DROUGHT RISK MONITORING
FOR THE SUDAN
USING NDVI
1982 - 1993
Alemayehu Kassa
A Dissertation submitted to the University College London
In part fulfilment of the requirementsfor the Degree of
Master of Science in Geographic Information Systems
August 1999
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ACKNOWLEDGEMENTS
Mr. Jeremy Morley of the Department of Geomatic Engineering, UCL, Dr. Simon Jones and Mr.
Tim Richards of the Global Vegetation Monitoring Unit, Space Applications Institute, Joint
Research Centre, Italy, and Mr. Yasir Mohieldeen of SOAS have given me their unreserved
support. I would like to thank them sincerely. Many thanks also go to Dr. David I. F. Grimes of
TAMSAT Group, Department of Meteorology, University of Reading for his generous help with
materials for the literature review.
I would like to say a very special thank you to Professor Tony Allan of SOAS for believing in
me and for his untiring support.
This project and the whole course of which it is part came to be because of the ever present
support and encouragement from Rachel, my wife and friend. I would like to dedicate this report
to Rachel and to our precious four month old daughter, Rosalie Etsub Tsedey.
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Contents
PageAcknowledgements 2
Contents 3
Abbreviations 5
Abstract 6
1. AIMS AND OBJECTIVES 7
2. BACKGROUND
2.1 Environmental Change in the Sahel 72.2 The Use of Remote Sensing in Monitoring Environmental
Change in the Sahel 82.3 Effectiveness of NDVI as an Indicator of Vegetation Change 102.4 Ecological Zones in Sudan 152.5 Problem of Drought Monitoring in the Sudan 16
3. METHODOLOGY
3.1 Introduction 173.2 Study Rationale 173.3 Datasets 183.4 Derivation of vegetation indices 183.5 ARTEMIS Integrated Time-series NDVI imagery 203.6 Rainfall – NDVI regression for Sudan 213.7 Drought Risk Classification 223.8 Use of GIS packages 22
4. RESULTS
4.1 Relationship between NDVI and rainfall in Sudan, 1982 – 1993 254.2 Drought Risk Classification 29
5. DISCUSSION OF FINDINGS
5.1 Relationship between NDVI and rainfall in Sudan 325.2 Implications for Drought Monitoring 33
6. COMPARISON WITH OTHER APPROACHES USING NDVI 36
7. CONCLUSIONS 38
8. KEY RECOMMENDATIONS 40
References 41
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List of Appendices
Appendix 1. Rainfall Stations and the Frequency of Reporting, Sudan, 1982-1991 44Appendix 2. Rainfall to NDVI Equivalent in the Sudan 45Appendix 3. Annual Rainfall Regression versus NDVI of Subsequent Year 46Appendix 4. Correlation between Annual rainfall and Cumulative NDVI (same
Years) 47
List of Tables
Table 1. Annual Rainfall-NDVI Regression Results, Sudan, 1982-1991 22Table 2. Drought Risk Classification, Sudan, 1982 – 1993 29
List of Maps
Map 1. IDRISI Drought Risk Classification Result, Sudan, 1982 – 1993 30Map 2. Classification Map Product Packaged in ArcView 31
List of Graphs
Graph 1. An Example of NDVI Time Profile for Sudan in 1991 20Graph 2. Correlation between Rainfall and NDVI in Sudan in 1982 25Graph 3. Correlation between Annual Rainfall and Cumulative NDVI for the
Subsequent Year 27Graph 4. Annual Rainfall against Annual Correlation Coefficient 28
Front page:
Annual Composite NDVI Image of part of the Horn of Africa, 1982. Data from FAO ARTEMISprogramme.
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ABBREVIATIONS
APAR Absorbed Photosynthetically Active RadiationARTEMIS The Africa Real Time Environmental Monitoring using Imaging
Satellite project of FAOAVHRR Advanced Very High Resolution RadiometerBGS Beginning of Growing SeasonBT Brightness TemperatureCCD Cold Cloud DurationDFID UK Department for International DevelopmentDN Digital NumberEGS End of Growing SeasonFAO Food and Agricultural Organisation of the United NationsGAC Global Area CoverageGIS Geographic Information SystemGUI Graphic User InterfaceGVI Global Vegetation IndexISR Incoming Shortwave RadiationKED Kriging with External Drift method of combining point and area dataLAC Local Area CoverageLACIE Large Area Crop Inventory Experiment, conducted in
the USA in the early 1980s.LAI Leaf Area Index, a qualitative measurement of vegetationLandSat MSS Land Satellite Systems mounted with the Multispectral ScannerMVC Maximum Value CompositingMVI METEOSAT Vegetation IndexNDVI Normalised Difference Vegetation IndexNIR Near Infra Red portion of the spectrum.NOAA National Oceanic and Atmospheric Agency of the USANPP (Terrestrial) Net Productivity IndexPC Personal ComputerMETEOSAT PDUS Ground system for receiving METEOSAT (Meteorological Satellite) dataPWU Plant Water UseRFE Rainfall Estimate obtained from satellitesSCF Save the Children Fund (United Kingdom)SOAS School of Oriental and African Studies, University of LondonSPOT Systeme Pour l’Observation de la Terre,
the French designed earth observation satellite systemsTAMSAT Tropical Agricultural Meteorology using SatellitesTCI Temperature Condition IndexTIFF Tagged Image Format FileTIR Thermal Infra Red portion of the SpectrumUEA University of East AngliaUN United NationsUSAID/FEWS United States Agency for International Development
Famine Early Warning SystemVCI Vegetation Condition IndexVIs Vegetation IndicesVIS Visible portion of the spectrumVT Vegetation and Temperature Condition IndexWFP World Food Programme of the United Nations
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ABSTRACT
This study employs GIS to examine the relationship between rainfall and the Normalised
Difference Vegetation Index (NDVI) in the context of the Sudan, and the value of NDVI as a
tool for drought monitoring. The relationship between rainfall and NDVI between 1982 – 1993
in the Sudan is examined using spatial analysis methods. A strong positive correlation is found.
However the vegetation response, as reflected in NDVI, is not exhibited until the following year.
This is due to the annual compositing of data and the inclusion of sub-tropical climatic zones
with Sahelian zones. The correlation is strongest during years of heaviest rainfall, indicating that
the relationship between rainfall and NDVI is not a simple linear one. A drought risk map is
produced in ArcView with the intention of being useful for decision makers involved in drought
monitoring. However it is argued that the input data accuracy has affected the quality of the
GIS output, and the shortcomings of the data are highlighted.
The study examines the implications of the results for operational drought risk monitoring.
Comparison is made with two other vegetation index based methodologies. The study stresses
the need for the use of remote sensing to provide real time data for forecasting. Whilst most
countries in the Sahel lack the resources (financial, technical and human) to establish the
information systems necessary for drought monitoring, this study concludes that remote sensing
is the only feasible data source to fill such a gap. NDVI is a valuable first cut indicator for such
systems, although analysing and interpreting its relationship to rainfall is complex, and must be
based on detailed analysis of its relationship to ecological zone, vegetation type and season.
A number of recommendations are made that would help to upgrade the methodology as an
effective tool for drought early warning in areas of the Sudan where drought is a recurring
threat. These include integrating NDVI with other socio-economic and bio-physical indicators in
a GIS, complementing rain gauge data with satellite rainfall data, and analysing the relationship
between NDVI and specific climatic zones, for each season and vegetation type.
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1. AIMS AND OBJECTIVES
This project aims to employ GIS to examine the relationship between rainfall and the
Normalised Difference Vegetation Index (NDVI), in the context of the Sudan, and the value of
NDVI as a tool for drought monitoring. The objective of the project is firstly, to examine
whether there is a relationship between rainfall and NDVI in Sudan. Once a positive
relationship is established, the project analyses the use of NDVI as a proxy indicator for the
occurrence of meteorological drought, that is, when precipitation is significantly below what is
normally required by vegetation. Secondly, the project explores the implications for drought
monitoring in the Sudan, through the production of drought risk maps, and the presentation of
results. This is done by integrating multi-source geo-referenced datasets in a GIS platform in
order to facilitate analysis and the generation of cartographic, statistical and modelling products.
