Abstract Land Cover Change in a Savanna Environment. A

ABSTRACT

LAND COVER CHANGE IN A SAVANNA ENVIRONMENT. A CASE STUDY OF BAWKU MUNICIPAL by Kwame Adusei Savanna environments currently are undergoing rapid changes. A key debate is whether these observed changes are of anthropogenic activities or simply seasonal or climatic variations. This study mapped and assessed land cover change in the Bawku Municipal through the application of remote sensing and GIS. Multi-date Landsat Thematic Mapper (TM) images acquired in 1989 and 2009 were used to characterize and assess the pattern of land cover change through a post- classification comparison. The results indicated 33% loss of forest cover, while bare fields increased by 68% and settlement increasing by 331%. Human activities like intensive cultivation and harvesting of wooded resources as fuel for both commercial and domestic use were the principal influence of the changes observed. The results also confirm findings of similar and earlier studies in the region indicating efforts to address the problem are not comprehensive enough.

LAND COVER CHANGE IN A SAVANNA ENVIRONMENT. A CASE STUDY OF BAWKU MUNICIPAL

A Thesis

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Arts

Department of Geography

Kwame Adusei

Miami University

Oxford, Ohio

2014

Advisor______(Dr. Mary C. Henry)

Reader______(Dr. John K. Maingi)

Reader______(Robbyn J. F. Abbitt)

Table of Contents ABSTRACT ...... i LIST OF TABLES ...... iv LIST OF FIGURES ...... v ACKNOWLEDGEMENT ...... vi CHAPTER ONE ...... 1 INTRODUCTION ...... 1 STUDY OBJECTIVES ...... 2 JUSTIFICATION ...... 3 CHAPTER TWO ...... 5 LITERATURE REVIEW ...... 5 LAND COVER CHANGE ...... 5 Causes of Land Cover Change ...... 5 DIGITAL CHANGE DETECTION ...... 6 Data acquisition and preprocessing ...... 7 Image Classification ...... 7 Change Detection Techniques ...... 9 Accuracy Assessment ...... 10 CHAPTER THREE ...... 11 METHODS ...... 11 STUDY AREA ...... 11 DATA ...... 13 Satellite Images ...... 13 Preparation before Field Work ...... 14 Field Sampling ...... 14 Field Data Collection ...... 14 Ancillary Data ...... 15 Image Enhancement ...... 15 LAND COVER MAPPING ...... 18 Classification Scheme ...... 18 Image classification ...... 20

Classification Accuracy Assessment ...... 22 Change Detection ...... 23 CHAPTER FOUR ...... 24 RESULTS ANALYSIS AND DISCUSSION ...... 24 LAND COVER MAPPING RESULTS AND DISCUSSION ...... 24 1989 Landsat TM Multi-date Image classification ...... 24 2009 Landsat TM Multi-date Image Classification ...... 26 Accuracy Assessment Results and Discussion ...... 29 CHANGE DETECTION RESULTS AND DISCUSSION ...... 30 Post Classification Comparison ...... 30 Land Cover Change Causes ...... 33 Vegetation Loss ...... 36 CHAPTER FIVE ...... 39 CONCLUSION AND RECOMMENDATIONS ...... 39 SUMMARY OF RESULTS ...... 39 CONCLUSION ...... 40 RECOMMENDATIONS ...... 41 REFERENCES ...... 42

iii

LIST OF TABLES Table 1: Landsat images used in the study ...... 13 Table 2: Percentage information contained in the standard principal components produced from the satellite images ...... 18 Table 3: Land cover classification scheme ...... 19 Table 4: Area and percentage area covered by each land cover class in 1989 ...... 26 Table 5: Area and percentage area covered by each land cover class in 2009 ...... 26 Table 6: 2009 land cover map error matrix ...... 29 Table 7: 1989 land cover map error matrix ...... 30 Table 8: Land cover change matrix for 1989 and 2009 land cover maps ...... 31 Table 9: Land cover changes between 1989 and 2009 ...... 31

LIST OF FIGURES

Figure 1: Map of Study Area ...... 12 Figure 2: A. Spectral reflectance signatures for the land cover types and B. EVI Values for cover types after the Landsat images were transformed to EVI products...... 17 Figure 3: Layer stacking process...... 21 Figure 4: Land cover map 1989 ...... 25 Figure 5: Land cover map for 2009 ...... 28 Figure 6: Change-No-Change 1989 – 2009 map ...... 33 Figure 7: Bawku Municipal, population density from 1970 to 2010...... 35 Figure 8: Human factors influencing land cover change ...... 36 Figure 9: Conversion of wooded vegetation to other land cover classes ...... 38

ACKNOWLEDGEMENT

As the popular Ashanti parlance says: “onipa yɛ bia, ɔsɜ ayeyi”. Meaning he that helps deserves gratitude. I recognize the motivation and efforts of my uncle Robert Dwamena, Director for Procurement Electricity Cooperation of Ghana for my being in the States and pursuing this program. Also, I appreciate the tremendous support from my junior brother Akwasi Danso Adusei.

Finally my heart felt gratitude to my advisor Mary C. Henry, committee members John K. Maingi and Robbyn J. F. Abbitt, the entire Geography Department for being wonderful and providing all the necessary assistance for the completion of this thesis. Ayeekoo!

CHAPTER ONE

INTRODUCTION Savannas are grassland with varying densities of scattered trees or shrubs. They are found within the tropical belts and are marked by an annual alteration of a rainy and a dry season with varied intensity and duration (Young and Solbrig, 1993). Some savannas however experience a bimodal pattern of rainfall. According to Shorrocks (2007) about 50% of Africa’s landmass is covered by savanna. It extends from the West Coast to East Africa and then round to Angola and Namibia.

It is an important ecological zone rich in biodiversity. A major feature of African savannas is its large population of herbivores and carnivores, which offers an advantage in tourism with regards to wildlife. In some African countries, this serves as a high foreign income earner to boost the national economy. For example, Kenya gains from tourism increased by 32.8 % estimated to be 97.9 billion Kenyan shillings in 2011 (African Economic Outlook, 2012). Savannas also serve as rangeland for livestock production and to a lesser extent for crop production. Nevertheless, exponential growth in population at the turn of the 21st century has increased the pressure on the use of resources. Ecosystems such as savannas, presently, are intensively used and are undergoing rapid changes. Population growth, overuse of woody plants for fuel, grazing and cultivation coupled with natural factors such as poor soils, low precipitation in densely settled savannas have raised concerns that they could become depleted (Lambin & Geist, 2006). It is therefore important to monitor the environment. Essential to the monitoring processes is understanding, accurate mapping, measurement and assessment of land cover change (Lambin & Geist, 2006).

Currently, remote sensing has become central in quantifying landscape patterns and processes, especially land cover change (Newton et al, 2009). Lambin & Geist (2006) noted that the use of remote sensing inputs as well as ancillary data sources improves mapping and assessment of land cover change. To map land cover changes, several change detection methods and algorithms have been developed and reviewed (e.g. Coppin et al., 2004; Lu et al. 2003 and Singh 1989). Each method has its own advantages and no method is optimal and applicable to all environments (Lu et al., 2003).

The Sahel region of Sub-Saharan Africa received the world’s attention in the 1970s, concerns were raised about widespread desertification. Desertification being a form of land degradation had been defined in many ways. The United Nations Conference on Environment and Development (UNCED) (1992), defined desertification as land degradation in arid, semiarid and dry sub-humid areas resulting from various factors, including climatic variations and human activities. Whereas land degradation is defined as the temporal or permanent reduction in the productive capacity of land (UNEP, 1992). According to Bai et al. (2008), Sub-Saharan Africa accounted for 13 percent global land degradation.

