia and the eastern high land
receives up to 8 months of rain. The climate of the country varies from humid to arid with
abundant and seasonal rainfall experienced in different areas more ever extreme
variations in rainfall occurs season to season and from year to year . Different
phenomena’s are happening due to the variation of rainfall consequences of non planned
agriculture activities and other socioeconomic activities in the country. The variation in
rainfall on different season leads variation of vegetation in that area. It shows the
Normalized Difference Vegetation Index (NDVI) always related to rainfall variation.
Generally the periods of a peak rainfall months are June to September in association with
the movement of inter Tropical Convergent Zone (ITCZ) across the country.
The topography of the country plays significant role in the distribution of seasonal
rainfall and the NDVI over the country. Normalized Difference Vegetation Index (NDVI)
is a measure of vegetation cover and biomass it also being used as an indicator for
primary productivity and crop yield. Vegetation is one of the important parameter for
human environment assessment and monitoring due to their roll in the biosphere,
atmosphere interactions and also plays an important role on global change.
7
crop dynamics and for yield production. Considerable efforts have also been made for
predicting the start and end of growing season using NDVI not only for crops but also in
natural ecosystem (Zheng,et.al 2003) nevertheless in all these cases the phonologic state
has been based on biomass accumulation (i.e leaf production ) and not by the
development and appearance.
We analyzed spatial rainfall and NDVI for the selected station of southern Ethiopia. The
Normalized Difference Vegetation Index (NDVI) data are calculated from the reflectance
measurement in red and Near Infra Red (NIR) portion of spectrum from satellite sensors.
The relationship between vegetation and rainfall in Ethiopia has been studied extensively
and in the southern region efforts have been made to explicitly dissent angle the effect of
rainfall and human impacts on vegetation dynamics.
1.12 .Objectives of the study
1. To study the vegetation variability in selected areas of southern part of Ethiopia
by using NDVI derived from NOAA-AVHRR.
2. To further explore trends in vegetation greenness, precipitation and thus spatial
pattern in the southern part of Ethiopia
3. To establish a relationship between NDVI and rainfall.
8
Chapter 2 2.1. LITARAURE REVIEW
Sarkar. 2004; Justice.et.al; 1985 studies on the normalized difference vegetation index by
calculating by difference between infra red and red reflectance by their sum provides an
effective measure of photosynthetic active biomass and also other studies discussed the
suitability of temporal NDVI profile for the studying vegetation phonologies especially
those of crops. Groten and Octare; 2002, reported significant positive relation between
satellite derived NDVI and observed greens.
A comparison with rainfall and vegetation response in east Africa around Sahel has been
studied by Sharon et .al (1990) with the result that the spatial pattern of annual integrated
NDVI closely reflect mean annual rainfall. Turker and Seller, 1986 estimated the
normalized difference vegetation index (NDVI) and found to be highly correlated to
green leaf density.
Rowland et al. 1996, processed satellite data for Normalized Difference Vegetation
Indices (NDVI) and used to indicate deficiencies in rainfall and portray meteorological
and/or agricultural drought patterns both timely and spatially, thus serving as an indicator
of regional drought patterns. NDVI is a measure or estimate of the amount of radiation
being absorbed by plants. The amount of radiation absorbed is directly related to evapo-
transpiration, since the plant must cool primarily by evaporating water. The evapo-
transpiration is constrained by the amount of water in the soil. Relatively low amounts
water in the soil is constrained by a decrease rainfall. Hence NDVI correlates with
rainfall. Drought will continue to occur, but the application of NDVI as a tool for
decision making will allow better integration and timely planning to promote food
security.
9
Running and Nemani (1998) estimated the strong correlation between normalized
difference vegetation index and vegetation cover which related to rainfall variation and
ground productivity.
However Normalized Difference Vegetation Index is found to be in sensitive change in
biomass at moderate to high vegetation density (Kanamasu; 1974, Buschamn and Nagel
1993, Gitelson et.al1996). Alterative index and vegetation fraction (Gitelson et.al, 2002)
for later, visible atmospherically resistant indices (VAR) were suggested which only use
channels in visible region of the electromagnetic spectrum.
