Zhang, Tao, Fu, Haiqiang, Zhu, Jianjun, Lopez-Sanchez, Juan M., Gómez, Cristina, Wang, Changcheng, He, Wenjie, Liu, Zhiwei
Estimation of Canopy Height from a Multi-SINC Model in Mediterranean Forest with Single-baseline TanDEM-X InSAR Data
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2024, 17: 5484-5499. https://doi.org/10.1109/JSTARS.2024.3363051
URI: http://hdl.handle.net/10045/140742
DOI: 10.1109/JSTARS.2024.3363051
ISSN: 1939-1404 (Print)
Abstract: 
TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model (DTM) is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this paper, a method for retrieving canopy height in Mediterranean forests, characterised by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semi-empirical parameters and then expand the single curve into a collection of three curves, forming the Multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of LiDAR data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared to the SeEm-SINC model (RMSE = 3.28 m and 2.36 m).
Keywords:Forest Height, Multi-SINC, TanDEM-X, InSAR, Coherence, Machine Learning
IEEE
info:eu-repo/semantics/article