T he Land Use Land Cover Classification and Mapping of South-West Nigeria is part of an intensive and extensive regional study initiated by the National Space Research and Development Agency (NASRDA) and its South-West Regional Centre, Cooperative Information Network (COPINE)/Advanced Space Technology Application Laboratory, Obafemi Awolowo University. (OAU) Campus, Ile-Ife, Osun State. The Land Use Land Cover of the South West is in line and in pursuit of one of the core mandates of the Agency supported by the Federal Ministry of Science and Technology, Ministry of Environment, States and the Federal Government.
The Land Use Land Cover Classification and Mapping concepts were discussed, planned and well-articulated by different working groups with valuable inputs from different sister agencies, researchers, experts, stakeholders, students and residents in all the communities visited during the fieldwork. In particular, the following agencies made immense contributions to the success of this project, they are, the Federal Ministry of Science and Technology, Obafemi Awolowo University, Ile-Ife, Government of Osun and Oyo states and the Department of Geography, O.A.U. Also to be acknowledged is the significant role of other institutions and individuals who responded and provided data for this project. Our parent agency, NASRDA, for the provision of the Nigerian Sat I and Sat 2 satellite images, Global andSat Facility for the provision of LandSat data, Google Earth Facility which aided the supervised classification and the Food and Agricultural Organisation,FAO, for providing the classification scheme used.
We further appreciate the supportive and inspiring roles of Professor S.O.Mohammad, Director-General, NASRDA, Mr A.T Alaga, National Coordinator of COPINE, Dr Shabba, Director Strategic Space Application, Mr Ogbole John, Mrs Adewoyin, all group leaders, and the scientific staffs of COPINE. We Remain Grateful to the people of South-West Nigeria who welcomed us with unexplainable hospitality, during our field work across the states. Their inputs, ideas, and suggestions were of great value to the success of this project.
Although, the study of Land Use/Land Cover and its dynamics has received considerable attention in Nigeria particularly in the South-West, the availability of data for public application is grossly insufficient. Information on Land Cover/Land Use and its changes is essential for regional planning, agricultural landuse, sustainable management of land resource, increase of crop production and environmental protection. However, in most parts of Nigeria, including the South- West, such scientific information are not available at the state and local government levels.
Of no doubt, considerable attention has been given to the study of Land Use/Land Cover and its drivers particularly in the South-West region of Nigeria in the last decade but these researchers were championed by individuals, post-graduate students and a few institutions. The outlook of these studies was localized, restricted, outdated and most often confined to arbitrary region of interest and therefore most of the results could not give the needed information for regional or state level analysis. However, they provide the framework and idea for the large-scale study. Study on Land Use / Land Cover has become imperative and a necessary fundamental dataset for planning in all ramifications. The impact of its absence in most states in the region is glaring and therefore needs to be addressed. In addition, Land Use Land Cover (LULC) changes in tropical regions are of major concern due to the widespread and rapid changes in the distribution and characteristics of tropical forests (Myers 1993, Houghton 1994, Daniel and Salami 2007). In South-West Nigeria, despite on-going research efforts, there remains a need for the development of basic dataset and thematic base maps providing quantitative and spatial Land Use Land Cover information. Moreover, there appears to be a gap in the available information at the national and local levels for use in regional and national decision- making process and sustainable management.
T he South West Nigeria has six states: Ekiti, Lagos, Ogun, Ondo, Osun and Oyo. The region is located within Latitude 5' 45' and 9°30' N and Longitude 2° 30' and 6 20' E. (figure 1). The region covers an area of 78,771 km and occupy 8.53 % of Nigeria landmass. It is majorly a Yoruba speaking area, although there are different dialects even within the same state. The weather conditions vary between the two distinct seasons in Nigeria; the rainy season (March-November) and the dry season (November of - February). The onset of dry season which usually falls within November comes with d the harmattan dust; cold dry winds characterized with low humidity from the northern deserts. Major part of the region falls within the rainforest vegetation belt while other parts, particularly the Northern parts of Oyo, Osun and Ekiti state fall within the Guinea Savannah. With respect to relief, the region is characterized with both plain land and trugged hills such as the Ikere-Ekiti hills, Efon Alaaye hills Ado-Ekiti and Idanre Hills (one of the most beautiful natural landscapes in Ondo State and Nigeria). The region is drained majorly by Osun-Ogun, Shasha, Owene, and Yewa rivers which form the hydro basin of the region. Other prominent water bodies include: the Lagos Lagoon, Eleyele and Ikere-Georg Dam in Oyo state etc. The major source of occupation and income in the region is agriculture, commerce, industries, and local craft. (https://www.myguidenigeria.com/regionalinfo/south-west-region).
