Improvement of Forest Height Retrieval By Integration of Dual-Baseline PolInSAR Data And External DEM Data

• Published in: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Main Authors: Xie, Q., Wang, C., Zhu, J., Fu, H.
• Format: Text
• Language: English
• Published: 2018
• Subjects: Northern Sweden
• Online Access: https://doi.org/10.5194/isprsarchives-XL-7-W4-185-2015; https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W4/185/2015/

In recent years, a lot of studies have shown that polarimetric synthetic aperture radar interferometry (PolInSAR) is a powerful technique for forest height mapping and monitoring. However, few researches address the problem of terrain slope effect, which will be one of the major limitations for forest height inversion in mountain forest area. In this paper, we present a novel forest height retrieval algorithm by integration of dual-baseline PolInSAR data and external DEM data. For the first time, we successfully expand the S-RVoG (Sloped-Random Volume over Ground) model for forest parameters inversion into the case of dual-baseline PolInSAR configuration. In this case, the proposed method not only corrects terrain slope variation effect efficiently, but also involves more observations to improve the accuracy of parameters inversion. In order to demonstrate the performance of the inversion algorithm, a set of quad-pol images acquired at the P-band in interferometric repeat-pass mode by the German Aerospace Center (DLR) with the Experimental SAR (E-SAR) system, in the frame of the BioSAR2008 campaign, has been used for the retrieval of forest height over Krycklan boreal forest in northern Sweden. At the same time, a high accuracy external DEM in the experimental area has been collected for computing terrain slope information, which subsequently is used as an inputting parameter in the S-RVoG model. Finally, in-situ ground truth heights in stand-level have been collected to validate the inversion result. The preliminary results show that the proposed inversion algorithm promises to provide much more accurate estimation of forest height than traditional dualbaseline inversion algorithms.