A Deep Learning Framework for the Estimation of Forest Height From Bistatic TanDEM-X Data

Abstract

Up-to-date canopy height model (CHM) estimates are of key importance for forest resource monitoring and disturbance analysis. In this article, we present a study on the potential of deep learning (DL) for the regression of forest height from TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) data. We propose a novel fully convolutional neural network framework, trained in a supervised manner using reference CHM measurements derived from the LiDAR LVIS airborne sensor from NASA. The reference measurements were acquired during the joint NASA–ESA 2016 AfriSAR campaign over five sites in Gabon, Africa, characterized by the presence of different kinds of vegetation, spanning from tropical primary forests to mangroves. Together with the DL architecture and training strategy, we present a series of experiments to assess the impact of different input features on the network estimation accuracy (in particular of bistatic InSAR-related ones). When tested on all the considered sites, the proposed DL model achieves an overall performance of 1.46-m mean error, 4.2-m mean absolute error, and 15.06% mean absolute percentage error. Furthermore, we perform a spatial transfer analysis aimed at deriving preliminary insights on the generalization capability of the network when trained and tested on datasets acquired over different locations, combining different kinds of tropical vegetation. The obtained results are promising and already in line with state-of-the-art methods based on both physical-based modeling and data-driven approaches, with the remarkable advantage of requiring only one single TanDEM-X acquisition at inference time.

Keywords

• Bistatic coherence
• convolutional neural network (CNN)
• deep learning
• forest height
• synthetic aperture radar (SAR)
• synthetic aperture radar interferometry