4.0. Classification of land use/land cover of Aniocha north local government area, Delta state using satellite

https://doi.org/10.30574/wjarr.2021.10.3.0273 Abstract Remote Sensing (RS) and Geographic Information System (GIS) have been established as indispensable tools in the assessment of Land use / Land cover (LULC) change. RS and GIS are important for the monitoring, modelling and mapping of land use and land cover changes across a range of spatial and temporal scales, in order to assess the extent, direction, causes, and effects of the changes. Change detection has provided suitable and wide-ranging information to various decision support systems for natural resource management and sustainable development. The main objective of the study is to assess and evaluate the extent and direction of changes in LULC of Aniocha North Local Government Area (LGA), Delta State, Nigeria to explain the changes and identify some of their effects on both the livelihoods of the local people and the local environment, and also to explore some of the conservation measures designed to overcome problems associated with land use and land cover changes. Landsat 7 Enhanced Thematic Mapper (ETM+) of 2002 with 30 meters resolution and landsat 7 Enhanced Thematic Mapper (ETM) 2014satellite images as well as GIS techniques were used to monitor the changes and to generate maps of the LULC of the area in these periods. Supervised Land Use/Land Cover classification algorithm (Maximum likelihood with null class) was used in the analysis of classification. The classification result of LandSat ETM+ (2002) revealed that farmland accounted for 36.34% of the total LULC class, followed by savannah which accounted for 24.15%. Forest built up area, and waterbody constituted 20.42%, 16.46% and 2.62% respectively. Also, the result of LandSat ETM (2014) shows that forest accounted for 38.59% followed by farmland with 30.93%. Built up area covers 25.55% while savannah and river cover 2.86% and 2.08% respectively. The classification shows 83.26 % average accuracy and 79.16 % overall accuracy for 2002 while the 2014 accuracy assessment showed 95.06% average accuracy and 94.99% overall accuracy. Growing population pressure and its associated problems, such as the increasing demand for land and trees, poor institutional and socio-economic settings, and also unfavorable government policies, such as lack of land tenure security and poor infrastructure development, have been the major driving forces behind the LULC


Introduction
Land is a complex and dynamic combination of factors: geology, topography, hydrology, soils, microclimates, and communities of plants and animals that are continually interacting under the influence of climate and peoples activities (1). It is a natural resource which provides space and raw materials for various developmental and other activities. Studies have shown that there are only few landscapes on the earth that are still in their natural state. Due to anthropogenic activities, the earth surface is being significantly altered in some manner and man's presence on the earth and his use of land has had a profound effect upon the natural environment thus resulting into an observable pattern in the land use/land cover change over time (2).
Land cover simply means the physical or natural state of the earth's surface while Land use is the way humans use the land and its resources. Environmental factors such as soil characteristics, climate, topography, and vegetation constrain the land use. Improper land use is causing various forms of environmental degradation. For sustainable utilization of the land ecosystems, it is essential to know the natural characteristics, extent and location, its quality, productivity, suitability and limitations of various land uses. Local land use and land cover changes are fundamental agents of global climate change at all scale and are significant forces that impact biodiversity, water and radiation budgets, and trace gas emissions (3).
The land use/land cover pattern of a region is an outcome of natural and socio -economic factors and their utilization by man in time and space. Hence, information on land use / land cover is essential for the selection, planning and implementation of land use and can be used to meet the increasing demands for basic human needs and welfare. This information also assists in monitoring the dynamics of land use resulting out of changing demands of increasing population (4).
According to Moore P. (5), deforestation is one of the major causes of land cover change and it is the most pervasive concerns in developing countries, especially in tropical moist forests, which covers some 550 million ha of the globe, with an annual harvesting rate of over 2%. The forest cover of the world is declining continuously and has global environmental implications. Moore P. (5), indicated the world wide consequences of deforestation as -change in the way of life of local people, extinction species, loss of undefined reservoir of genetic resources, increased erosion from wind and water, and increased desertification; increased runoff to rivers, resulting in flooding and future erosion; reduced transpiration from vegetation and thus less precipitation, and change in the regional albedo.
The variety of land use and land cover data needs is exceedingly broad. Current land use and land cover data are needed for equalization of tax assessments in many States. Land use and land cover data also are needed by Federal, State, and local agencies for water-resource inventory, flood control, water-supply planning, and waste-water treatment etc. Many Federal agencies need current comprehensive inventories of existing activities on public lands combined with the existing and changing uses of adjacent private lands to improve the management of public lands. Federal agencies also need land use data to assess the environmental impact resulting from the development of energy resources, to manage wildlife resources and minimize man-wildlife ecosystem conflicts, to make national summaries of land use patterns and changes for national policy formulation, and to prepare environmental impact statements and assess future impacts on environmental quality.
Land use/Land cover change is one of the major influencing factors for landscape changes. RS and GIS are important for the monitoring, modelling and mapping of land use and land cover changes across a range of spatial and temporal scales, in order to assess the extent, direction, causes, and effects of the changes.
Change detection and monitoring involve the use of several multi-date images to evaluate the differences in LULC due to various environmental conditions and human actions between the acquisition dates of images (6). Successful use of RS for land use/land cover change detection largely depends on an adequate understanding of the study area, the satellite imaging system and the various information extraction methods for change detection in order to fulfil the aim of the present study (7).