Debates on dry lands degradation is however surrounded by controversy (Dregne, 2002). This has been the result of inadequate information on the extent and severity of change and degradation in dry lands. Even remotely sensed data for this environment is difficult to interpret and to distinguish human-induced trends from climate-driven variations in vegetation cover (Lambin & Geist, 2006). To date no remote sensing study in this region has demonstrated widespread vegetation change. In fact, recent studies cast doubts on this earlier assumption of widespread degradation in the Sahel. Begue et al (2011), used NOAA-AVHRR 8 km resolution vegetation index to study vegetation dynamics in the Bani catchment in Mali. They reported the greening of the region. The critical question yet to be answered is whether these reported changes in vegetation are caused by human impacts or climatic variation. Herrmann et al. (2005) in their study of vegetation dynamics in Africa Sahel and their relationship to climate between 1982 and 2003 found a positive trend in the Normalized Difference Vegetation Index (NDVI) values. It indicated the greening of the Sahel and thus an increase in green biomass during the study period. Hence, they refuted earlier claims of widespread human-induced vegetation loss (Eklundh & Olsson, 2003; Prince et al., 1998) and also challenged earlier notions of irreversible changes in the region. These debates indicate the need to re-examine land cover change in savanna regions especially at the local level. Therefore this study aims to map and detect land cover change, as well as to elaborate on the possible causes within a local context.

STUDY OBJECTIVES The goal of this study was to detect and map land cover changes in Bawku Municipal in the Upper East Region of Ghana. Specific objectives were:

a) To map land cover of Bawku Municipal for the years 1989 and 2009 using multi- temporal Landsat images b) Map land cover changes between 1989 and 2009. c) Examine possible causes for the land cover changes.

JUSTIFICATION Savanna zones of Ghana experience the same environmental problems as other savannas of Sub-Saharan Africa. Ghana’s environment is rapidly changing, especially in the savanna zones. Approximately 21% of Ghana’s total land area is categorized as degraded through land cover changes (Bai et al., 2008). Population growth, intensive land use for crop and livestock production has led to serious changes in the environment. Earlier studies have shown the northern part of Ghana suffers adverse environmental changes. Hunter (1972) noted and attributed environmental change in Northern Ghana to increase in livestock population and over population which exerted pressure on land resources. Similarly, the Environmental Protection Agency of Ghana has earmarked Northern Ghana as highly susceptible to degradation through land use land cover changes (EPA - Ghana, 2002). Briamoh and Vlek (2003), studying the impact of land cover change on soil properties in Northern Ghana regarding the EPA - Ghana (2002) report found there were no major changes between soil properties under natural vegetation and soils under cultivation. However, soils under continuous and permanent cultivation showed signs of deterioration.

Bush fallow had been practiced to restore soil fertility and to check extensive use of land resources. In that adjacent farmland is left to fallow until the next farming season before it is cultivated again. However, with current population growth this customary practice of bush fallow is impractical to reduce pressure on land resources. In a recent study within the Volta Basin, Briamoh and Vlek (2004) found the most dominant land cover change in Ghana was the conversion of natural vegetation to croplands at an annual rate of 5%. This poses a serious threat to natural ecosystems.

Without a formal national remote sensing unit; poor environmental monitoring and management system raises concern about the reality of land cover change in Ghana. Previous studies in northern Ghana including the Bawku Municipal, however, indicated that land cover

3 change is an issue (Nsiah-Gyabaah, 1994; Yiran et al., 2012). Nevertheless, data and methodological difficulties constrained a detailed analysis.

Yiran et al. (2012) assessed degradation in the Bawku area using remote sensing and local knowledge. Their study was limited in the remote sensing analysis. The satellite image scene could not cover the entire study area and this affected the change statistics. Also, their study would have benefited more from multi-temporal images. Multi-temporal images would have helped in discriminating active agricultural fields from other land cover types especially pasture or rangeland through phenological patterns of vegetation. This study therefore utilized a moderate spatial, spectral and multi-temporal resolution satellite data with additional data such as precipitation, human and livestock population to produce a detailed analysis of land cover change in the Municipal.

CHAPTER TWO

LITERATURE REVIEW LAND COVER CHANGE Land cover change is identified as a critical influence of environmental change. It affects surface albedo, hence altering surface-atmosphere energy exchanges. Local and regional climate are especially impacted. This aggregates globally to affect the earth system processes (Lambin & Geist, 2006, U.S. Geological Survey, 2013). Land cover change constitutes modification or conversion of land cover (Turner & Meyer, 1994). Modification in a land cover represents a change in the attributes of the cover but not its classification. However, land cover conversion represents a complete change from one cover type to another. According to Ramankutty and Foley (1999), about 6 million sq. km of forests and woodland and 4.7 million sq. km of savanna/grassland/steppes were converted to croplands since 1850. Even so, Diouf and Lambin (2001) argued land cover modification is the most common form of change. This makes it a challenge to detect and map actual changes due to anthropogenic impact or inter-annual climatic variations.

Causes of Land Cover Change Most land cover change studies demonstrate the role of nature and society in shaping landscapes. Human activities, however, are recognized most to influence land cover change (Antrop, 1998; Bastian & Steinhardt, 2002). Most narratives recognize human activities such as agricultural expansion, forest exploitation and urbanization and population growth as land cover change forces. Land cover change presently is the major change variable affecting environmental change and biodiversity loss (Burel & Baudry, 2003; Lambin & Geist, 2006). For example, Kiage et al (2007), found land cover change was a factor for land degradation in the Lake Baringo Catchment, Kenya. They argued the changes have resulted in the lake’s sedimentation and threatens its rich biodiversity. They attributed this development to human and livestock population growth. Naturally land cover can change responding to extreme weather events that cause wildfires, lasting floods, or multiple-year drought (Mölders, 2012). The Sahelian droughts in the 1970s and 1980s for instance raised concerns about widespread land cover change and advancing desert.

Studies have shown the significance of land cover changes, however the causes are not examined holistically. For instance in the Sahel savanna zone, it is debated if the changes are the result of widespread anthropogenic impacts (Eklundh & Olsson, 2003; Prince et al., 1998) or climatic variations (Herrmann et al., 2005). Eklundh and Olsson (2003) argued the greening of the Sahel during their period of study is attributed to best land management practices and land abandonment during and after the Sahelian droughts. They concluded the observed changes were anthropogenic impacts. On the contrary, Herrmann et al. (2005) argued that the changes were drought induced and hence temporary with a minor human role. They also concluded that the observed greening of the Sahel during their study period indicates a recovery of the ecosystem.

The basic logic underlying these varying opinions is that impact of land cover change is recognized globally. Reliable land use land cover data are therefore essential in making decisions at all levels especially with recent concerns about environmental sustainability. Despite its significance in monitoring and assessing the environment, land cover change data are lacking in rural Africa (Otukei & Blaschke, 2009). This is attributed to the fact that in developing countries mapping agencies and research institutes lack the necessary government support; insufficient funds for data acquisition and expensive software and hardware.