10
Chapter 3
3.1. MATERIALS AND METHODS
3.1.1. Description of study area The study area comprises of three zones and one Special Woreda which are situated in
the southern part of Ethiopia (Fig. 1). The zones are GamoGoffa (Approx. lat & long
5.5N-6.75N; 36.50E-37.80 E), Wolyita (6.5N-7.15N; 37.50E-38.25E), Guraghe (7.75N-
8.5N; 37.50E-38.75E) and Konso (Sp.Woreda-5.1N-5.5N; 37.0E-37.50E)
Fig.a. Map showing study area under SNNPR
11
3.2. Methodology
3.2.1. Remotely sensed Normalized Difference Vegetation Index
(NDVI)
Living green plants absorb solar radiation in the photosynthetic ally active radiation
(PAR) spectral regions. Which they use as a source of Energy in process of
photosynthesis, leaf cells have also evaluated to scatter solar radiation in the near
infrared spectral region. NASA’s, satellite National Oceanic and Atmospheric
Administration (NOAA) - Advanced Very High Resolution Radiometer(AVHRR)
derived NDVI data for the zone under the study were obtained from secondary sources
(P.G. Department, Arba Minch University) which was primarily downloaded from the
website (wwwhttp : //iridil.ldeo. columbia.edu/sources/NOAA/.NCEP.) for the period
of 1994-2004. NDVI is calculated from the formula,
NIR-RED
NDVI = ------------
NIR+RED
Where,
NDVI = Normalized Difference Vegetation Index
NIR = Reflection from Near Infrared wavelength region
RED = Reflection from Red wavelength region
The data were averaged for every month from January to December for ten
year’s period and yearly averages were also calculated for the same period.
NDVI is calculated from the visible and near-infrared light reflected by
vegetation. Healthy vegetation (left) absorbs most of the visible light that hits it,
and reflects a large portion of the near-infrared light. Unhealthy or sparse
vegetation (right) reflects more visible light and less near-infrared light. The
numbers on the figure above are representative of actual values, but real
12
vegetation is much more varied. NDCalculations of NDVI for a given pixel
always result in a number that ranges from minus one (-1) to plus one (+1);
however, no green leaves gives a value close to zero. A zero means no vegetation
and close to +1 (0.8 - 0.9) indicates the highest possible density of green leaves.
The NDVI values range from -1 to +1 (pixel values 0-255).
VI = (NIR — VIS)/(NIR + VIS)
The NDVI value lower than 0.18 Can be considered drought/(moisture
stress) condition
13
3.2.2. Rainfall Data: Rainfall data pertaining to the study area for ten years i.e. 1995-2004 were
availed from secondary sources (P.G. Department, Arbaminch University) which
were initially provided by NMA. The data were averaged for yearly and monthly
for ten years. The rainfall stations under the four zones are, Arbaminch,
Mirababaya, Konso, Sawla, Araka, Bilate, Buie, Emdibir and Wolikite. The
average rainfall of the stations under each zones were taken as the zonal average.
3.2.3. Statistical analysis of data:
The analysis was performed by using M.S. office Excel. Bar diagrams of
monthly rainfall and NDVI were created taking ten years of averages. Also the
line diagrams were made for yearly averages of rainfall and NDVI. Linear
regression between rainfall and NDVI were also calculated and linear regression
coefficient “R2” values for the four zones were made.
14
Chapter 4
4.1. RESULTS AND DISCUSSION 4.1.1. GAMOGOFA ZONE
AVG. MONTHLY RAIN FALL DISTRIBUTION AT GAMOGOFA
0.0
50.0
100.0
150.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MONTHS
RA
IN F
AL
L(m
.m.)
GAMOGOFA MONTHLY RAIN FALL
Fig.1. Monthly avg. rainfall at Gamogofa
Monthly rain fall distribution (Fig.1) showed bimodal oscillation with peak
values in April, May and October months. This is according to Belg and Kiremt
seasons, a minimum rain fall recorded in the month of February. The highest
15
value was found to be 137.6 mm which has recorded in April, whereas the lowest
of 25.5 mm was observed in February.
According to Diro et al., (2008) Ethiopia gets rainfall due to two reasons, first in
connection with the penetration of troughs at both medium and upper
troposphere. At the beginning of March, the ITCZ arrives at the southern parts of
the country and moves to the north. During this time the southern, southeastern
and some parts of western Ethiopia receive rainfall in association with ITCZ.
The ITCZ moves gradually northwards the areas that get maximum rainfall from
March to May.
MONTHLY AVG. NDVI AT GAMOGOFA
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MONTHS
ND
VI
MONTHLY AVG. NDVI
Fig.2. Monthly avg. NDVI at Gamogofa
In the case of NDVI monthly average show mono modal oscillation (Fig.2)
showing peak value from July to September and the lowest is January.
16
AVG. YEARLY RAIN FALL DISTRIBUTION AT GAMOGOFA
0.0
20.0
40.0
60.0
80.0
100.0
120.0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
RA
IN F
AL
L (
m.m
)
GAMOGOFA YEARLY RAIN FALL Vriability
Fig.3. Yearly avg. rainfall at Gamogofa
Yearly rainfall distribution shows the peak value in 1997 and the lowest value in
1999 which have values of 103.0mm and 57.8 mm respectively (Fig 3).
17
GAMOGOFA YEARLY NDVI Variability
0.560.570.580.59
0.60.610.620.630.640.65
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
ND
VI
GAMOGOFA YEARLY NDVI Variability
Fig.4. Yearly avg. NDVI at Gamogofa
From yearly average NDVI distribution the highest vegetation cover was
observed in 2001 and the lowest vegetation cover occur in 1996 which have
values of 0.637 and 0.589 respectively(Fig 4). This is contrary to the rainfall
pattern.
