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SPATIAL DE-AGGREGATION OF CROP AREA STATISTICS USING REMOTE SENSING, GIS AND

EXPERT KNOWLEDGE

Edith B. Kahubire March 2002

A Case Study of Ghana

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Spatial de-aggregation of Crop Area Statistics using Remote Sensing, GIS and Expert Knowledge

By

Edith Kahubire

Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation (Natural Resource Management/Sustainable Agriculture) Degree Assessment Board Chairperson: Prof. A. K. Skidmore, Head, ACE Division, ITC External Examiner: Prof. Paul Driessen, Wageningen Agricultural University Internal Examiner Dr. Michael J. C. Weir, Forestry Division, ITC Primary Supervisor: Dr. C. A. J. M. de Bie, ACE Division, ITC Secondary Supervisor: Ir. L. M.van Leeuwen, Forestry Division, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH

OBSERVATION ENSCHEDE, THE NETHERLANDS

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Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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To

Mr. & Mrs. S.A.K Magezi for their Inspiration and Guidance

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Acknowledgements

This research was carried out at the International Institute for Geo-information Science and Earth Observation and supported by the Netherlands Fellowship Programme. The author is indebted to Dr. Kees de Bie for his dedication, guidance and scientific support during the research process. Ir. Louise van Leeuwen, your insights on Ghana and the methods used in this thesis are highly appreci-ated, and many thanks for the bringing the GIS data from Ghana. To Dr. Michael Weir and Dr. Herman Huizing – your words of encouragement and support throughout the whole programme do not go unnoticed. The author wishes to acknowledge CERGIS under the Directorship of Dr. Emmanuel Amamoo, for providing the crop suitability, land cover/ land use data and the facilities that were used for the initial data analysis and genera-tion of expert knowledge while in Ghana. Many thanks, to AGHRYMET, for providing NDVI dataset that was pri-marily used in the study. Special thanks are extended to Albert Allotey for his support in the generation of expert knowledge from various institutions in Ghana and for attaining the historic crop area statistics from the Ministry of Food and Agriculture, Ghana. Steve Duadze, Nii Amasah Namoale, Alfred Mensah and John Boateng, your discussions and inputs were very helpful. Mohammed Said, Onisimo Mutanga, Alfred Duker, Zuze Dulanya Samuel Mugisha and Isah Kiti Nabide your comments are highly appreciated. Finally, the author is grateful for the continual support of fellow colleagues, family friends and the Ugandan community in Enschede.

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Abstract In order to achieve sustainable agriculture, national planners and decision makers require timely, accurate and

detailed information on land resources. Available crop area statistics are disseminated as administrative aggre-

gates in tabular format. The aggregate form of such data is incompatible with data formats that attempt to inter-

pret spatial relationships of factors related to crop distribution and production potential. This study primarily aims

at generating detailed crop maps by de-aggregating crop area statistics from second level administrative aggre-

gates to more meaningful production areas. The study proposes procedures that apply remote sensing and GIS

techniques incorporated with expert knowledge to spatially distribute crop areas. The method is that significant

vegetation cover and biophysical variables are used to statistically redistribute crop using their regression coeffi-

cients that represent the percentage under crop production in one hectare. The fuzzy logic is used improve sta-

tistical distributions by incorporating expert knowledge in order to produce final crop area distribution maps. The

relative importance of variables is observed from regression results that indicate determination coefficients of the

redistributed crop area statistics as 76.7% for maize, 55.5% for cowpea, 76.7% for sorghum, 82.7% for pearl

millet, 56.6% for groundnuts and 41.4% for rice. The final outputs were detailed crop area distribution maps at

100 m pixel level based on their biophysical, vegetation considerations and refined by expert knowledge. The

validation of the final maps indicates that compared to the available land cover and land use map of Ghana, dis-

tributed crop area statistics fall within areas that were mapped out for possible crop production. The method pro-

vides a cheap, rapid and efficient method for mapping crop distribution over large areas in tropical environments

where in situ information is limited or incompatible with satellite data. It is hoped that the detailed national crop

area maps will benefit planners in most developing countries where over 50% of the total population depend on

rain fed agriculture for their livelihoods.

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Table of Contents 1 Introduction ....................................................................................................................................1

1.1 Background ............................................................................................................................1 1.2 Justification ............................................................................................................................1 1.3 Research Problem...................................................................................................................1 1.4 Objective ................................................................................................................................1 1.5 Research Questions ................................................................................................................2 1.6 Research Hypothesis ..............................................................................................................2 1.7 Research Approach ................................................................................................................2

2 Approaches To Crop Area Identification.......................................................................................4 3 Methods and Materials...................................................................................................................6

3.1 Study Area..............................................................................................................................6 3.1.1 Geographical Location ...................................................................................................6 3.1.2 Climate ...........................................................................................................................6 3.1.3 Biophysical Characteristics............................................................................................7 3.1.4 Agricultural characteristics ............................................................................................7

3.2 Methods..................................................................................................................................8 3.2.1 Georeferencing ...............................................................................................................8 3.2.2 Satellite data processing.................................................................................................8 3.2.3 Crop Suitability Data......................................................................................................9 3.2.4 Creation of the Area of Interest....................................................................................10 3.2.5 Generation statistical map attributes ............................................................................11 3.2.6 Transformation of Crop Area Statistics .......................................................................11 3.2.7 Determination of Significant Crop Area Variables......................................................14 3.2.8 Development of Regression Equations to Estimate Crop Area ...................................14 3.2.9 Statistical distribution of crop area ..............................................................................15 3.2.10 Application of the Fuzzy Set Theory ...........................................................................15 3.2.11 Validation Procedure....................................................................................................17

4 Results ..........................................................................................................................................18 4.1 Crop Area Statistics..............................................................................................................18

4.1.1 Maize............................................................................................................................18 4.1.2 Cowpea.........................................................................................................................20 4.1.3 Rice...............................................................................................................................21 4.1.4 Sorghum .......................................................................................................................23 4.1.5 Groundnuts ...................................................................................................................25 4.1.6 Pearl Millet...................................................................................................................26

4.2 Crop Area Distribution Maps...............................................................................................28 4.3 Evaluation of Crop Area Disribution Maps .........................................................................35

5 Discussion ....................................................................................................................................36 5.1 The de-aggregation method..................................................................................................36

5.1.1 Regression Analysis .....................................................................................................37 5.1.2 Fuzzy logic Analysis ....................................................................................................37

5.2 Limitations of the Approach ................................................................................................38 6 Conclusions & Recommendations ...............................................................................................40 References ............................................................................................................................................42

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Appendices...........................................................................................................................................45

List of Figures Figure 1:1: Present Crop Map of Ghana ................................................................................................2 Figure 1:2: The Methods Framework ....................................................................................................3 Figure 3:1: Location Of Ghana. .............................................................................................................6 Figure 3:2: The NDVI unsupervised classified image...........................................................................9 Figure 3:3:The district map of Ghana for which crop area statistics were distributed..........................9 Figure 3:4 The variability in tabular maize crop area crop area statistics 1992 - 2000.......................12 Figure 3:5 The variability in tabular rice crop area crop area statistics 1992 - 2000 ..........................12 Figure 3:6: An example showing the variability in historic crop area statistics for selected districts 12 Figure 3:7: An example of a logarithmic equation used to generate a consolidated crop area statistic13 Figure 3:8: An example of logarithmic function for maize crop area in South Tongu district ...........13 Figure 3:9: An example of a transformation for Ejura Sekyidumasi, that yielded a negative

consolidated statistic ....................................................................................................................14 Figure 4:1: Residual plot for the regression to redistribute maize crop area .......................................20 Figure 4:2: Residual plot for the regression to redistribute cowpea crop area ....................................21 Figure 4:3:Residual plot for the regression to redistribute rice crop area ...........................................23 Figure 4:4 : Residual plot for the regression to redistribute sorghum crop area..................................24 Figure 4:5: Residual plot for the regression to redistribute groundnut crop area ................................26 Figure 4:6: Residual plot for the regression to pearl millet crop area .................................................27 Figure 4:7: Maize Crop Area Distribution...........................................................................................29 Figure 4:8: Brown Rice Crop Area Distribution..................................................................................30 Figure 4:9: Groundnut Crop Area Distribution....................................................................................31 Figure 4:10: Cowpea Crop Area Distribution......................................................................................32 Figure 4:11: Sorghum Crop Area Distribution ....................................................................................33 Figure 4:12: Pearl Millet Crop Area Distribution................................................................................34

List of Tables Table 3:1: An Overview of Data Used for the study .............................................................................7 Table 3:2 The Georeference Parameters ................................................................................................8 Table 3:3: An overview of crops used in the study and their length of maturity.................................10 Table 4:1: Stepwise Regression Results for Maize Crop.....................................................................18 Table 4:2: Multiple Linear Regression Results to Redistribute Maize Crop Area..............................19 Table 4:3: Stepwise Regression Results for cowpea crop area............................................................20 Table 4:4 : Multiple Linear regression results to redistirbute cow pea crop area................................21 Table 4:5: Stepwise regression results for rice crop area ....................................................................22 Table 4:6: Multiple Linear Regression results to redistribute rice crop area ......................................22 Table 4:7: Stepwise regression results for sorghum crop area ............................................................23 Table 4:8: Multiple Linear Regression results to redistribute sorghum crop area...............................24 Table 4:9: Stepwise regression results for groundnut crop area ..........................................................25

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Table 4:10: Multiple Linear Regression results to redistribute groundnut crop area .........................25 Table 4:11: Stepwise regression results for pearl millet crop area. .....................................................26 Table 4:12 :Multiple linear regression results to redistribute pearl millet crop area...........................27

List of Equations Equation 3:1: Fuzzy Product for Crop Area ........................................................................................16 Equation 3:2: Fuzzy Sum for Crop Area..............................................................................................16 Equation 3:3: Factor Gamma for Crop Area........................................................................................16 Equation 4:1: Regression equation to redistribute maize crop area.....................................................19 Equation 4:2: Regression equation to redistribute cowpea crop area ..................................................21 Equation 4:3: Regression equation to redistribute rice crop area ........................................................22 Equation 4:4: Regression equation to redistribute sorghum crop area ................................................24 Equation 4:5: Regression equation to redistribute groundnut crop area..............................................25 Equation 4:6: Regression equation to redistribute pearl millet crop area............................................27