Aim and Objectives of the Study
The aim of the study is to assess the land cover and land use changes in Aniocha North local government area, Delta State Nigeria from 2002 to 2014.
The specific objectives are:  To acquire satellite images of the study area with at least five years interval.  To create land use land cover classification scheme for the study area.  To carry out spatial change detection and analyses of land cover/use in the study area.

Study Area
Aniocha North LGA of Delta State is the study area. Aniocha North was created in 1991 having been carved out of the old Aniocha local government. It has a total of seventeen communities with its administrative headquarters in Issele uku. It is located west of the majestic River Niger. It lies between longitude 06 0 14 1 and 06 0 30 1 North of the equator and Latitude 06 0 18 1 and 06 0 37 1 East of the Greenwich meridian. It is bounded to the North by Edo state, to the West by Ika North-East local government area of Delta State, to the East by Oshimmili North local government area of Delta State and to the South by Aniocha South local government area of Delta State.

Status
Aniocha North local government has a tropical climate with two distinct seasons, the dry and wet seasons. Average rainfall is between 1800mm and 3000mm. average temperature is between 25℃ to 32℃. The vegetation ranges from mangrove swamp along the coastal region and rain forest in the other places. Most of the communities are highly susceptible to erosion due to its nature of topography which is steeply particularly in the northern part of the local Government area.
The local government is situated on a highland and covered with clayey brick-red soil. Some traces of solid rock can be found in the towns located at the extreme north of the local government. The topography of the towns at the extreme north is also steeply and they are properly drained towards the north by river Ohe which is a tributary of the River Niger. River Ohe is actually the boundary line between Aniocha North and the neighbouring Edo state and runs through the entire northern corridor of Aniocha North local government. Consequently, most of the communities on the northern part are highly susceptible to erosion due to the steeply nature of the topography around that area.
Aniocha North has a tropical climate with two distinct seasons, the dry and wet seasons. The wet season comes from about April/May and ends about October/November of the same year while the rest is the dry weather/harmattan period. Average rainfall is between 1800mm and 3000mm.

Methodology
The data input for this study were remotely sensed satellite images downloaded from GLCF (Global landcover facility) and USGS (United State Geological Survey), which covered the study area and its environs for a period of 12 years.

Figure 2 Landsat scene covering the study area, in Ilwis environment
The pre-processing activities were carried out in order to enhance the quality of the image and the readability of the features. A major preprocessing activity carried out was image restoration, which is concerned with the correction of distortion, degradations and noise introduced during the imaging. These errors can degrade the quality of the remote sensor data collected, which may have impact on the accuracy of human or machine assisted image. The radiometric enhancement was performed to sharpen the area of interest.
The coordinates of Ground Control Points such as road intersections and popular spots were collected using the handheld GPS.A standard technique is adopted for georeferencing the base map using PCI Geomatica V10.1 software. Image to image registration of the satellite image was made using the georeferenced base map. The landsat images of 2002, and 2014, acquired from the Global land cover facility were geometrically corrected and the projection was set to World Geodetic System (WGS 84) and UTM zone 32N.
Three bands (7, 4 and 2) were used in the color composite operation, the Pseudo Color composite were applied where red color was assigned to band 7. This is to display the built up area in Red color, the near infrared band 4 was used to display the spatial distribution of the vegetation in Green color and the band 2 was used to display the Water body in blue color.  (5) Savannah which is dominated by grass and few trees. Selected ground control points (GCP) that include the major land use and land cover classes were sampled to create a signature file to help train the software to classify the entire study areas. Care was taken to minimize error by avoiding mixed pixels. Location of the 'training sites' in the image and selection of sample set was done. A sample set stores training pixels for supervised classification. Prior to image classification, training pixels were selected in a sample set. A map list, containing the set of images used for classification and a background map on which the training pixels are located, and a class domain was specified. Maximum likelihood was employed in the analyses of the classification in which the output image 'max like' was displayed. A supervised maximum likelihood classification technique was used because the data of the study area were available, and the author has a prior knowledge of the study area.

Figure 4
The selection of sample set in the training site environment The change detection method used in this study in ILWIS ACADEMIC was the Cross Operation. Pixels on the same positions in both images are compared and the occurring combinations of class names, identifiers or values of pixels are stored. These combinations give an output cross map, and a cross table. The cross table includes the combinations of input values, classes or IDs, the number of pixels that occur for each combination and the area for each combination.
The cross table of land use land cover change for study areas shows combination classes in the change matrix. The analysis is based upon area-based comparison so that recorded changes from one class to another represent actual features.

Results and discussion
The results indicating the different land cover classes and the amount of changes that had occurred are found in    Table 2 and 3). Figure  6 further explains the land use/ landcover dynamics from 2002 to 2014.

Figure 6
Accuracy assessment for 2014 classification

Conclusion
This research aims at investigating land use/land cover changes that occurred in Aniocha North Local Government Area, Delta State between 2002 and 2014 using remote sensing and GIS. The main changes observed for the time period between 2002 and 2014 were in the built-up and Forest areas. Data registered in table 5 reveal that there were positive and negative changes in the landuse/land cover pattern of Aniocha North Local Government Council during the period under study.
The study shows that multi temporal satellite imagery and GIS play very important role in quantifying spatial and temporal phenomenon which otherwise is not possible using conventional methods. The study reveals that the major land use in the study area is farmland which covers a total of 67.27% from 2002 to 2014, followed by Forest which has a total coverage 59.01%. Both of them also have positive or significant increase through the study period.
Finally, remote sensing and GIS have made change detection possible in less time, at low cost and with better accuracy than the conventional method.