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\begin{document}

             \author[1]{A.  Dami.}

             \author[2]{H. K.  Ayuba}

             \author[3]{J. O.  Odihi}

             \affil[1]{  University of Maiduguri}

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\date{\small \em Received: 10 December 2013 Accepted: 5 January 2014 Published: 15 January 2014}

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\begin{abstract}
        


The study examines land use and land cover change in Kwale Ndokwa-East Local Government area Delta State, Nigeria between 1975 and 2008 using GIS and remote sensing technique. The satellite data that were employed included LandSat (MSS) 1975, LandSat (TM) 1987, LandSat (ETM+) 2001, downloaded from Global Landcover Resources Website (http:www.glcf.com), while images from NigSat1 2008 were obtained from the National Centre for Remote Sensing, Jos, Plateau state, Nigeria. The software used for the processing and analysis for this study includes ARCGIS 9, ERDAS 8.1 and ILWIS 3.2a. Results of the study revealed that on the average, between 1975 and 2008, bare surfaces decreased to by 93.51%: forest vegetation by 30.98%: settlement by 25.61% and woodlands by 37.19. Marshlands, cultivated lands;, shrublands and water bodies increased respectively by 54.45%, 24.42%;, 3.21% and 319.91%. This showed that bare surfaces, forest vegetation, settlements and woodlands were gradually being replaced by marshlands, cultivated lands, shrublands as well as water bodies. Settlements were found to be aggregating within specific geographic regions, over time. It is therefore recommended that concerted efforts be made to reclaim the areas occupied by bare surface and marshlands into arable agricultural lands. And finally, further efforts should be devoted towards reducing gas flaring, increasing afforestation strategies while discouraging lumbering, oil spillage as well as gas flaring within the region.