List of Appendices Appendix 1: Present land cover / land use map of Ghana ...................................................................45 Appendix 2: An overview of area covered by each land cover type in the First Level .......................46 Appendix 3: Legend of the present land use / land cover map of Ghana as Second Level .................46 Appendix 4: Legend of the present land use / land cover map of Ghana as Third Level ....................47

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Acronyms and Abbreviations AGHRYMET: Agro Hydro Meteorological Centre ASTER: Advanced Space borne Thermal Emission and Reflection Radiometer AVHRR: Advanced Very High Resolution Radiometer CERGIS: Centre for Remote Sensing and GIS FAO: Food and Agricultural Organisation FEWS: Famine Early Warning Systems GAC: Global Area Coverage GIS: Geographical Information System ILWIS: Integrated Land and Water Information System ISODATA: Iterative Self-Organising Data Analysis Technique JERS-1: Japanese Earth Resources Satellite -1 LAI: Leaf Area Index LAC: Local Area Coverage LANDSAT: Land Remote Sensing Satellite MODIS: Moderate-Resolution Imaging Spectroradiometer MODLAND: MODIS Land NEMA: National Environment Management Authority NDVI: Normalised Difference Vegetation Index NOAA: National Oceanic and Atmospheric Administration SAR: Synthetic Aperture Radar SPOT 4: Systeme pour l’Observation de la Terre 4 TM: Thematic Mapper

SPATIAL DE-AGGREGATION OF CROP AREA STATISTICS USING REMOTE SENSING ,GIS AND EXPERT KNOWLEDGE

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1 Introduction

1.1 Background In order to address global problems related to food security, regional and national planners using multidiscipli-nary decision support systems require among others adequate information on where crops are grown in order to monitor agricultural production over vast areas (McGuire, 1997). Advances in remote sensing, GIS and semi quantified techniques for land use assessments can go a long way in sustainable land use planning (De Bie, 2000). The use of remote sensing techniques has become increasingly important in describing a variety of satel-lite-derived data sets and their application to understanding changes in the landscape (Kouchoukos et al., 1997). LAC NOAA is by far the most efficient satellite for large-scale agricultural applications.

1.2 Justification Patterns of agricultural resource use and the scope of resource demand are always changing (Anderson, Hardy, T.Roach, & Witmer, 1976). Information requirements for agricultural planning at national and sub-national levels and decision-making are immense. Detailed land use maps on location of major croplands are not readily avail-able for many Sub-Saharan countries (McGuire, 1997). Agricultural land is too often classified into broad classes like tree cropping, irrigated cropping and mixed cropping (Agyepong & Duadze, 1999). Having knowledge on meaningful production area allows decision makers to locate populations that are most vulnerable to food insecu-rity and poverty.

1.3 Research Problem In many developing countries like Ghana, agricultural statistics disseminated in incompatible formats (tabular and graphs) that lack Georeferencing as second level administrative aggregates. Yet administrative areas are arbi-trary in geographical terms varying in size, shape and time posing serious problems for attempts to map or inter-pret spatial patterns in statistical data or integrate such data with other data sets (Fresco, Stroonsnijder, Bouma, & Keulen, 1994) (McGuire, 1997). In addition to this, analyses based on such synoptic data make extrapolations to lower administrative levels difficult (Ocatre, 1997). Monitoring of potential yield based administrative aggre-gates is disadvantageous because of the wide variety of ecological conditions and land use types that may occur within their boundaries. National policy makers need to monitor the spatial extent of agricultural production in all districts in order to address the constraints hindering farmers from achieving their potential yield (Huizing, Zon-neveld, & Bronsveld, 1980). The aim of this study is to show that remote sensing when used in integration with other data sets can be used to explain spatial relationships in crop area that can be used to de-aggregate 2nd district level tabular crop area statistics to more meaningful production areas.

1.4 Objective Spatial information on the expansion and identification of agricultural production is an important aspect in the generation of spatial agricultural statistics. This study is based on the observation that there is an increasing need for accurate and timely information on crop area information at national level.


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The primary objective of this study was to generate detailed crop area maps (maize, sorghum, rice, millet, cow-pea and groundnuts) for Ghana contrary to the available ones. (See Fig.1: 1) The specific objective of this study was to spatially redistribute tabular crop area statistics from a number of spatial data sets and expert knowledge.

1.5 Research Questions The research objectives will be achieved by answering the following ques-tions:

• How can spatial remote sensing and GIS data be used to de-

aggregated crop area statistics that are based on administrative boundaries?

• Which vegetation cover and biophysical characteristics of land can

be used as predictors to identify cropped areas by crop type?

Figure 1:1: Present Crop Map of Ghana

1.6 Research Hypothesis Assuming that crop area is a linear combination of a number of variables and that other factors affecting crop area are already mapped, this study aims at distributing crop area statistics using the statistical model:

Crop Area by district (ha) = f (GIS Maps, Remote Sensing products, Expert knowledge) The distribution of crop area is expressed as a function of mapped GIS and Remote sensing products and expert knowledge.

1.7 Research Approach The approach to this study was that remotely sensed data from different sources and resolutions is merged with GIS maps in order to distribute aggregated crop area statistics within district boundaries. All data sets were geo-referenced to the same coordinate system and resampled to 100 m x 100 m grid cells. Considering that in some areas no crops are grown, the study used prior land use and land cover information like water bodies, forest ar-eas, game reserves and urban area to map out possible crop areas. Historic crop area data was transformed in order to generate a consolidated statistic that is reflected the events in crop production in the recent years. Step-wise and multiple linear regressions are used to redistribute crop area statistics. The procedure is that consoli-dated crop areas statistic is used as the response variable while using significant vegetation classes from NDVI and crop specific suitability assessments as the independent variables. The regression coefficients are applied to the original NDVI classified and suitability classes maps and then combined. The result is a crop area distribution map representing percentage area under crop production in one hectare or at a 100 x 100 grid cell level. The mathematical combination of regression coefficients in some case did not reflect real life situations. It was there-


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fore necessary to integrate expert knowledge on the study area using the fuzzy set logic theory. The final outputs are maps showing the distribution crop area in percentages per hectare with a more realistic view of the experts.

NDVI Time series

(March 97 - March 99)

Layer Stacking

Radar Image(100m)

Aster Images (15 m)

Suitability Maps / 6

crops & 3 tech (shp)

Land cover Masks (parks, forests)

Crop Area Statistics (9 years)

NDVI Image (69 bands)

Ghana NDVI image

Subseting

Classfied Image(30 classes)Resampled to 100m (NDVI map)

Classified Image (2 classes - Field &

Non field)

Unsupervised Classification

Supervised Classification

Attribute Algorithm

Suitability Raster Maps

Fuzzy Set Algorithm

log(n) eqaution

Linear Regressions

Resample and Rasterising to (100 M)

CropsSuitability Index

Masked NDVI Classified Image

Raster (parks, forests,urban

&waterbodies)

( MapCross Algorithm)

Ghana district map

(shp)

District Raster Maps

Masked Crop Suitability Maps

Landuse landcover map (shp)

ISUNDEFAlgorithm

Landcover masks(Area of Interest

map)

District Raster Map

ConsolidatedCrop Area Statistics

Map Addition of Crop Varieties

Median technology suitability index per crop

6 Crop Area Distribution Maps

Ghana Radar image

Masking

Masked District Map

District and Suitability Cross Table

NDVI and Districts Cross table

Density Slicing

Classified Suitability maps

6 (Normalised)Crop Factor Maps6 maps based on expert knowledge

Figure 1:2: The Methods Framework


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2 Approaches To Crop Area Identification

The traditional notion of thematic mapping presumes that every spot on the ground surface can be labelled as belonging to only one category (Schowengerdt, 1997). However, the identification of object depends on the spa-tial resolution of the remote sensing imagery (Tomar & Maslekar, 1974). The landscape is a heterogeneous area consisting of a mosaic of local and interacting ecosystems, which may include forests, cropland, shrub land, open grassland and built up areas (DeFries, Hansen, & Townshend, 1996). In agricultural applications, remote sensing imagery has been used to identify different crop types, estimate crop area and, predict yield at small scales (Kanemasu, 1974). In operational remote sensing carried out at national level, coarse resolution data is used for most analyses for yield prediction. This is the reason why many researchers have tried to derive bio-physical characteristics about vegetation cover that can be used to estimate crop area. The structure of most crops is identical causing spectral mixes within crops and other types of vegetation. These high spectral overlaps makes attempts to understand the relationships between crops and the ecosystems within which they occur in order to classify remote sensing imagery difficult. Results from such studies show that with high-resolution imagery there was a clear distinction between broad land cover classes (Tomar & Maslekar, 1974) with a classification accuracy of over 80%. However, in areas where agricultural crops are raised in con-junction with forestry plantations or commercial crops like coffee, some difficulty is likely to be experienced in recognising correct land use classes. While automated image classification assumes that there is a direct relationship between crop canopy data and their spectral reflectance, biophysical conditions and temporal aspects also affect the way in which spectral re-flectance of crops can be interpreted. The spectral signature for vegetation is highly variable in nature since it changes completely during the seasonal cycle of many plants (Schowengerdt, 1997). Therefore, a number of contexts like spatial, spectral and temporal have been used over the years the in order to carry out crop identifi-cation in remotely sensed data (Byeungwoo & Landgrebe, 1992; Wharton, 1982). In order to capture the seasonal variability in vegetation characteristics the NDVI has been used in a multitempo-ral approach to image classification. In a study in South West Asia, (Kouchoukos et al., 1997) vegetation cover analysis was done using a one-year 1 km AVHRR data composite in ten-day periods stacks so that each pixel was characterised by 36 NDVI values. The k-means clustering algorithm was used to assign each pixel to one of the thirteen classes having similar seasonal cycles of vegetation cover. The resulting distribution of vegetation classes was complex and patterns of land use and land cover were better differentiated by the shape of the an-nual cycle especially in areas where there are multiple cropping cycles. The study assumed that a scale of 1 km resolution was appropriate for crop identification, however, in a multiple cropping system with very small farms, more than one crop type are likely to occur in a 1 km pixel.


