Dr Frederick Numbisi
Remote sensing and vegetation analytics are at the core of my professional interests. Over more than a decade, I have amassed a skill set that bridges forestry, agroforestry, remote sensing, and data science, through extensive work in vegetation inventory and analytics. After transitioning from a natural science background to the fields of data science and ecological informatics, I have held roles focusing on data management and remote sensing science at universities, international NGOs, and prominent biodiversity research and conservation organizations.
In my career, I enjoy opportunities to conduct field surveys across diverse landscapes, including both human-altered ecosystems and ecologically unique regions. I am dedicated to harnessing remotely sensed data—drawing on the rapidly expanding global archives of accessible imagery—and applying advanced spatial analyses to bridge field observations with informed decision-making. This approach supports plant conservation and ecosystem restoration initiatives across various ecological and spatial scales.
In biodiversity assessment and conservation research, I actively employ machine learning algorithms to reveal complex, non-linear relationships between spatial datasets (both remotely sensed and engineered) and field-verified characteristics of plant communities and vegetation types. By integrating these methods, I aim to discern and model the spatial distribution of plant biodiversity, contributing to more effective monitoring and management strategies.
More info: https://www.fredericknumbisi-dev.com/