Geoinformatics & Surveying

This research explores the implementation of a loosely coupled logistic regression model and geographic information systems in modelling and predicting future urban expansion of Lagos from historical remote sensing data (Landsat TM images of Lagos acquired on 1984, 2000 and 2005). ArcGIS and MATLAB software are used for the modelling. The three Landsat images are classified using the k-means unsupervised algorithm in MATLAB. Ten salient explanatory land use variables are extracted for the calibration of the model. The model is calibrated by running a simulation for period 1984 to 2000. The computed logistic coefficients of the 10 explanatory variables show that all the 10 explanatory variables are significant at 95% confidence level based on a two-tailed test, since all the 10 variables yields p-values <0.05. The simulated map in 2000 is compared with the reference data in 2000; and evaluated using the Kappa statistic. The computed Kappa statistic is 0.7640; which implies a substantial agreement between the predicted and the reference data. The calibration model for 1984-2000 is used to predict 2005 map. A comparison of the predicted and reference data in 2005 yields Kappa statistics estimate of 0.6998; which indicates a substantial agreement between the predicted and the reference data. A prediction of 2030 is derived upon satisfactory result obtained for the 2005 prediction based on the 1984-2000 calibrated model. An urban expansion of 129.49% is predicted between 1984 and the forecasted 2030.

This research examines the spatio-temporal changes of Land use/Land cover of Akwa Ibom state from historical remote sensing dataset (Landsat TM, ETM+ and OLI images acquired on 1986, 2001 and 2016).Three set of Landsat images were classified into five land use/land cover classes (built up/bare lands, water, mangrove/primary vegetation, secondary vegetation and cultivated/mixed vegetation) using unsupervised and supervised algorithm in ERDAS IMAGINE and ArcGIS. The Overall Classification Accuracy and KAPPA (K^) STATISTICS was 91.50% and 0.8301; 90.77% and 0.8227; 91.81% and 0.8491 for 1986, 2001 and 2016 respectively. The spatio-temporal analysis of the change trend indicated that from 1986 to 2016, built up/bare lands increased by 62,828hectares (19.56%), water bodies decreased by 3,425 hectares (1.07%), mangrove/primary vegetation decreased by 96,333hectares (29.99%), secondary vegetation increased by 97,486hectares (30.34%) and Cultivated/mixed vegetation decreased by 61,191hectares (19.04%). The result demonstrated that historical remote sensing images can be used toinvestigate change trendof land use/land over of the study area. Also, the result raisesconcernover the unabated alteration of the natural environment particularly the depletion of mangrove/primary vegetation and conversions of arable cultivated lands to settlements, thus, calling for urgent review of land use planning process.

Nowadays, there are efforts around the globe to coordinate all mapping activities using the earth-centred WGS 84. Therefore the need to transform Nigerian coordinates hitherto based on the Nigerian non-earth centred Minna Datum to the global WGS 84. This research presents a 3D coordinate transformation between the local Minna Datum and the global WGS 84 datum in Nigeria using total least squares. The Bursa-Wolf and Molodensky-Badekas similarity/conformal transformation models are used for the experiment. One hundred and ten points are selected, of which sixty points are used to compute the values of the unknown parameters while the remaining fifty points are used to compute the accuracy of the model. The experiment shows that total least squares yields better result than the least squares; and also that the Molodensky-Badekas model results are better than those of the Bursa-Wolf model.

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show ...