DEPARTMENT OF SURVEYING AND GEO-INFORMATICS

Monitoring forest dynamics is essential for understanding ecosystem health and supporting sustainable conservation efforts in tropical environments. This study assessed forest cover changes in the Okomu Forest Reserve, Edo State, Nigeria, between 2015 and 2024 using geospatial techniques, including Normalized Difference Vegetation Index (NDVI) analysis and supervised land-use/land-cover (LULC) classification. Landsat satellite imagery for 2015, 2020, and 2024 was processed to generate NDVI maps and classify LULC patterns using a maximum likelihood algorithm. Temporal NDVI statistics revealed a moderate increase in vegetation greenness from 0.346 in 2015 to 0.360 in 2020, followed by a slight decline to 0.359 in 2024, indicating fluctuating vegetation health over the study period. Anomaly results further highlighted localized decline in vegetation vigour, suggesting increasing disturbance pressure. LULC analysis showed a decline in dense vegetation from 409.28 km² (37%) in 2015 to 375.82 km² (34%) in 2024, alongside an increase in moderate vegetation from 360.36 km² (33%) to 411.04 km² (37%), reflecting secondary regrowth in disturbed areas. Settlement areas expanded from 59.34 km² to 72.02 km², underscoring growing anthropogenic influence. Bare ground and light vegetation also exhibited reductions, suggesting conversion to built-up areas or natural regeneration. Overall, findings indicate progressive forest degradation coupled with evidence of vegetation recovery in specific zones. The observed changes are primarily attributed to human activities such as agricultural expansion, settlement growth, and logging. The study emphasizes the need for strengthened protection measures, community-based conservation strategies, and continuous remote
sensing monitoring to safeguard the ecological integrity of the Okomu Forest Reserve.

This study was conducted at the university of Benin block of flat to evaluate the accuracy, efficiency, and applicability of both surveying methods in terrain mapping. The research aimed to improve the reliability of topographic data acquisition and to recommend appropriate techniques for modern surveying practices. It focused on determining how both instruments perform under similar field conditions and how their outputs can be effectively compared and integrated. Field data were acquired using both Total Station and Global Navigation Satellite System (GNSS) instruments. Four temporary benchmarks were established using GNSS, which served as reference points for Total Station observations. The collected data were processed using Autodesk Civil 3D, ArcGIS, and Surfer software to generate contour maps, Digital Elevation Models (DEMs), and 3D surface models representing the terrain. These outputs provided a clear visualization of the area’s elevation pattern and slope behavior. The results revealed that the elevation within the study area ranged from 85. meters to 93.5 meters, indicating a gently sloping terrain with drainage flowing from north to south. Both datasets showed consistent terrain patterns, with the Total Station producing more detailed elevation variations and the GNSS providing faster and broader coverage. The study concludes that while the Total Station offers higher precision for detailed surveys, the GNSS method enhances efficiency and ease of data collection. Integrating both techniques is therefore recommended for achieving optimal accuracy and productivity in modern surveying and mapping projects.

Urban sprawl is a significant challenge facing rapidly growing cities, particularly in developing countries, where unplanned expansion can strain infrastructure, degrade the environment, and complicate land-use management. This study evaluates the urban sprawl of Oredo Local Government Area (LGA), Edo State, Nigeria, using geospatial techniques to understand the patterns, extent, and implications of urban growth from 2004 to 2024. Multi-temporal Landsat satellite images were analyzed using Geographic Information Systems (GIS) to map land use and land cover (LULC)changes, while population data were projected to assess growth and density patterns. The analysis revealed substantial transformations within Oredo LGA over the twenty-year period.Built-up areas expanded consistently, particularly between 2014 and 2024, often replacing vegetation and bare land. Vegetation cover declined steadily, while water bodies and open land diminished, reflecting environmental pressures associated with urban expansion. Population density increased sharply from approximately 1,504 persons per square kilometer in 2006 toabout 2,488persons per square kilometer in 2024, highlighting the strong correlation between demographic growth and land-use change. Areas such as Ring Road, Ugbowo, and Sapele Road were identifiedashigh-intensity development zones, whereas peripheral neighborhoods exhibited lower level sofurbanization. The study demonstrates that integrating remote sensing and GIS provides a robust framework for monitoring spatial development, identifying hotspots of urban growth, and supporting evidence-based urban planning. Based on the findings, recommendations include the institutionalization of geospatial monitoring, development of a GIS-based land information system, preservation of green spaces, targeted management of high-density zones, predictive modeling for future urban growth, integration of geospatial data into planning policies, and capacity building for planners and community engagement initiatives.