DIGITAL CHANGE DETECTION The environment is in a constant change (Antrop, 1998) at different spatial and temporal scales (Coppin et al., 2004). For sustainability issues, there is the need of accurate and up-to-date data (Coppin et al., 2004). This requires timely and accurate change detection methods to understand human-environment relations to make better decisions (Lu et al., 2003). Digital change detection has become a major application of remotely sensed data. The objective is that changes in land cover will result in changes in reflectance values captured in the data.

Although digital change detection involves many challenges, it offers consistent and repeatable procedures compared to a visual detection (Coppin et al., 2004). In addition, it is easy to utilize the non-optical range of the electromagnetic energy, hence its application in many land use land cover change studies. Nevertheless a digital change detection involving a post- classification comparison is constrained to detect land cover conversions only, whereas land cover modifications are undetected or misrepresented Foody (2001). However, improvement in

6 computing power, data acquisition and change detection methods still makes it a viable technique.

A digital change detection analysis requires a consideration of the nature of change detection problem, geographic environment, remote sensing system, appropriate classification system and image processing methods (Jensen, 2005). Failure to consider and understand the impact of these parameters leads to inaccurate results.

Data acquisition and preprocessing Recognizing land cover data importance, advances in Remote Sensing and Geographic Information Systems (GIS) have facilitated the data’s availability. Remote sensing provides spatio-temporal data useful for environmental studies (Campbell, 2011; Chuvieco & Huete, 2010). Historically, aerial photos and ground surveys have been used in land cover change analysis, but this made it difficult to obtain data over a large data. The process of data acquisition is also time consuming and expensive. Satellite remote sensing has provided the advantage of regional and global coverage, high temporal and spatial resolution and data easily accessible (Kiage et al., 2007). However, not all satellite data are in high resolution. Aerial photos and field data are therefore used to supplement satellite data to analyze and quantify land cover change patterns. Newton et al (2009) confirms that incorporating field data, expert knowledge and remote sensing data increases the accuracy of a land cover change analysis.

Digital imagery also contains some errors in both the radiance captured and position of resulting pixels (Chuvieco & Huete, 2010). These errors are the result of atmospheric effects, terrain variations, earth’s curvature and rotation, variations in orbit altitude and sensor’s miscalibration (Chuvieco & Huete, 2010; Jensen, 2005). These errors must be corrected through radiometric and geometric corrections. It is an important step in any digital change detection as any misregistration will produced false results.

Image Classification Classification methods group pixel values within an image into informational classes (categories of interest to the analyst). It involves assigning each pixel in an image a class label that identifies what is on the ground at that point (Brislawn et al., 2003). It is used to produce a map or one of several processes used to obtain information from an image. In a digital change

7 detection such as post classification comparison, it is an important stage to ensure the accuracy of the study. Generally, classification methods are grouped into two: supervised and unsupervised (Chuvieco & Huete, 2010).

Supervised classification is mostly based on acquaintance of the study area through field work or other sources (aerial photographs or conventional cartography) or personal experience (Chuvieco & Huete, 2010). The analyst provides the classification algorithm with sufficient reference data that are homogenous examples of the defined land cover types. Hence the reference data are a training sample use to train the classifier to process the image into a map (Campbell, 2011; Jensen, 2005). Statistical parameters such as means, standard deviation, etc. for each training data are computed. Then pixels both within and outside the training data are weighed and assigned a class of which it has the maximum likelihood of membership (Jensen, 2005).

Unsupervised classification, on the other hand, uses cluster algorithms to classify an image into several spectral classes based on statistical information within the image (Lu & Weng, 2007). It requires no prior definition of the classes. The analyst is responsible for identifying and assigning the spectral classes to informational classes. Although unsupervised is more an automatic process compared to supervised classification, the accuracy is generally lower (Tso & Mather, 2009). Supervised classification gives the analyst control over selecting the informational classes and this is vital when generating a classification for a change analysis of the same geographic area at different dates (Campbell, 2011).

Deriving accurate change analysis from satellite data requires a robust classification method. According to Lu and Weng (2007), traditional classifiers such unsupervised classifiers produce ‘noisy’ results especially in complex environments. Also integrating ancillary data, spatial and contextual rules and non-statistical information in the classification process is limited. Nonparametric classifiers such as Support Vector Machine (SVM) provide a better alternative. Comparative analysis has proven SVM to be a more accurate classifier with a small training data than decision trees and neural networks (Huang et al., 2002). SVM classifier uses an optimal hyperplane to distinguish between classes using training data at the edge of class distribution and class centroids (Foody & Mathur, 2006). This has made it possible for the classifier to attain a higher accuracy results with a small training data set.

Change Detection Techniques There are several change detection methods and most have been reviewed by Coppin et al., 2004; Jensen, 2005; Lu et al. 2003 and Singh, 1989. A Good change detection provides details on the extent of change, change distribution and change trajectories (Lu et al., 2003). This is dependent on several factors including: knowledge of the region, temporal dimension of the study, classification system, environmental factors (atmospheric and soil conditions, phenological characteristics, etc.), sensor’s resolution (temporal, spatial, spectral and radiometric), ground reference data, geometric registration between multi-temporal images (Jensen, 2005). For these complex impacts, several researchers have diverse opinions and often debated conclusions about an efficient change detection method (Lu et al. 2003).

Literature indicates the heavy use of Image Differencing, Principal Component Analysis and Post-classification Comparison (Lu et al., 2003). This current research adopts a Post- classification Comparison method as it is not affected by atmospheric and environmental differences between multi-temporal images (Lu et al, 2003). This has been a constraint for other multi-temporal change detection methods.

Post-classification Comparison examines independently classified images for different times. It compares pixel by pixel in the classified images for a change (Coppin et al., 2004; Lu et al., 2003). A higher classification accuracy enables the analyst to attain a good change analysis. For example, Mas (1999) obtained a high change accuracy using Post-classification Comparison to detect land cover change in a coast of Mexico as compared to methods like Image Differencing, Vegetation Index Differencing, and Selective Principal Component Analysis (two bands were selected out of the multi-date image used opposed to all bands). This accuracy correlates to higher classification accuracy of the land cover maps. Thus, the accuracy of this method is corroborated by the accuracy of the classified maps (Jensen, 2005).

In spite of the advantages class errors in each land cover map propagate into the post- classification image, this gives a false indication of changes (Singh, 1989). Foody (2001), studying desert fluctuations in the Sahel, found Post-classification Comparison underestimated the areas of land cover change and where change was detected, it overestimated its magnitude. This poor performance is attributed, in part, to difficulty in obtaining constant, similar and accurate classification from one date to another (Coppin et al., 2004). Also Post-classification

9 based on pixel-based classifiers is affected by atmospheric effects and sensor’s optic properties, thus constrained to indicate land cover conversions. Hence, adopting a fuzzy classifier that allows partial and multiple class membership aids a greater degree of land cover change to be detected (Chuvieco & Huete, 2010; Foody, 2001).

Accuracy Assessment As remotely sensed data is essential in environmental models, accuracy assessment is needed to understand the results generated before it is used in analysis or to make decisions. Most studies now adopt site-specific accuracy assessment. This is a detailed assessment of agreement between the maps at specific locations (Campbell, 2011).