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\keywords{land use, change, remote sensing, GIS, kwale, nigeria.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
wale falls within the Niger Delta region of Nigeria. The area is located within latitudes 5º40 1 N and 5º50 1 N and longitudes 6º15 1 E and 6º30 1 E (Figure \hyperref[fig_0]{1a}, b) (Anonymous, 2011 and \hyperref[b2]{Avbovbo and Ogbe, 1978)}. The Niger Delta is located within the southern part of Nigeria. It is home to numerous creeks, rivers and possesses the world's largest wetland with significant biological diversity  {\ref (Twumasi and Merm, 2006)}. Okpai/Aboh region is within Ndokwa East Local Government Area and is situated within the Sombriero Warri deltaic plain deposit invaded by mangroves. The geographical Niger Delta has been said to cover an estimated area of between 19,100 km 2 to 30,000 km 2  based on hydrological, ecological as well as political boundaries \hyperref[b14]{(Keddy, 2010;}\hyperref[b13]{Ibe, 1988;}\hyperref[b18]{Merki, 1972 and}\hyperref[b20]{Murat, 1972)}. Okpai/Aboh region is a low-lying area with elevation of not more than 3.0 metres above sea level and generally covered by fresh water, swamps, mangrove swamp, lagoonal marshes, tidal channels, beach ridges and sand bars along its aquatic fronts (Dublin-Green et al, 1997). The area has a characteristic tropical monsoon climate at the coast with rainfall peaks in June and September/October with prevailing tropical maritime air mass almost all year round with little seasonal changes in wind directions \hyperref[b27]{(Olaniran, 1986)}. Annual mean total rainfall is about 2,500mm. The mean monthly temperature range from 24-25 o C during the rainy season in August to 27-29 o C during the end of dry season in March/April. \hyperref[b15]{Leroux (2001)} reported that maximum temperatures are recorded between January and March (33 o C) while minimum temperature are recorded in July and December (21 o C), respectively. Temperatures are moderated by cloud cover and damp air. It experiences a humid tropical equatorial climate consisting of rainy season (April to November) and dry season (December to march). The average annual rainfall is about 2,500mm while the wind speed ranges between 2-5m/s in the dry season to up to 10m/s in the rainy season especially during heavy rainfall and thunderstorms. The region is criss-crossed with distributaries and creeks. This area has been classified geomorphologically as tidal flat and large flood plains lying between mean, low and high tides. Three different highs exist within the Kwale, in Ndokwa-East Local Government block, namely a central high where most of the wells have been drilled, an eastern high housing one well and a north western high whose extent has not been clearly defined. The area lies within the freshwater forested region of the Niger Delta. The coastal areas of the Niger Delta are the home to oil exploration and exploitations in Nigeria \hyperref[b23]{(Nwilo and Badejo, 1995)}. This is largely due to the huge deposits of crude oil and natural gas deposits within the region. The World Bank report of 2002 succinctly stated that Rivers and Delta states alone produced about 75\% of Nigeria's petroleum, which represents over 50\% of national government's revenues. The report also rated, Nigeria as the fifth largest supplier of crude oil to the United States (EIA, 2003). Nigeria's proven oil reserves drives the economy because it is almost exclusively dependent on earnings from the oil sector, which generates about 20\% of GDP, 95\% of foreign exchange and about 65\% of budgeting revenues (CIA World fact Book, 2008). No doubt, human activities like oil exploration and production have impacted negatively on the delicate balance of nature and the fragile ecosystems of the study area.\par
Land use and land cover have become very important parameters in highlighting such environmental changes that have taken place over time within the earth's surface \hyperref[b17]{(Matiko et al, 2012)}. It has become one of the major parameters for environmental change monitoring and natural resource management \hyperref[b39]{(Zhang et al, 2008)}. Thus, \hyperref[b11]{Fuchs (1996)} aptly stated that land use and land cover and impacts on terrestrial ecosystems including forestry, agriculture, and biodiversity have been identified as high priority issues at global, national, and regional levels. The indirect impact of landuse and land cover is altering climate on the waters \hyperref[b37]{(Weng, 2001)} while the direct effect could be compromising water quality \hyperref[b29]{(Rogers, 1994)}. Kwale region is not alone with respect to deterioration of its landscape. \hyperref[b38]{Woodgate and Black (1988)} reported that an estimated 66\% of Victoria's native vegetation has been cleared as a result of the growth and economic development of the State. Geographic Information Systems (GIS) Global Positioning System (GPS) and Remote Sensing (RS) have become indispensable tools in almost all environmental endeavors (UN, 1986). These concepts have been employed in various studies including atmospheric studies \hyperref[b10]{(Fagbeja, 2008)}, lithospheric (Maruo et al, 2002), hydrologic (Nwilo and Badejo, 1995)   biodiversity \hyperref[b25]{(Salami and Balogun, 2006)}, assessment of developmental change over time  {\ref (Twumasi and Merem, 2006)}, land use and land cover categories (Ehlers et al.,  1990; Treitz et al., 1992) as well as ground water (Maruo  et al, 2002). Kwale region's landscape had undergone environmental changes over a long period of time as a result of oil exploitation in the area. This environmental change, therefore, has necessitated the need to carry out a holistic approach to land use and land cover inventory of the area with a focus of establishing the geospatial infrastructure for policy makers as well as for proper planning and management of the environmental conditions of the region. 
\section[{II.}]{II.} 
\section[{Methodology}]{Methodology}\par
The types of data acquired for this study are shown in Table \hyperref[tab_0]{1}. They were sourced from global Land cover resources website (http:www.glcf.com), while the image from the Nigsat1 2008 was obtained from the  
\section[{Data Extraction Process}]{Data Extraction Process}\par
Following the acquisition of the required satellite images from their respective sources for the aforementioned years, the extraction of the study area portion from the entire image covering the entire South Western /South Southern corner of the country was done using ArcGIS. The georeferencing of the satellite data as well as the subset operation using ILWIS 3.3 Academy software was performed.\par
IV. 
\section[{Digital Image Processing and Analysis}]{Digital Image Processing and Analysis}\par