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Due to the enormous problems related to crop identification using remote sensing imagery many researchers have attempted to estimate crop area using an integration of sampling techniques and satellite sensor images. The method is that regression estimators are used to combine ground data with satellite sensor images. Confu-sion matrices obtained by evaluating the performance of the supervised classification are used to generate a matrix of conditional probabilities. The conditional probability is the probability of obtaining a reference class as a result of the supervised classification (González-Alonso & Cuevas, 1993). Attempts have been made in improving agricultural statistics using area frame sampling techniques with the con-sideration that classical methods of generating of agricultural statistics are time consuming, costly and subject to a variety of errors (Pradhan, 2001) (Leeuwen, Zourngrana, & Groten, 2001). Results from a study in Burkina Faso (Leeuwen et al., 2001) indicated that the method reduces the underestimation of remote crop fields and increases overall accuracy and reliable estimates. However, the initial costs are high and benefits can only be spread over a period of 5 to 10 years. The relative importance of AVHRR NDVI from NOAA for regional and national agricultural research is highlighted in studies on the Crop Use Intensity by a FEWS Project (McGuire, 1997), which stratifies NDVI imagery into agri-cultural and non-agricultural land to redistribute agricultural statistics from general administrative units to sub administrative units using the NDVI statistics. The study redistributed crop area statistics using crop use inten-sity simulations for different areas and NDVI values. Maselli & Rembold, (2001) also tried to identify cropland while using NDVI statistics and the NDVI profiles for operational purposes. Results indicated high determination coefficients of over 80% and that different scenarios show a seasonal pattern and can be used to identify crop area from other vegetation types. Research into crop area identification through spectral information has not been very successful due to high de-gree of uncertainty in the spectral information. Many researchers have used the fuzzy theory in order to classify and map continuous variables. According to Molenaar, (1998), there will always be some doubt whether the mapping of the real world onto discrete categories has been done correctly or adequately. In this context, Serra (2001) tried to assess the spatial distribution of non-traditional crops by analysing the relationships between their distribution, biophysical and infrastructure condition. The result of the fuzzy set algorithm showed that though the method was highly subjective, it was easier to identify and classify areas where ferns and flowers were grown. Statistical de-aggregation techniques have been applied to different geographical phenomena like haze assess-ment using spectral data (Abuzar & Al-Ghunaim, 1997) to socio- economic data (Dougall, 1992). This study fo-cuses on crop and takes into consideration the complexity and cost of identifying and mapping crop area in a tropical environment. It uses a number of spatial remotely sensed and GIS datasets and applies statistical esti-mations of crop area, then incorporates expert knowledge through the fuzzy algorithm to de-aggregate crop area statistics. The fuzzy set operators are used for many spatial analyses because discretisation of objects through data models cannot be done with absolute precision and accuracy (Molenaar, 1998; Carranza, 2002) (Verhoeye & Wulf, 2002). Fuzzy logic assumes that an object has a degree of membership rather than chance or probability (Ding & Kainz, 1998) and is appropriate for spatial analysis where data used is highly subjective. It is hoped that the method used in this study will contribute to further research in agricultural studies where primary data is costly and time consuming to generate.


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3 Methods and Materials

3.1 Study Area

3.1.1 Geographical Location

Ghana is a relatively small West African country along the Northern Coastline of the Gulf of Guinea. The country stretches over 565 km from South to North and 540 km from east to west and has a total area of 238 538 km2. Extreme longitudes are 3o 07' W and 1o14' E and extreme latitudes 4o 45' N and 11o 11' N. The total area of

Ghana is 238 538 km 2. At Port Tema, the Greenwich Merid-ian zone transverses the East of Ghana.

3.1.2 Climate

The climate of the country is tropical with some seasonal variation in rainfall. The southern part of the country has two rainy seasons (April-July and September-November). Rain-fall is highest in the southwest, ranging from 1,250 to 2,150 mm. The south east also has a bimodal rainfall pattern but with less annual rainfall (750 mm). In the northern part, there is only one rainy season with annual rainfall ranging from 1,100 –1,250 mm. The mean annual temperature of Ghana averages between 26 and 29ºC (FAO 2000).

Figure 3:1: Location Of Ghana.

While Ghana on average receives adequate rainfall, this resource is unevenly distributed both geographically and seasonally (Kranjac et al., 1999). Even in the high rainfall belt (above 1,500 mm per annum) in the south of the country, available soil moisture for plant development can be very scarce in the dry season. In the northern and south eastern regions, where annual rainfall amounts to less than 1,500 mm pa and in some areas below 500 mm per annum, the dry season spreads over seven to eight months. The most severe constraint to tradi-tional agriculture in Ghana is the dry spells, which sometimes lead to drought conditions. Dry spells occur during the cropping season leading to depletion of soil moisture, which renders the soil less able to support traditional rain fed agriculture. Even where rainfall is considered adequate individual events are extremely variable, leading to major difficulties in calculations of runoff generation and erosion control. Typically, the rainy season starts in the last week of April or the first two weeks of May. However in some years, it can start as late as the last week in June. Temperature in this region is consistently high with relatively small variations, particularly in the south-ern part. The annual average temperatures increase from south to north alongside an increase in solar radiation


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and a decrease in annual rainfall. The hottest month in the year is March or April, before the beginning of the rainy season, while the coolest month of the year is August. The humid season in the area extends from April to the end of October.

3.1.3 Biophysical Characteristics

The country is generally undulating with broad, poorly drained valleys and extensive flood plains adjacent to the Volta and the Nasia Rivers where altitudes vary between 108-138 meters above sea level. Within Gambaga highlands and on a few out lie of the Upper Volatain sandstone formations. The land is gently to steeply rolling and the rise to maximum height of just over 523 meters above sea level in the Northeastern part of the region. At the Northern edge of the hills is a scarp overlooking the White Volta River and the Morago River, which lie some 308 metres below. The crest forms the northern boundary of the Nasia basin. Neither of the other scarps along the lines Walewale-Parago-Bunkpurungu and Piga-Gushiega is such a prominent topographical feature, as they rarely exceed a height of 30 metres above the rivers and do not give rise to a range of hills on their deep slope. Despite the low inherent fertility of Ghanaian soils, land in most agro ecological zones is suitable for cereal crop production (Asuming-Brempong et al., 2001). About a third of the million hectares of Ghana’s arable land is under cultivation.

3.1.4 Agricultural characteristics

The poverty profile of Ghana is largely rural and agricultural: 54% of those living in poverty are subsistence (crop) farmers. As in most sub Saharan Africa, farming systems in the Ghana are complex and often sophisti-cated to suit sophisticated systems, which have adjusted to external factors. Farmers generally practice mixed cropping. Maize is grown in almost all agro ecological zones and other major cereal like upland and lowland rice are grown in the savannah zone; cassava, cowpea and sorghum in the savannah and transitional zones (Asum-ing-Brempong et al., 2001). Rain fed cereal crops are commonly in the Northern part of Ghana, which is gener-ally drier than the rest of the country. Most cash crops like cocoa and coffee are grown in the forests and semi deciduous zones.

Table 3:1: An Overview of Data Used for the study

Data/Maps Source LAC NDVI 1 km resolution AGRHYMET JERS SAR 100 m resolution scene Taken between July 1994 -1996 ASTER 15 m resolution ASTER Classified Landsat TM 30 m resolu-tion

Taken in 1996,4th February, 2000

USGS Land use Map, Scale 1:250000

Based on TM 1990

Crop Area Statistics by 2nd administration areas

Ministry of Food and Agriculture, Ghana

Ghana Crop Suitability Maps CERGIS, University of Ghana, Legon Literature CERGIS, University of Ghana, Legon Ghana District Polygon Map Dept Geography, University of Ghana, Legon


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3.2 Methods

Table 3:2 The Georeference Parameters

3.2.1 Georeferencing

The study used a number of remotely sensed and GIS data sets that had to be merged and analysed together. There fore they were georefenced to the same coordinate system. The parameters that were used are shown in Table 3:2.

3.2.2 Satellite data processing

The primary satellite that was used for this study is the NOAA AVHRR NDVI imagery that was attained from the AGRYMET data-base. The ten-day (dekad) NDVI images were taken of the same ground scene, in a period of two years (June 1997 – March 1999) with spatial resolution of I km at nadir view. NDVI was considered to be the most appropriate data set for this study because it has suc-cessfully been used to determine crop vigour (Avery & Berlin,

1992). In addition to this, the dataset provided a multitemporal aspect to the study a complete coverage of the study area, which in most cases may not be possible with other sensors due to cloud cover effects. Since the images were covering areas of West Africa, an area of interest was created using the Subset algorithm in Erdas Imagine. This tends to reduce on classification errors and amount of time and space taken in image processing. The NDVI imagery was composted into one image using the Layer Stacking algorithm as explained in (Erdas, 1999) to create an image with sixty-nine (69) bands. In this way, the data set could be analysed as multitemporal image in lieu of individual decadal images. The image was the classified into thirty classes using the ISODATA algorithm providing a map with homogenous units so that each pixel was characterised by its NDVI response over the two-year period (See Fig.3.2). The image was then converted to the ILWIS format resampled to 100 meters resolution. The 100 by 100 grid cell was preferred for this study because it would provide the basis for the crop area distribution and also considering that in a 1 km by 1 km grid cell more than one crop type is likely to occur. JERS-1 SAR imagery of West African forest areas taken January-February, 1996 with 100 m spatial resolution, covering most of Ghana’s forest zone was be used to delimit of areas of interest. The high resolution imagery from ASTER, taken on dates 19 January, 2001, 27 December 2000 and 27 April 2001 with a 15 m resolution and <5% cloud cover will be used as ancillary data. The 100-meter radar imagery was classified into 2 broad classed using the supervised classification algorithm based on the spectral reflectance of land cover in the 15 m ASTER imagery. The classes were defined as fields and non-fields. The classified radar image with integrated with other land cover maps to create a mask that would be used to map out the area of interest for the study.

