Updating

Accurate topographic mapping is vital for effective land-use planning, infrastructure development, and environmental monitoring. The integration of advanced remote sensing techniques, particularly the use of Unmanned Aerial Vehicles (UAVs), is highly advantageous for creating efficient and precise terrain models. The importance of high-resolution topographic data cannot be overstated, as it is integral to engineering applications and geospatial analysis. This study aims to produce a detailed topographic map of the University of Benin's Ugbowo Campus, located along the Benin-Lagos Expressway in Benin City, Nigeria, utilizing the DJI Phantom 4 RTK drone. The methodology employed key topographic parameters, including elevation, slope, aspect, and terrain variation, to create a high-accuracy Digital Elevation Model (DEM). A UAV was operated at an altitude of 120 meters in a 3D flight mode, capturing high-resolution aerial imagery. To ensure precise geo-referencing of the orthophoto, Real-time Kinematic (RTK) GPS technology was utilized with an RTK-enabled drone, thus eliminating the need for Ground Control Points (GCPs). The acquired imagery was then processed to produce an orthophoto, which served as the basis for deriving the DEM, and contour lines were extracted at 5-meter intervals to illustrate elevation variations. The accuracy of the model was assessed through a positional accuracy analysis, revealing that the generated topographic data achieved a remarkable precision of less than 5 cm. This outcome underscores the high accuracy of UAV-based mapping techniques. The resulting topographic map provides a comprehensive representation of the terrain, facilitating improved decision- making in urban planning, construction, and geospatial analysis. In conclusion, this research showcases the effectiveness of UAV photogrammetry, particularly through the integration of RTK technology, in producing precise topographic maps. It highlights the promise of UAV-based surveys as a cost-effective and efficient alternative to traditional surveying methods, especially in challenging or inaccessible terrain. By achieving exceptional positional accuracy, these techniques not only enhance the quality of the collected data but also significantly contribute to improved decision-making across various domains, including urban planning and construction.

The soils of Owan East Local Government Area of Edo State were studied in order to update the classification, mapping and suitability evaluation done by Federal Department of Agricultural Land Resources (FDALR 1985). Soil mapping was at the reconnaissance scale; soil classification was according to the USDA and WRB systems while suitability evaluation was patterned after FAO guidelines, as modified by several scientists for rubber, oil palm, cacoa, maize and cassava. Reliability of the soil maps were
determined by the variability indices of: Coefficient of Variation (CV), Variance Ratio Test (VRT), Inter – class Correlation Coefficient (P1), Relative variance (RV) and its Complement (1 – RV). Validation of the updated work was done through a free soil survey procedure and suitability assessment for rubber on the 17.7 ha parcel of land for RRIN within the project area. The study revealed that six major mapping units were found and classified as Alfisols/Lixisols, (occupying some 66,160.26 ha), Inceptisols/Cambisols (37,803.07 ha) and Entisols/Arenosols (19,471.17 ha). The FDALR work showed two mapping units classified as Alfisol/Lixisol and without areal distribution of the units. In terms of the reliability of the soil maps for the study area, the results showed that the findings in this study is quite superior to that of the FDALR. In terms of suitability for the selected crops, for the current findngs, 70,201.5 ha (56.87%) was best suited for maize, cassava and cocoa, while 26, 624.49ha (21.57 %) was best suited for cocoa only. An area of 19,471.17 ha (15.77%) was best suited for maize, cassava and rubber while an area of 7,137ha was found not suitable for any of the 5 crops under study. The FDALR study had the same tree crops examined for their suitability, no area was
rated unsuitable and only one map was used to represent all the crops, while for arable crops, no specific crop was mentioned and it had only one map and two suitability classes. For Indices of variability: FDALR study had 46 % homogeneity within mapping units by CV while it was 87 % for the 2019 findings. For variance ratio test (VRT), no property was significantly different for the 1985 study while for the 2019, 13 properties were significantly different; Intra class correlation coefficient (pi) , in the 1985
work only CEC was accurately predicted (pi > 0.5) while in the 2019 work 7 properties were accurately predicted (pi > 0.7); for Relative variance only one property, CEC was accurately predicted compared with 9 properties (RV = 0.26 – 0.53) - 1985 and 2019 respectively. The results of the 17.71ha classification and suitability ratings agreed with that of the updated findings – being largely Inceptisol/Cambisol and only marginally suitable for rubber cultivation. Thus it can be concluded that while the FDALR study served its purpose as a pioneering attempt and hence overdue for updating. The study was highly necessary for accurate prediction of crop performance and sustainable management of the study location.