The stage of analysis include a reconnaissance field survey (ground truthing) with GPS to obtain coordinates of each location; the 1975 topographic sheet (1: 25,000) covering the entire region was used to aid in identifying notable spatial features of the area. This process proved very useful in unraveling, demystifying and harmonizing the disparity between what was observed on ground and their respective spectral signatures displayed in the images. In this regard however, it was observed that both bare surfaces and settlements exhibited somewhat similar spectral characteristics as both randomly did have a mix of cyan and white color, which are the standard color representations for both settlements and bare surfaces.\par
The procedure developed for the sample dataset of the submap was carried out based on the supervised classification techniques using the eight (8) land use/cover classes (features) of the area as indicated in Table \hyperref[tab_1]{2}. Furthermore, the maximum likelihood method of classification (MLC) in the ILWIS 3.3 Academic software was adopted for the classification. The maximum likelihood method is a statistical decision rule that examines the probability function of a pixel for each of the classes, and assigns the pixel to the class with the highest probability. The classifier assumes that the training statistics (sample sets) for each class have a normal or 'Gaussian' distribution. The classifier then uses the training statistics to compute a probability of whether of a pixel belonging a particular land cover. This allows for within-class spectral variance. MLC usually provides the highest classification accuracies. Accordingly, it has a high computational requirement because of the large number of calculations needed to classify each pixel (Natural Resources Canada, 2005).\par
Three softwares were used to analyse the spatial data. ARCGIS was used for curve fitting processing while ERDAS Imagine was used for land use land cover classification, evaluating the quality of input data and ensuring that thematic maps were accurately classified. Finally, ILWIS (Integrated Land and Water Information System) was very useful in combining raster (image analysis), vectors and thematic data operations in one comprehensive phase.\par
V. 
\section[{Results and Discussion}]{Results and Discussion}\par
Table \hyperref[tab_2]{3} shows that bare surfaces rose astronomically from 35,395 km² in 1975 to 154,630 km² in 1987 representing an area change of 119,235 km² (336.87\%). This could be due to the establishment of the Agip Gas Plant. which started operation within the area in 1975 (NAOC, 2007). Oil exploration and production activities abound in the region \hyperref[b24]{(Oboli, 1978)}. From 1975 to 1987 oil exploration and exploitation activities were at their peak.\par
Close observation reveals that areas covered by thick oil sleeks after oil spillage, do become bare with time \hyperref[b9]{(Fabiyi, 2008)}. This could be responsible for huge leap of bare surfaces from the 1975 and 1987. In 2001, there was a significant decrease in land area to 61,374 km², accounting for 25,979 km² (73.40\%) area change. By 2008, there was a further significant decrease to 2,296 km representing -33,099 km² (-93.51\%) area change. This gross reduction from the 1987 estimates to those of the 2001 and 2008 could be as a result of the frantic efforts of prospecting oil and gas companies at carrying out environmental remediation and mitigation mainly through phytoremediation within the study area. This showed that bare surfaces are losing their space to marshlands, cultivated lands shrub lands and water bodies (Figure \hyperref[fig_3]{2}).\par
The area had a forest reserve that was recognized by the Federal Government of Nigeria as far back as 1975 (Mensah and Amukali, 2000; \hyperref[b8]{Ekine and Iyabe, 2009)}. Also, the Green Revolution of  Field observations revealed that the study area is exposed to massive deposition of organic agents like silt, clay, debris and a host of other decomposable materials as supported by \hyperref[b12]{(GGFRI, 2009 and}\hyperref[b1]{Allen, 1972)}. Thus, the increase in marshlands area prior to 1987, but when oil related activities increased within this area, after the 1980's, easy formation, transportation and deposition of marshlands became affected and this could be  
\section[{Conclusion}]{Conclusion}\par
The delicate balance of nature and fragile ecosystem of the Kwale in Ndokwa-East Local Government area has been altered by natural and human factors over time. This study was able to model the long term land use and land cover changes between 1975 when the area was still free of exploration and exploitation activities to 2008 when oil-related activities reached their peak and provide analysis of LUCC information in the area which helped in showing significant trends. The results of this study showed that between 1975 and 2008, bare surfaces decreased by 33,099 km² representing 93.51\%, forest vegetation to 14,054 km² amounting to 30.98\%, settlement to 5,654 km² which is equivalent to 25.61\% and woodlands 133,377 km² representing 37.19\%. Furthermore, scattered cultivation, scrublands and water bodies correspondingly increased by 68,294 km² (54.45\%), 77,603 km² (124.42\%), 6,322 km² (3.21) and 31,274 km² (319.91\%), respectively. This indicate that bare surfaces, forest vegetation, settlements and woodlands were gradually being replaced by marshlands, scattered cultivation, shrublands as well as water bodies. This study therefore, recommends the reclaiming of the areas occupied by bare surfaces and marshlands to agricultural activities to reduce poverty and improved food security in the region.\par
VII.\begin{figure}[htbp]
\noindent\textbf{1a}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1a :}\end{figure}
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\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.19029850746268656\textwidth}P{0.34888059701492535\textwidth}P{0.1966417910447761\textwidth}P{0.11417910447761194\textwidth}}
\multicolumn{2}{l}{S/No Satellite imagery}\tabcellsep \multicolumn{2}{l}{Year Resolution}\\
1\tabcellsep Landsat MSS\tabcellsep 1975\tabcellsep 60m\\
2\tabcellsep Landsat TM\tabcellsep 1987\tabcellsep 28.5m\\
3\tabcellsep Landsat\textunderscore ETM\tabcellsep 2001\tabcellsep 28.5m\\
4\tabcellsep Nigsat 1\tabcellsep 2008\tabcellsep 28.5m\\
III.\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_0}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2} \par 
\begin{longtable}{P{0.28159509202453986\textwidth}P{0.5684049079754602\textwidth}}
\tabcellsep the Study Area\\
Code\tabcellsep Class\\
1.\tabcellsep Bare Surface\\
2.\tabcellsep Forest Vegetation\\
3.\tabcellsep Marshland\\
4.\tabcellsep Cultivated land\\
5.\tabcellsep Settlement\\
6.\tabcellsep Shrubland\\
7.\tabcellsep Water Body\\
8.\tabcellsep Woodland\\
\multicolumn{2}{l}{Sources : Adapted from Dami (2003)}\end{longtable} \par
 
