Onyemaechi John Nnamani | Climate Change | Excellence in Research award

Mr. Onyemaechi John Nnamani | Climate Change | Excellence in Research award

Federal University of Technology Akure | Nigeria

Onyemaechi John Nnamani is an academic and geospatial researcher in the Department of Surveying and Geoinformatics at the Federal University of Technology, Akure, Nigeria. He holds B.Tech and M.Tech degrees in Surveying and Geoinformatics from FUTA. Currently an Assistant Lecturer, he teaches surveying-related courses and supervises undergraduate research. His research interests span geoinformatics, remote sensing, GIS applications, land use and land cover change, urban sustainability, and climate adaptation technologies. He has published widely in reputable journals and actively participates in professional development and conferences. His contributions have earned him awards, including Lecturer of the Year, reflecting his commitment to impactful research, teaching, and sustainable development.

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Featured Publications

Ayomide Olubaju | Remote sensing | Best Researcher Award

Mr. Ayomide Olubaju | Remote sensing | Best Researcher Award

Abiola Ajimobi Technical University | Nigeria

Olubaju Ayomide Emmanuel is a dedicated and innovative researcher specializing in Geographic Information Systems (GIS) and Remote Sensing, with growing recognition for his contributions to geospatial science. He holds an M.Tech. in Surveying and Geoinformatics (Remote Sensing) and a B.Tech. in the same discipline from the Federal University of Technology, Akure, Nigeria. Currently serving as an Assistant Lecturer at Abiola Ajimobi Technical University, Ibadan, he combines teaching and research to advance environmental monitoring and sustainable urban planning. His research interests encompass environmental degradation, climate change impact assessment, urban informatics, multi-sensor remote sensing, forest species monitoring, and machine learning applications in geospatial analysis. Olubaju has authored and co-authored several peer-reviewed publications focusing on urbanization, forest ecology, and mining-induced land degradation, accumulating 3 documents, 4 citations, and an h-index of 2. He has participated in national and international conferences, workshops, and collaborative projects addressing climate resilience and spatial data science. A member of professional societies including ISPRS, IAENG, and the Nigeria Institution of Surveyors, Olubaju’s academic and professional journey reflects a commitment to interdisciplinary research and data-driven solutions for sustainable environmental management. His goal is to pursue a Ph.D. to deepen his expertise and contribute to innovative geospatial applications in global environmental research.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Akinbiola, S., Akinsola, J. E. T., Ajagbe, S. A., Salami, A., Olubaju, A., Awotoye, O., & Awoleye, O. M. (2025). Artificial intelligence technique for prediction of carbon stocks and uncertainty estimates in tropical forests. SN Computer Science.

Akinbiola, S., Salami, A. T., Olubaju, A. E., & Awotoye, O. O. (2025). Assessing the impact of environmental variables on the distribution of keystone tree species in Omo-Shasha-Oluwa forest complex using MaxEnt modelling techniques. SSRN Electronic Journal.

Ibukun, J. A., Olubaju, A. E., Thomas, S. F., Sodipo, E. O., Akinbiola, S. A., Oyetunji, S. O., Shitu, K., Kucher, D. E., & Tariq, A. (2025). Modeling mining-induced land degradation in Itagunmodi: A multi-temporal machine learning approach with random forest and gradient boosting. Trees, Forests and People, 21, 100926.

Ibukun, J. A., Olubaju, A. E., Thomas, S. F., Sodipo, E. O., Akinbiola, S. A., Rebouh, N. Y., Said, Y., & Tariq, A. (2025). Assessing vegetation degradation and thermal effects of artisanal small-scale mining using remote sensing time series data. Land Degradation & Development.

Akinbiola, S., Salami, A. T., Olubaju, A. E., & Awotoye, O. O. (2024). Assessing the impact of environmental variables on the distribution of keystone tree species in Omo-Shasha-Oluwa forest complex using MaxEnt modelling techniques. SSRN Electronic Journal.