The final output comprises the spatial analysis products and aims to be useful in the decision
making process for drought monitoring and to avert its consequences on lives and livelihoods.
Finally, a critique is made of drought monitoring based on NDVI, through a comparison with
two other NDVI based approaches. The first of these relates NDVI to useful rainfall during the
plant growing-season, and the second uses NDVI and net useable soil moisture.
Recommendations are made for refining operational drought monitoring systems based on
NDVI.
2. BACKGROUND
2.1 Environmental Change in the Sahel
There are conflicting propositions regarding the dynamics of the Sahelian desert that lies in the
Northern part of the African continent. The absence of a universally agreed definition of drought
and an understanding of its relationship to desertification makes understanding the Sahelian
ecosystem difficult. The definitions of the term desert and desertification are complex issues in
themselves, and open to various interpretations (Richards, 1994; Toulmin, 1995). Lamprey, in a
1975 report, stated that
“it is evident that the desert’s southern boundary has shifted south by an average of 90 –100 km in the last 17 years” (Lamprey, in Richards, 1994),
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representing a southwards shift of 5-6 km per year. This assertion is contested on the ground that
the basis of Lamprey’s comparison was wrong, and that the “shift” as a result of a severe
drought has been stabilised (IISD, 1999). Hellden does not concur with such expansion in the
Sudan, and asserts that there is no evidence that patches of desert were spreading outward from
villages and water holes into the dry lands of the Sahel area (Hellden, 1991 quoted in IISD,
1999). In the Rio Earth Summit, desertification was defined less controversially as:
“land degradation in arid, semi-arid, and dry sub-humid areas resulting from climaticvariations and human activities” (Toulmin, 1995).
Consequently most existing criteria of desertification have limitations in demarcating areas
assumed to be affected. However, there is agreement that rainfall in the Sahel band is highly
variable and that these areas are frequently subject to drought. Rainfall variations are often 30 –
40% of average (Nana-Sinkam, 1995). While the majority of the populations of arid and semi-
arid lands are predominantly agro-pastoralists, these zones exhibit ecological constraints which
set limits to nomadic pastoralism and settled agriculture. These constraints include rainfall
patterns that are inherently erratic. Rains fall mostly as heavy showers and are lost to run-off. A
high rate of potential evapotranspiration further reduces yields, resulting in frequent drought
(Salih and Ahmed, 1993). Progressive degradation of land resources, due to a combination of
climatic and human factors, is a serious threat to sustainable livelihoods for people living in
these areas.
2.2 The Use of Remote Sensing in Monitoring Environmental Change in the
Sahel
There is an immediate operational need to keep track of drought conditions and environmental
changes for the purpose of monitoring and predicting the production of the rangelands and
marginal agricultural areas, whether they be the result of shifting climatic zones, human actions
or a combination of these. Assessing risk of drought is a first step in this direction. Closer
attention can then be paid to areas and population groups identified as most at risk.
Very little data exists in Sahelian areas which can be used to measure trends in land degradation
or desertification (Toulmin, 1995). Instead proxy measures, such as vegetation cover or crop
yields, have to be used. Vegetation in areas at risk will show moisture stress that then results in
changes in vegetation cover. Information on vegetation cover can in turn be used as a first cut
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indicator of the possible occurrence of drought, although additional indicators will also be
required to carry out a full drought assessment, since the causes and consequences of drought
are multi-faceted (de Waal, 1989).
Vegetation studies based on remotely sensed data began to be used in the mid-1970s with data
obtained from Landsat MSS (MultiSpectral Scanner) . Attempts were also made to try to study
change or dynamic phenomena, for example in the LACIE (Large Area Crop Inventory
Experiment) in the USA (Cracknell, 1997). The use of remotely sensed data from satellite
platforms for crop and drought monitoring has become wide spread since the mid and late
1980s. This has been due mainly to the efforts of the FAO and its ARTEMIS program (African
Real-Time Environmental Monitoring and Information System), as well as work carried out by
USAID/FEWS (Bonifacio et al., 1998).
Today the application of remote sensed data in vegetation studies is widely operational.
Applications as sophisticated as highly accurate yield estimates and crop disease and water
stress detection at sub-pixel level have been operational in northern America and Europe
(Cracknell, 1997; EWSE, 1999; Logica, 1999), while its application for drought monitoring is
operational world wide (Kogan, 1997). The increasing use of satellite remote sensing for
civilian use has proved to be the most cost effective means of mapping and monitoring
environmental changes in terms of vegetation, rainfall and non-renewable resources (Richards,
1994). Data can be obtained as frequently as required. Mapping and modelling environmental
changes as they progress can be achieved by integrating digital datasets obtained by remote
sensors with relevant ground information. This is facilitated by Geographic Information Systems
(GIS). GIS also have the added value of enabling the presentation of findings from such analysis
in a comprehensive format. The GIS products form the basis for environmental monitoring and
decision support.
However, systems for monitoring environmental change based on remote sensing are not yet
perfected. FAO (1993) argue that improvements are still needed in three major areas:
acquisition of data and data types, storage and analysis, and dissemination and communication
of results. There is a particular need to improve and harmonise data from meteorological
satellites such as NOAA and METEOSAT, in order to produce standard, compatible outputs
which can be used to aid decision-making in agricultural warning and forecasting, in particular
by estimating rainfall and plant activity. Another problem is the need to improve assessments of
soil degradation at all levels, from the global to the local. This could be accomplished by
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combining remote-sensing information from satellites, such as LANDSAT or SPOT, with data
from ground observation sites. This has the potential to provide information for continuous
monitoring of climatic change and land degradation. FAO stresses the importance of integrating
“high-resolution spatial and spectral satellite pictures such as LANDSAT or SPOT,combined with those from satellites with higher temporal frequency and low spatialresolution, such as METEOSAT or NOAA….into GIS and spatial models andcomplemented by new methods of gathering ground data through navigation satellites”(FAO, 1993).
2.3 Effectiveness of NDVI as an indicator of change in vegetation cover
Whether the desert is expanding or not, there is agreement that patterns of vegetative cover in
Sahelian areas are dependent on rainfall (Tucker et al., 1991, quoted in IISD, 1999), with the
exception of some irrigated areas. Rainfall is the key limiting factor in crop and rangelands
production.
Increasingly dense and accurate rainfall observations that can be analysed in real time are
required to monitor closely the progression of the cropping season. This is because in areas such
as the Sahel, great spatial and temporal variability of rainfall mean that interpolating between
raingauge values to obtain estimates of the rainfall at a particular point can give rise to serious
errors. Proper monitoring for this region therefore requires an impractically large number of
gauges (Bonifacio et al., 1991). Even if such dense coverage were possible, the rainfall data on
its own is insufficient to draw useful information regarding the status of the crop.
The methodology employed in this study is based on the relationship between remote sensed
data about vegetation, in the form of vegetation index, and rainfall. The main application of
vegetation indices is the monitoring of vegetation conditions. Vegetation indices (VIs) are
functions of the spectral contrast between the reflected Near Infrared (NIR) and Visible (VIS)
radiance from a given surface. This contrast is greater for vegetation than for soil, and hence the
higher the vegetation index the denser the cover and vigour of vegetation. Although there are
many new indices that are theoretically more reliable than the NDVI (such as soil-adjusted,
transformed soil-adjusted, atmospherically resistant, and global environment monitoring
indices), they are not yet widely used with satellite data (Rondeaux et al., 1996). The most
widely used index is the NDVI, the Normalised Difference Vegetation Index, using AVHRR
(Advanced Very High Resolution Radiometer) data. Its wide use is partly due to its
effectiveness as a surrogate measure of biophysical parameters, and partly due to the fact that its
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calibration requires no a priori information concerning the imaged scene (Belward and Taylor,
1986).
The relationship of NDVI to rainfall is used as a basis for employing NDVI as an indicator of
drought. The onset of suitable moisture conditions for vegetation causes the emergence and
growth of plants. The resulting increase in the amount of vegetation and in the photosynthetic
activity leads to a consistent increase in the NDVI. When these conditions cease, the resulting
moisture stress will reduce biophysical rates (photosynthetic rate and transpiration) which will
result in a substantial fall in the NDVI (Bonifacio et al., 1993a). The vegetation response to
rainfall is well marked in the Sahel and detailed studies of the relationship between NDVI and
biomass have been undertaken (Justice and Hiernaux, 1986). The integrated NDVI over a
suitable base or background value has been used previously as a measure of total biomass
production (Tucker and Sellers, 1986 quoted in Bonifacio et al., 1991; Justice and Hiernaux,
1986).
