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

Abstract

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.

Determination of Factors Responsible for the Change in Vegetal Cover in Katsina Town

“…Similar findings like (Maina, et al, 2017), (Nwaogu, et al, 2017) and (Okosun, 2018) report that built up areas were increasing while vegetal cover were decreasing. The vegetation cover in Katsina town is decreasing as result of increase in built up environment and climatic factors.…”

How do African elephants utilize the landscape during wet season? A habitat connectivity analysis for Sioma Ngwezi landscape in Zambia

“…Normalized difference vegetation index (NDVI) raster layer was created in ENVI software 5.6 (Nwaogu et al, 2017) using NDVI tool.…”

Fusion of Sentinel-1a and Landsat-8 Images for Improving Land Use/Land Cover Classification in Songkla Province, Thailand

“…This might be explained by the fact that the forest in dominated by dense evergreen and high-density trees. Plants and paddy were confused between each other and, consequently, this decreased the reliability of their accuracies when compared to other land use and land cover types classified (Nwaogu et al, 2017;Johnson et al, 2016). Another reason, might be that some areas from the secondary data were attributed another class in comparison to the classification of the satellite images in land use types (lake, forest, city, plants and paddy).…”