18
MONTHLY CORRELATION BETWEEN NDVI & RAINFALL AT GAMOGOFA
y = 0.0073xR2 = -3.4072
0
0.2
0.4
0.6
0.8
1
1.2
0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0
RAINFALL
ND
VI
NDVI Vs. RAINFALLLinear (NDVI Vs. RAINFALL)
Fig.5. Correlation between monthly NDVI & Rainfall at Gamogofa
Monthly averages between NDVI and rainfall, when we analyzed statistically,
linear regression coefficient (r 2) was found to be negatively correlated (Fig.5).
But from the bar diagrams it could be seen that, Belg rainfall enhanced the plant
production and subsequent increase of NDVI in April onwards. Till October
NDVI showed steady value which coincides with the Kiremt rainfall.
19
YEARLY CORRELATION BETWEEN NDVI Vs. RAINFALL AT GAMOGOFA
y = 0.0083xR2 = -45.441
00.10.20.30.40.50.60.70.80.9
1
0.0 20.0 40.0 60.0 80.0 100.0 120.0
RAINFALL (m.m)
ND
VI
AVG. YEARLY NDVI Vs. RAINFALL Linear (AVG. YEARLY NDVI Vs. RAINFALL)
Fig.6. Correlation between yearly NDVI & Rainfall at Gamogofa
Whereas the yearly average rainfall and NDVI, did not show any correlation
(Fig.6). This should be further analyzed with the ground realities and events
occurred during the respective years.
20
4.1.2. WELAYTA ZONE
WELAYTA ZONE MONTHLY RAINFALL
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
160.0
180.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMONTHS
RA
INF
AL
L(m
.m)
WELAYTA ZONE MONTHLY RAINFALL
Fig.7. Monthly avg. rainfall at Welayta
The average monthly rainfall observed in Welayta zone is a bi-modal oscillation
(Fig.7) with highest value of 155.2 mm registered in April and the lowest of 23
mm in February. A highest peak could be seen in April and followed by the
second peak in August. Generally the rainfall is highly variable in Belg season.
21
MONTHLY AVG. NDVI AT WELAYTA
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MONTH
ND
VI
WELAYTA ZONE MONTHLY NDVI Variability
Fig.8. Monthly avg. NDVI at Welayta
Even though maximum rainfall registered in Belg, prolonged rainfall could be
observed during Kiremt season. Based on the rainfall variability a good
vegetation cover over this zone was observed (Fig.8) just after having rainfall.
22
AVG. YEARLY RAINFALL DISTRIBUTION AT WELAYTA
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004YEAR
RA
INF
AL
L (
m.m
)
AVG. YEARLY RAINFALL DISTRIBUTION AT WELAYTA
Fig.9. Yearly avg. rainfall at Gamogofa
From the figure it could be seen that average yearly rainfall distribution
registered a maximum value of 115.7 mm in 1996 and the lowest of 78 mm in
1999(Fig.9).
23
YEARLY AVG. NDVI AT WELAYTA
0.56
0.57
0.58
0.59
0.6
0.61
0.62
0.63
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
ND
VI
WELAYTA ZONE YEARLY NDVI Vriablity
Fig.10. Yearly avg. NDVI at Gamogofa
From the yearly mean NDVI variability (Fig.10) the highest value 0.622 was
observed in 2001 and the lowest value observed in 2000.
24
MONTHLY CORRELATION BETWEEN NDVI & RAINFALL AT WELAYTA
y = 0.0057xR2 = -1.3901
00.10.20.30.40.50.60.70.80.9
1
0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 160.0 180.0RAINFALL (m.m.)
ND
VI
NDVI Vs. RAINFALL Linear (NDVI Vs. RAINFALL)
Fig.11. Correlation between monthly NDVI & Rainfall at Welayta Even though the regression coefficient showed a negative (Fig.11) value for
monthly average rainfall and NDVI, but from the bar diagrams it could be seen
that after the prolonged rainfall an increase in vegetation indices was observed.
25
YEARLY CORRELATION BETWEEN NDVI Vs. RAINFALL AT WELAYTA
y = 0.0061xR2 = -43.359
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0
RAINFALL (m.m.)
ND
VI
NDV Vs. RAINFALL Linear (NDV Vs. RAINFALL)
Fig.12. Correlation between yearly NDVI & Rainfall at Welayta
Whereas for yearly average rainfall and NDVI negative correlation (Fig.12) was
observed, this has to be further studied based on the ground realities.
26
4.1.3. GURAGE ZONE
MONTHLY AVG. RAINFALL AT GURAGE
0.0
50.0
100.0
150.0
200.0
250.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MONTHS
RA
INF
AL
L (
m.m
.)
