Forest Height Estimation with TanDEM-X SAR and InSAR Features using Deep Learning

Abstract

Accurate forest height estimates lead to improved accuracy of biomass estimation and are crucial for monitoring and conservation efforts. Interferometric synthetic aperture radar (InSAR) techniques use two synthetic aperture radar (SAR) images to measure the interferometric coherence that includes the volumetric decorrelation which is known to be related to forest canopy height. Several approximations and assumptions are made in different steps to compute volumetric decorrelation and to invert it to forest canopy height using physical models. Data-driven approaches overcome the potential bias introduced by these assumptions by directly estimating forest canopy height. However, the question of optimal representation and level of processing of the input data is often neglected. We address this gap comparing different SAR and InSAR input features such as single-look-complex (SLC) images, backscatter, coherence, and volumetric decorrelation. The resulting best model has a root-mean-squared error (RMSE) of 6.12 m with volumetric decorrelation as primary input feature. It is followed using coherence as primary input with an RMSE of 6.30 m.