Grid: Ghana National Projection: Transverse Mercator Datum: Leigon Ellipsoid: Clarke 1880

a 6378249.145 1/f = 293.465

Unit of Measurement: Meters Meridian of Origin: 1-degree West ofGreenwich Latitude of Origin: 4 degrees 40 minutesNorth Scale Factor at Origin: 0.99975 False Coordinates of Origin: 274,320 me-ters Easting; Nil Northing


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Figure 3:2: The NDVI unsupervised classified image

Figure 3:3:The district map of Ghana for which crop area statistics were distributed

3.2.3 Crop Suitability Data

The crop suitability data used in this study was produced by the Environmental Information Systems Develop-ment Project (EISD) was attained from the University of Ghana. The attributes for the suitability maps used for Ghana included, among others suitability index and attainable yield (Boateng et al., 1999). The suitability index


10

was considered to be the most appropriate predictor of crop area. The crop suitability assessments that were used for the study included, maize, pearl millet, sorghum brown rice, groundnuts and cowpeas at three different management technologies; high, intermediate and low input levels with cultivars of at least two different lengths of maturity (See Table: 3: 2). The suitability data was converted to the ILWIS raster format and resampled to 100-meter resolution. The data set included at least three different varieties depending on the maturity date. Considering that all data crop area statistics are reported as aggregates of the varieties by crop, the crop variety data were averaged to produce one map for each management level. The levels of management input included high, medium and low. Assuming that all farmers in Ghana practice subsistence and commercial crop production with intermediate credit on accessible terms and plant-improved cultivars, the intermediate input level was considered for further processing. The suit-ability index maps were classified into 5 classes, displaying an index characterised by a percentage of each pro-duction potential as explained in (Boateng et al., 1999). The classes are as follows: Very suitable: 80-100% Suitable: 60-80% Moderately Suitable: 40-60% Marginally Suitable: 20-40% Not Suitable: >20%

Table 3:3: An overview of crops used in the study and their length of maturity

Crop Variety Length of Maturity Brown Rice 110 days Brown Rice 130 days Brown Rice 90 days Cowpea 60 days Cowpea 80 days Groundnuts 120 days Groundnuts 90 days Maize 105 days Maize 120 days Maize 90 days Pearl Millet 70 days Pearl Millet 90 days Sorghum 105 days Sorghum 120 days Sorghum 90 days

3.2.4 Creation of the Area of Interest

The land cover and land use vector maps attained from The Ghana Country at a Glance database were con-verted to the ILWIS raster format and resampled to 100 meters resolution. The classified radar image was inte-grated with land cover and land use maps was used to create a mask using the ISUNDEF algorithm as explained in (ITC, 2001) that was used to delimit areas of interest. The land cover/land use maps that were used to mask


11

out area of interest and these included water bodies, forests, game reserves, urban areas, rocks and reservoirs. The mask was applied to the district map of Ghana (attained from the University of Ghana), the NDVI classified image and the crop suitability maps in order to generate the area interest for the study.

3.2.5 Generation statistical map attributes

It was necessary to generate statistical attributes from the maps that were to be used for the statistical proce-dure. The masked NDVI and crop suitability maps were multiplied with the district map using the MapCross algo-rithm in ILWIS so that each pixel from the district map in the outputs was characterised by an NDVI class on one hand and a suitability class. Considering that one 100 x 100 m pixel is equivalent to hectare, the pixel was used as the unit of de-aggregation or redistribution of the historic crop area statistics that were reported in hectares. The outputs were both in tabular format and maps and included among others the frequency of number of pixels. The tables were converted to the Microsoft Excel format for further statistical analysis.

3.2.6 Transformation of Crop Area Statistics

Nine year agricultural statistics of major crops grown in Ghana were attained from the Ministry of Food and Agri-culture of Ghana in tabular format. The statistics included annual harvested area, production and yield reported at district level and total by region. The analogue crop area data reported in hectares was entered into Microsoft Excel used in the data processing for this study as an agricultural parameter to distribute crop area. There was a lot of variability in the historic statistics for all the 110 districts over the nine years (See Fig 3:5 – 3:7) with maize crop area ranging from 0 to over 25 000 hectares. This could probably be explained by interannual variations in rainfall, which are a characteristic of tropical environments. In the first instance, an average crop area statistic was calculated for all districts. It was observed that averaged crop area statistics tended to smooth out the variability that would not have reflected the real life situation. Average statistics are frequently inflated by a few extreme events or outlier events that may have occurred in a particular year, which is the reason why it was not considered for further processing.

The trend of variation in Variation in Historic Maize Crop Area Statistics

0

5,000

10,000

15,000

20,000

25,000

30,000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

District ID

Are

a (h

a)

199219931994199519961997199819992000


12

Figure 3:4 The variability in tabular maize crop area crop area statistics 1992 - 2000

The trend of variation in Historic Rice Crop Area Statistics

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

District ID

Are

a (h

a)

199219931994199519961997199819992000

Figure 3:5 The variability in tabular rice crop area crop area statistics 1992 - 2000

Variabilty in Historic Maize Crop Area Statistics for selected districsts

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1992 1993 1994 1995 1996 1997 1998 1999 2000Years

Are

a (H

a)

Amansie East

Amansie West

Ejura Sekyidumasi

Sekyere West

Sekyere East

Afigya Sekyere

Ahafo Ano North

Figure 3:6: An example showing the variability in historic crop area statistics for selected districts


13

In order to make realistic distribution of the crop area data, the statistics were transformed using logarithmic transformation, which assigned more weight to the events in crop area data that occurred in the recent years. The logarithmic transformation is used because it assumes that crop area data does not increase linearly. The procedure was carried out independently for each district by generating a logarithmic equation that gave a con-solidated crop area statistic that was highly influenced by the events of the recent years.

Logarithmic transformation on the Average of the Total Rice Crop Area Statisitc

y = 215.76Ln(x) + 640.23

0200

400600

8001,000

1,2001,400

1,600

1 2 3 4 5 6 7 8 9

Years

Are

a (h

a)

Figure 3:7: An example of a logarithmic equation used to generate a consolidated crop area statistic

Transformed Maize Crop Area Statistic For South Tongu

y = 245.74Ln(x) + 1447.1

0

500

1,000

1,500

2,000

2,500

3,000

3,500

1 2 3 4 5 6 7 8 9

Years

Are

a (h

a)

Figure 3:8: An example of logarithmic function for maize crop area in South Tongu district

In the case of rice negative results were yielded which could be explained by the poor reported statistics that reflected rice was grown in the first three years (See Fig. 3:8). Considering that negative crop area cannot occur, the negative values were excluded from further statistical analyses and replaced with a zero.


14

Transformed Rice Crop Area Statistics yielding negative values

y = -314.51Ln(x) + 638.51

-200

0

200

400

600

800

1 2 3 4 5 6 7 8 9

Year

Are

a(ha

)

Figure 3:9: An example of a transformation for Ejura Sekyidumasi, that yielded a negative consolidated statistic

The consolidated crop area statistic that was generated reflected what could be the most realistic crop area sta-tistic and was considered for further statistical analyses as a response variable.

3.2.7 Determination of Significant Crop Area Variables

The statistical and spatial data pre-processing, provided a data set in tabular format that included 30 classes of the NDVI and the 5 classes of the suitability maps. The 35 classes were as the predictors with the consolidated crop area statistic as a response variable. The multiple stepwise regression analysis was used to select variables that were significant distributors of crop area. The attribute that was used was number of pixels since 1 pixel is equivalent to one hectare. The 35 predictors were subjected to a no constant stepwise linear regression in order to eliminate variables that were not significant to the regression. A non-constant model was used because in the redistribution of the crop area statistics, the model assumes no crop area has been mapped or distributed. The elimination was carried out iteratively first entering the variable that explains the most variance in the data, until no more variables could be eliminated. Selected variables that yielded a negative T-Value though significant were eliminated since they seemed to suggest that negative crop area is existent. The selected variables were then used to run the multiple linear regressions for the different crop areas.

3.2.8 Development of Regression Equations to Estimate Crop Area

The multiple linear regression analysis was used to generate the regression equations because recent research into crop area identification has shown that regression models fit crop area data (González-Alonso & Cuevas, 1993; Maselli & Rembold, 2001) with more than 70% of the variability explained. Assuming that variables and crop area statistics were independent of each other and that crop area is a linear combination of multiple predic-tors, the multiple linear regressions of the 110 districts were carried out as explained by (Moore & McCabe, 1999) using the selected predictors by the stepwise regression. Individual regression equations were developed for each of the six crop types. No constant was used in the linear regression model and the relationship between the selected variables and the response was analysed. Only significant predictors of crop area were included in the multiple linear regression models. The results of the multiple linear regressions were coefficients that had to be spatially distributed in the form of value maps. The coefficients of the resultant multiple linear equations were


15

applied to the six maps so that the final maps are a mathematical combination of the coefficients of different classes that were used in the regression model.

3.2.9 Statistical distribution of crop area

The results of the multiple linear regression in the form of coefficients were applied to the original masked NDVI classified image and the masked crop suitability classes. The procedure was done using the conditional state-ments each significant variable was represented their coefficient spatially in a value map. The coefficient repre-sented the percentage under crop area in each corresponding map. The different maps representing crop spe-cific variables were combined using simple arithmetic so that each pixel in identified crop areas was character-ised by a percentage of crop area based on the combined variables. The result of the addition of the maps meant that the percentage of crop area for some pixels that were characterised by two or more variables would in-crease. This would mean that if a pixel were redistributed for crop area by more than one variable then the crop area would increase. These statistical considerations are not valid for environmental situations. The fuzzy set algorithm was used to refine the statistical distribution procedure where the crisp but uncertain crop area per-centages had to be matched with expert knowledge.

3.2.10 Application of the Fuzzy Set Theory

Since there was no in situ information used in this study, expert knowledge was used to further distribute the crop area statistics in order to reduce on the statistical error in the redistribution. Expert knowledge can be used for analyses if primary information and quantitative analytical means are limited (Driessen & Konijn, 1992). Probabili-ties of crop area were attained from various experts all of whom were familiar with the study area. These were mainly officials from the Ministry of Food and Agriculture and the University of Ghana. The classified NDVI map (Fig. 3:2) was presented to the experts who were asked to give a probability that each of the six crops is grown for each NDVI class. The probabilities were averaged in order to reduce on bias, and then used for analysis of the final maps. The coefficient maps suggested that there are crisp boundaries between crop area classes and that percentage of crop area per hectare did not vary significantly from one location to another. The statistics were initially gener-ated from homogenous units of the NDVI unsupervised classified image and the crop suitability classes. At local-ised levels, the same homogeneity is not valid because environmental phenomena vary due to site-specific fac-tors. In order to distribute crop area under the assumption that it is a function of socio - economic conditions. It was therefore necessary to evaluate a scenario that closely resembled the reality. The method selected for this procedure was fuzzy logic algorithm, which handles information that is highly subjective. The fuzzy logic could be regarded, as a departure from Boolean logic where the result is continuous unlike the binary predictors where there result is either yes or no. The method was that resultant coefficient maps and maps with probabilities of crop area attained the experts were rescaled or normalised by multiplying the values in each map with the inverse of their total so that they could be analysed at a scale between 0 and 1. The maps were normalised so that they were easier to manipu-late without holding anomalies when changes occurred. The maps indicated respectively: 1. The fraction per hectare of statistically redistributed crop area for each crop type


16

2. The fraction per hectare according to experts with a probability of crop area for crop type The combination of the two maps would result in crop area factor maps de-aggregated to local levels based on a method that does overestimate or underestimate the percentage crop area in the view of the expert. The two maps were assigned weights using subjective judgement where the crop factor map that were generated from statistical predictions were assigned a weight of 0.5 while the factor map of possible crop areas from the experts was assigned a higher weight of 0.8 since it was assumed that it had a higher degree of belief. The two maps were used to calculate different fuzzy operations as shown below. The algebraic fuzzy product is a combination of the two maps and fuzzy membership values tend to be small due to the effect of multiplying several numbers that are less than 1. The output is a ‘decreasive’ always smaller than, or equal to, the smallest contributing fuzzy membership value (Carranza, 2002).

