ASSESSMENT OF SPATIAL PREDICTION TECHNIQUES FOR ELEVATION DETERMINATION IN AKURE SOUTH LOCAL GOVERNMENT AREA, ONDO STATE

Abstract

This research explores the prediction methods used in generating digital elevation models (DEM) and also assesses the relative accuracies of Spatial Prediction techniques used for elevation determination in Akure South Local Government, Ondo State, to generate a better continuous Surface. The Research employed eleven (11) interpolation/extrapolation algorithms, including Inverse Distance Weighting, Natural Neighbor, Spline Regular, Spline Tension, Universal Kriging, Empirical Bayesian Kriging, Topo to Raster, global (trend surface), local polynomial, kernel interpolation with barriers and radial basis functions in DEM surface creation using Advanced Space borne Thermal Emission Reflectometer (ASTER) and Shuttle Radar Topographic Mission (SRTM) Global Digital Surface models (GDSM) which were integrated with Global Positioning System elevation data (X, Y, Z). The prediction models were generated with the aid of Arc-Gis 10.3 software. Moreover, vertical accuracy assessment using the Root Mean Square Error based on the distributed data points reported at the 95% confidence level. The Inverse distance weighting, Natural Neighbour, Spline R, Spline T, Topo to Raster, Universal Krigging, Empirical Bayesian krigging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation with barrier has the vertical accuracy of ±8.446, ±8.648, ±9.532, ±10.707, ±10.020, ±9.795, ±8.452, ±14.565, ±9.266, ±10.802 and ±11.303 respectively for SRTM while The Inverse distance weighting, Natural Neighbour, Spline R, Spline T, Topo to Raster, Universal Krigging, Empirical Bayesian krigging, Global polynomial interpolation (GPI), local polynomial interpolation (LPI), Radial basis function and Kernel interpolation with barrier for ASTER has vertical accuracy of ±15.170, ±14.340, ±11.520, ±12.336, ±13.551, ±14.707, ±13.711, ±15.363, ±13.964, ±13.590 and ±15.376 respectively. This study revealed the possibility of generating elevation data from any Global digital surface models that exhibits continuous characteristics of physical surfaces at large, medium and small scale. This research is recommended for future elevation determination from GDSM (SRTM & ASTER).