Tomographic SAR Reconstruction for Forest Height Estimation

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.

Plant phenotyping relevance

SAR画像と深層学習による森林キャノピー高推定手法の開発・比較・精度評価が中心であり、植物群落の明示的な形態形質を測定している。

Code and data availability

The paper's phenotyping inputs are the public TomoSense P-band SAR SLC stack and LiDAR DTM/CHM over Eifel Park, Germany, which directly underpin all canopy-height experiments; the authors' own processing/analysis code is not yet public (only promised via the corresponding author), and the EO-College tomography tutorial

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