Site-specific accuracy is reported through an error matrix (confusion matrix). It identifies both overall errors for each category and misclassifications by category. It consists of a collection of statistics which represent the count of pixels assigned to a particular category relative to the actual category verified on the ground (Congalton, 1991). An error matrix furnishes the analyst with errors of omission and commission, as well as overall, user and producer accuracies. Other researchers have recommended Kappa Statistics to measure the difference in the observed agreement between the maps and the agreement attained by chance matching of the maps. Not every accuracy can be related to the success of the classification, kappa statistics, therefore eliminate accuracies due to chance to reflect agreement between the classified maps and reality. Fitzgerald and Lees (1994) testing the functionality of both site- specific accuracy and kappa statistics confirmed kappa statistics more robust. It proved more sensitive assessing classification accuracy of different classifiers over overall accuracy index. As a map cannot be a perfect representation of reality, a higher Kappa value will indicate a good performance of the classification. Hence, a general confidence in the map produced.

CHAPTER THREE

METHODS STUDY AREA Bawku municipal is among the nine administrative districts in the Upper East Region of Ghana. Geographically located in the north-eastern corner of Ghana, it is bordered by Burkina Faso in the north, Togo to the east, Bawku West District to the west and to the south is Garu- Tempane District. It covers an area of approximately 1,215 km sq. between latitudes 10º15’ and 11º12’N and longitudes 0º03’E and 0º23’W (Figure 1). Generally, topography is gentle and a bit undulating. Average elevation ranges between 120 – 150 meters above sea level. There are few isolated peaks, the prominent being Zawse hills with an average elevation over 430 meters. Rock outcrops are also notable in most areas.

Bawku is within the interior continental climate zone characterized with pronounced rainy and dry seasons. The rainy season occurs from May to October, followed by mostly a severe dry season from November to March. Rainfall pattern is unimodal with an annual mean between 800 and 850mm. Rainfall is also erratic and at times begins early or late April and ends late September. Daily temperatures are high, 38º C on the average (Ghana Metrological Agency, 2013). Long dry periods and high day temperatures makes the municipal dry and dusty during the dry season. This condition is mainly influenced by the dry Hammattan air mass that blows from the Sahara desert over most of the West African sub-region during this period. This dry condition makes this agricultural dominated landscape prone to bush fires especially where bush burning and charcoal production are practiced. This has accelerated the rate of environmental degradation in the municipal.

The vegetation is mainly Sahel savanna woodland (Open). It consists of short grass and drought resistant scattered dwarf trees. Most dominant tree types are the Baobab (Adansoia) and Shea Tree (Vitellaria paradoxa). The Shea tree is very important; the nuts are used as food and for making shea butter for domestic and commercial use. Bawku has some gazetted forest reserves which have been encroached upon (Yiran et al. 2012). A notable of these are the Zawse and Binduri reserves. It is drained by the White Volta and its tributaries.

Bawku Municipal has an estimated population of 217,791 and a density of 179 persons per km sq. It is one of most populous districts in the Upper East Region. The main socio- economic activity in the area is agriculture which includes crop production and animal husbandry and employs about 70% of the people (Ghana Statistical Service 2010). According to the Ghana Statistical Service (2010), about 83.7% households are engaged in agriculture. Major crops grown are millet, sorghum, maize and groundnuts. Agriculture is heavily dependent on rains, however there are a number of dams to support dry season farming.

Figure 1: Map of Study Area

DATA Satellite Images Landsat Thematic Mapper (TM) images for 1989 and 2009 were used in this study (Table 1). These images were used because they had minimal or no cloud cover. Both wet and dry season images were used. The wet season images were ideal for distinguishing farmlands as by this time most of the crops are in the growing stage or near maturity. Also the dry season images were used to distinguish vegetated and non-vegetated areas. These data were downloaded from the United States Geological Survey website: http://earthexplorer.usgs.gov/.

Table 1: Landsat images used in the study

Data Type Path/Row Acquisition Date Usage Landsat 4 TM 194/52 October 26, 1989 Wet Image Landsat 4 TM 194/52 November 11, 1989 Dry Image Landsat 5 TM 194/52 August 6, 2009 Wet Image Landsat 5 TM 194/52 December 12, 2009 Dry Image

Image Preprocessing Radiometric Correction A satellite image in the raw state contains digital numbers. To be able to use it in analysis and compare it with other satellite data over time, it must be calibrated into reflectance values through radiometric correction (Chuvieco and Huete, 2010). The satellite images were processed to obtain sensor’s reflectance values using ENVI 5.0 classic software. The software uses the metadata of the images to compute for actual top-of-atmosphere reflectance captured by the sensor at the time of image acquisition.

Geometric Correction The satellite images were already geo-rectified and projected to WGS 1984 UTM Zone 30 North. The study area shapefile (municipal boundary) was however re-projected to the spatial reference of the satellite images. This enabled the satellite images to be subset to the study area.

Preparation before Field Work Knowledge of the study area before the field work was inadequate, so unsupervised Iterative Self Organizing Data Analysis Technique (ISODATA) classification was used to classify the 2009 reflectance image into a land cover map using ENVI 5.0 classic software. A minimum number of 26 and a maximum of 36 spectral clusters was specified. Maximum iterations was set to 25 and 95% change threshold was specified to increase the spectral clusters separability. 23 spectral clusters were identified by the algorithm in the reflectance image. The spectral clusters were combined and assigned their target land cover classes based on the reflectance image and high resolution aerial photographs (DigitalGlobe, 2012) embedded in Bing maps.

After the classification some classes were found mixed or confused with others. Bare fields were found to be confused with settlements that are built with mud bricks and thatch roofs. Dry river beds were confused with bare fields and settlements and cropland that were bare or harvested. Grassland was also confused with active croplands. Coordinates and photos of these confused areas were recorded and taken to the field for verification. A Geographical Positioning System (GPS) unit (Garmin GPSMAP 62x) was used to locate the confused areas in the field. Once located, description of the cover type, condition and location were recorded and photographs taken. These verified areas were especially noted and used in the training samples of the 2009 image during the supervised classification.

Field Sampling The study area is relatively large with a gentle topography which made it relatively accessible. Therefore a stratified random sampling was used to ensure most part of the Municipal was sampled. The Municipal was divided into five (5) observation strata with a size of approximately 240 km sq. each. Within each strata, observation points were selected randomly. In all a total of 201 sample points were taken, majority of which were for croplands being the most dominant cover type.

Field Data Collection Field data were collected and used to verify and evaluate the accuracy of the classified image (2009). Field data included GPS points and digital photos which were acquired during site study during summer 2013. During the field study, each observation strata was visited as

14 identified in the field sample map. Within each strata, target land cover classes were identified and selected randomly. Upon identification, locational information was recorded with a GPS with an average of ± 2 meters accuracy. Detail description of the land cover attributes (type, elevation, and conditions) were also documented to help in the data analysis. For every GPS point record, high resolution digital photos which were geo-tagged were taken systematically (east, north, south and west) to accompany it. This was done to provide a visualized support for the documented land cover attributes, hence supplementary information for accuracy assessment.

Ancillary Data Other data used in this study included a shapefile of the Municipal (boundary) which was obtained from http://www.diva-gis.org/Data. Digital aerial photographs of the study area were acquired from the Ghana Lands Survey and Mapping Division at a scale of 1: 10,000, acquired at 1993 and 2008. Digital topographical maps at the scale of 1:50,000 were obtained from East View Geospatial Inc. These were based on aerial photographs acquired at 1960 and 1961. The topographical maps were georeferenced through IRAS/C image processing software using a second order polynomial at an accuracy of 10 meters. Population data (for 1984, 2000, and 2010) of the district was obtained from the Ghana Statistical Service. A spread sheet of monthly rainfall, temperature and relative humidity (from 1990 to 2012) recorded from the two main sub- stations in the municipal was obtained from the Ghana Meteorological Agency and Livestock census data from the Municipal Agricultural Directorate. These data were used in analyzing the changes in the Municipal.