Equation 3:1: Fuzzy Product for Crop Area

Where: CA = Resultant Crop Area membership map CF = Crop factor map generated from statistical predictions EF = Expert knowledge factor map of probable crop area The product gave the decreased fraction of crop per hectare when combined with the expert knowledge

Equation 3:2: Fuzzy Sum for Crop Area

CA = Resultant Crop Area membership map CF = Crop factor map generated from statistical predictions EF = Expert knowledge factor map of probable crop area Mathematical computation of the sum is incremental which means that the result of the operation is always larger or equal to the largest contributing fuzzy membership value. The algebraic fuzzy sum was complementary to the algebraic product and their combination was used to gener-ate the final crop maps, which is the gamma factor The combined evidence of crop area from the two resultant maps using the gamma factor reduces the maximisation or minimisation of results (Serra, 2001).

Equation 3:3: Factor Gamma for Crop Area

Where CA(gamma) = Combination of the fuzzy sum and fuzzy product CA = Resultant Crop Area membership map

CA (gamma) = (pow (CA (sum), 0.6))*(pow (CA (product), 0.4))

CA (sum) =1- (1- (CF*0.5) * (1-(EF*0.8))

CA (product) =(CF*0.5)+(EF*0.8)


17

The result of this operation balances the incremental result of the sum and the decreased result of the product. The results of the maps were generated using the gamma factor which indicated the fraction of crop area on one hectare after taking into consideration of maximised and minimised effects of statistical redistributions combined with expert knowledge. This could be explained as a conditional statement that implied that if the sum of the crop area percentages and the probabilities is low, then increase is by 0.6 and if the result of the product decreases the percentage crop area per hectare, then increase it by 0.4.

3.2.11 Validation Procedure

The resultant maps by crop type were evaluated by comparing them with the available land cover map legend of Ghana that was generated form the LANDSAT TM 1990. (See Appendix 1) Since the available map has broad classes, only visual evaluation was carried out to confirm whether the broad classes corresponded with the crop area factor maps. This was done by comparing the two maps and seeing in which classes the redistributed crop area statistics and analysing the possibilities of agricultural land in the broad classes. It should be noted that even at the lower levels in the present land use/land cover map, areas classified as agricultural land are not as informative, therefore, more research should be targeted at validating the procedure.


18

4 Results

4.1 Crop Area Statistics

4.1.1 Maize

The variables that were used in the stepwise regression were 30 NDVI classes and 5 crop suitability classes for the six crops in 110 districts. The final results of the regression and the selected variables are shown in the sub-sequent tables and graphs.

Table 4:1: Stepwise Regression Results for Maize Crop

Step

R 0.876 R-sq 0.767 Variable Coefficient Std. Error Std. Coef. Df F P' IN C28 0.168 0.067 0.166 1 6.27 0.014C30 0.152 0.031 0.309 1 23.823 0.000MS 0.076 0.010 0.507 1 63.483 0.000C3 0.038 0.018 0.120 1 4.261 0.041NS 0.023 0.008 0.162 1 7.926 0.006OUT Part. Corr C9 0.101 1.08 0.301 The stepwise regression results (See Table 4:1) seems to suggest that NDVI Classes 28 and 30 are the most important variables for maize crop area distribution respectively with each hectare in each class having an effect of 16.8 and 15.2 % on crop area. The classes when analysed have a high NDVI values and lie in the forest and semi deciduous zones, which means that farmers in Ghana grow maize in conjunction with tree crops (Asuming-Brempong et al., 2001). Results also show that if a hectare falls in the crop suitability classes “Moderately Suit-able” and “Not Suitable” then only 7.6 and 2.3% respectively will be maize crop area and the rest will be cassava since most farmers in Ghana practice intercropping of maize and cassava. It can also be concluded that in areas that are classified, as “Very Suitable” and “Suitable” for maize crop farmers would rather shift to more lucrative crops like crops like cocoa or cassava.


19

Table 4:2: Multiple Linear Regression Results to Redistribute Maize Crop Area

Dependent Var. Maize (ha)

N 110 R 0.831

R-sq 0.691 R-sq (adj) 0.679

Standard Error 4254.1 Variable Coefficient Std. Error Std. Coef T P

C28 0.168 0.067 0.166 2.504 0.014 C30 0.152 0.031 0.309 4.881 0.000 MS 0.076 0.010 0.507 7.968 0.000 NS 0.023 0.008 0.162 2.815 0.006 C3 0.038 0.018 0.12 2.064 0.041

Equation 4:1: Regression equation to redistribute maize crop area

Where: C28 = NDVI class 28 C30= NDVI class 30 MS = Maize Suitability class “Moderately Suitable NS = Maize Suitability class “Not Suitable” C3 = NDVI class 3 In the case of maize, the regression model seems to fit the data since it explains 69.1% of the variability in the data. (See Table: 4:2). This means that the best additive combination of variables could only explain over half of the data with significant variables. However, the residual plot shows that the standard error is fairly normally dis-tributed over the zero line which means that the relationship between the predictors and the response is a causal one and that variance is evenly distributed over the estimate. It can be concluded that if maize is grown in areas that are not suitable then external inputs like chemicals and organic fertilisers are affordable and are widely used. The statistical redistributions may be misleading especially in the case of this study where maize is grown widely even though the soils are inherently infertile and that farm-ers are generally very poor and lack capital to purchase external inputs.

Maize (ha) = 0.168 C28 + 0.152 C30 + 0.076 MS + 0.023 NS + 0.038 C3


20

Figure 4:1: Residual plot for the regression to redistribute maize crop area

4.1.2 Cowpea

The regression analysis results for cowpea show that pixels falling into NDVI classes 9 and 7 have an effect on cowpea crop area (See Table: 4:3) distribution. The model seems to suggest that farmers’ decision as to where cowpeas are grown does not depend on the suitability of the land but on other factors probably socio-economic factors. Reported statistics show that most of the cowpea is grown in the Northern region which is generally dry. It is possible that most of the cowpea could significantly be explained using areal extents that could be due to the fact and that it is grown is for mainly home consumption. It could also be deduced that these areas in the where cowpea is grown have a particular seasonal trend that is suitable for its production.

Table 4:3: Stepwise Regression Results for cowpea crop area

Step R 0.745

R-sq 0.555 IN

Predictor Coefficient Std Error Std Coef Df F P' No Constant

C9 0.049 0.006 0.621 1 76.65 0.000 C7 0.026 0.008 0.227 1 10.253 0.002

OUT Part Corr. C4 -0.064 0.70815 1 0.508


21

Table 4:4 : Multiple Linear regression results to redistirbute cow pea crop area

Dependent Variable: Cowpea

N 110 R 0.773 R-sq 0.597 R-sq (adj) 0.593 Standard Error 2015.822 Predictor Coefficient Std Error Std Coef T P C7 0.026 0.008 0.245 3.502 0.001 C9 0.049 0.006 0.623 8.905 0.000

Equation 4:2: Regression equation to redistribute cowpea crop area

Where: C7 = NDVI Class 7 C9 = NDVI Class 9

Figure 4:2: Residual plot for the regression to redistribute cowpea crop area

The multiple linear regression equation to redistribute cowpea crop area explains 59.3% of the variance in the data though the residual plots show that the standard error is not normally distributed and that the data needs to be transformed further in order remove the effect of outliers and missing values.

4.1.3 Rice

The model to redistribute rice crop area seems to suggest that in every one hectare that falls in NDVI Class 7 only 3.4% is crop area and in every hectare that falls in Class 22 contributes 2.5% to rice crop area. It is also interesting to note that the model only considers areas that are not suitable for rice crop as having 0.7% effect on

Cowpea (ha) = 0.026C7 + 0.049C9


22

redistributed crop area and finds other suitability classes not significant. Like any other cereal in Ghana upland and lowland rice could be grown anywhere and depending on water availability.

Table 4:5: Stepwise regression results for rice crop area

Step

R 0.644 R-sq 0.414

Predictor In Coefficient Std. Error Std. Coef Df F P"

C7 0.034 0.006 0.429 1 30.372 0.000 C22 0.025 0.01 0.197 1 6.346 0.013 NS 0.007 0.002 0.286 1 12.162 0.001

Part. Corr OUT C8 0.028 1 0.082 0.775

Table 4:6: Multiple Linear Regression results to redistribute rice crop area

Dependent Var. Rice ha

N 110 R 0.644

R-sq 0.415 R-sq (adj) 0.398

Standard Error 1788.4

Predictor Coefficient Std. Error Std. Coef. T P C7 0.033 0.007 0.421 5.043 0.000 C8 0.003 0.003 0.026 0.287 0.775

C22 0.025 0.025 0.201 2.523 0.013 NS 0.007 0.007 0.275 3.014 0.003

Equation 4:3: Regression equation to redistribute rice crop area

Where: C7 = NDVI Class 7 C8 = NDVI Class 8 C22= NDVI Class 22 NS = Rice Suitability Class “Not Suitable”

Rice (ha) = 0.033 C7 + 0.003C8+ 0.025C22 + 0.007NS


23

Figure 4:3:Residual plot for the regression to redistribute rice crop area

The model to redistribute rice crop area did not fit the data since only 41% of the variability of the data could be explained (See Table: 4:5). This could have been due to the poor reported rice crop area statistics that were re-distributed due many outliers and missing values. The relation between the response and the independent vari-ables is not causal.