A review of the literature revealed that methods based on vegetation indices derived from the
AVHRR data abound for the monitoring of drought in the Sahel region, many of them following
similar methods. Two methods based on NDVI that are both distinctive and widely used are
summarised here. These methodologies were selected for the purpose of comparison with the
one used in this study.
Method using the relationship between NDVI and rainfall during the plant-growing season
This method was developed by the TAMSAT group in Reading (Bonifacio, 1991 and 1992;
Bonifacio et al., 1993a and b; Bonifacio and Grimes, 1998) for application in Sahelian and
southern Africa. It is based on the relationship of NDVI to rainfall during the plant-growing
season. A similar method is also used by the FAO of the United Nations (FAO, 1997). Based on
previous work that concluded that rainfall estimates could be used to forecast biomass
production as indicated by the NDVI (Justice et al., 1991), the method characterises the
dynamics of the vegetation development via its growing season’s parameters on a consistent
spatial scale. These parameters are related to a rainfall derived moisture satisfaction index.
The NDVI data set used here is that provided by FAO Rome and NASA/GSFC as 10-day MVC.
The rainfall data used is 10 day (dekadal) Rainfall Estimates (RFEs) derived from cold cloud
statistics and obtained from the TAMSAT archive (Bonifacio et al., 1993a). Both datasets are
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derived from satellite sources. The plant water use model which is used to determine net water
requirement during the growing season uses inputs of dekadal rainfall estimates, potential
evapo-transpiration and crop coefficients. This was the model used by FAO in crop monitoring
and forecasting.
Whilst both this project and the TAMSAT methodology use the same NDVI dataset, they differ
in two major aspects. Instead of raingauge data, the TAMSAT methodology uses rainfall
estimate data (RFE) that is obtained by a cold cloud thresholding technique, in which the
thresholding temperature varies in time and space to reflect the meteorological conditions. The
estimate’s statistical relationship with rainfall over a ten-day period is established using
raingauge data (Bonifacio et al., 1998). Moreover, it relates the NDVI (after defining parameters
that characterise the growing season and that can be derived objectively from NDVI*) to the
RFE during a specific period in a given year, the plant-growing season. The plant-growing
season is defined by the beginning of the growing season (BGS) and end of the growing season
(EGS) parameters that relate to the existence of suitable moisture conditions for vegetation
development. As the timing and duration of the growing season varied widely within the study
area, parameters for the BGS and EGS were defined objectively on a pixel by pixel basis
(Bonifacio et al., 1993a and b). The RFE data is then used as input for the crop water model that
enables the calculation of plant water use (PWU) on a ten daily basis. The objective of the crop
water model is to convert raw observations of the atmospheric environment into a set of
parameters that are of direct importance to crop production (Gommes et al., 1999).
In its operational mode, the methodology provides image outputs such as areas where the
Growing Season status is imminent, where stress is imminent, and has an option to cumulate
with previous dekads to show length of uninterrupted stress period. It also provides Vegetation
Production (also called METEOSAT Vegetation Index, MVI) that is a forecast of the real
cumulative NDVI, _NDVI 1 or 2 dekads in advance, depending on the lag chosen.
* The definitions of the beginning and end of the growing season, BGS and EGS respectively, are slightmodifications of the ones used by Henricksen and Durkin (1986). It is taken as the dekad when the NDVI begins toincrease towards the maximum (with a growing trend of at least three dekads) and the NDVI for that dekad is abovethe NDVI used as base value. The EGS is taken as the dekad, within the decreasing trend that starts after themaximum, when the largest drop in NDVI occurs, or that in which the NDVI has decreased from its maximum byone third of its seasonal amplitude, which ever comes earlier. The base value for any year, NDVI0, was selectedobjectively as the minimum value on a time series of three point running means of the individual NDVI dekadaldata points. This procedure eliminates the effect of those corrections to NDVI values from different satellites inNOAA series (Bonifacio et al. 1993b).
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The main feature of this operational scheme, according to the developers, is that it provides
information on the vegetation and growing season status that could previously only be obtained
from NOAA AVHRR data. The scheme thus offers advantages, such as greatly reduced data
processing with no need for the complicated and demanding NOAA receiver stations. This
results in reduced implementation costs since the PC based PDUS systems are much cheaper
(even than PC based NOAA receiver stations) and have a proven record of operational
simplicity, reliability and robustness. Relying on METEOSAT derived data, the information
can be obtained in centres that are geographically prevented from acquiring NOAA data over
the region of interest, such as European and American centres whose activities focus on Africa.
Perhaps most importantly, it provides advance warning on vegetation conditions of up to 2
dekads. Further advantage is gained from thermal infrared monitoring from which the RFE is
derived, since it is relatively insensitive to changes in atmospheric aerosols, such as those which
disrupted NDVI monitoring following the Mount Pinatubo volcanic eruption in 1991.
These advantages do not make the RFE based technique a substitute for the NDVI data, but a
valuable complement to it. Its ability to forecast enables risk areas to be highlighted, prompting
a monitoring centre to relay acquired NOAA NDVI data on the following dekads, for a finer
assessment of the situation as it evolves.
The large area coverage and low cost of the above method makes it attractive. However the
rainfall estimate, is derived from geostationary satellite thermal infrared (TIR) imagery. The
calibration coefficients may be unstable both spatially and temporally if such a method relies on
empirical calibration against ground data. Furthermore, although rainfall amounts averaged over
long time periods or large areas may be adequately estimated, localised intensity variations are
not well represented. In particular, heavy rainfalls are underestimated. Bonifacio and Grimes
(1998) tried to reduce the errors associated with these problems by modulating the satellite
estimates with available real-time raingauge data.
However this approach poses a problem. While the raingauge data are point measurements, the
satellite values are averages over pixel-sized areas. This was dealt with by deriving pixel
average values from the gauge data, and merging these with the satellite estimates in a way that
reflects the likely accuracy of the two data sets. This is done using the geostatistical technique
of kriging that was developed for the DFID funded Drought and Flood Warning project in
Southern Africa (Bonifacio and Grimes, 1998). The method reportedly provides optimal areal
estimates in any given situation and is applicable both for drought monitoring and flood
14
forecasting. Following an evaluation procedure, it was established that the merged estimate is
more accurate than the CCD alone or estimates based on the Kriging with External Drift (KED)
method (Bonifacio and Grimes, 1998). Delineation of rain areas on a daily basis was then made
using logistic regression. This yielded Boolean images of occurrence of rain over areas of over
10,000 km2.
Method using the relationship of NDVI with rainfall and surface temperature
The second method used for comparison was developed by Kogan (1995; 1997). It is based on
the relationship of the Global Vegetation Index (GVI) and the Temperature Condition Index
(TCI) with rainfall.
The GVI, unlike the GAC data, is in the form of a ready mapped processed geophysical product,
namely the NDVI. The GVI is of coarser resolution than the GAC from which the NDVI for the
FAO datasets is derived. This is because the GVI is sampled, both spatially and temporally from
GAC, with the final product made from a composite of the daily NDVI arrays over a seven-day
period such that the maximum NDVI value is retained for each location (Belward et al., 1986).
The TCI is derived from 10.3 – 11.3 _m thermal band AVHRR measured radiances, converted
to brightness temperature, (BT). The VT (the Vegetation and Temperature Condition Index) is a
combination of the VCI and TCI indices. It is used to monitor the water- and temperature-
related vegetation stress occurring during drought.
The methodology is based on the assumption that
“first impression of drought climatology can be obtained from the global distribution ofthe surface moisture balance, that is, the difference between annual precipitation andannual potential evaporation as an approximate measure of estimating vulnerability ofterritories toward drought” (Gol’dsberg 1972, quoted in Kogan 1997).
