RT info:eu-repo/semantics/article
T1 Forest height inversion by combining single-baseline TanDEM-X InSAR data with external DTM data
A1 He, Wenjie
A1 Zhu, Jianjun
A1 Lopez-Sanchez, Juan M.
A1 Gómez Almaraz, Cristina
A1 Fu, Haiqiang
A1 Xie, Qinghua
K1 Remote sensing
K1 Forests and forestry - Remote sensing
K1 Forest canopies
K1 Ecología del dosel forestal
K1 Forests and forestry
K1 Synthetic aperture radar (SAR)
K1 Forest management
K1 Bosques - Gestión
K1 TanDEM-X
K1 3106 Ciencia Forestal
AB Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R2 = 0.88) in coniferous forests of Teruel and 1.97 m (R2 = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation.
PB MDPI
SN 2072-4292
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/67151
UL https://uvadoc.uva.es/handle/10324/67151
LA eng
NO Remote Sensing, 2023, Vol. 15, Nº. 23, 5517
NO Producción Científica
DS UVaDOC
RD 12-mar-2025