MONTHLY AVG. RAINFALL
Fig.13. Monthly avg. rainfall at Gurage As we can see from the bar diagram of monthly rainfall (Fig.13), the maximum
value (158.7, 222.8, 194.0 mm) is recorded during the Ethiopia Kiremt (Jun,
July, August) respectively. This is also true for the south. Since the southern
parts shows a bimodal rainfall pattern, a small amount of rainfall also recorded
during the Belg season (Feb._ May) .The ITCZ which is the source of Ethiopian
Kiremt returned back to south at the end of rainy season.
27
MONTHLY AVG. NDVI AT GURAGE
0
0.1
0.2
0.30.4
0.5
0.6
0.7
0.8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMONTHS
ND
VI
MONTHLY AVG. NDVI
Fig.14. Monthly avg. NDVI at Gurage
Where as in the case of NDVI (Fig14), it shows an enhancement from the small
rainy (Belg) season to the main rainy season (Kiremt) due to continues rainfall
supply and also extends up to December. The maximum value of NDVI
0.727631 is recorded during the periods which have high amount of rain fall. It
shows enhancement of production due to successive rainfall.
28
YEARLY AVG. RAINFALL DISTRIBUTION AT GURAGE
020406080
100120140160
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
RA
INF
AL
L(m
.m.)
YEARLY AVG. RAINFALL DISTRIBUTION
Fig.15. Yearly avg. rainfall at Gurage
In the case of yearly average rainfall the maximum value is shown during the
period 1996(Fig.15).
29
AVG. YEARLY NDVI DISTRIBUTION AT GURAGE
0.50.510.520.530.540.550.560.570.580.590.6
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
ND
VI
AVG. YEARLY NDVI
Fig.16. Yearly avg. NDVI at Gurage
In contrast the maximum value of NDVI 0.5935 is recorded during the period
2001-2002 which has low value of rainfall (Fig.16).The minimum value of NDVI
0.537 is observed during the period which has high value of rainfall. This shows
having high amount of rainfall may not result good vegetation cover. This
requires further climatic condition studies.
30
MONTHLY CORRELATION BETWEEN NDVI & RAINFALL AT GURAGE
y = 162.19xR2 = 0.3134
0.0
50.0
100.0
150.0
200.0
250.0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
NDVI
RA
INF
AL
L
NDVI Vs. RAINFALL Linear (NDVI Vs. RAINFALL)
Fig.17. Correlation between monthly NDVI & Rainfall at Gurage The monthly graph shows appositive correlation of rainfall and NDVI (Fig.17).
Having sufficient supply of rain fall result a good vegetation cover .
31
YEARLY CORRELATION BETWEEN NDVI & RAINFALL AT GURAGE
y = 165.34xR2 = -0.0817
0
20
40
60
80
100
120
140
160
0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6
NDVI
RA
INF
AL
L
NDVI Vs. RAINFALL Linear (NDVI Vs. RAINFALL)
Fig.18. Correlation between yearly NDVI & Rainfall at Gurage
Unlike the monthly average, yearly average of rainfall and NDVI shows negative
correlation (Fig.18).This may be due to over grazing or flooding.
32
4.1.4. KONSO ZONE
MONTHLY AVG. RAINFALL DISTRIBUTION AT KONSO
0.0
100.0
200.0
300.0
400.0
500.0
600.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MONTH
RA
INF
AL
L (
m.m
.)
KONSO MONTHLY RAIN FALL Variability
Fig.19. Monthly avg. rainfall at Konso Konso monthly average rainfall showed bimodal oscillation with the highest
value of 566.3 mm registered (Fig.19) in March and the second peak of about
521.1 mm. in August. This is according to Belg and Kiremt season. Whereas
minimum rainfall of 389.5 mm. recorded in the month of November. Generally
from the above figure the rain fall variability during the Belg season was found
to be increased from January to March and decreased during April to June.
During Kiremt season gradual increase in rainfall could be observed from June-
August and afterwards it decreased from September - November. So Konso gets
higher rainfall during Belg season than Kiremt season.
33
MONTHLY AVG. NDVI DISTRIBUTION AT KONSO
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MONTH
ND
VI (m
.m.)
KONSO MONTHLY NDVI Variability
Fig.20. Monthly avg. NDVI at Konso
In the case of NDVI (Fig.20) it gradually increased from March and attained
peak in June and steady value could be seen in July also. More or less steady
values could be seen till October.
34
YEARLY AVG. RAIN FALL DISTRIBUTION AT KONSO
0.0
100.0
200.0
300.0
400.0
500.0
600.0
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
RA
IN F
AL
L(m
.m.)
KONSO YEARLY RAIN FALL Variability
Fig.21. Yearly avg. rainfall at Konso Yearly average rainfall distribution (fig.21) indicates with peak value of about
523.5 m.m. in 1997 and the lowest value of about 402.1 mm in 1996.