Image Enhancement Image enhancement improves an analyst’s chance of distinguishing different features in an image. Based on the analysis of previous research, this study adopted an Enhanced Vegetation Index (EVI), Principal Component Analysis (PCA) and Tasseled Cap Transformation (TCT) to enhance land cover class separation. This was adopted especially to distinguish vegetated from non-vegetated areas to improve the accuracy of classifying the satellite images into land cover maps.

Enhanced Vegetation Index The EVI is an optimized vegetation index designed to minimize soil background and atmospheric effects and improve the VI sensitivity in high biomass areas (Huete et al., 2002).

EVI is based on the Soil-adjusted Vegetation Index (SAVI) and the Atmospherically Resistant Vegetation Index (ARVI). It incorporates the blue band to stabilize aerosol effect in the red band. Hence, the EVI is optimized to distinguish canopy structural variations (Chuvieco and Huete, 2010). In this study, EVI products were produced from the Landsat image for both years. The study area has lots of open canopy, therefore soil background noise has impact on the vegetation reflectance recorded in the images. The EVI products produced were used to minimize the soil background effects and to aid the classifier to distinguish vegetated from non-vegetated areas (Figure 2). From the figure below it is clear when EVI was added to the data vegetated areas were easily distinguished from the non-vegetated areas (Figure 2 B). Vegetated areas reflect more infrared energy and therefore have a higher EVI values than other cover types.

A. Spectral Profile of Cover Types 40.0%

35.0%

30.0% Open Water 25.0% Settlement 20.0% Closed Woodland

15.0% Open Woodland Reflectance Grassland 10.0% Cropland 5.0% Bare Field 0.0% 0 . 5 0 . 6 0 . 7 0 . 9 1 . 2 3 Wavelength (µm)

B. EVI Values for Cover Types 0.7

0.6

0.5

0.4

0.3

0.2 EVI Values EVI 0.1

-0.1

-0.2 Open Water Settlement Closed Open Grassland Cropland Bare Field Woodland Woodland

Figure 2: A. Spectral reflectance signatures for the land cover types and B. EVI Values for cover types after the Landsat images were transformed to EVI products.

Tasseled Cap Transformation Tasseled cap orthogonally transforms an image to separate features through a linear combination of the original bands (Jensen, 2005). It generates spectral feature component with specific meaning. It includes brightness, greenness and wetness features. Where wetness indicates water content of vegetation and soil; brightness represents variation soil background reflectance, and greenness is the contrast between the visible and near infrared bands (Campbell, 2011). This was also used to aid the classifier to separate vegetated areas, built-up areas from soils by utilizing the green and brightness component of the transformation.

Principal Component Analysis PCA transforms multispectral data into a few interpretable uncorrelated components representative of most of the information in the original data. This transformation reduces redundant spectral information present in the multispectral data. It involves a linear combination of correlated variables to distinguish independent sources of variability in multispectral images (Chuvieco and Huete, 2010). Therefore, it produces a better distribution of data to aid separation of different covers in an image. Mostly the first components contain a greater percent of the variance while the higher order components often contain noise (Jensen, 2005). A standardized

17 principal component transformation was adopted for the study to identify informative bands and also aid the classifier in the land cover class separation. This was adopted as it is recognized to be superior when evaluating change in multi-temporal images to the non-standardized PCA (Jensen, 2005). The standardized PCA is computed based on correlation matrices. Therefore each band has equal weight in determining the new components unlike the non-standardized PCA which is based on covariance matrices.

The transformed images showed most of the spectral information was retained in the first principal component (PC1) (Table 2). Therefore PC1 was used to facilitate the image classification.

Table 2: Percentage information contained in the standard principal components produced from the satellite images (2009 August image)

Principal Eigen values Percentage Components Variance PC1 5.803753 96.7 PC2 0.11661 1.9 PC3 0.068174 1.1 PC4 0.006307 0.1 PC5 0.003975 0.1 PC6 0.001181 0.0

LAND COVER MAPPING Classification Scheme The classification scheme used in this study (Table 3) has seven classes. The class scheme is based on previous study by Yiran et al. (2012) and field work experience with modifications. The modifications were made based on the U.S. Geological Survey Land- Use/Land-Cover Classification System for use with remotely sensed data.

Table 3: Land cover classification scheme

Land cover class Description This represents dense woody vegetation with little or no undergrowth. These are mostly evergreen the entire year and include forest reserves and natural growth along rivers. Closed Woodland

They are less dense woody evergreen vegetation. Trees are scattered interspersed with shrubs and grass.

Open woodland

This is characterized by mainly grass, shrubs/scrubs with a few scattered trees. It included grazing fields and aquatic vegetation.

Grassland

This included rivers, ponds and dammed reservoirs.

Open Water

This represented farmlands with temporary crops followed by harvest and a bare-soil period. They include very sparse farm settlements which are built of mud houses and thatched roof. Croplands

This included highly built areas and other human-made structures.

Settlements

It included eroded areas, excavated areas, rock outcrops and exposed soils with ≤ 10% vegetated cover during the year.

Bare Fields

Image classification A Support Vector Machine classification algorithm was used to classify the images. It enabled the incorporation of ancillary and non-statistical information in the classification process to increase accuracy as possible. A Radial Basis Function kernel type was used. This maps training samples nonlinearly into a higher dimensional space. Hence, it is able handle nonlinear relations between class attributes (Hsu et al., 2010). This was chosen as a classification approach with robust computation rules and accuracies mostly higher than other classifiers from evaluations prior to study (Huang et al., 2002).

The classification was carried out in ENVI 5.0 Classic. First the wet and dry season multispectral and enhanced images (EVI, TCT and SPCA) for the study area were layer stacked according to their respective years (Figure 3). This resulted in a 22 band image for both years used in the classification.

1. Landsat TM 2. SPCA derived 3. TCT derived 4. EVI derived Multispectral bands from the Landsat from the Landsat from the Landsat (excluding the thermal data (Component 1). data (Greenness, data. band). brightness, wetness components).

2 4 1 3

LAYER STACKING

1989 MULTI-BAND 2009 MULTI-BAND IMAGE IMAGE

Figure 3: Layer stacking process.

Regions of interest (ROIs) were created for each cover class with their respective color codes. An average of 51 ROIs were created for each class. For the 2009 image, the ROIs were created based on the interpretation of 2008 aerial photographs, Bing Map data (30 cm natural color DigitalGlobe aerial orthomosaics, 2012), observation during the field work, and false color composites of the Landsat data. The 1989 image ROIs were created based on the topographical maps, 1993 aerial photos, and false color composites of the Landsat data. Band combination like 7, 4, 2 (That is, displaying mid-infrared reflectance as red, near-infrared as green, and green as blue respectively) for instance provided a good amount of information about the vegetation of the study area. This helped to distinguish woody vegetation from grassland and croplands. In creating the ROIs, the dry season image helped in distinguishing water areas especially reservoirs as in the wet season image some areas were flooded because of the abundance of rain. The wet season image helped in distinguishing actual river beds as in the dry season most rivers are dried up. The ROIs were used to generate decision rules to classify the images.

