Dr. Bill Donatien Loubelo Madiela is a distinguished Congolese lecturer-researcher in Atmospheric Physics and Remote Sensing, currently teaching at Denis Sassou-N’Guesso University. Born on February 21, 1981, in Brazzaville, Republic of Congo, Dr. Loubelo earned his PhD in Atmospheric Physics from Marien Ngouabi University in 2018. His research focuses on the use of satellite imagery for vegetation mapping and carbon stock assessment, particularly in Congo’s northern forests. He has conducted extensive research in collaboration with international institutions, including the National Institute for Space Research (INPE) in Brazil and the French CNRS. Dr. Loubelo has published widely on geospatial techniques and environmental monitoring.
Publication Profile
Scopus
🎓 Educational Background
Dr. Loubelo Madiela holds a PhD in Atmospheric Physics (2018), with a thesis on “Cartography of Vegetation Gradients by Remote Sensing and Forest Carbon Stock Evaluation” from MARIEN NGOUABI University. His research received high recognition with honors. He also earned a Master’s degree (2008) and advanced diplomas in Physics and Environmental Sciences.
🌱 Research Focus
Dr. Loubelo Madiela’s work primarily revolves around geospatial techniques and remote sensing, particularly focusing on Land Use and Land Cover (LULC) assessment and climate change. His expertise has been showcased in international forums like IGARSS (2016, China).
Conclusion
Based on his extensive academic training, international collaborations, and contributions to geospatial research, Dr. Loubelo Madiela is a strong candidate for the Best Researcher Award. His impactful research in atmospheric physics and innovative use of remote sensing make him a valuable contender.
Publication Top Notes
Correction to: Identification of extreme rainfall events in the Republic of Congo from analysis of meteorological tendencies (Theoretical and Applied Climatology, (2024), 155, 6, (5033-5049), 10.1007/s00704-024-04889-8)
Identification of extreme rainfall events in the Republic of Congo from analysis of meteorological tendencies
Semantic pre-classification of vegetation gradient based on linearly unmixed Landsat time series