\caption{\label{tab_1}Table 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.4893496932515337\textwidth}P{0.005006134969325154\textwidth}P{0.011263803680981595\textwidth}P{0.32164417177914106\textwidth}P{0.011263803680981595\textwidth}P{0.011472392638036809\textwidth}}
\multicolumn{2}{l}{Main Landuse/ Cover Class}\tabcellsep 1975\tabcellsep 1987\tabcellsep 2001\tabcellsep 2008\\
\multicolumn{2}{l}{Bare Surface ( km²)}\tabcellsep 35,395\tabcellsep 154,630\tabcellsep 61,374\tabcellsep 2,296\\
\multicolumn{2}{l}{Forest Vegetation ( km²)}\tabcellsep 45,363\tabcellsep 10,910\tabcellsep 46,873\tabcellsep 31,309\\
Marshland\tabcellsep ( km²)\tabcellsep 125,431\tabcellsep 197,752\tabcellsep 154,105\tabcellsep 193,725\\
\multicolumn{2}{l}{Scattered Cultivation( km²)}\tabcellsep 62,374\tabcellsep 61,916\tabcellsep 88,156\tabcellsep 139,977\\
\multicolumn{2}{l}{Settlement ( km²)}\tabcellsep 22,074\tabcellsep 17,437\tabcellsep 13,375\tabcellsep 16,420\\
Shrubland\tabcellsep ( km²)\tabcellsep 196,724\tabcellsep 79,998\tabcellsep 267,613\tabcellsep 203,046\\
Water Body\tabcellsep ( km²)\tabcellsep 9,776\tabcellsep 13,215\tabcellsep 4,556\tabcellsep 41,050\\
Woodland\tabcellsep ( km²)\tabcellsep 355,979\tabcellsep 317,258\tabcellsep 217,064\tabcellsep 225,293\\
\multicolumn{3}{l}{responsible for the decrease in marshlands noticed in}\tabcellsep \multicolumn{3}{l}{and spread of settlement areas must have contributed}\\
\multicolumn{3}{l}{2001 while the further increase in the 2008 value could}\tabcellsep \multicolumn{3}{l}{to the initial increase in shrub lands but later}\\
\multicolumn{3}{l}{be attributed to factors like lumbering and farming as}\tabcellsep \multicolumn{3}{l}{unavoidable reduction in shrub lands in the area. This}\\
\multicolumn{3}{l}{well as those factors that earlier favored increases.}\tabcellsep \multicolumn{3}{l}{scenario depicts high amount of human and economic}\\
\multicolumn{3}{l}{Mensah and Amukali (2000) described the rural}\tabcellsep \multicolumn{3}{l}{activities within the area after 1975. But, since most}\\
\multicolumn{3}{l}{communities in the area as rural subsistence farmers. In}\tabcellsep \multicolumn{3}{l}{shrubs are seasonal plants that grow massively during}\\
\multicolumn{3}{l}{1975, scattered cultivated areas were estimated at}\tabcellsep \multicolumn{3}{l}{rainy seasons, it could be deduced that the time the}\\
\multicolumn{3}{l}{62,374 km² which slightly decreased to 61,916 km²}\tabcellsep \multicolumn{3}{l}{images were taken could have influenced the results,}\\
\multicolumn{3}{l}{representing area change of -458 km² (-0.73\%) in 1987.}\tabcellsep \multicolumn{3}{l}{hence the huge jump from the 1975 to that of 1987 and}\\
\multicolumn{3}{l}{In 2001, there was a massive increase to 88,156 km²}\tabcellsep \multicolumn{3}{l}{later from 2001 to 2008 respectively. Water bodies area}\\
\multicolumn{3}{l}{representing area change of 25,782 km² (41.34\%) and}\tabcellsep \multicolumn{3}{l}{were calculated to be 9,776 km² in 1975 and increased}\\
\multicolumn{3}{l}{this continued till 2008 where an increase of 139,977}\tabcellsep \multicolumn{3}{l}{to 13,215 km² representing area change of 3,439 km²}\\
\multicolumn{3}{l}{km² representing area change of 77,603 km² (124.42\%)}\tabcellsep \multicolumn{3}{l}{(35.18\%) in 1987 but dwindled to 4,556 km²}\\
\multicolumn{3}{l}{occurred.. The slight decline of scattered cultivated}\tabcellsep \multicolumn{3}{l}{representing area change of -5,220 km² (-53.40\%) in}\\
\multicolumn{3}{l}{areas from 1975 to 1987 must have been influenced by}\tabcellsep \multicolumn{3}{l}{2001. In 2008, water bodies increased to an estimated}\\