The initial classification had some class confusion. Bare croplands for instance were confused with exposed soils and eroded areas. Also dried river beds were confused with settlements. To resolve this interclass confusion, some classes were sub classified. Open water was classified into rivers and reservoirs; cropland was classified active croplands and bare croplands. These sub-classification help reduced the class confusions. For instance most of the bare fields were classified as croplands when the general classification theme was used as some croplands were bare. With the sub-classes and increase in the training samples the class confusion was improved. The sub-classification classes were reclassified to reflect the initial classification scheme.

Classification Accuracy Assessment Accuracy assessment of the 2009 land cover map was based on the GPS and photo points taken during the field work. The 1989 image land cover map accuracy was assessed based on sample points taken from interpretation of the 1993 aerial photos and topographic maps. The accuracy points were independent of the points used in the image classification. A visual examination of the land cover maps was performed. They were reasonably accurate therefore a confusion matrix was created for both maps showing the user, producer and overall classification accuracies and kappa statistic accuracy was also calculated for both maps.

Change Detection After the accuracy of the classified images were assessed, a change detection was performed. Post classification change detection was utilized to detect changes between the 1989 and 2009 land cover maps. This technique was adopted for this study as it had the capacity showing the nature of the changes. The maps were compared on a pixel by pixel basis to produce a change map and a change matrix. The changed map was assessed and color coded to indicate areas of change and no change between the two dates.

CHAPTER FOUR

RESULTS ANALYSIS AND DISCUSSION LAND COVER MAPPING RESULTS AND DISCUSSION Two land cover maps were produced for the study area. These were produced from Landsat 4 and 5 TM multi-date images for 1989 and 2009 respectively (Table 1). A Support Vector Machine algorithm was used to produce the land cover maps after unsatisfactory attempts with other classifiers. The 1989 images were cloud-free, however the 2009 wet season image had some clouds and shadows. These had to be classified as no data, therefore some parts of the land cover map were missing class information. After the initial classification using the target classification theme, there were some interclass confusions. Generally, settlement class was confused with dry river beds and bare croplands confused with bare fields. To improve the confusions; classes like cropland were classified into two sub-class as bare and active croplands. This helped to reduce the confusion between bare croplands and bare fields which would have been classified as Croplands. Also the training samples for the classes were increased till the land cover maps were improved and class confusions reduced.

1989 Landsat TM Multi-date Image classification The 1989 image was classified into seven main land cover classes. After the initial classification, there was obvious class confusion. Notably dry river beds were classified as settlement, some bare croplands were classified as bare fields and some parts of the closed woodland being classified as grassland. In the case of the river beds being classified as settlement, both wet and dry season image did not indicate much water in the rivers at the time of image acquisition, therefore both the dry river bed and settlements had similar reflectance. This made it difficult for the classifier to separate these classes accurately. After several runs of the classification while improving the training data, a satisfactory result was achieved (Figure 4). The dominant cover type was cropland with a percentage cover of 71.97%, the least being settlements with 0.35% (Table: 4). However, it must be noted this figure does not encompass all of settlements, as most of the settlements in the study area were very sparse at this time period. Significantly, the building materials are made of mud bricks and thatch roofs, such settlements were classified to include croplands as attempt to separate them yielded no significant results. Bawku being the principal town of the Municipal was easily mapped because it is a built

24 environment built of cement blocks and aluminum roofing sheets.

Figure 4: Land cover map 1989

Table 4: Area and percentage area covered by each land cover class in 1989

Land cover Area (Hectares) Percentage Cover (%) Closed Woodland 7,596.99 7.73 Open Woodland 17,223.66 17.52 Grassland 570.51 0.58 Open Water 425.07 0.43 Croplands 70,764.75 71.97 Settlements 343.71 0.35 Bare Fields 1,404.54 1.43 Total 98,329.23 100.00

2009 Landsat TM Multi-date Image Classification As indicated the initial classification using the broad thematic classes did not yield a satisfactory results. Some of the classes were further sub classified to reduce the interclass confusion. After several classification runs with the sub classes, some classes like settlements, bare fields and dry river bed were still confused. Therefore coordinate location of these points were identified and checked using Bing maps. New training data were created and assigned to the right class and the classification algorithm was run again for a satisfactory result. The classes were combined to their respective thematic class. In all seven land cover classes were produced from the 2009 image (Figure 5). The dominant class was Croplands covering 68.0% indicating Bawku Municipal is an agrarian landscape, while the least class was Settlements covering 1.6% (Table 5).

Table 5: Area and percentage area covered by each land cover class in 2009

Land cover Area (Hectares) Percentage Cover (%) Closed Woodland 6,463.44 6.62 Open Woodland 13,884.93 14.21 Grassland 4,828.59 4.94 Open Water 2,212.20 2.26 Croplands 66,465.81 68.03

Settlements 1,482.03 1.52 Bare Fields 2,366.91 2.42 Total 97,703.91 100.00

A comparison of the two tables (Tables 4 and 5) shows that there was more open water in 2009 than 1989. While this is true, it’s overestimated. The wet image used in the 2009 image indicated the main rivers in the study area have overflown its banks as it was acquired at the peak of the rainy season. Again, there is more Cropland in 1989 than 2009. The statistics here was affected by cloud cover and shadows on the 2009 image which most covered Cropland areas. Considering the inter-tribal conflicts in the municipal during 2008 and 2009, lots of croplands were abandoned. This is evidenced by the production figures for 2010. While crop production was 102656 metric tonnes in 2010, production was 215143 metric tonnes (Ministry of Agriculture – Bawku Municipal, 2012).

Figure 5: Land cover map for 2009

Accuracy Assessment Results and Discussion The 2009 land cover map yielded an overall accuracy of 88.22% and a Kappa Coefficient of 0.86 (Table 6). This is within the acceptable range and shows the map is fairly accurate. Open water had the highest accuracy for both user and producer accuracies, 98.70% and 100% respectively. This does not come as a surprise as water is the most accurately classified class in most classification. Croplands had the least user accuracy 75.31% and grassland the least producer accuracy 76.32%. The other classes were fairly accurate having user and producer accuracies ranging between 84% to 100%

Table 6: 2009 land cover map error matrix

Ground Reference (Pixels) Open Closed Open Bare Classified User's Commission Classified Image Water Settlements Woodland Woodland Croplands Grassland Fields Total Accuracy Error Open Water 76 0 0 0 0 0 1 77 98.70% 1.30% Settlement 0 64 0 0 0 0 0 64 100% 0.00% Closed Woodland 0 0 72 4 0 1 0 77 93.51% 6.49% Open Woodland 0 0 0 65 7 2 0 74 87.84% 12.16% Croplands 0 11 1 5 122 15 8 162 75.31% 24.69% Grassland 0 0 3 2 6 58 0 69 84.06% 15.94% Bare Fields 0 1 0 0 3 0 67 71 94.37% 5.63% Reference Total 76 76 76 76 138 76 76 594 Producer's Accuracy 100% 84.21% 94.74% 85.53% 88.41% 76.32% 88.16% Omission Error 0.00% 15.79% 5.26% 14.47% 11.59% 23.68% 11.84%

Overall Accuracy 88.22% Kappa Coefficient 0.86

The 1989 land cover map had an overall accuracy of 85.20% and Kappa Coefficient of 0.82 (Table 7). Open Woodland and Bare Fields were poorly classified with producer accuracies of 50% and 63.16% respectively. This shows that more of the pixels for these classes were omitted and labeled as other classes. Only 50% Open Woodland was actually labeled correctly, 40% was labeled as Closed Woodland which indicates a confusion between the two classes. Similarly, Bare Fields were also confused with bare croplands, 61% was actually labeled as Bare Fields the rest was labeled as Croplands. This shows these classes exhibited similar reflectance patterns in the image making it difficult for the classifier to separate the actual classes accurately. Aside these classes, other land cover classes were classified satisfactorily.

