Enhanced Forest Above Ground Biomass estimation using LIDAR, Machine Learning and Earth Oberservation data

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The project aims to transformationally improve the accuracy of estimates of carbon stocks in above ground biomass using remote sensing data. The ability to accurately measure carbon stocks in the world's forestry is of vital importance to the development of voluntary carbon markets, the development of international climate policy and the scientific community's understanding of the effects of the climate crisis. The project aims to improve the accuracy of these estimates by deploying ultra-high resolution LIDAR data to train the latest generation of machine learning models.