35
YEARLY AVG. NDVI DISTRIBUTION AT KONSO
0.63
0.64
0.65
0.66
0.67
0.68
0.69
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
YEAR
ND
VI
KONSO YEARLY NDVI Variability
Fig.22. Yearly avg. NDVI at Konso
From the average monthly NDVI distribution the highest value of 0.686 in 2001
and the lowest of about 0.654 in 2003 is observed (Fig.22).
36
MONTHLY CORRELATION BETWEEN NDVI V & RAINFALL AT KONSO
y = 0.0014xR2 = -0.8458
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.0 100.0 200.0 300.0 400.0 500.0 600.0
RAINFALL (m.m.)
ND
VI
NDVI Vs. RAINFALL Linear (NDVI Vs. RAINFALL)
Fig.23. Correlation between monthly NDVI
& Rainfall at Konso From (fig.23) linear correlation between monthly average rain fall and monthly
average NDVI failed to give any positive trend, but from the bar diagrams
enhancement in plant production could be seen after the Belg and during the
Kiremt season.
37
YEARLY CORRELATION BETWEEN NDVI & RAINFALL AT KONSO
y = 0.0014xR2 = -40.554
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.0 100.0 200.0 300.0 400.0 500.0 600.0
RAINFALL (m.m.)
ND
VI
NDVI Vs. RAINFALL Linear (NDVI Vs. RAINFALL )
Fig.24. Correlation between yearly NDVI & Rainfall at Konso In the case of yearly averages of rainfall and NDVI (Fig.24) any correlation
could not be seen, this has to be further analyzed based on historical data of
related parameters.
38
Chapter five 5.1. Conclusion and Recommendation Based on our study on rainfall and NDVI and the effect of rainfall variability on
NDVI which can be used as a tool for decision making which will allow better
integration and more timely planning of methods to promote food security. We
recommend that to study other meteorological parameters also (temperature, soil
type, wind etc) to get more vegetation yields. For our study we took data of ten
years for both NDVI and rainfall, if historical data is available for identifying the
long term relation of NDVI and Rainfall other causative factors effecting on
NDVI can also be studied.
Therefore from the experience gained from this project we would like to propose
that,
Remote sensing data are also more important in addition to station
Data, so more emphasis on Remote sensing data are to be given.
To conserve the vegetation covers any agricultural sectors and
Investors must know the effect of rain fall on NDVI.
The government must provide risk efficient strategies on Rainfall and
NDVI distribution.
The studies must be done every years and months to know the
Relationships. Agricultural research projects must be implemented to
Study rainfall in association with NDVI.
Government must play a great role in order to conserve the
Vegetation cover.
39
5.2. References
1 Bethake.S (1976), Basic zonal rainfall patterns in Ethiopia REVAB Drought and
famine in Ethiopia international Africa Institutes in association with the
Environment training program UNEP –IDEP-SIDA, Africa Environment special
report 2.
2 Buschamn.C and. E. Nagel 1993. In vivo spectroscopy and international optic of
serves as bias for remote sensing of vegetation int.remote sens.14.711-722.
3 Diro .C.T. 2008 seasonal forecast of Ethiopian spring rains Department of
meteorology. Campus of ready U.K
4 Degefu .C (1987), some aspects of meteorological drought in Ethiopia, M.H.
Galt(ed). Drought and hunger in Africa; Denying famine in the future Cambridge
university press, Cambridge.p23-36.
5 FAO (Food And Agriculture Organization)(1984),Assistance to land use planning
in Ethiopia; Geomorphology and soil Report prepared and submitted to the
government of Ethiopia 0,AGDA ETA/78/003,Field Document3.
6 Gitelson, A.A., J.J. Kaufman, and M.N. Merzlyak.1996.Use of a green channel in
remote sensing of global vegetation from EOS-MODIS.Remote sensing. Environ.
58:289-298.
7 Gitelson, A.A., Y.J. Kaufman, R.Stark, and D. Rundquist. 2002. Novel
algorithms for remote estimation of vegetation fraction. Remote sensing. Environ.
80:76-87.
8 Hang J.R and Schmugge .T.J.1980.an empirical model for the complex diclutrive
permitti of soilvity as a functions weather content IEEE Trans Scosu. Remote
sens.18 .288-295.
9 Justice. C .O.J.R.G.Townshed. B.N and Holben.C.J; 1985, Analysis of the
phenology of global vegetation using meteorological international journal
remote sensing vol.6 pp .1271_1318
40
10 Kanamasu E.T 1974 seasonal canopy reflectance patterns of wheat, sorghum and
soybean Remote sense. Enviro.3.43-48.
11 Lee, R., L.A. Kastens, L.P. Price, and E.A. Martinko. 2000. Forecasting corn
yield in lowa using remotely sensed data and vegetation phonology information.
Proc. Int. Conf. on Geospatial Information in Agric. and Forestry, 2nd
12 Prince, S. 1991, Satellite Remote Sensing of Primary Production: Comparison of
Results for Sahelian Grassland 1981-1988-Special Issue - Coarse Resolution
Remote Sensing of Sahelian Environment. International Journal of Remote
Sensing. Vol. 12, pp. 1301-1311.
