Deakin Marine Research and Innovation Centre
Deakin University, Burwood campus
221 Burwood Hwy, VIC 3125
Omosalewa Odebiri is a specialist in Remote Sensing and GIS/Machine Learning (Deep Learning) with over 8 years of experience developing technology solutions and conducting research, as well as teaching in environmental sciences with a focus on climate change, carbon emissions, forestry, ecology, land use and land cover changes, among others.
At the Blue Carbon Lab, he hopes to use remote sensing strategies in modelling greenhouse gases (GHGs) within agricultural dams, as well as provide active solutions to minimise GHG emissions.
Remote sensing, Climate change, Ecology, Teal carbon, Wetlands, Farm dams, Greenhouse gases
Arogoundade, A.M., Mutanga, O., Odindi, J., & Odebiri, O. (2023). Leveraging Google Earth Engine to estimate foliar C: N ratio in an African savannah rangeland using Sentinel 2 data. Remote Sensing Applications: Society and Environment. 30. 100981.
Odebiri, O., Mutanga, O., Odindi, J., Naicker, R., Slotow, R., & Mngadi, M. (2023). Evaluation of projected soil organic carbon stocks under future climate and land cover changes in South Africa using a deep learning approach. Journal of Environmental Management. 330. 117127. 10.1016/j.jenvman.2022.117127.
Odebiri, O., Mutanga, O., Odindi, J., & Naicker, R. (2022). Mapping soil organic carbon distribution across South Africa’s major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks. Science of The Total Environment. 865. 161150.
Odebiri, O., Odindi, J., & Mutanga, O. (2021). Basic and deep learning models in remote sensing of soil organic carbon estimation: A brief review. International Journal of Applied Earth Observation and Geoinformation. 102. 102389.
Odebiri, O., Mutanga, O., Odindi, J., Peerbhay, K., & Dovey, S. (2020). Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province, South Africa using remotely sensed data. GIScience & Remote Sensing. 57. 1-14.