4.1.4 Sorghum

The results of the stepwise regression seem to suggest that sorghum and cowpea are grown in the same areas since regression results show that the two are grown in the same classes. However, in the case of sorghum, if a hectare falls in Class 7 it would contribute 14% to sorghum crop area and 7.4% if in class 9. The model also sug-gests that farmers decision growing sorghum because of biophysical land suitability is 10.2% and that the other percentage is determined by socio economic factors.

Table 4:7: Stepwise regression results for sorghum crop area

Step R 0.876 R-sq 0.767 Predictor Coefficient Std. Error Std. Coef. Df F P' In C7 0.14 0.014 0.526 1 94.801 0.000C9 0.074 0.012 0.38 1 40.768 0.000S 0.102 0.036 0.159 1 8.168 0.005


24

Table 4:8: Multiple Linear Regression results to redistribute sorghum crop area

Dependent Var. Sorghum

(ha) N 110 R 0.876

R-sq 0.767 R-sq (adj) 0.762

Standard Error 3799.6

Predictor Coefficient Std. Error Std. Coef. T P C7 0.140 0.014 0.526 9.737 0.000 C9 0.074 0.012 0.38 6.385 0.000 S 0.102 0.036 0.159 2.858 0.005

Equation 4:4: Regression equation to redistribute sorghum crop area

Where: C7 = NDVI class 7 C9 = NDVI class 9 S = Sorghum Suitability class “Suitable “ The regression equation used to estimate sorghum crop area for 110 observations explained 76.7% of the vari-ability of the data. However, the test for normality shows that the standard error is not normally distributed over the variance which could be explained by the fact that reported statistics for sorghum crop area are only for a few districts in Ghana. The results are valid because the predictors fall in the savannah zone where crops like sor-ghum; cowpea and millet are widely grown.

Figure 4:4 : Residual plot for the regression to redistribute sorghum crop area

The residual plot shows that the model though explains 76.7% of the variance in the data. The test for normality shows that the standard error is not normally distributed and has several outliers.

Sorghum (ha) = 0.0744 c9 + 0.140 c7 + 0.102 S


25

4.1.5 Groundnuts

The stepwise regression results for groundnuts show similar variables as those of cowpea where it is suggested that crop suitability variables are not significant predictors in the redistribution equation. The results suggest that Classes 7 and 8 are significant variables for groundnut crop area contributing 10.7% and 5.6 % respectively.

Table 4:9: Stepwise regression results for groundnut crop area

Step R 0.752

R-sq 0.566

Predictor Coefficient Std Error Std Coef Df F P IN

C7 0.107 0.012 0.611 1 74.593 0.000 C8 0.056 0.016 0.244 1 11.918 0.001

OUT Part.Corr.

C3 0.107 1 1.240 0.268 C9 -0.087 1 0.822 0.367

Table 4:10: Multiple Linear Regression results to redistribute groundnut crop area

Dependent Vari-able

Groundnut (ha)

N 110 R 0.752 R-sq 0.566 R-sq (adj) 0.562 Standard Error 3381.4

Predictor Coefficient Std. Error Std. Coef T P C7 0.107 0.012 0.611 8.637 0.000 C8 0.056 0.016 0.244 3.452 0.001

Equation 4:5: Regression equation to redistribute groundnut crop area

Groundnuts (ha) = 0.056 c8 + 0.107 c7

Where:


26

C8: NDVI Class 8 C7: NDVI Class 7 The model seems to suggest that groundnut crop area per hectare will be 5.6 % if located in Class 8 and 10.7 % if in Class 7. While groundnuts are only grown in areas that have a particular seasonal trend it can be concluded that the limiting factor is water. The model explains 56.6% of the variability in the data though the regression plot shows that the data is not normally distributed and that the model is not suitable for the redistribution of ground-nut crop area due to outliers between 0 – 5000 hectares.

Figure 4:5: Residual plot for the regression to redistribute groundnut crop area

4.1.6 Pearl Millet

The stepwise regression results for pearl millet crop area suggest that at an R-sq of 82.7%, Classes 3 and 7 are significant distributors of crop area with each of the contributing 1.8 and 12.4 % respectively to the total crop area. Crop Suitability class “Suitable” contributes 3.1 percent to the total crop area. The model seems to suggest that there are no pressures on land that would force farmers to cultivate on land that is not suitable or marginally suitable for pearl millet. The redistribution model for pearl millet crop area is well suited for the data has a deter-mination coefficient of 82.7%. The normality test shows that there are several outliers between 0 and 5000 ha and the relationship improves when crop area estimated increases from 5000 – 20000 (See. Fig: 4:11).

Table 4:11: Stepwise regression results for pearl millet crop area.

Step R 0.910

R-sq 0.827


27

Variable Coefficient Std. Error Std. Coef Df F P IN C3 0.018 0.009 0.097 1 4.390 0.039 C7 0.124 0.007 0.819 1 361.85 0.000 S 0.031 0.01 0.149 1 9.288 0.003

OUT Part. Corr C10 0.063 0.423 0.517

Table 4:12 :Multiple linear regression results to redistribute pearl millet crop area

Dependent Var. Pearl Millet (ha)

N 110 R 0.910 R-sq 0.827 R-sq (adj) 0.824

Predictor Coefficient Std. Error Std. Coef T P C3 0.018 0.009 0.097 2.095 0.039 C7 0.124 0.007 0.819 19.015 0.000 S 0.031 0.01 0.149 3.038 0.003

Equation 4:6: Regression equation to redistribute pearl millet crop area

Where: C7 = NDVI Class 7 C3 = NDVI Class 3 S = Pearl Millet Suitability Class “ Suitable”

Figure 4:6: Residual plot for the regression to pearl millet crop area

It would also be interesting to note that according to the redistribution models 43% of area that falls in Class 7 could area that is under cereal crop production, which can be broken down as follows: groundnuts contributing 10.7%, pearl millet 12.4%, sorghum 14%, cowpea 2.6% and rice 3.3%. Historic cowpea crop statistics were re-ported only after 1994, which would probably explain why it could have the least contribution to crop area. There-fore it could be concluded that cowpea is not widely grown by farmers. The other 57% of crop area could be

Pearl Millet (ha) = 0.124 C7 + 0.0310 S + 0.018C3


28

attributed to other crops like cassava and coco yam which are widely grown in the drier areas as those in the Northern part of Ghana.

4.2 Crop Area Distribution Maps The resultant crop area maps indicated that crop area had not only been distributed using statistical prediction but were also integrated with expert knowledge. The final crop distribution maps represented the gamma factor that showed information on crop area on the basis of mathematical combinations of statistical distribution and probability per pixel of crop area. The visual analysis of the maps showed that no areas were eliminated during the computations. Areas that were considered with low probability in crop area but high crop area percentages were leveraged in order to portray the real life situation. The maps gave an indication that the method is optimistic and can redistribute crop area on the basis of the re-mote sensing, GIS and expert knowledge. The redistribution of crop area statistics based on the fuzzy logic works very well for crop areas that had more than two together with suitability assessment classes such that there was variability in crop area percentages due to the map additions. Maize and rice, that are grown through out the country, crop area percentages were optimally redistributed over the areas based on the expert knowl-edge. Areas where the experts indicated zero probability for certain crops were not excluded from the final maps but rather their statistical crop area percentages reduced based on probabilities given by the experts. In the case of maize, statistical predictions showed that predictor effects on maize crop area ranged from 3.8 – 16.8%. The final results seem to suggest that farmers in Ghana take into consideration a number of biophysical factors as well as socio cultural norms in different areas and zones before growing maize. They also suggest that since such conditions do not change significantly farming systems have been adapted to local environmental conditions and socio economic factors such that experts that have knowledge of the different areas can perceive the different environments effectively.


29

Figure 4:7: Maize Crop Area Distribution

The maize crop area distribution map shows that maize is widely grown all over the country though the percent-age per hectare in the Northern regions is only 0 - 4% and the percentage increases towards the South. In the forest, semi deciduous and transitional zones maize crop percentages increase up to 7% per hectare. The fuzzy operators reduced the crop area percentages for maize in most areas as compared to the statistical distribution. It indicates that farmers in the North cannot intensify production because they are generally poor and lack capital to purchase inputs as had earlier been indicated thought is a possibility that people in the North have more ac-cessibility to land to clear to grow more maize as compared to other areas.


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Figure 4:8: Brown Rice Crop Area Distribution

The situation with rice is that while statistical considerations redistribute that 0.7 – 3.4 % could be area under rice crop production, the final map based on the fuzzy logic algorithm seems to suggest an over estimation of 26%. Rice is one of the most significant food export, yet only 0 – 2.5 %/ ha could be redistributed as area under for rice production. It could be interpreted that smallholder productivity for rice is generally low. Rice could be grown in irrigated lowland area where most farmers are faced with problems in attaining credit for inputs and marketing.


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Figure 4:9: Groundnut Crop Area Distribution

Results indicate that in the case of groundnuts and cowpeas, which are redistributed by two homogeneous units based on the NDVI, the fuzzy algorithm did not consider environmental complexities but rather gave a more real-istic estimate based on the expert knowledge. In some areas in the Northern region, in one hectare 10.1% and 6.9 % which are almost similar to the statistical coefficients of 10.7 and 5.6 % respectively. It could be concluded that farms in the northern region are generally fragmented and that farmers’ decision on where to grow cowpea does not depend on land suitability due to their high level of illiteracy rates.


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Figure 4:10: Cowpea Crop Area Distribution

Some areas in the Accra plains have been identified for possible groundnut and cowpea crop area mainly be-cause the areas are characterised by savannah vegetation, which is the same as that in the Northern region. The same crop area percentages were assigned to these areas in the final maps, yet their cropping systems are dif-ferent. However, the maps give a clear indication that most of the cowpea produced in Ghana is grown in the Northern Region with crop area percentage going up to 12.5% per hectare. This is higher than statistical distribu-


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tion of sorghum crop area making the fuzzy operators appropriate due to the input of the experts that increased the crop area percentages.

Figure 4:11: Sorghum Crop Area Distribution

The distribution of sorghum crop area shows that percentages per hectare are generally higher than those of rice and maize. This would imply that while maize is grown all over the country, farmers in the Northern region will grow drought resistant cereal crops like sorghum that can tolerate erratic rainfall (Schulze, 1997). The ever-increasing population in Ghana especially in the less dry areas may contribute to the increased demand for ce-


34

real products for food security. This explains the higher crop area percentage that was attained after the fuzzy operators that assign higher probabilities of crop area to areas in the North as compared to the statistical redistri-bution.