, Lake
Buena Vista, FL. 10-12 Jan. 2000. ERIM Int., Ann Arbor, MI.
13 Rowland, J., Nadeau, A., Brock, J., Klaver, R. and Moore, D; 1996, Vegetation
Index for Characterizing Drought Patterns. Raster Imagery in Geographic
Information Systems. Santa Fe, New Mexico, Onward Press, pp. 247-254
14 Running S.W and Nemani R.R; 1998, relating season pattern of the AVHRR
vegetation index to simulated photosynthesis and transpiration of forest in
different climate. Remote sensing of environment vol 24.2 pp.347-367
15 Sarker.S.M.Kafatos .2004 inter annual variability of vegetation over the Indian
subcontinent and it relation to the different meteorological parameters, remote
sensing of environment 90, pp.268_280.
16 Tucker, C.J., Townshed, J.R.G., and Goff, T.E; 1985, African Land cover
Classification using Satellite Data. Science. Vol. 227, pp. 369-375. Nicholson.
And Sharon. (1989) Long-term Changes in African Rainfall. Weather. Vol.44, pp.
46-56.
17 Townshed, J. R. G. and Justice, C. O; 1986, Analysis of the dynamics of African
vegetation using the normalized difference vegetation index.. International
Journal of Remote Sensing, 7, pp. 1435-1445.
18 Zhang X.M.A friends C.B ScheafA.H Strahhar J./C.F Hogas Gao C,B AND B.C
Reed and Hvet 2003 monitoring vegetation phenology using models Remote
sens.Environ 84.471-475. Abate.K (1994) The climatology of drought over arts of
41
Ethiopia and their Impacts on crop prediction with special reference to the impact
of Drought on the prediction of Barley and MazePHD this is climatology
University of Nairoby,Kenya.
42
INDEX
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg. 1995 0.2 23.0 62.7 266.3 106.0 61.3 36.5 23.5 90.1 79.0 52.9 19.0 68.3 1996 20.8 14.8 90.5 120.0 168.3 117.6 60.0 44.4 79.5 50.3 43.4 4.1 67.8 1997 9.9 0.2 18.6 201.4 142.9 29.8 53.7 47.8 32.9 249.7 272.2 176.8 103.0 1998 126.5 122.0 24.5 122.0 131.4 56.7 31.8 46.2 27.4 146.9 36.2 1.5 72.7 1999 14.5 0.4 109.8 87.2 35.7 61.2 85.4 49.6 49.5 167.3 6.1 27.0 57.8 2000 0.7 0.0 10.3 84.5 220.9 36.1 69.5 57.7 64.8 173.2 61.3 45.4 68.7 2001 38.4 47.3 67.7 123.3 209.3 87.5 23.7 56.5 66.5 122.7 85.0 9.0 78.1 2002 29.2 23.6 74.9 102.7 46.6 56.5 52.6 26.7 79.5 80.0 20.6 126.4 59.9 2003 10.9 9.0 40.6 222.6 134.7 80.3 45.9 107.5 31.7 72.7 23.4 55.7 69.6 2004 44.4 34.8 21.6 178.7 79.3 45.5 32.0 52.3 97.0 44.1 115.6 29.9 64.6
Monthly Avg. 29.5 27.5 52.1 150.8 127.5 63.2 49.1 51.2 61.9 118.6 71.7 49.4
Table.1. Average monthly and yearly rainfall (m,m,) at Gamagofa
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg. 1995 0.4 0.43 0.52 0.59 0.672 0.73 0.7 0.714 0.69 0.67 0.57 0.41 0.593070083 1996 0.41 0.42 0.49 0.64 0.707 0.68 0.7 0.696 0.68 0.66 0.54 0.45 0.589641917 1997 0.42 0.46 0.46 0.57 0.697 0.73 0.7 0.756 0.75 0.68 0.56 0.48 0.606627583 1998 0.44 0.44 0.48 0.52 0.647 0.71 0.7 0.724 0.73 0.69 0.63 0.5 0.604445333 1999 0.38 0.35 0.51 0.55 0.661 0.76 0.7 0.734 0.75 0.7 0.6 0.49 0.600648833 2000 0.39 0.39 0.48 0.59 0.683 0.71 0.7 0.759 0.72 0.64 0.62 0.53 0.6023195 2001 0.44 0.44 0.51 0.63 0.728 0.76 0.8 0.741 0.79 0.72 0.59 0.52 0.636869333 2002 0.42 0.46 0.5 0.61 0.716 0.75 0.8 0.751 0.77 0.71 0.62 0.51 0.63273825 2003 0.44 0.44 0.47 0.56 0.687 0.74 0.7 0.74 0.77 0.68 0.59 0.54 0.616915167 2004 0.4 0.44 0.49 0.56 0.665 0.71 0.7 0.716 0.73 0.71 0.59 0.5 0.6021485
Monthly Avg. 0.42 0.43 0.49 0.58 0.686 0.73 0.7 0.733 0.74 0.68 0.59 0.49
Table.2. Average monthly and yearly NDVI at Gamagofa Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg.