The VCI-TCI values for a given region for each week are calculated and compared with yields
of agricultural crops. The results reportedly showed a very strong correlation between these
indices and yield, particularly during the critical periods of crop development. The method
follows the consideration that the absolute maximum and minimum of NDVI and BT calculated
from several years of data that contain the extreme weather events (drought and non-drought
years) can be used as criteria for quantifying the extreme conditions (Kogan, 1995). Accordingly
the largest and smallest NDVI and BT values during 1985-1993 were calculated for each of the
15
52 weeks of the year and for each pixel. These form the criteria for estimating the upper
(favourable weather) and the lower (unfavourable weather) limits of the ecosystem resources.
These limits
“characterise the ‘carrying capacity’ of the ecosystems and the range in which NDVI andBT fluctuate due to weather changes. These fluctuations were estimated relative to themaximum and minimum intervals of both NDVI and BT variations and named thevegetation (VCI) and temperature (TCI) condition indices” (Kogan, 1997).
The methodology has already been tested globally, in very diverse environments, including
Kazakhstan, China, Ukraine, Zimbabwe, Ethiopia, USA and Argentina, with encouraging results
(Kogan, 1995).
Global or regional assessment methodologies based on GVI have limitations for applications at
the sub-regional level, particularly those areas with high spatial variation such as the Sudan.
Data such as the TCI above, derived from TIR, need to be treated with caution, since the
information content of composited TIR measurements is uncertain, as TIR emissions from the
earth change rapidly with time of day and atmospheric conditions, (Goward, et al., 1991).
Furthermore, existing algorithms have limitations in accounting and correcting for factors such
as emissivity variations in space, in time, different wavebands and view angle.
2.4 Ecological Zones in Sudan
This study is concerned with the monitoring of environmental changes in Sudan, particularly in
the central areas that are highly dependent on rainfall for agriculture and pasture regeneration.
The Sudan is the largest country in Africa, with an area of 2,505,813 km2. Flat or slightly
undulating plains dominate the surface relief.
Sudan can be divided into four major ecological zones. The Sahara desert dominates the
northernmost third of the country, stretching about 700 km from the capital city Khartoum to the
Egyptian border. The Sahel belt lies between the desert and tropical biomes that are farther
south. Short rains, totalling 250 – 500 mm occur during the months of July, August and
September. The semi-arid zone is an important domain for pastoralism in western Sudan and
irrigated agriculture in the eastern region. Dry savannah vegetation predominates in the central
portion of the country. The climatic zone here broadens near the Ethiopian highlands, which
flank the eastern border of the Sudan. Stretching as long as eight months, there is sufficient
16
precipitation to sustain short grasses, scattered trees and the country’s principal food crops-
sorghum, and sesame. Most rainfed cultivation, parcelled out in small holdings or extensive
farms, occurs in this climatic region of Sudan. A more humid climate extends approximately 240
km into south western Sudan from the neighbouring countries of the Central African Republic
and the Democratic Republic of Congo. Annual precipitation of more than 1,000 mm produce a
mixture of diverse tress and tall grasses. Important export crops – tea, tobacco, and coffee – as
well as a variety of foodstuffs – most notably, corn, yams and bananas – are grown under these
moist conditions. Such variation in the ecological characteristics, with annual rainfall varying
from 130 mm in the central regions to 1,270 mm in the south makes determination of common
environmental parameters a very complex undertaking.
2.5 The problem of drought monitoring in Sudan
The climate and environment in the Sudan have shown localised changes during the course of
this century, and recurrent droughts in the last 30 years (Richards, 1994). It is estimated that
60% of the country is affected by desert or desertification (IteM, 1995). In 1984/5, Sudan
experienced a particularly severe drought and famine (Richards, 1994; de Waal, 1989), resulting
in widespread deaths. Despite this, there is little available information with which to monitor
drought and environmental changes.
The vastness of the Sudan, poverty, conflicts, and its poorly developed communications
infrastructure, pose great difficulty for the collection of data for operational use by a ground
based method. Consequently, remote sensing becomes the only feasible source of data that can
be used as a decision tool for timely action to avert the negative consequences of drought.
Gutman et al. (1991) argue that systems based on remote sensing data from satellites have
demonstrated the advantages accrued in timeliness and consistency. There is a need in the
Sudan, for a system which can provide timely, reliable and useful information for decision
makers on the risk of drought and environmental change. This is a particular need in the drought
prone Sahelian areas. This study examines the feasibility of a GIS system based on NDVI for
drought risk determination and monitoring.
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3. METHODOLOGY
3.1 Introduction
The following section outlines the study rationale, the data used, and any inherent problems in
the data sets. It then outlines the methodology used in the project. The mathematical relationship
between NDVI and vegetation biomass is explained. Regression techniques are used to verify
whether there is a correlation between NDVI and rainfall in Sudan, between 1982 – 1993. Once
a positive correlation has been established, the NDVI values over a twelve-year period are used
to classify ecological areas in the Sudan and produce a drought risk classification map. Finally,
these results are packaged in a customised ArcView, in order to provide a useful format for
decision making.
3.2 Study Rationale
Sudan was chosen for this study, since it is representative of the problems of other Sahelian
countries. The severe problems of drought and land degradation, and the lack of capacity to
develop an effective drought monitoring system in Sudan, mean that a cost-effective
methodology for drought monitoring is critical. However, there is a severe paucity of data with
which to monitor environmental change.
This study explores the use of NDVI, since it has been widely used in drought monitoring, and
is one of the most reliable and widely available of indices. The methodology selected in this
project was determined by the available data (NDVI and rainfall). Efforts were made to collect
Cold Cloud Duration from FAO, and relevant ground data from Sudan WFP, but these data sets
were not available. NDVI and rainfall data sets from 1994 to the present year were also
unavailable. The choice of systems for spatial analysis was limited to those available in SOAS,
which are IDRISI and ERDAS Imagine. Two front-end GIS software were also available,
MapInfo Professional and ArcView, of which the latter is used for packaging.
The methodology employed in this project is compared with two other vegetation index based
methodologies (Section 2.3). These two methods were chosen because they are applicable to
the same or similar datasets, and due to their applicability to the region.
18
3.3 The Datasets
This method relates two sets of data, firstly NDVI derived from satellite sources and secondly
rainfall obtained from ground rainfall stations, for the Sudan. The area of interest extends
roughly from 30 N to 230N and 210E to 380E. The NDVI is derived from the polar orbiting
NOAA AVHRR system by combining data from its channel 1 (red 0.58 – 0.68 _m) and channel
2 (infrared 0.75 – 1.1 _m). NDVI data from the FAO ARTEMIS programme covering the period
from 1982 up to and including 1993 was used for this study.
The rainfall data covers the period 1982 to1991. Its origin is Sudan’s Ministry of Environment
with some additional data from the Climatic Research Centre at UEA Norwich. Data for the
earlier years is from 76 stations. The number of stations gradually diminished to 20 in 1991.
Among the last 20 stations, some became operational toward the end of the data period, while
others were dropped, indicating data source inconsistency. In general, the data was not collected
consistently from all stations. Appendix 1 shows how inconsistently rainfall stations ID numbers
13 to 43 reported during 1982 – 1991. This should have a bearing on the consistency of the year
on year correlation.
Very little digitised map data was available for the Sudan. Even the UN agencies operating in
that country do not have complete detail maps that are vital for any spatial analysis (personal
communication with staff of WFP and SCF UK). The available digitised political map does not
show the administrative divisions. The only available digital maps are for certain sub-regions
(such as Darfur) where UN and non-governmental agencies are in operation. One such map
data, the land use dataset obtained from SOAS, outlines the general topography of the Sudan
and was not found to be a useful input for the analysis beyond its use for segregation and
masking-out irrigated areas.
None of the data sets are sufficiently documented to meet the basic requirements of useful
metadata.
3.4 Derivation of Vegetation Indices
Calculating vegetation indices from AVHRR data involves the transformation of the original
spectral channels into a new synthetic vegetation index channel. Creation of a vegetation index
channel from AVHRR involves the arithmetic combination of channel 1 and 2 data using
19
appropriate image processing routines. Building up regular vegetation index images from
sequential satellite overpasses adds the temporal dimension necessary for vegetation monitoring
(Belward and Taylor, 1986).
