Transcript The Normalized Difference Vegetation Index (NDVI): An index of rainfall, productivity, and over-grazing James Osundwa, Nasser Olwero and Nicholas Georgiadis, Mpala.

The Normalized Difference Vegetation Index (NDVI): An index of rainfall, productivity, and over-grazing
James Osundwa, Nasser Olwero and Nicholas Georgiadis, Mpala Research Centre
RATIONALE and PURPOSE
‘OVER-GRAZING’ REDUCES RAINFALL USE EFFICIENCY
• In African drylands, ecological and economic processes, from livestock production, to household and rural
economies, to wildlife and ecosystem dynamics are, by definition, driven by rainfall. However, gauged rainfall data are
typically lacking (Laikipia is an exception; Fig. 1).
Opportunities to test effects of intensive grazing on primary production are rare, because key factors like amount of
rainfall, mean annual rainfall, soil type, evapo-transpiration, and vegetation type can seldom be controlled. The
arrangement of adjacent properties representing different land use types in Laikipia permits direct assessments to
be made of the effects of grazing on primary production, by comparing NDVI dynamics on either side of common
boundaries (Fig. 4).
• Remotely sensed indices have long been available to estimate rainfall (e.g. products from Meteosat) and monitor
gross vegetation responses to rainfall. For example, vegetation ‘greenness’ indices, such as NDVI (a measure of
phototsynthetic biomass, derived as the difference between red and far-red wavelengths, divided by their sum), have
been available from the AVHRR satellite since 1981 at low resolution (8.1 km pixels), and from the TERRA platform via
the MODIS website at high resolution (250m pixels) since 2000. However, these indices are underused, partly due to
lack of awareness about their availability, and what they ‘mean’.
• We use a modeling approach to demonstrate the strong rainfall ‘signal’ in NDVI time series, greatly increasing
confidence in the utility of NDVI as an index of rainfall.
Fig. 4 (left) Common boundaries between
commercial ranches, group ranches, and
settled areas in Laikipia District were
selected for NDVI contrasts.
Pixels
(250m square) were randomly selected
within 500m of each boundary.
• We then use NDVI data to assess the effect of overgrazing on rainfall use efficiency (amount of primary production
per mm of rainfall), by comparing NDVI dynamics sampled either side of common boundaries separating different land
use types in Laikipia District.
(right) Mean MODIS NDVI for the Laikipia
region from 2000-2005, depicting the
rainfall gradient at more than 1000 times
resolution than in Fig. 1.
MEAN RAINFALL AND MEAN NDVI ARE CLOSELY RELATED
Fig. 2. The non-linear but close
relationship
between
mean
annual rainfall (R) and mean
NDVI can be modeled by:
Fig. 1. (above) Spline interpolation
of the rainfall gradient in the Ewaso
region (left; based on long-term
data from >50 gauging stations)
broadly resembles the ‘greenness’
map (below), based on 20-year
NDVI means from AVHRR. But the
NDVI map shows local areas of
higher rainfall (darker green) in the
north, and around Mukogodo
Forest, missed in the rainfall
interpolation due to lack of gauged
data.
(r2=0.70)
NDVI=0.20 ln R-0.98
0.4
0.8
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NDVI
Mean NDVI .
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0
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Ol Naishu
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1500
1/1/03
Loldaiga
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1/1/06
Date
Fig. 5. Examples of differences in NDVI time series across common
boundaries between two commercial ranches (top), a commercial
ranch and a group ranch (middle), and a commercial ranch and a
communally used area (bottom). Differences were not marked
during the drought, which ended in March 2001, but subsequently
became so, when annual rainfall exceeded the long term mean.
0.6
NDVI
Mean Annual Rainfall (mm)
0.4
1.1
NDVI CAN BE PREDICTED FROM RAINFALL
0
1/1/00
Where:
• a is a scaling factor relating the NDVI response to rainfall;
• b is a constant controlling the rate of decline of greenness;
• c is the baseline NDVI value;
• d is no. of preceding decads contributing to current NDVI;
• e is the base of natural logarithms;
• f is exponent of R;
• Ri is the total rainfall in decad i.
0.5
400
0.4
300
0.3
200
0.2
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0.1
0
0.0
Jan-90
Jan-91
Jan-92
Jan-93
Jan-94
Jan-95
Jan-96
Jan-97
Jan-98
Jan-99
NDVI
bi
500
Jan-00
1.0
600
500
Mpala Research Centre
0.8
0.6
400
300
200
100
0
Jan-00
0.4
0.2
0.0
Jan-01
Jan-02
Jan-03
Jan-04
NDVI
f
Rainfall (mm)
NDVI  i  d (aRi .e )  c
2
Rainfall (mm)
Fig. 3 Comparison of observed NDVI (black line) with NDVI estimated from gauged rainfall data using the modified
model of Hess et al. (green line) shows a good fit for both AVHRR data (upper panel) and MODIS data (lower panel).
For the Archer’s Post dataset, the model was fit to data for the period 1996-2000 and then used to ‘predict’ NDVI for the
0.7
period 1990-1995
A. Archer's Post
600
0.6
Archer’s
Post
1/1/01
1/1/02
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1/1/03
Makurian
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1/1/05
1/1/06
Date
0.6
NDVI
We modified the model of Hess et al. (1996 Modeling NDVI from decadal rainfall data in the North East Arid Zone of
Nigeria. Journal of Environmental Management 48:249-261) to assess the rainfall ‘signal’ in NDVI time series, and
better understand how vegetation ‘greenness’ responds to rainfall. Examples of best fit model solutions (Fig. 3) show
that NDVI time series can be closely predicted solely from rain falling over preceding months, for both AVHRR and
MODIS data.
Ol Naishu
0.4
0.2
0
1/1/00
Mogwooni
1/1/01
1/1/02
1/1/03
Date
Kimungandura
1/1/04
1/1/05
1/1/06
Fig. 6 (right). Compared to
commercial ranches, rainfall
use efficiency on group
ranches (growth per mm of
rainfall) is reduced by a
mean of 14% by overgrazing, but by as much as
25% in some cases.
Relative Rainfall Use Efficiency
0.2
1
0.9
0.8
0.7
Commercial to
Commercial
Commercial to
Settlement
Commercial to
Pastoralist
Land Use Contrast
CONCLUSIONS
•, NDVI is a useful surrogate for rainfall (like having a rain gauge every 250m across the entire ecosystem).
• NDVI is an informative index of green biomass, its integral an estimator of net vegetation production, and is a good
way to monitor degradation and recovery of rangelands.
• NDVI data are available to all researchers at Mpala Research Centre – simply submit a list of GPS locations, and
the NDVI series at that location will be returned to you.