Is Nigeria losing its natural vegetation and landscape? Assessing the landuse-landcover change trajectories and effects in Onitsha using remote sensing and GIS

Onitsha is one of the largest commercial cities in Africa with its population growth rate increasing arithmetically for the past two decades. This situation has direct and indirect effects on the natural resources including vegetation and water. The study aimed at assessing land use-land cover (LULC...

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Bibliographic Details
Published in:Open Geosciences 2017-01, Vol.9 (1), p.707-718
Main Authors: Nwaogu, Chukwudi, Okeke, Onyedikachi J., Fadipe, Olusola O., Bashiru, Kehinde A., Pechanec, Vilém
Format: Article
Language:English
Subjects:
Classification
Floodplains
geoinformatics
Growth rate
Kappa coefficient
Land use
Natural resources
Natural vegetation
Plant species
Population growth
Remote sensing
Riparian vegetation
Sand
Sandy soils
supervised and unsupervised classifications
urbanization
Vegetation
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Summary:Onitsha is one of the largest commercial cities in Africa with its population growth rate increasing arithmetically for the past two decades. This situation has direct and indirect effects on the natural resources including vegetation and water. The study aimed at assessing land use-land cover (LULC) change and its effects on the vegetation and landscape from 1987 to 2015 using geoinformatics. Supervised and unsupervised classifications including maximum likelihood algorithm were performed using ENVI 4.7 and ArcGIS 10.1 versions. The LULC was classified into 7 classes: built-up areas (settlement), waterbody, thick vegetation, light vegetation, riparian vegetation, sand deposit (bare soil) and floodplain. The result revealed that all the three vegetation types decreased in areas throughout the study period while, settlement, sand deposit and floodplain areas have remarkable increase of about 100% in 2015 when compared with the total in 1987. Number of dominant plant species decreased continuously during the study. The overall classification accuracies in 1987, 2002 and 2015 was 90.7%, 92.9% and 95.5% respectively. The overall kappa coefficient of the image classification for 1987, 2002 and 2015 was 0.98, 0.93 and 0.96 respectively. In general, the average classification was above 90%, a proof that the classification was reliable and acceptable.
ISSN:2391-5447
2391-5447
DOI:10.1515/geo-2017-0053