For NOAA AVHRR, NDVI is universally defined as (Lillesand and Kiefer, 1994; Cracknell,
1997):
NDVI = (Ch 2 – Ch1 )/(Ch 2 + Ch1)
Direct validation of NOAA NDVI is difficult because of the spatial subsampling applied to a
number of the NOAA products, and because of the large area covered by each pixel.
It has been shown that:
APAR = f[LAI, ISR, Canopy geometry]
Where
- APAR = Absorbed Photosynthetically Active Radiation
- LAI = Leaf Area Index
- ISR = Incoming Short wave Radiation.
Furthermore
NDVI = f[APAR]=f[LAI]
Different biomes have different canopy structures and can produce different NDVI while having
identical LAIs. Relating NPP to integrated APAR, absorbed over a growing season:
NPP = f[ ∑ APAR] x _
NPP = ∑ APAR x _
Where _ = energy-conversion efficiency in g/MJ.
20
3.5 ARTEMIS Integrated Time-series NDVI imagery
The FAO ARTEMIS project provides calibrated NDVI imagery on a dekad basis – i.e. every ten
days.
Graph 1. An example of NDVI Time Profile for Sudan in 1991
A temporal profile can be constructed for each pixel, from which one can obtain a single
annually integrated NDVI value, as is shown in Graph 1. A grid of these values makes an
integrated NDVI image.
A serious problem can occur in the profile in that the short ten-day compositing period can mean
that cloud contamination persists in the imagery, leading to gaps in the profile. This problem is
compounded if gaps occur either at the beginning or the end of the year.
The FAO ARTEMIS database has cloudy pixels marked with a special code. It is therefore
possible to identify where serious cloud contamination persists in the data.
These problems can be overcome by calculating a cubic spline function for the whole time-
series and interpolating the missing values, and by adding supplemental values from the
preceding and subsequent years, to allow interpolation at the beginning and end of the series.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35Dekads
Cummulative
21
Cubic splines are piece-wise cubic functions that are fitted to a time-series. A curve describing
the whole series is therefore built up from a family of cubic functions. Cubic splines honour the
original data points. Numerical integration of the profile is achieved using Simpson’s rule
(Richards, 1994).
The maximum daily NDVI value over a ten-day period is retained to make a dekad image,
known as Maximum Value Compositing (MVC). Such maximum values are assumed to
represent the maximum vegetation “greenness” during the period. One year’s worth of dekad
images is then integrated to construct an annually integrated NDVI image. It has been shown
that these images are strongly correlated with NPP for a given biome (Goward 1985; Justice et
al., 1991; Justice and Hiernaux, 1986).
3.6 Rainfall - NDVI Regression for Sudan
In addition to NPP, NDVI also has a strong relationship with rainfall in natural savannah
vegetation communities. There is obviously a causal link here in that where it rains the
vegetation grows (Richards, 1994). Each annual rainfall vector data was rasterised, copying
parameters from the NDVI raster datasets. Using the land-use image, the irrigated areas were
masked out by Boolean operation, so that only the rainfall dependent surface was left. The rain-
dependent surface image was re-classed by Boolean operation, in order to define the points at
which regression values are to be taken from the rainfall and the NDVI images. This masking
image was reformatted to a binary file of byte data. The image obtained was used as the
independent variable for the regression against the annually integrated NDVI image. The
resulting regression graph for 1982 rainfall (that was collected by 76 rainfall stations) is shown
below (Graph 2). The process was repeated for all the years.
Within the Sudan the relationship between rainfall and NDVI is very strong (Richards, 1994).
Once this relationship is established, a binary mask is created. The threshold for the mask is
determined on the basis of the knowledge that the 300-mm isohyet is of significance to rainfed
farming (Richards, 1994). The binary mask shows only those areas which had integrated annual
NDVI values of greater than 300 mm rainfall equivalent in any year. Although rainfall data for
the years 1992 and 1993 was not available, the 10-year average shown in Table 1 was
substituted to calculate the respective threshold values for these years.
22
Table 1. Annual Rainfall – NDVI Regression Results, 1982 - 1991
3.7 Drought Risk Classification
For the purpose of this project, a drought condition is defined (expanding the definition used by
Richards, 1994), as a meteorological situation where precipitation becomes significantly below
that required for rainfed agriculture. In the Sudan, rainfed agriculture requires a minimum of
300 mm annual rainfall (Richards, 1994). The yearly average DN values for the 12 years were
obtained by substituting for ‘x’ this amount of rainfall as the threshold in the linear equation y =
a + bx for each year (Table 1). The ten-year average was used as a threshold for segregating
areas with vegetation from those without, in order to create binary masks from each year of
annual integrated NDVI images.
The resulting twelve binary masks were added together, yielding pixel values in the range of 0 -
12. These values represent the number of years that exceeded the threshold. For instance, areas
showing a value of 6 will have 50% of green vegetation occurrence. These pixel values were re-
classed into the six categories. The resulting classification is presented in Table 2.
Year Y intercept Slope r Population DN 300 mm threshold(annual) (Daily Average)
1982 19.520273 0.073487 0.8589 76 41.57 0.111983 21.743002 0.078973 0.8123 64 43.75 0.121984 23.757212 0.067238 0.7396 59 43.93 0.121985 22.086809 0.065783 0.7775 56 41.82 0.111986 18.897245 0.085479 0.8425 49 44.54 0.121987 22.658373 0.081857 0.8117 30 47.22 0.131988 21.817945 0.067617 0.8754 38 42.10 0.121989 23.931011 0.069746 0.8043 32 44.85 0.121990 25.602036 0.072654 0.7878 25 47.40 0.131991 22.817852 0.058062 0.7378 20 40.24 0.11
Mean 45 43.74 0.12
23
3.8 Use of GIS Packages
The project uses IDRISI GIS package for data processing, modelling and analysis. The final
output is processed through ERDAS Imagine and Arc/Info and presented in ArcView.
IDRISI is a raster-based GIS and image processing software. Its raster analytical functionalities
cover a wide spectrum of GIS and remote sensing needs, such as database query, spatial
modelling and image enhancement and classification. It also incorporates special facilities for
environmental monitoring and natural resource management, such as change and time series
analysis, multi-criteria and multi-objective decision support, uncertainty analysis such as
Bayesian and Fuzzy Set analysis and simulation modelling. However, whilst this software was
found suitable for the interrogation of the data and visualisation, it was not efficient in vector
data handling, and it cannot produce high quality presentation outputs. Furthermore, whilst
importing data in suitable formats into IDRISI might be possible, exporting data from IDRISI is
time consuming and incompatible with many formats.
Although IDRISI claims that it allows for highly interactive and flexible on-screen, cartographic
composition, in reality it does not allow automatic inclusion of legends from overlaid datasets.
Manual manipulation is frustrating. Even with the automated legend provided by the
programme, displaying and printing with desired colour is difficult (what you see is not what
you get).
An attempt was made to package the map products in MapInfo Professional. Whilst this
software allows the process of overlay of images by its image registration facility, it recognises
raster data as a single value and therefore image manipulation, creation of legends and true scale
bars is not possible. Consequently, it was decided to present final outputs in ArcView.
This involved the exporting of image files from IDRISI to ERDAS Imagine as TIFF files. The
TIFF files needed to be geo-registered as one-band images and exported as grids. The grids were
imported into ArcView. Vector data sets had to be exported from IDRISI to Arc/Info where they
were converted to arc covers. The arc cover files were imported to and opened in ArcView and
converted to shape files. The lineage of each dataset was documented in the spaces provided by
ArcView. The necessary integration and cosmetics were applied to obtain the result as shown in
Map 2. Using the Avenue customisation and development environment, ArcView’s standard
tools were modified and customised. The customisation of the graphic user interface (GUI) to
24
suit the needs of the packaging involved the writing of scripts for the various views. Buttons
were created for each view to which the corresponding script, help string and icon were
assigned. Finally the scripts were embedded in the project.
The result is an easy to navigate ArcView project with each area of interest displayed by a
simple click of a designated button. Each button has a corresponding label that indicates the
view it will display. Other GIS products such as graph and tabular data are also included in the
package. The main map product that shows the classification of the Sudan into the six ecological
zones is displayed in the layout option with legend, scale bar and reference map.