Table 7: 1989 land cover map error matrix

Ground Reference(Pixels)

Open Closed Bare Open Classified User's Commission Classified Image Water Settlements Croplands Grassland Woodland Fields Woodland Total Accuracy Error Open water 76 0 0 0 0 0 0 76 100.00% 0.00% Settlement 0 71 0 0 0 0 0 71 100.00% 0.00% Croplands 0 4 134 4 4 28 2 172 77.91% 22.09% Grassland 0 0 0 59 3 0 5 67 88.06% 11.94% Close Woodland 0 0 0 0 69 0 31 100 69.00% 31.00% Bare Fields 0 1 4 0 0 48 0 53 90.57% 9.43% Open Woodland 0 0 0 0 4 0 38 42 90.48% 9.52% Reference Total 76 76 138 63 76 76 76 581 Producer's Accuracy 100.00% 93.42% 97.10% 93.65% 90.79% 63.16% 50.00% Omission Error 0.00% 6.58% 2.90% 6.35% 9.21% 36.84% 50.00%

Overall Accuracy 85.20% Kappa Coefficient 0.82

Overall, a considerable high accuracy was achieved considering how complex the landscape was and how some classes exhibited similar reflectance in the image. This accuracy is accounted to the fact that multi-date (dry and wet season) images were combined with other enhanced images before the classification. This improved the chance of distinguishing each class which would have been more difficult if a single image was used. Also using false color composites during the training data selection helped in distinguishing most target land cover classes and therefore efficient training data selection.

CHANGE DETECTION RESULTS AND DISCUSSION Post Classification Comparison To assess changes that have occurred in the study area between 1989 and 2009, each pixel value in the 1989 land cover map was compared with that of 2009. This produced a change matrix with a detail “from-to” change information about the land cover types (Table 8). From a general view there was significant land cover conversions and modifications (Figure 6). Closed Woodland had a loss of 14% equivalent to 1133.53 hectares, Open Woodland had 19% loss equivalent to 3338.73 hectares (Table 9). Croplands also lost 6.1% equivalent to 4298.98 hectares. This decrease was influenced by the clouds and shadows found on the 2009 image that were classified as no data. These covered most of the Croplands, hence a reduction in the actual size of Croplands. Settlements gained 331.2% equivalent to 1138.32 hectares, Bare Fields gained by 68.5% equivalent to 962.37 hectares, Grassland gained 746.4% equivalent to 4258.08

30 hectares. Open Water also gained 420.4% equivalent to 1787.13 hectares. Although, additional dams had been constructed which would affect the size of Open Water, the actual increase was influenced by the fact the 2009 wet image had the main river bodies over flowing their channels (Table 9).

Table 8: Land cover change matrix for 1989 and 2009 land cover maps

2009 Land Cover 1989 Closed Woodland Open Woodland Grasslands Open Water Croplands Settlements Bare Fields Class Total (Ha) Closed Woodland 1652.67 2490.84 912.15 349.83 2159.37 7.2 7.29 7596.99 Open Woodland 1830.42 6008.13 1169.01 737.01 7329.69 22.95 25.92 17223.66 Grassland 81.72 15.12 103.5 80.01 272.25 2.97 13.23 570.51 Open Water 24.39 30.87 23.22 295.29 43.2 0.45 7.47 425.07 Croplands 2868.21 5332.5 2605.05 591.12 55892.88 1123.02 1857.87 70764.75 Settlements 0.18 0.9 2.43 84.87 37.17 202.95 14.76 343.71 Bare Fields 5.85 6.57 13.23 74.07 731.25 122.49 440.37 1404.54 Class Total (Ha) 6463.44 13884.93 4828.59 2212.2 66465.81 1482.03 2366.91

Table 9: Land cover changes between 1989 and 2009

Land Cover Type 1989 (Hectares) 2009 (Hectares) Change (hectares) Percentage Change Closed Woodland 7596.99 6463.44 -1133.55 -14.9 Open Woodland 17223.66 13884.93 -3338.73 -19.4 Grassland 570.51 4828.59 4258.08 746.4 Open Water 425.07 2212.2 1787.13 420.4 Croplands 70764.75 66465.81 -4298.94 -6.1 Settlements 343.71 1482.03 1138.32 331.2 Bare Fields 1404.54 2366.91 962.37 68.5

Assessing the change matrix (Table 8), there was loss of wooded vegetation in the study area for the period of study. Open Woodland and Croplands accounted for a greater loss in Closed Woodland. Only the protected areas remain unchanged for the period of study. An obvious area of change between 1989 and 2009 indicating forest loss is around the Zawse Hills. The cover was mostly Closed and Open Woodlands, but it has been converted to Grassland and Croplands in 2009. Similarly, Croplands accounted for a greater loss of Open Woodland. This indicates human encroachment on these wooded resources. Aside cutting down the trees to make space for farming, this reduction is due to harvesting the trees for charcoal, firewood and building materials.

Some transitions are also observed among the vegetation classes. Over 2000 hectares of Closed Woodland changed to Open Woodland and about 900 hectares to Grassland. About 1800 hectares of Open Woodland also transitioned to Closed Woodland. This figure is doubtful as a low producer accuracy was recorded for this class for 1989, however a decent amount was transitioned to Croplands and Grassland. Additionally, during the site visits, most of the wood in the Open Woodlands were harvested for fuel and to give way for further cropping (Figure 8 D).

The matrix also indicated Croplands have reduced, where Closed and Open Woodlands accounted for the greater loss of Croplands, this is true to some extent as the local government and non-profit organizations have increased afforestation efforts in the Municipal, these efforts were focus at the reserve areas like the Binduri Reserve area. It can also be attributed to the fact that many households abandoned their Croplands when the 2008 – 2009 Bawku inter-tribal violence erupted in the Municipal. Such Croplands easily transitioned to Grassland and these were forage grounds for livestock during the field study.

Settlements also gained, principal settlements like Bawku, Pusiga, Pulmakoma and Sankaase expanded significantly. Mainly, Croplands were affected by the outward expansion of these commercial centers of the Municipal. Bare Fields also increased for the study period. Intensive cultivation, grazing and stone quarry activities have rendered some lands especially Croplands bare. During the field visits, it was recognized that these areas were highly eroded (Figure 8 C).

Figure 6: Change-No-Change 1989 – 2009 map

Land Cover Change Causes The current observed changes in the Municipal can be attributed to both human and environmental factors. While environmental factors can influence the way humans use land, at the current population growth, humans are perceived to alter the environment at unprecedented rate. These factors are explained as follows:

Demographics A look at inter-census (1970, 1984, 2000 and 2010) statistics indicated the population of the municipal has increased significantly (Figure 7). The population structure of Bawku Municipal further reveals most households are engaged in some form of agricultural activity either crop production or animal rearing or both. According to the Ghana Statistical Service (2010), about 83.7% households are engaged in agriculture as of 2010. The land cover maps produced for both dates support this fact as Croplands recorded the highest amount of total hectares. With this figure, more land resources are needed to meet the demands of this growing population. This pressure to cultivate more is increased as demand for grains from cities like Kumasi increases. During the field visits, some form of small-scale systematic rotation was recognized, but as population density is increased and continues cropping is intensified. Cultivation has further extended into reserve lands, which has increased the risk of environmental depletion.

