Uncertainties Analysis in Forest Height Estimation Using Polarimetric Interferometric SAR Data

Quantifying the uncertainty of forest height estimation is crucial for accurate global carbon computation. Forest height have been estimated by random volume over ground (RVoG) model using polarimetric interferometry synthetic aperture radar (PolInSAR) data in recent two decades. By far, RVoG derived forest height uncertainty estimation has been limited to comparison against "true" validation data, which then lead to the impossible use of uncertainties analysis at a national, continental or global landscape. In this paper, Bayesian theorem were introduced to RVoG forest height estimation procedure and used to estimated the uncertainties of the estimated forest height. The results revealed the feasibility of Bayesian method in forest height uncertainties estimation.