1995 89.5 30.5 34.6 200.5 82.7 158.1 133 242.9 124.9 165.3 34.0 29.6 110.4 1996 28.2 32.8 151.6 174.2 143.6 144.2 107 106.4 172.6 72.2 31.0 42.4 100.5 1997 73.0 11.1 106.3 196.9 151.1 215.2 139 119.5 130.1 111.4 125.9 58.4 119.9 1998 11.3 0.0 8.5 146.5 76.2 57.4 72 35.6 89.2 164.3 150.1 41.3 71.0 1999 64.1 19.1 77.9 70.9 118.5 111.4 128 126.8 100.5 123.2 10.9 2.8 79.5 2000 3.0 0.1 26.7 139.4 155.5 80.3 153 125.9 97.3 214.2 29.4 16.9 86.8 2001 7.2 48.6 134.6 133.4 152.9 116.5 119 141.4 217.5 125.5 43.8 26.7 105.6 2002 50.7 11.2 161.2 97.1 74.5 53.2 93 110.5 97.1 80.3 15.0 133.2 81.4 2003 45.7 15.8 105.7 168.3 95.5 125.9 96 147.2 113.7 61.0 46.5 76.5 91.5 2004 89.0 43.5 15.9 212.4 91.9 27.2 71 66.7 86.4 59.3 39.0 11.3 67.8
Monthly Avg 46.2 21.3 82.3 154.0 114.2 108.9 111 122.3 122.9 117.7 52.6 43.9 Table.3. Average monthly and yearly rainfall (m,m,) at Welayta
43
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg.
1995 0.4 0.41 0.52 0.59 0.662 0.72 0.8 0.744 0.7 0.68 0.58 0.43 0.600676417 1996 0.42 0.4 0.49 0.61 0.679 0.65 0.7 0.733 0.71 0.68 0.56 0.46 0.591731417 1997 0.39 0.43 0.47 0.56 0.705 0.72 0.7 0.738 0.74 0.67 0.57 0.49 0.598106 1998 0.43 0.42 0.47 0.57 0.686 0.7 0.7 0.722 0.72 0.68 0.62 0.5 0.600239583 1999 0.34 0.34 0.51 0.56 0.639 0.76 0.7 0.735 0.77 0.66 0.6 0.5 0.595421333 2000 0.41 0.36 0.5 0.58 0.618 0.66 0.7 0.749 0.72 0.6 0.6 0.54 0.58678675 2001 0.44 0.41 0.49 0.6 0.685 0.73 0.8 0.731 0.79 0.73 0.58 0.51 0.62228475 2002 0.39 0.42 0.5 0.57 0.676 0.71 0.8 0.743 0.77 0.69 0.64 0.54 0.61912875 2003 0.47 0.4 0.47 0.54 0.645 0.69 0.8 0.693 0.75 0.67 0.6 0.56 0.605179417 2004 0.4 0.41 0.49 0.58 0.688 0.7 0.7 0.706 0.73 0.7 0.58 0.49 0.59890275
Monthly Avg. 0.41 0.4 0.49 0.58 0.668 0.7 0.7 0.729 0.74 0.68 0.59 0.5 Table.4. Average monthly and yearly NDVI at Welayta
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg. 1995 0.0 27.1 44.4 303.3 118.1 127.2 225 242.5 71.3 9.3 0.0 38.4 100.6 1996 105.4 12.1 195.2 103.5 181.7 304.7 282 303.5 141.8 33.6 15.6 1.7 140.1 1997 74.5 0.0 76.4 154.0 55.5 107.8 220 167.0 72.6 132.7 57.5 4.1 93.6 1998 22.3 20.7 103.0 50.7 101.1 163.6 304 252.7 124.9 72.9 0.0 0.0 101.4 1999 4.2 0.0 30.9 22.1 72.3 213.2 260 199.5 111.8 155.8 0.7 0.5 89.2 2000 0.0 0.0 7.0 49.8 68.5 104.5 228 204.9 184.4 87.5 51.9 9.4 83.0 2001 6.2 10.9 114.9 44.6 133.9 179.1 261 186.6 81.7 59.5 9.4 1.8 90.8 2002 47.0 6.0 63.8 45.5 110.4 191.8 179 182.4 81.8 0.7 0.0 30.7 78.3 2003 17.6 63.5 81.6 124.4 39.0 164.8 263 157.1 128.6 10.8 5.6 22.9 89.9 2004 46.3 9.9 28.9 108.8 46.5 142.2 166 221.2 148.4 44.6 7.6 7.8 81.6
Monthly Avg. 32.4 15.0 74.6 100.7 92.7 169.9 239 211.8 114.7 60.7 14.8 11.7
Table.5. Average monthly and yearly rainfall (m,m,) at Gurage
44
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg.