Figure 4:12: Pearl Millet Crop Area Distribution

The results seem to suggest that while cowpea, groundnuts, pearl millet are concentrated in the drier savannah areas, they are of relative importance to the subsistence farmers. The final crop area percentage maps give ranges of 0 -10.15% (See Fig. 4:9), 1 – 12.50% (See Fig.4: 10), 0 – 12.76% (See Fig. 4:11) and 0 – 13% per-centage crop area/ha for groundnuts, cowpea, sorghum and pearl millet respectively. The final maps seem to


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suggest that taking into consideration the knowledge of experts, and with an average of 2 hectares per holding in Ghana, farmers will not use 3.8-16% of their land for maize alone but rather 0 - 7% together with variety of other cereal and legumes with the integration of some areas for livestock rearing in order to improve the soil condi-tions.

4.3 Evaluation of Crop Area Disribution Maps The land use / land cover map is classified into 9 major land cover types of which agricultural land cover 63% of the total area (See Appendix 2). Agricultural land in the second level of the present land cover / land use map was classified as percentage ranging between 0.5 – 29.3%. This means that most of the land in Ghana is under agricultural production. The visual evaluation of the final maps confirmed that areas that had been distributed as crop area corresponded to the areas that were mapped out for possible crop area. This is indicative of the application of the land use mask brought major improvements in the distribution of crop area statistics to meaningful production areas by excluding areas that are definitely not under crop production. The crop distribution maps gave more information as compared to the broad classes at all the three levels of the land use land cover map (See Appendix 4). When the maps were explored, most distributed sorghum, pearl mil-let, cowpea and groundnuts crop area percentages were identified in areas that have been classified as open widely cultivated savannah woodland, open savannah woodland and grassland areas (See Appendix 1). In the areas where most of the redistributed cereal and legumes areas fall were classified as 1700, 1800, 1900 having 4.6%, 29.3% and 17.4% respectively of agricultural land though no details are given as to which crops are grown. Maize crop distribution areas with 7% crop are area per hectare mostly fall in areas that are classified as moder-ately closed tree and moderately dense bush and herbs. The percentage of agricultural land in such areas ranges from 19 – 22% though most of it is likely to be tree crops like cocoa as they tend to be grown in the forest and semi deciduous zones. The rest of the redistributed maize crop area fell in the open widely cultivated savan-nah woodland and open savannah woodland where conditions are similar to the ones identified for the pearl mil-let and sorghum. Redistributed rice crop area percentages mostly fall in open savannah woodland, open widely cultivated savan-nah woodland and riverine savannah vegetation. This indicates that the “not suitable” class of rice suitability classes used was a significant predictor of its crop area, it gave an idea of where rice could be grown which may have included the riverine vegetation. Rivers are found almost all over the country such that even in areas that are dry, healthy vegetation is likely to be found along rivers where rice could be grown.


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5 Discussion

5.1 The de-aggregation method The increasing need for information on the spatial distribution of crop areas that is compatible with other spatial datasets is clearly evident in countries where vegetation characteristics are highly dynamic throughout the grow-ing season. (Fresco et al., 1994). This thesis presents procedures that spatially distribute crop area statistics using a number of GIS and remotely sensed data sets combined with expert knowledge. The lesson learnt from the study is the ability of using remote sensing, GIS and expert knowledge to redistribute crop area statistics based on crop suitability assessments and NDVI characteristics of vegetation. Image processing and analysing of Geographical Information Systems becomes insufficient when geometrically corrected data sets are merged (Skidmore, 1991). Such procedures were observed in the statistical models that assume that crop area is a linear combination of variables. The integration of expert knowledge into the statistical distribution was appropriate since it takes into account views of several agronomists, government planners and indigenous people on the spatial probability of crop grown. The method tries to divert from other approaches of crop area identification based on NDVI profiles during the growing season. Maselli & Rembold, (2001) tried to identify croplands by analysing statistical correlations with crop yield and NDVI profiles depending on the peak of the growing season using GAC data. Such methods as-sume that crop area is a function of vegetation biophysical characteristics, which include leaf area index and biomass. While statistical considerations based on meteorological factors could explain where crops are grown, they tend to ignore geographical extents and effects on response to production inputs that are accounted for in crop specific land suitability (Kassam, Higgins, & Velthuizen, 1984). It is also necessary to incorporate socio economic and biophysical analysis in order to improve information on land use systems at farm level (Beek, Bie, & Driessen, 1997). The proposed method took into consideration the repercussions of using NDVI profiles using coarse resolution data to identify croplands in a tropical environment with different cropping systems and crop varieties. González-Alonso & Cuevas, (1993) tried to generate agricultural statistics based on crop area estimation from ground data and satellite images. Though the method gave a high accuracy assessment, it was very costly and the method works well in the temperate countries. The proposed method takes into account the time and costs that have to be invested in activates that generate national crop area statistics through extensive field trips as those that may be required in a tropical environment by using expert knowledge to refine the outputs based on the views of people that know the area very well. McGuire, (1997) tried to de-aggregate agricultural statistics using the crop use intensity model. The procedure was used to estimate crop area based on interpretation of remote sensing imagery and represented depending on the level of cropping intensity and correlating it with the NDVI profiles. The method not only requires intensive field visits but also assumes that crop use intensity is measure for crop area distribution. The proposed method takes into account the implications of using such procedures by using crop suitability data which gives the poten-


37

tial of the land in areas that have fragmented farms that change every season depending on the farmers socio-economic demands. The heterogeneity of land use and land cover data needs to be improved in terms of spatial temporal consis-tency, completeness and compatibility (De Bie, 2000). The method is promising but requires additional research and systematic fieldwork to expand and fine-tune each of these procedures that may ultimately provide a level of completeness in modelling large-scale crop area distribution. With new sensors coming up like MODIS with 250 m resolution could be used instead of the 1 km NOAA AVHRR. The SPOT 4 could be used to discriminate crop types since it has multiple channels that can be analysed in a spatio - temporal context in order to discriminate crop types accurately.

5.1.1 Regression Analysis

The relative importance of NDVI and biophysical characteristics as spatial distributors of crop area is reflected in the significance levels of the multiple linear regression coefficients, which is similar to results of (Maselli & Rem-bold, 2001; Kouchoukos et al., 1997; McGuire, 1997; Ocatre, 1997). Regression analysis tries to produce the best additive combination of independent variables that produce a best linear relationship between the observed values and the redistributed values by the resulting equation (Moore & McCabe, 1999). The regression model fit the data because of the significant P-values and R2 percentages of 76.7% for maize, 55.5% for cowpea, 76.7% for sorghum, 82.7% for pearl millet, 56.6% for groundnuts and 41.4% for rice variability in the data. The model to redistribute rice had many outliers in the reported statistics, which explains why the model did not fit the data. The NDVI data that was used is a measure for vegetation condition and when classified will give the variability in vegetation condition over a period of two years. Results indicated that farmers in Ghana would grow more maize in the much wetter areas that have high NDVI values throughout the growing season. Such areas will have high vegetation cover throughout the growing season (Schowengerdt, 1997; Kouchoukos et al., 1997). While rice is grown in most of the country, the extent to which is grown will depend on the availability to water. It can also be deduced if areas that are suitable for rice production other lucrative crops will be grown instead. The results for the pearl millet and cowpea indicate that biophysical characteristics do not affect farmers’ on where grown such crops but rather most farmers are illiterate and rely normally on their traditional norms in deciding where and what they will grow in a particular season. The variables that are used to redistribute the crop area statistics are crop specific. This is because agricultural systems face continual changes due to effects of the world economy agro-environmental and socio – economic variables (Hervé, Genin, & Migueis, 2002). Most farms are fragmented and farmers generally decide on what and where to plant on the basis of social and economic norms. It should be noted that crop area is influenced by complex relationships that are a function of seasonal variations in tropical environments. Given enormous impact of these changes further research should be carried out using the temporal aspect in order to incorporate the nature of different crop types throughout the growing season if there is sufficient knowledge on the rainfall pat-terns.

5.1.2 Fuzzy logic Analysis

Spatial partitioning of areas from aggregated units to smaller sub areas of different sizes can have an effect on the statistical results derived from the same region (Arlinghaus, 1996). The stochastic element de-aggregation


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assumes that elements very close together may be similar. Beyond this assumption, detailed spatial complexities prevalent in environmental phenomena cannot be depicted accurately using statistical predictions. Expert knowledge that includes information on the area provided by a number of specialists and indigenous peo-ple gives a clear idea on the analysis of the areas of interest (Driessen & Konijn, 1992; Skidmore, 1991). The fuzzy logic was used to cater for dissimilarity in de-aggregated units of crop area. The combinations of the fuzzy product and fuzzy sum into a gamma factor were used td derive the final maps in the form of a continuous output with minimised effects of changes based on the expert knowledge. In the case of cowpea, original crop area percentages were 2.67 and 4.9% per hectare and those redistributed using the fuzzy logic being 1.55 % and 12.33% per hectare respectively. The fuzzy operators are appropriate since they increase the crop area percentages of cowpea in most areas in the Northern region where it is widely grown (Asuming-Brempong et al., 2001). Maize crop area percentages were reduced as compared to the ones that were generated from the statistical considerations. This means that while maize is widely grown all over the country, less will be grown in the Northern region in favour of other drought resistant crops like pearl millet and sorghum. The higher crop area percentages for sorghum, pearl millet, cowpea and groundnut reflects the view that these crops are only grown in a savannah areas with a major contribution to the farmers’ livelihood. There is possibility of having market from the much wetter areas that have limited land to grow food crops especially in the forest and semi deciduous zones. It can be concluded that the results of the fuzzy operators gave a realistic percent-age of crop area based on experts’ views on the study area. In some areas, the experts considered as not having certain crop types for example the Accra Plains for such crops as pearl millet, sorghum and cowpea. The result of the fuzzy operators gave a lower percentage of crop area rather than a zero. It can be concluded that the fuzzy operators do not eliminate areas with zero probabilities as would have been the case for Boolean operators but rather gives such areas a certain degree of association that is lower than the rest.

