Tree Height Estimates In Boreal Forest Using Gaussian Process Regression

Tree height is one of the continuous essential climate model variables. This work applies Gaussian process regression to tree height estimation. Estimates are produced in a coniferous boreal forest area in Southern Finland. GP regression produces both estimate and predictive variance for each pixel. Results show that Gaussian process regression produces estimates which are as good as the ones produced by existing methods. The root-mean-square error from GP regression is lower, estimates have less bias and maximum value is closer to the actual maximum. Predictions can be applied and ingested to carbon modelling where estimates are either direct input parameters or they serve for validation the parameters computed by the models.