Novel tree leaf distribution inversion method for close-range LiDAR data

In this project, we will develop and validate a novel method to invert leaf area and orientation distributions using close-range LiDAR data. The idea is to represent the leaf distributions as simple parametric models based on the tree's structural dimensions. We will then deduce the optimal parameter values for these distributions from the LiDAR data by formulating and maximizing likelihood functions that quantify the probability of a given distribution given the data. This new approach will provide unprecedented detail in expressing leaf covers for trees, enabling new ways to study them. We will also produce a freely available and easy-to-use software tool for leaf distribution inversion.