25
4. RESULTS
4.1 Relationship between NDVI and rainfall in Sudan from 1982 – 1993
Regression analysis was undertaken on the rasterised annual rainfall against the NDVI annual
composite data. The regression coefficients obtained for each year under consideration fall
between 0.74 and 0.88 (Table 1). This indicates a strong positive relationship of NDVI to
rainfall in the Sudan within each year.
ANNUAL RAINFALL (mm)
Graph 2. Correlation between Rainfall and NDVI in Sudan in 1982
In order to see the trend across the 10 years, the annual rainfall was plotted against the
cumulative annual NDVI (DN) values (Appendix 4). However, it appeared that the NDVI values
corresponded to the rainfall, but with a time lag of one year. Two methods were used for
exploring this hypothesis. First, each year’s rainfall image data was regressed against the
subsequent year’s NDVI data in IDRISI. The results obtained from regression of rainfall data
against the subsequent year’s NDVI and reclassifying on the basis of the new threshold also
produced a strong positive correlation. The average DN value obtained was 44.88 compared to
the 43.74 from the previous exercise. The regression coefficients varied between 0.64 and 0.85,
with an average of 0.79 (Appendix 3), compared to 0.8 in the original regression (Table 1). The
Integrated
NDVI
26
only significant change from the previous classification was the increase of the Desert Condition
class by 0.75%.
In the second method the rainfall values were plotted against the NDVI values for the
subsequent year using a spreadsheet (Graph 3). The graph depicts a strong positive correlation
(coefficient of 0.74 for the entire period). This reinforces the hypothesis that there is a time lag
between the rainfall and the NDVI response. In 1984/85 the Sudan experienced severe famine,
as a result of poor agricultural seasons since 1983. The national annual average rainfall in 1984
of 261.6mm was the lowest of the 10 years. Yet the cumulative annual NDVI values for 1983
and 1984 were 43.75 and 43.93 respectively, representing only average DN values. It was not
until the subsequent year, 1985, that the NDVI value was low, reflecting the poor rain of 1984.
Sahelian Sudan and most of the Sahel region received the highest rainfall in 1988. Here again,
the NDVI reflected this only in the subsequent year’s image.
Similarly, the below average rainfalls of 1987 and 1990 are not reflected in the NDVI until the
following years (Graph 3 and Appendix 4).
This indicates that NDVI can only be a delayed outcome indicator. In this case the delay is
shown in the subsequent year’s data, partially due to annual compositing. The most likely
explanation for the time lag is the influence of non-Sahelian climatic bands in the south of
Sudan, where the vegetation response to rainfall in a sub-tropical climate is slower and more
sustained than in the Sahelian areas (see Section 5.1).
27
Graph 3. Correlation between Annual rainfall and Cumulative NDVI for the Subsequent Year(NB: The line used for the NDVI is for visualisation purpose and does not imply continuous data).
The correlation coefficient obtained after the regression of the shifted NDVI data in IDRISI was
0.80, compared to 0.79 for the unshifted data. However, the coefficient calculated by simple
linear regression in Excel was only 0.74 for the shifted data (Graph 3), and showed only a very
weak negative correlation for the unshifted data (Appendix 4). The difference in the correlation
coefficients for the shifted data between the IDRISI and Excel calculations is likely to be due to
the use of spatially continuous data in IDRISI. As explained in Section 3.6 the regression was
carried out between NDVI and spatially continuous rainfall data, after rasterising the rainfall
point data. However, Graphs 3, 4 and Appendix 4 all represent “point” data.
For 1991, the NDVI value was surprisingly low (40.24), even taking into account the effect of a
delayed vegetation response to the poor rains of 1990. This is due to the impact of the June
1991 Mount Pinatubo eruption. Following this, the ARTEMIS data supply was interrupted for a
year and half, since the eruption resulted in lower NDVI values and the normal peak values were
not obtained. For this reason, the data for the period June 1991 to December 1993 should have
been obtained from other sources. However, the use of multiple source datasets would have had
problems of compatibility, and influenced the results.
0
50
100
150
200
250
300
350
400
450
500
1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991Years
Ann
ual R
ainf
all (
mm
)
36
38
40
42
44
46
48
Cum
ulat
ive
ND
VI
Annual Rainfall (mm)
Cumulative NDVI (DN)
28
Graph 4. Annual Rainfall against Annual correlation Coefficient
It is also interesting to note that when the correlation coefficient for annual rainfall against
NDVI is graphed against annual rainfall (Graph 4), the correlation is stronger in years of higher
rainfall, than in years of below average rainfall. This suggests that the vegetation response to
rainfall is not a simple linear one, and that as rainfall increases, the vegetation vigour increases
geometrically, as is reflected in NDVI. The exception is that of 1991, when the correlation
coefficient fell, despite high rainfall. As explained above, this is likely to be due to the
influence of the Pinatubo eruption.
0
50
100
150
200
250
300
350
400
450
500
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
Avera
ge
Years
Ann
ual R
ainf
all (
mm
)
0.65
0.70
0.75
0.80
0.85
0.90
Cor
rela
tion
Coe
ffici
ent
Annual Rainfall (mm)
Correlation Coefficient
29
4.2 Drought Risk Classification
The IDRISI drought risk classification map is shown in Map 1. Those areas that were below the
threshold throughout the twelve years were classified as desert areas, accounting for 48% of the
total area of the Sudan. Pixels that fall below the threshold for five or more years out of the
twelve years are classified between the categories very severely and moderately at risk of
drought. These sub-regions, comprising 5%, 3% and 4% of the country’s surface area
respectively, form the transition from desert to tropical ecosystems and as such are of primary
interest in terms of drought monitoring. Areas that were above the threshold for ten or more
years and which make up some 41% of the country were classified as areas at relatively less risk
of drought, between slight and very slight.
CATEGORY RANGE (of years with
Green Vegetation)
PERCENTAGE
Desert Condition 0 48
Very Severe 1 – 3 5
Severe 4 – 6 3
Moderate 7 – 9 4
Slight 10 – 11 4
Very Slight 12 37
Table 2. Drought Risk Classification, Sudan, 1982 - 1993
30
Map 1. IDRISI Drought Risk Classification Result, Sudan, 1982 - 1993
The final classification map was imported into ArcView and packaged as a final presentation
product showing the six levels of drought risk in the Sudan, international boundaries of the
Sudan, legend, and scale (Map 2). On screen, the customised ArcView GUI facilitates easy
navigation within the project and GIS products such as maps, statistical tables and graphs can be
viewed and printed as required. In addition, the customisation protects the project from the
inadvertent alteration.
31
Map 2. Classification Map Product Packaged in ArcView
32
5. DISCUSSION OF FINDINGS
5.1 Relationship between NDVI and rainfall data in Sudan from 1982 – 1993
Section 4.1 established that a strong positive relationship exists between NDVI and rainfall,
within each year (Graph 2 and Table 1). However, the relationship between NDVI and rainfall
is a complex one, as is indicated by the delayed vegetation response (Graph 3). The lag period
with NDVI in the Sahel is usually considered to be less than 10 days (Ulanova, 1975, quoted in
Kogan, 1997). With the current dataset, it has been shown that where the data includes sub-
tropical climatic zones as well as Sahelian areas, the response time can be considerably greater
than this.
NDVI has to be analysed and used with caution, as a number of factors unrelated to vegetation
conditions can influence NDVI observations. This section highlights some of these factors
which are likely to have affected the data set, and mean that it should be used with caution. Such
factors include “incident solar radiation, radiometric response characteristics of the sensor,
atmospheric effects (including the influence of aerosols), and off-nadir viewing effects”
(Lillesand and Kiefer, 1994). The effect of the Mount Pinatubo eruption has already been noted
(Section 4.1). Despite the strong positive correlation found, the analysis could be improved
further by rectifying errors in the NDVI data, and addressing limitations in the pre-processing
and modelling methods.
The performance of the sensors can distort the data. Rao and Chen state that
“during 1985-1994, the performance of the VIS and NIR channels differed betweenNOAA-9 and NOAA-11 satellites and most importantly, degraded over time for eachsatellite differently.” (Rao and Chen, 1995)
This may have had a bearing on the data sets used in this study.