In addition Bawku Municipal has a higher livestock population. As of 1999 the livestock population density was 76 per sq. km when the last livestock population census was taken (Ministry of Agriculture – Bawku Minucipal, 2012). Such a high density of livestock population is a proof of higher probability of over grazing as indicated by the UNEP, 1997.

Population Density: 1970 - 2010

2010 169

2000 103

CensusYears 1984 55

1970 36

0 20 40 60 80 100 120 140 160 180 Number of people per sq. Km

Figure 7: Bawku Municipal, population density from 1970 to 2010. Data source: Ghana Statistical Service

Wood Fuel Economy Nationwide biofuel is the main source of energy for cooking. The 2010 census indicated that 40% of Ghanaians used wood or firewood as cooking fuel and 33.7% charcoal. Out the national figure 7 of every 10 household in the northern regions where Bawku Municipal is located use wood or firewood (Ghana Statistical Service, 2010). Furthermore, charcoal is in high demand in the southern regions. Places like Kumasi and Accra demand most and charcoal used in these parts comes from northern districts like Bawku. It is no surprise there was loss in wooded vegetation while an increase in bare fields in the change detection. During the field visits, it was observed the wooded resources were being processed into charcoal (Figure 8 A and D). This situation is further worsened with current hike in price of Liquefied Petroleum Gas (LPG). Besides fuel, wooded resources in the district are used for constructing local houses, cattle kraals and support frames for vegetable garden fence made of mud.

Figure 8: Human factors influencing land cover change

Precipitation Savannas are also affected by variable rainfall pattern, low soil nutrients and regular burning (Young and Solbrig, 1993). Analysis of precipitation data for Bawku Municipal using two rainfall gauge stations from 1990 to 2009 indicated a normal rainfall distribution pattern which was fairly above the annual average. Only a couple of years between the time interval recorded below the annual average which indicates that there was no drought to have influence the loss of wooded vegetation recorded in the change detection.

Vegetation Loss A comparison of known areas of natural vegetation indicated there was a significant loss of wooded vegetation for the study period. The change detection analysis indicated a total of

33% equivalent to 4472.26 hectares of wooded vegetation was lost to other land cover classes (Figure 9). This has facilitated an increase in Bare Fields. The analysis indicated that Bare Fields increased by 68.5% equivalent to 962.37 hectares. Moreover, most of these Bare Fields are heavily eroded (Figure 8 C). The only dense wooded vegetation left were found in the protected areas. Wooded vegetation close to rivers were the most affected. They have been harvested as result of intensive farming along these features.

Figure 9: Conversion of wooded vegetation to other land cover classes

CHAPTER FIVE

CONCLUSION AND RECOMMENDATIONS SUMMARY OF RESULTS Overall two land cover maps were produced for the study area for the years 1989 and 2009. These were used in observing and assessing land cover changes in the Bawku Municipal for a 20 year period. A supervised classification based on Support Vector Machine (SVM) was used to classify the images. An overall accuracy of above 85% and a Kappa statistics of over 0.82 was achieved, with the 1989 image being the least accurate of the two cover maps. In this regard, Open Woodland and Bare Fields were poorly classified on the 1989 cover map with low producer accuracies of 50% and 63% respectively. The SVM tended to over-classify some of the land cover classes, cover class like Closed Woodland, Croplands and Grassland were observed to be to be over-classified. Accuracy was however improved when the training samples was increased. This produced a better results as compared to other classifiers used at the initial stage of the study.

There are several change detection techniques that could have been used in this study, however post classification comparison was used to detect land cover changes in the Municipal. It proved to be effective as atmospheric influence on the images used did not account so much. It produced a detailed “from-to” change trajectory for the land cover class for the study period. It must be noted that accuracy of the change detection results depends on the accuracy of the classified maps used. In this case, the overall accuracy of the individual maps were satisfactory. For the study period, the results indicated that land cover at Bawku Municipal have changed significantly. Although land cover modification was common, land cover conversions was also observed. Notably, wooded vegetation was converted to grassland or croplands. A total of 4472.26 hectares of wooded vegetation was converted mainly to croplands and grassland. This results is an indication of environmental degradation and confirms the earlier works of Nsiah- Gyabaah, 1994 and Yiran et al., 2012.

There are some limitations to study that must recognized. As a typical problem within the tropics, cloud free images were difficult to come by. This problem also made it difficult

39 obtaining anniversary dates to reduce seasonal variability in the images used. Ancillary data that was used to make inferences from for the classification of the 1989 multi-date image was limited. Training sites were selected based on the interpretation of 1993 aerial photographs and topographical maps based on 1960 and 1961 aerial photographs. Although, most features were recognized to be fairly unchanged, close or actual 1989 aerial photographs would have influenced and improved the results achieved. In addition, the field work was conducted in 2013 while a 2009 multi-date image was used. A more recent image could have produced a more accurate results.

Furthermore, similarities in the reflectance patterns of some of classes had limitation on the image classification. For instance, Open Woodland and Closed Woodland exhibited similar reflectance and Bare Fields, dry river beds, settlements and bare croplands had similar reflectance. This affected the accuracy of these classes, especially the Open Woodland and Bare Fields.

Changes in a landscape are not continuous, some of the changes observed are reversible. A third image between the two dates could have improve the understanding of the land cover change trajectories in the Municipal. Changes that were reversible could have been identified, hence concrete inferences could have been made whether the change observed is positive or negative.

CONCLUSION It is evidenced by the study that land cover changes have occurred at the Bawku Municipal. While the changes observed were the result of both human and natural agents, human action was a strong influence on the changes observed. Population growth, unsustainable farm practices and expansion and poor environmental management have caused the vegetation to deplete. While there was 33% decrease in wooded vegetation, there was increase of 68% in Bare Fields. This result supports and emphasizes findings of earlier studies that Bawku Municipal and other regions with savanna ecosystems are at risk of the impact of high population growth.

Post classification comparison is also proven to be a useful indicator of change especially for arid and semi-arid environments. It is able to make both qualitative and quantitative inferences about changes in a landscape. However, it is recommended that intensive field data

40 should be gathered to aid in the image classification, as the change results depend on the accuracy of the classified maps. In savanna environments where there is a complex reflectance patterns among the cover types, training samples or data should be taken directly from the field and not solely based on high resolution images.

RECOMMENDATIONS It is recommended that government agencies, especially the Environmental Protection Agency and other non-governmental agencies dedicated to environmental work should focus more on education rather than afforestation practices. Although government and institutions have pushed for afforestation over the years, it is barely enough. In fact more resources should be invested in sustainable farming and management.

The government should include remote sensing and Geographic Information Systems as integral part of environmental monitoring efforts in Ghana to establish detail trends and impacts of land cover changes. This will facilitate efforts to address environmental issues that has plagued the country.

Future research on this problem should undertake an intensive field measurements to achieve higher image classification accuracy. The use of a higher temporal and spatial resolution image can also be considered. Also biophysical data like rainfall should be included and analyzed quantitatively to improve the understanding of role of climate in the changes observed. If possible future research should consider a model that could integrate biophysical data with change data to establish a correlation between the data and the changes observed. In addition, a third image should also be considered when the duration of the period study is lengthy.

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