1995 0.39 0.45 0.5 0.5 0.589 0.67 0.7 0.704 0.73 0.66 0.54 0.36 0.566735583 1996 0.39 0.4 0.43 0.53 0.629 0.66 0.7 0.718 0.7 0.65 0.52 0.44 0.560875333 1997 0.41 0.44 0.45 0.49 0.653 0.66 0.7 0.711 0.75 0.65 0.56 0.46 0.5737415 1998 0.42 0.44 0.44 0.48 0.609 0.68 0.7 0.744 0.7 0.66 0.61 0.47 0.580687583 1999 0.39 0.4 0.48 0.51 0.552 0.68 0.7 0.713 0.76 0.64 0.59 0.45 0.573363917 2000 0.36 0.37 0.48 0.54 0.581 0.61 0.6 0.678 0.62 0.56 0.56 0.47 0.537251333 2001 0.46 0.46 0.47 0.51 0.556 0.65 0.7 0.735 0.76 0.72 0.58 0.45 0.589390667 2002 0.41 0.46 0.48 0.48 0.547 0.63 0.7 0.764 0.79 0.72 0.63 0.49 0.593510833 2003 0.43 0.44 0.45 0.49 0.562 0.68 0.7 0.694 0.75 0.69 0.58 0.5 0.58362525 2004 0.39 0.47 0.5 0.53 0.635 0.63 0.7 0.688 0.69 0.67 0.52 0.41 0.567120667
Monthly Avg. 0.4 0.43 0.47 0.51 0.591 0.66 0.7 0.715 0.72 0.66 0.57 0.45 Table.6. Average monthly and yearly NDVI at Gurage 37
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg. 1995 435.3 510.5 541.5 545.6 561.0 503.3 526 525.0 507.8 204.0 0.0 0.0 405.0 1996 174.5 556.2 559.8 516.4 534.4 485.7 474 507.5 498.1 518.2 0.0 0.0 402.1 1997 593.0 540.3 609.0 512.0 513.8 369.8 519 525.8 529.4 527.7 511.5 531.2 523.5 1998 545.8 498.6 585.3 564.7 527.2 499.0 507 486.0 502.2 518.9 501.7 526.9 522.0 1999 539.7 514.9 548.6 500.3 617.5 593.9 497 518.5 371.0 379.5 514.0 532.8 510.6 2000 554.5 537.5 575.5 515.5 519.5 493.5 426 489.5 476.6 260.8 451.5 445.5 478.8 2001 502.5 435.0 500.5 420.1 0.0 0.0 500 564.8 619.2 439.0 453.5 541.6 414.7 2002 540.5 527.2 532.7 474.3 444.5 417.0 425 521.2 521.1 480.6 547.4 559.2 499.2 2003 520.8 563.7 615.5 549.1 549.9 530.2 540 528.9 537.6 365.6 389.5 411.7 508.5 2004 72.6 555.4 594.6 525.5 537.3 509.1 522 543.7 522.2 545.4 525.6 567.7 501.8
Monthly Avg. 447.9 523.9 566.3 512.4 480.5 440.2 494 521.1 508.5 424.0 389.5 411.7
Table.7. Average monthly and yearly rainfall (m,m,) at Konso
45
YEAR Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Yearly Avg. 1995 0.52 0.52 0.62 0.68 0.731 0.75 0.7 0.692 0.71 0.75 0.67 0.55 0.662928833 1996 0.59 0.57 0.57 0.67 0.744 0.69 0.8 0.692 0.69 0.74 0.62 0.58 0.65931325 1997 0.57 0.58 0.53 0.69 0.676 0.77 0.8 0.773 0.79 0.73 0.61 0.54 0.66772575 1998 0.57 0.56 0.6 0.63 0.657 0.72 0.7 0.73 0.73 0.7 0.69 0.6 0.659749417 1999 0.48 0.47 0.62 0.64 0.709 0.79 0.7 0.726 0.76 0.72 0.65 0.56 0.654408833 2000 0.5 0.52 0.59 0.64 0.706 0.73 0.7 0.78 0.74 0.71 0.65 0.62 0.660478667 2001 0.53 0.54 0.6 0.7 0.782 0.78 0.8 0.72 0.8 0.74 0.64 0.63 0.686118417 2002 0.54 0.56 0.58 0.7 0.78 0.79 0.8 0.736 0.77 0.7 0.66 0.57 0.682096333 2003 0.55 0.57 0.51 0.6 0.745 0.71 0.7 0.714 0.78 0.71 0.62 0.6 0.653753417 2004 0.51 0.58 0.61 0.66 0.724 0.77 0.7 0.734 0.75 0.76 0.64 0.6 0.6728315
Monthly Avg. 0.54 0.55 0.58 0.66 0.725 0.75 0.7 0.73 0.75 0.73 0.65 0.58
Table.8. Average monthly and yearly NDVI at Konso