A Fourier–Legendre Polynomial Forest Height Inversion Model Based on a Single-Baseline Configuration

Abstract

In this article, we propose a Fourier–Legendre (FL) polynomial forest height estimation algorithm based on low-frequency single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. The algorithm can obtain forest height with a single-baseline PolInSAR configuration while capturing a high-resolution vertical profile for the forest volume. This is based on the consideration that the forest height remains constant within neighboring pixels. Meanwhile, we also assume that the coefficients of the FL polynomials remain unchanged within neighboring pixels, except for the last polynomial coefficient. The idea of using neighboring pixels to increase the observations provides us with the possibility to obtain high-order FL polynomials. With this approach, it is possible to obtain a high-resolution vertical profile that is suitable for forest height estimation without losing too much spatial resolution. P-band PolInSAR data acquired in Mabounie in Gabon and Krycklan in Sweden were selected for testing the proposed algorithm. The results show that the algorithm outperforms the random volume over ground (RVoG) model by 18% and 16.7% in forest height estimation for the Mabounie and Krycklan study sites, respectively.

Forest Canopy Height Estimation by Integrating Structural Equation Modeling and Multiple Weighted Regression

“…The combination of SAR and LiDAR offers the possibility of accurately detecting the height of forest canopies over a wide area. Forest canopy height estimation methods based on SAR observations can be broadly classified into backscatter model-based methods, interferometric synthetic aperture radar (InSAR)-based methods, polarimetric InSAR (PolInSAR)-based methods, and data-driven empirical modeling-based methods [16][17][18][19][20].…”