Name of United Graduate School of Agricultural Sciences:Tottori University
Assigned university:Tottoriuniversity
Specialized field:UAV remote sensing, Artificial intelligence, Biomass modelling
Research Theme:Tree species detection and classification using UAV data and Artificial Intelligence techniques.   Modelling aboveground biomass from remote sensing data
Obtained (planned) degree/date:Doctor of Philosophy (Science)  Scheduled: September 2023


Forests are important forest carbon sinks, storing the highest amount of terrestrial carbon, measured through above ground biomass. Monitoring of forest condition is the only way of monitoring and accounting for this biomass storage. Traditional methods of monitoring involve field visits and felling of trees, followed by intensive laboratory work. These are time consuming and destructive to the forests, especially when trees are cut. In addition, traditional methods are not applicable to restricted/inaccessible areas. Their scope thus remains shallow, owing to these limitations.

Recently, the world has seen increased availability/accessibility of drones for remote monitoring, advances in Artificial Intelligence (AI), hence machine vision, and increased computation power. Researchers have previously used drone images and AI methods to detect and identify trees with accuracies exceeding 95%. This is a promising method to solve aforementioned limitations of traditional methods. In my research, I use deep learning to detect and classify trees, then later estimate above ground biomass in a small mixed forest. In future, I plan to apply the same methods in tropical/Dryland areas, especially in finding suitable, invasive or other unique featured trees within the given ecosystem. My career goal is to research with international research organizations or regional/international multidisciplinary and collaboration research projects.