\multicolumn{3}{l}{farmers giving up farming to taken in juicy jobs in the oil}\tabcellsep \multicolumn{3}{l}{area of 41,050 km² representing area change of 31,274}\\
\multicolumn{3}{l}{industry. Settlement areas decreased from 22,074 km²}\tabcellsep \multicolumn{3}{l}{km² (319.91\%). Seasonality must have influenced the}\\
\multicolumn{3}{l}{in 1975 to 17,437 km² representing area change of -}\tabcellsep \multicolumn{2}{l}{trend as observed in the study.}\tabcellsep \\
\multicolumn{3}{l}{4,637 km² (-21.01\%) in 1987 and later to 13,375 km²}\tabcellsep \multicolumn{3}{l}{Massive accumulation of marshlands, drastic}\\
\multicolumn{3}{l}{representing area change of -8,699 km² (-39.41\%) in}\tabcellsep \multicolumn{3}{l}{reduction in the number of forest vegetation, shrub}\\
\multicolumn{3}{l}{2001. However, by 2008 the areas covered by}\tabcellsep \multicolumn{3}{l}{lands and woodlands all expose surface water bodies to}\\
\multicolumn{3}{l}{settlements were shown to be 16,420 km² representing}\tabcellsep \multicolumn{3}{l}{the direct influences of the vagaries of weather, thereby}\\
\multicolumn{3}{l}{area change of -5,654 km² (25.61\%), respectively. As}\tabcellsep \multicolumn{3}{l}{contributing to increased evaporation. Thus, water-}\\
\multicolumn{3}{l}{shown from the interpreted satellite images (Figure 2),}\tabcellsep \multicolumn{3}{l}{holding capacities of soils decrease, making them lose}\\
\multicolumn{3}{l}{settlements were initially seen to be scattered but in}\tabcellsep \multicolumn{3}{l}{same to ground, surface and atmospheric sources.}\\
\multicolumn{3}{l}{2008, the settlements became more concentrated within}\tabcellsep \multicolumn{3}{l}{Woodlands reduced from 355,979 km² in 1975 to}\\
\multicolumn{3}{l}{specific geographical regions. This trend could be}\tabcellsep \multicolumn{3}{l}{317,258 km²m in 1987 representing area change of -}\\
\multicolumn{3}{l}{explained by the recent resettlement of some}\tabcellsep \multicolumn{3}{l}{38,721 km² (-10.88\%) and to 217,064 km² representing}\\
\multicolumn{3}{l}{communities within the study area to pave way for more}\tabcellsep \multicolumn{3}{l}{area change of -138,915 km² (-39.02) in 2001. Finally, in}\\
\multicolumn{3}{l}{oil exploration and exploitation. Shrub lands decreased from 196,724 km² in 1975 to 79,998 km² representing area change of -116,726 km² (-59.34\%) in 1987. This later increased to 267,613 km² representing area change of 70,889 km² (36.04\%) in 2001 but decreased 203,046 km² in 2008 representing area change of 6,322 km² (3.21\%). Afforestation efforts or seasonal regeneration of plants during the 1990's as at the time the images were captured must have been responsible}\tabcellsep 2008, the VI.\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{for the increase noticed from 1987 to 2001. It could also}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{be due to decreased activities of oil prospecting and}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{production companies within the area owing to the}\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{3}{l}{activities of militants, increased agricultural cultivation}\tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_2}Table 3 :}\end{figure}
 			\footnote{Assessment of Land use and Land Cover Change in Kwale, Ndokwa-East Local Government Area, Delta State, Nigeria} 		 		\backmatter   			 
\subsection[{Acknowledgement}]{Acknowledgement}\par
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