Tomographic SAR Reconstruction for Forest Height Estimation

TL;DR

This paper explores a deep learning approach to estimate forest height directly from SAR images, aiming to simplify the process and reduce latency compared to traditional tomographic methods, with implications for scalable global biomass monitoring.

Contribution

The study introduces a minimal deep learning method that bypasses traditional tomographic processing for forest height estimation from SAR images, analyzing its accuracy and operational implications.

Findings

Deep learning can estimate forest height from SAR images with reasonable accuracy.

Reducing tomographic processing increases error by 16-21%.

Varying the number of SAR images affects height estimation accuracy.

Abstract

Tree height estimation serves as an important proxy for biomass estimation in ecological and forestry applications. While traditional methods such as photogrammetry and Light Detection and Ranging (LiDAR) offer accurate height measurements, their application on a global scale is often cost-prohibitive and logistically challenging. In contrast, remote sensing techniques, particularly 3D tomographic reconstruction from Synthetic Aperture Radar (SAR) imagery, provide a scalable solution for global height estimation. SAR images have been used in earth observation contexts due to their ability to work in all weathers, unobscured by clouds. In this study, we use deep learning to estimate forest canopy height directly from 2D Single Look Complex (SLC) images, a derivative of SAR. Our method attempts to bypass traditional tomographic signal processing, potentially reducing latency from SAR capture to end product. We also quantify the impact of varying numbers of SLC images on height estimation accuracy, aiming to inform future satellite operations and optimize data collection strategies. Compared to full tomographic processing combined with deep learning, our minimal method (partial processing + deep learning) falls short, with an error 16-21\% higher, highlighting the continuing relevance of geometric signal processing.