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

Ugbelase Vincent Nwacholundu *, Igbokwe Joel Izuchukwu, Emengini Josephine Ebele, Ejikeme Joseph Onyedika and Igbokwe Esomchukwu Chinagorom

Department of Surveying & Geoinformatics, Nnamdi Azikiwe University, Awka, Nigeria.
 
Research Article
World Journal of Advanced Research and Reviews, 2021, 10(03), 207–216
Article DOI: 10.30574/wjarr.2021.10.3.0273
 
Publication history: 
Received on 04 May 2021; revised on 08 June 2021; accepted on 11 June 2021
 
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 changes.
 
Keywords: 
Land use; Land cover; Satellite Imagery; GIS
 
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