5.2 Limitations of the Approach In theory, there is a relationship between NDVI and vegetation condition (Avery & Berlin, 1992; Schowengerdt, 1997; Avery & Berlin, 1992) and that NDVI and crop suitability characteristics can explain where crops are grown. The investigation of this study relied on LAC NDVI data, which was resampled to 100 m resolution. The current study relied on remote sensing imagery and cartographic data obtained from different sources. The multisource data set may have been prone to a lot of errors in terms of scale and radiometric correction during the transfor-mation from different formats and scales. According to Weir, (1991) procedures like vector raster conversions can cause positional and area errors. Mathematical computations of maps using the fuzzy operators could also be a source of error. Though the data set was consistent such inherent error that may not have had a high influ-ence at national level could render positional accuracy of de-aggregated units insufficient. In addition to this spectral mixes in crop characteristics at a coarse resolution as was used in the study, may have rendered to ISODATA classification on which most map calculations were based inappropriate. The crop suitability data used in this study was generated using the agro ecological zoning based on the 1: 5 000 000 soils map of the world. The accuracy of yield estimates from such data is low and insufficient for regional and national planning (Driessen & Konijn, 1992). If land evaluation data were based on a 100 m soils map, the yield estimates would be more accurate and realistic at national level. The method assumes that crop phonologies for most of Ghana are constant and that farmers operate at several technologies. In addition to this, the study as-


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sumes that all farmers operate at medium management level which assumption may not hold for the de-aggregated units. While it is easier to manipulate crop suitability data that is based on averaged suitability indi-ces, such procedures do not consider that on farm production situations are different from general situations. According to Driessen & Konijn, (1992) on farm production is not solely determined by biophysical factors and that socio economic factors should be put into consideration. The study did not take into account such factors, which could have included demographic characteristics and market for the crops. The 100 m spatial resolution that was used in this study can be regarded as a higher level of detail at national level. However, in agricultural applications where size of holdings are approximately 2 hectares (Asuming-Brempong et al., 2001), and Ghanaian farmers practising mixed cropping with generally short fallow, optimal de-tail may not be reached. Land use data like crop area is affected by biophysical and climatic factors in association with technological con-straints (Kassam et al., 1984). The redistribution of crop area in a tropical environment based on variables used is the study is almost unrealistic because crop areas in the real sense are a function of so many variables that have complex spatial interrelationships. The statistical estimates are generated from models that assume distrib-uted crop area is a function of a combination of linear relationships between selected significant variables, which statistical considerations are not valid for environmental conditions. It would also be interesting to understand the interrelationships between the different variables that were used in the study. The use of subjective means in crop area identification is in a way trade off between accuracy and cost effective-ness in data collection. The fuzzy set algorithm used in this study was based on expert knowledge and used to refine the final maps. The procedure was appropriate due to the nature of the data set but an inconsistent method. This is because human perceptions about the environment differ in such subjective procedures though appropriate in cases where in situ information or ground truth is limited or not available. Intended users of the crop area percentage maps should take into consideration the fact that while GIS is an important tool in decision-making, there are a number of errors that may be propagated within the system (Weir, 1991). Though the district boundary map for which the redistribution crop area statistics were redistributed was last updated in the year 2001, its status is still questionable due to conflict between districts boundaries. The er-ror caused the conflict between boundaries that were originally represented as lines is propagated when con-verted to raster format. Attempts should be made to constantly integrate changes in administrative boundaries over time.


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6 Conclusions & Recommendations Sustainable land development based on effective, clear, national and regional policy requires provision of timely accurate and detailed information on land resources (Skidmore, Bijker, Schmidt, & Kumar, 1997). Spatial distri-bution of crop areas at pixel level is of great importance to most decision makers and planners in most tropical countries where vegetation characteristics are highly dynamic throughout the growing season. A feasible method that incorporates remotely sensed and GIS data with expert knowledge has been developed. The procedure pro-duced meaningful representations of crop areas in the form of detailed crop area percentage maps at 100 x 100 m grid cell level. The method is promising but could be refined using other environmental and socio-economic variables and understanding the interrelationships between them. Generating more quantified expert knowledge in relation to environmental parameters could be used to train a more complex fuzzy expert system to redistribute crop area statistics. The study showed that there were interesting relationships between the variables that were used to redistribute crop area. While there were a total of thirty-five variables used in this study the, the significant ones were crop specific. However, it is important to note that the data that was utilised may not have been optimal and that linear models are not realistic models (De Bie, 2000). The model to redistribute rice crop area had an R2 of 41.4%, which means that linear model is not suitable for its kind of data. Though it is attractive to fit a linear regression line for many models, such may not clearly relate to real environmental conditions. A more complex study should be undertaken on the basis of this study since several interactions were not taken into account. A scenario that could be tested could be in understanding the relationships between variables using quadratic fitted line plot. The procedure performs regression in polynomial terms and could take into consideration the environmental interac-tions between different variables. Recent research has shown that the use of LAC NDVI provides not only information on vegetation condition but also provides a cheap data set that covers large areas especially if the study area is at country level. The method relied on a cheap primary dataset that had a multitemporal coverage of the same ground scene, from NOAA AVHRR. The procedures could be improved by using other data sets that do not rely solely on NDVI values but on multiple spectral channels. With the introduction of new higher resolution datasets, the procedures of the method could be improved. The MODIS 250 m resolution Land cover product, MOD12Q1, identifies 17 vegeta-tion classes (Land, 2002), which could be beneficial to such studies with high spectral mixes. The recent launch of SPOT IV could be an added advantage to large area mapping. The spectral bands of the instruments have been carefully selected to monitor among others crop condition and health (CNES, 2000). The validation of the final products using the available land cover/land use maps showed that the method was effective in distributing crop area statistics. However since the present map used to validate the outputs is de-fined in broad classes, future research should be directed to validation of the method through selection of sample points by administrative areas based on their NDVI response throughout the growing season. A scenario could be explored using area frame sampling. Possible data that can be collected would be information on cropping systems, patterns and other local and environmental factors that can improve the performance of the method. In


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this way, the method would be effective in redistributing crop area in most tropical environments that have com-plex agricultural systems. While the prime objective of the Government of Uganda is alleviation of poverty through sustainable agriculture, information on crop area data is disseminated in tabular formats and cannot easily be integrated with other spa-tial data. Current vulnerability assessments carried out by FEWS (Uganda) and the Agro meteorological Section are based on the NDVI information that reflects the crop condition during the growing season. Such crop monitor-ing systems need detailed crop area maps in order to improve on the results that are forecast. The method if refined, would benefit many developing countries like Uganda that do not have national detailed crop area maps. Uganda has five major farming systems and the average size of land holding ranges from 0.4 to 3 hectares per typical holding of seven persons (NEMA, 1998). There are great disparities in crop production in all the systems due to different environmental impacts. The Uganda Bureau of Statistics will generate national crop statistics through extensive field visits in the year 2003. The representation of such data collected through field trips could give a more realistic distribution of crop area as compared to generalized expert knowledge. The detailed maps would also be relevant to organisations like OXFAM, FEWS (Uganda) and World Food Programme that are in-volved that are working towards providing sustainable livelihood options for rural people.


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Appendices

Appendix 1: Present land cover / land use map of Ghana


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Appendix 2: An overview of area covered by each land cover type in the First Level

Land Cover Code Land cover type Area (ha) % 1000 Agricultural land 15,022,385 63.06 2000 Forest 1,487,772 6.25 3000 Savannah 4,782,591 20.08 4000 Shrub-thicket 23,192 0.10 5000 Constructed surface 73,899 0.31 6000 Bare land 11,778 0.05 7000 Water body 774,786 3.25 8000 Wetland 84,914 0.36 9000 Unclassified land 1,561,360 6.55 23,822,683 100.0

Source: Land Use and Land cover of Ghana (Technical Bulletin (Draft) 1990/1991)

Appendix 3: Legend of the present land use / land cover map of Ghana as Second Level

Level II Area (ha) % at level II Level II Area (ha) % at level II

Agricultural land Constructed surfaces1100 80,289 0.5 5100 73,698 99.71300 775,334 5.2 5200 201 0.31400 2,891,897 19.3 73,899 100.01500 3,327,744 22.21600 242,869 1.6 Bare land1700 687,178 4.6 6100 11,739 99.71800 4,397,132 29.3 6300 39 0.31900 2,619,942 17.4 11,778 100.0

15,022,385 100.0Water bodies

Forest 7100 9,226 1.22100 1,310,200 88.1 7200 703,804 90.82200 166,404 11.2 7300 5,078 0.72300 11,167 0.8 7400 56,678 7.3

1,487,772 100.0 774,786 100.0

Savanna Wetland3100 1,797,363 37.6 8000 84,914 1003200 1,729,310 36.23300 942,893 19.7 Unclassified3400 313,024 6.5 9100 1,380,415 88.4

4,782,591 100.0 9200 180,945 11.6Unattributed 5 0.0

Shrub thicket 1,561,365 100.04100 23,127 99.74200 65 0.3

23,192 100.0 Source: Land Use and Land cover of Ghana (Technical Bulletin (Draft) 1990/1991)


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Appendix 4: Legend of the present land use / land cover map of Ghana as Third Level

Ar e a ( h a ) o f a g . l a n o f t o t a l

A g r ic u ltu ra l la n d 1 1 1 0 7 2 , 3 1 1 0 . 5 0 . 31 1 2 0 4 , 6 6 2 < 0 . 11 1 3 0 2 , 5 5 4 < 0 . 11 1 4 0 7 6 1 < 0 . 11 3 1 0 5 2 1 , 3 8 9 3 . 5 2 . 21 3 2 0 2 5 3 , 9 4 6 1 . 7 1 . 11 4 1 0 # # # # # # # # # 1 6 . 9 1 0 . 71 4 2 0 3 5 0 , 7 3 1 2 . 3 1 . 51 5 1 0 # # # # # # # # # 1 2 . 6 8 . 01 5 2 0 # # # # # # # # # 9 . 5 6 . 01 6 1 0 1 2 0 , 6 1 5 0 . 8 0 . 51 6 2 0 1 0 4 , 3 3 3 0 . 7 0 . 41 6 3 0 1 7 , 9 2 1 0 . 1 0 . 11 7 1 0 2 9 1 , 2 5 0 1 . 9 1 . 21 7 2 0 3 9 5 , 9 2 8 2 . 6 1 . 71 8 1 0 # # # # # # # # # 1 3 . 3 8 . 41 8 2 0 # # # # # # # # # 1 5 . 9 1 0 . 11 9 1 0 # # # # # # # # # 9 . 5 6 . 01 9 2 0 # # # # # # # # # 7 . 9 5 . 0

# # # # # # # # # 9 9 . 9 6 3 . 1 Source: Land Use and Land cover of Ghana (Technical Bulletin (Draft) 1990/1991)

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