A Deep Learning Study on the Retrieval of Forest Parameters from Spaceborne Earth Observation Sensors

Abstract

The efficient and timely monitoring of forest dynamics is of paramount importance and requires accurate, high-resolution and time-tagged predictions at global scale. Despite numerous methodologies have been proposed in the literature, existing approaches often compromise on accuracy, resolution, temporal fidelity or coverage. To tackle these challenges and limitations, the main objective of this doctoral thesis is the investigation of the potential of artificial intelligence (AI) for the regression of bio-physical forest parameters from spaceborne Earth Observation (EO) data. This work explores for the first time the combined use of TanDEM-X single-pass interferometric products and convolutional neural networks for canopy height estimation at country scale. To achieve this, a novel deep learning framework is proposed, leveraging the capability of deep neural networks to effectively capture the complex spatial relationships between forest properties and satellite data, as well as ensuring the adaptability to different environmental conditions. The design and the understanding of the model is driven by explainable AI principles and by considerations on large-scale forest dynamics, with a great emphasis set on the challenges related to the variable acquisition geometry of the TanDEM-X mission, and by relying on the use of LVIS-derived LiDAR measurements as reference data. Moreover, several investigations are conducted on the adaptability of the developed framework for transferring knowledge to related domains, such as digital terrain model regression and above-ground biomass density estimation. Finally, the capability of the proposed approach to be extended to the use of other EO sensors is also evaluated, with a particular emphasis on the ESA Sentinel-1 and Sentinel-2 missions. The developed deep learning framework sets a solid groundwork for the generation of large-scale products of bio-physical forest parameters from spaceborne EO data. The approach achieves cutting-edge performance, significantly advancing the current state of forest assessment and monitoring technologies.