Data obtained from AVHRR sensors is most suited for environmental monitoring at regional
and continental scales due to the coarseness of the spatial resolution. The spatial resolution of
the NDVI data that is finally used in the above procedure is even further degraded due to
technical considerations. The generalisation of data such as Local Area Coverage (LAC) data
into the final ARTEMIS product with a lower resolution can lead to errors. Kogan (1995)
claims that up to 20% error can occur during data sampling and processing etc.
33
Kogan also highlights the problems of spatial and temporal aggregation:
“Space aggregation (average, interpolation) is inappropriate because areas with differentenvironmental resources could be combined together. For example, an area of 10 lat X 10
long in the Sahel of Africa combine such different ecosystems as desert, grassland, andsemi-forest with differences between annual precipitation up to 1000 mm (Lebedev1998). Temporal aggregation (bi-weekly, monthly) based on a compositing technique isbiased because a later time interval is normally selected, giving preference to moredeveloped vegetation. If the longer term average technique is used, the results are quiteoften biased over space because neighbouring pixels might characterise a different timeinterval (start and end of month)....” (Kogan, 1997)
Some of the problems of the rainfall data set have already been outlined (Section 3.3). Data
obtained from raingauges have shortcomings in terms of spatial representation. Rainfall gauges
can tell us the amount of rainfall that fell at that point to a high degree of accuracy, but no
information can be gleaned from them as to the rainfall distribution in the surrounding area.
Bonifacio argues that in areas such as the African Sahel with high spatial and temporal
variability of rainfall, interpolation between raingauge values to obtain an estimate of the
rainfall at a particular point may give rise to serious errors (Bonifacio et al., 1991). This is due
to the fact that the degree of correlation between stations weakens the further a point is from a
station. In this study, the distribution of rainfall recording stations varied both temporally and
spatially, and in some years their geographic distribution was not statistically representative. For
example, for most stations in North Darfur, (the north-western province of Sudan) there are no
records for the early 1980s, as the government failed to pay the data collectors (de Waal, 1989).
Consequently data from too few points were interpolated to represent a large and diverse region,
especially towards the final years under consideration. The high degree of human involvement
during the collection, reading and recording, transmission, compilation and processing of
raingauge data is prone to introduce further errors. The problem of atmospheric distortion,
performance of the sensors and data processing affect the reliability of the model, and are likely
to have weakened the correlation. However, the impact of spatial interpolation will have biased
the relationship towards positive correlation.
5.2 Implications for Drought Monitoring
This model for drought classification based on NDVI and rainfall data enables decision-makers
to have a basic overview of areas at risk of drought in the Sudan. In the long-term, it enables
them to build a better understanding of the phenomenon that brings about changes in regional
34
climates. Annual data can be integrated in such a methodology, in order to update the dataset
and eventually establish a classification based on long term data. However, for more detailed
planning purposes, it has a number of weaknesses.
The method has enabled the classification of the Sudan into six rough rainfall zones. This is a
crude zoning mechanism, due to the data resolution, and the data limitations. Such
classifications based on very short time series data cannot help us to distinguish between natural
short-term variations and long-term climatic changes. As it stands, the model can only classify
the vegetation performance of large areas. It cannot be effective as a monitoring tool in real
time, since it does not allow for the monitoring of seasons as they develop. It lacks the
dynamism that would lend itself as a tool for proactive intervention. It can only be used on an
annual basis, based on historical data.
The implications of the lagging effect of the NDVI data for drought monitoring are significant.
Firstly, it illustrates that this kind of analysis needs to be done separately for each climatic zone,
and the nature of the relationship between NDVI and rainfall studied for each. The technique
may be more useful for drought monitoring in the critical Sahelian zone, where the relationship
between NDVI and rainfall is established and not subject to a long time lag. However it may
not be practicable in more humid sub-tropical and wetland zones where vegetation cover is less
seasonal.
Secondly, NDVI is a late or outcome indicator of vegetation stress. The use of annually
composited data and the time taken for the acquisition and processing of the data further
exacerbate the timeliness of the information. The end result limits the effectiveness of the
method.
The drought classification also has shortcomings arising from limitations in the input data. The
quality of GIS data is related to its ‘fitness for use’ (Bernhardsen, 1999). Fitness for use can be
measured in terms of criteria such as geo-referencing accuracy, attribute data accuracy, logical
relationship consistency, completeness, resolution and currency. Data selection is facilitated if a
dataset is accompanied by metadata that describes the procedure, content, quality, condition and
other characteristics of the data including its history and source (Bernhardsen, 1999). It is
desirable that input data be of higher quality than the intended use requires, in order to ensure
output quality. In this respect the reliability of the ground data was a limiting factor to this
analysis. The scale, data compatibility, accuracy and reliability of the paper map copies that
35
were sources of the digitised ground information were below the required standard. For instance,
the national boundary map does not seamlessly fit with the NDVI data along the border with
Chad to the West, with Egypt in the north-east, the Red Sea coast to the East and Kenya in the
south. The accuracy and reliability of the co-ordinates in the geographical datasets and accuracy
and reliability of statistical data were all substandard (Richards, 1994). There is insufficient
documentation accompanying the ground datasets. Consequently the value of the GIS product
obtained by this method will be limited.
Finally, the methodology fails to present a true “drought risk” map for two reasons. The map
produced approximates a map of rainfall bands, rather than risk of drought, defined as a fall in
rainfall below the average required. To produce a meaningful map of risk, there would also
have to be further bio-physical and socio-economic data integrated into the model. In addition,
an analysis is required of climatic zones, and the particular vegetation response to rainfall. This
would enable a more useful definition of drought, based on rainfall thresholds in different areas.
Drought is as much a socio-economic construct as a bio-physical one. For instance, the amount
of rainfall required for pasture regeneration that is needed by pastoralists might not be as much
as that needed for cereal crop production. However, although the advances in GIS are such that
socio-economic data can be integrated and modelled with bio-physical data, the structures for
obtaining the desired datasets from regions such as the Sudan is difficult and prohibitive in
terms of cost.
36
6. COMPARISON WITH OTHER APPROACHES USING NDVI
This section makes a comparison with the two VI based approaches presented in Section 2.3,
with the aim of making recommendations to improve this methodology. The methodology used
here has one outstanding difference from the two other VI based approaches. Whereas both
methods are dynamic, it is static. The major shortcomings of the method used here lie in the type
of input datasets, as well as in its global approach that fails to take into account the ecological
variations of the Sudan. While it has much similarity with the Kogan model, the latter has the
advantage of giving a ‘first impression’ of drought on a near real-time basis.
In order to make recommendations to improve on the methodology, it is useful to draw on some
features of the methodologies developed by the TAMSAT group and Kogan.
Initially, it would be useful to define the parameters derived from the NDVI that characterise the
growing season in order to relate the NDVI and the rainfall within such a time window. The
TAMSAT group have suggested parameters that are the beginning and end dates of the growing
season, and a threshold NDVI value that is assumed to be representative of the bare soil or pre
growing season conditions (Bonifacio et al., 1993 a and b). Objective definition of such
parameters on a pixel-by-pixel basis would accommodate the wide variation in the timing and
duration of the growing season in the Sudan.
It is essential to determine the growing season, so that only the useful rainfall (PWU) and
accumulated NDVI during the growing period are correlated.
“The total seasonal rainfall does not always correspond to the useful rainfall and maytherefore be a poor indicator of the seasonal production” (Justice and Hiernaux, 1986).
Better results could be obtained by correlating ‘useful rain’ and the vegetation response. The use
of a national average rainfall threshold in the correlation, in a country with diverse ecological
zones, is misleading.
The method should account for useful soil moisture, which is critical during the crop-growing
season. Bonifacio accounted for this variable using the water balance model and due
consideration of the vegetation dynamics. Kogan incorporated the Thermal Condition Index to
determine the net soil moisture available for vegetation. Kogan uses a weekly time frame in
order to capture morphological changes and leaf appearances that occur in 3-7 days (Kogan,
37
1997). The finding of the TAMSAT group does not concur with this, and they assert that given
that vegetation is a slow varying parameter, a time frame of 10 days is sufficient (Bonifacio and
Grimes, 1998). The calculated plant water use seems to give a considerably more robust
relationship with integrated NDVI than does the total rainfall (Bonifacio et al., 1991), as it takes
into account all prevailing factors at localised level.