P rimary and secondary data which contain both spatial and non-spatial attribute was utilized for this project. The primary data include, Nigerian Satellite image (Nigeria Sat1 and NigeriaSat 2), LandSat 8, coordinate values of observation points and their attributes, parallel interview and focus group discussion records. The secondary data include topographic maps on the scale of 1:50,000 covering the entire state, administrative maps, historical data and literature, google earth image facilities and patches of other satellite imagery covering different parts of the state.
Imagery from the same sensor type with the same anniversary date or within a given anniversary window was utilized. Using imageries within the same anniversary window helps in minimizing discrepancies of reflectance caused by seasonal vegetation, land flosses and sun angle differences (Coppin and Bauer 1996). In line with the recommendation of Hame (1986), Burns and Joyce (1981), NigeriaSat image acquired within December and March was utilized. NigeriaSat multi-spectral images acquired within this period are the best for Land Use Land Cover and other related studies and this is because of the dryness, and reflectance stability, enhance spectral separability and minimized cloud cover and spectral similarity due to excessive wetness prevailing in other period of the year,
NigeriaSat I is Nigeria's contribution to the international Disaster Monitoring Constellation (DMC) project. It is an earth observing microsatellite built by SSTL on the Microsat-100 platform. It features a 32m resolution imager in 3 spectral bands. The Disaster Monitoring Constellation (DMC) is a novel international cooperation in space, launched by CSSTC bringing together organizations from seven countries. Algeria, China, Nigeria, Thailand, Turkey, the United Kingdom and Vietnam. The DMC consortium is forming the first ever micro-satellite constellation bringing remarkable earth observation capabilities both nationally to the individual satellite owners, and internationally to benefit World-wide Humanitarianism and efforts (www.space,skyrocket.de).
Landsat 8 is an America Earth Observation satellite which form part of the Landsat Data Continuity Mission (LDCM). The LDCM spacecraft is mounted with Operational Land Imager (OLI) and Thermal Infra-Red Scanner (TIRS) sensors. The Operational Land Imager (OLI) provides two new spectral bands in respect to the Landsat 7 ETM+ instrument, one tailored especially for detecting cirrus clouds (band 9, new Near Infra-Red (NIR) band) and the other for coastal zone observations (band 1, new deep blue visible channel). It measures in the visible, NIR, and Shortwave Infra-Red (SWIR) portions of the electromagnetic spectrum and offers 15 metre panchromatic, and 30 metre multi-spectral spatial resolution. The landsat Thermal Infra-red Scanner (TIRS) provides two more narrow spectral bands in the thermal (originally covered by a single band in the previous TM and ETM+ sensors) and is a QWIP (Quantum Well Infra-Red Photodetector) based instrument intended to supplement the observation of the OLI instrument (https://landsat.usgs.gov/landsat-8-history).
G Ground trothing was carried out before and after the satellite image classification. The ground trothing and field work exercise lasted for two years between 2016 and 2017. And was intensively and extensively carried out across the South-West states. The ground trothing exercise was significant and enabled the physical conformation of spatial information and features from the imageries. It also aids the generation of attribute information that was useful for the training of the image data. Furthermore, characteristic photographs and coordinate values were taken to enhance the whole process.
T The participatory imagers which include: Nigeria Sat 1 and Land Sat 8 were imported in Geo-tiff format. Gram-Schid spectral sharpening was used sharpen bands 5,4,3 of Landsat image, all with 30 m to 15 m resolution using band 8 (15 m resolution). While image equalization was carried out on Nigeria Sat 1to enhanced the radiometric quality, subsequently, the bands of interest were selected and stacked. From the stacked bands, a colour composite of near infrared, Red and Green were generated and re-sampled in a new display. This combination has been regarded as efficient and adequate when using Nigeria Sat and Landsat image data for studying Land Use / Land Cover especially if it has to do with vegetation, farmland, water body, wetland, bare surface and built-up area. (Jensen, 1986). After the colour composite, the image subset was created using the Region of Interest (ROI) vector frame representing the different Administrative Boundary of State and Local Government Area (LGA).
A fter the False Colour Composite (FCC), to commence the image classification, a classification schema (which include: Built up, Natural vegetation, Wetland, Water body, Bare surface, altered vegetation) were created. Homogeneous pixels identifiable from the image and through ground truthing were trained and exported to an n-D visualizer to check the separability of the trained pixels. (The n D visualizer locates, identify and cluster the purest pixels and the most extreme special responses in a dataset. Supervise Classification is used to check the accuracy of the trained pixels). After the n D visualization check the trained image is classified -- using maximum likelihood supervised classification techniques. The methodology flowchart is shown in figure 2.
F actors limiting the classification accuracy of Land Use Land Cover for this study includes: