Ecological Characterization through Spectral Analysis and Deep Learning-based Vegetation Extraction from High-resolution Imagery

This project aims to address challenges posed by climate change and human activities that transform agroforestry systems at an unprecedented rate. To effectively monitor and map ecological changes in these systems, this project focuses on testing and developing deep learning algorithms for characterizing vegetation cover/types in remote sensing imagery of varying resolutions. Currently, there is a need to establish optimal deep learning algorithms using multi-platform (aerial and spaceborne) and multi-spectral datasets specifically targeting vegetation cover. By developing a new deep learning-based characterization algorithm, this project aims to enable efficient and rapid mapping and monitoring of land cover and ecological changes. The research outcomes will promote collaboration and improve the management of agroforestry systems, enabling them to adapt-to and mitigate the impacts of climate change on agriculture.