The TAMSAT group highlights the problems of accuracy in rainfall data, particularly for the
Sahel:
“estimates are more accurate at the lower range of rainfall values (<50 mm) precisely theamounts where rainfall becomes the limiting factor for vegetation development”(Bonifacio et al., 1993a and b).
Such problems associated with rainfall data from rain gauges (Sections 3.3 and 5.1), suggest that
using RFE data might lead to improved accuracy due to their high spatial and temporal
coverage. In addition, as RFE is a spatially continuous data, it is easier to establish
correspondence with NDVI. Effective use of rainfall estimate requires establishment of the
RFE’s statistical relationship with rainfall in each specific ecological zone.
A detailed cost evaluation could not be undertaken due to lack of data, particularly on the inputs
for the Kogan methodology. However, the system described in this study is relatively low cost,
since it uses free pre-processed data and can be run on an ordinary desktop computer. It is also
easy to implement and operate compared to the other systems, and does not require extensive
training for users. The TAMSAT method however, requires the additional input of METEOSAT
PDUS system to receive the RFE data.
38
7. CONCLUSIONS
This project aimed to employ GIS to examine the relationship between rainfall and the NDVI, in
the context of the Sudan, and to explore the implications for drought monitoring, through a
comparison with other VI based approaches. In the process it has been demonstrated that in
regions such as Sahelian Africa, where there is a dearth of digital data from which useful
monitoring and management information can be drawn, GIS using remotely sensed data
obtained from satellites is technically feasible. Furthermore, it is a relatively low cost system, as
it uses free data for input and can be run on an ordinary desktop computer. The training required
for running the system is also limited since once set, it uses pre-processed data inputs.
The spatial modelling method has shown the strong positive relationship between rainfall and
vegetation vigour as reflected by the NDVI in the Sudan for the period 1982 - 1993, in spite of
data limitations. The ArcView GIS package was then used to produce a final “drought risk” map
of Sudan, showing 6 classes of areas at risk of drought. This was presented in a customised
GUI.
However, this analysis has shown that NDVI is a complex indicator, difficult to interpret, as
well as being a delayed outcome indicator. Whilst previous studies have highlighted a lag in
NDVI response of a few weeks, this study showed a delayed response between the annual data
sets. This is due to the influence of sub-tropical humid and wetland zones on the predominantly
Sahelian/desert areas. The precise extent of the time lag is hidden by the annual composited
format. Furthermore, on its own, NDVI is a crude indicator of drought risk, and needs to be
related to other socio-economic and bio-physical data in order to be useful. The precision of
NDVI as a vegetation index also needs to be strengthened through establishing its relationship to
the growing season, for each specific climatic zone, on the basis of local vegetation and crop
types.
The methodology adopted here has demonstrated the importance of GIS as a tool for integrating
various sources of data. Visual analysis of graphed data facilitated the data processing and
analysis, as was demonstrated by the shifting of the NDVI by one year in order to explore the
best correlation of the data sets. The plotting of the rainfall data with the correlation coefficient
is another example where visualisation of data helped, highlighting that the higher the rainfall
the stronger the correlation. GIS tools enable datasets to be brought together and used in
modelling and analysis. The products from such procedures were indispensable for the analysis
39
and are designed to play a crucial role in decision-making processes. The presentation of
products in a customised graphic interface was carried out with the aim of facilitating the
utilisation of outputs by non-GIS technicians. It has been shown that GIS can help in
understanding and analysing complex environmental situations, such as the Sudan, even where
data is relatively scarce and there is a limited knowledge base.
The GIS tools used, however, have highlighted the shortcomings of the data sets and the
method. Further improvements are required to both, in order for the methodology to be refined.
For any system for monitoring environmental change, the objectives need to be specific. In
particular, it should be clear whether the aim is to monitor environmental change across years, in
which case a long time-series data is required, or whether it is to monitor vegetation and crop
changes within seasons for purposes of drought warning. As it stands, the method is static and
does not exploit the dynamic potential presented by remote sensing and GIS tools. An effective
drought warning system using NDVI should take advantage of remote sensing sources in using
real time data, in order to facilitate timely decision making. If this were to be done, NDVI can
be a valuable first cut indicator, and provide a key input for cost-effective, reliable and timely
drought monitoring systems.
40
8. KEY RECOMMENDATIONS
The following recommendations are made to upgrade the methodology described in this study as
an effective tool for drought early warning in the Sudan.
1. NDVI on its own is a crude indicator of drought risk. It must therefore be related and
integrated to bio-physical data (such as soil types, evapotranspiration etc.), in order to
determine areas at risk, and other socio-economic information (such as livelihoods in each
ecological zone), in order to determine groups at risk of drought. GIS is an essential tool for
integration of such diverse data sets, in order to facilitate timely decision making.
2. Where ground based rainfall data quality is poor, as in Sudan, it should be complemented by
satellite rainfall data (RFE) which is both economic and timely. This can assist in advance
warning of drought.
3. Since the total seasonal rainfall does not always correspond to the useful rainfall, the
growing season should be determined in order to relate the NDVI to the useful rainfall. This
would yield a better correlation between NDVI and rainfall.
4. The usefulness of NDVI for drought monitoring should be strengthened through establishing
its relationship within specific climatic zones. Within each zone, further analysis is required
on the basis of growing season, and major local vegetation and crop types.
5. Any proposed drought monitoring system should extend the relationship between NDVI and
other remote sensed information, such as RFE, in order to obtain forecasts and real time data
for monitoring.
6. Interoperability between the various GIS would greatly facilitate the task of exchanging data
between systems. Ultimately however, a GIS that is versatile with both raster and vector data
as well as capable of presentation quality products is desirable.
41
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44
APPENDIX 1
Rainfall Stations and the Frequency of ReportingSudan, 1982 – 1991
1982 1983 1984 1985 1986 1987 1988 1989 1990 199113 13 13 13 13 13 13 13 13 1314 14 14 14 14 1415 15 15 1518 18
19 1920 20 2021 21 21 21 21
24 24 24 24 24 24 2426 26 26 26 26 26 26 26 26 26
27 27 27 27 27 27 27 27 2728 28 28 28 28 28 28 2832 32 32
33 33 33 33 33 33 33 33 3335 35 35
36 36 36 3637 37
39 3941 41
4243 43
Notes
1. Blank cell means no report2. Numbers refer to the ID numbers of the rain stations
45
APPENDIX 2
Rainfall to NDVI EquivalentIn the Sudan
Rainfall(mm)
NDVIEquivalent
Rainfall(mm)
NDVIEquivalent
100 0.077 1000 0.270200 0.098 1100 0.291300 0.119 1200 0.313400 0.141 1300 0.334500 0.162 1400 0.355600 0.184 1500 0.377700 0.205 1600 0.398800 0.267 1700 0.420900 0.248
Notes
1. Source: (Richards, 1994)
46
APPENDIX 3
Annual Rainfall Regression VersusNDVI of subsequent year
Year Y intercept Slope r Population DN 300 mm threshold(annual) (Daily Average)
1982 21.807657 0.073151 0.8481 76 43.75 0.121983 16.471920 0.076215 0.7642 64 39.34 0.111984 28.554474 0.074391 0.7850 59 50.87 0.141985 29.704552 0.059809 0.7633 56 47.65 0.131986 17.801397 0.089189 0.8262 49 44.56 0.121987 25.646868 0.085653 0.8370 30 51.34 0.141988 18.047853 0.071259 0.8444 38 39.43 0.111989 25.709478 0.050077 0.6410 32 40.73 0.111990 22.504648 0.068344 0.7909 25 43.01 0.121991 27.696959 0.067977 0.7656 20 48.09 0.13
Mean 0.7866 45 44.88 0.12
47
APPENDIX 4
Correlation between Annual Rainfall andCumulative NDVI (same years)
Note
1. The line used for NDVI is for visualisation purposes, and does not represent continuous data.
0
50
100
150
200
250
300
350
400
450
500
1982 1983 1984 1985 1986 1987 1988 1989 1990 1991
Years
Ann
ual R
ainf
all (
mm
)
36
38
40
42
44
46
48
ND
VI (
DN
)
Annual Rainfall (mm)
Cumulative NDVI (DN)
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