A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation

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Title: A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation
Authors: Xie, Qinghua | Zhu, Jianjun | Wang, Changcheng | Fu, Haiqiang | Lopez-Sanchez, Juan M. | Ballester Berman, Josep David
Research Group/s: Señales, Sistemas y Telecomunicación
Center, Department or Service: Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática
Keywords: Forest height | Polarimetric SAR interferometry (PolInSAR) | Dual-baseline | Synthetic aperture radar (SAR) | Sloped random volume over ground (S-RVoG) model | P-band
Knowledge Area: Teoría de la Señal y Comunicaciones
Issue Date: 9-Aug-2017
Publisher: MDPI
Citation: Xie Q, Zhu J, Wang C, Fu H, Lopez-Sanchez JM, Ballester-Berman JD. A Modified Dual-Baseline PolInSAR Method for Forest Height Estimation. Remote Sensing. 2017; 9(8):819. doi:10.3390/rs9080819
Abstract: This paper investigates the potentials and limitations of a simple dual-baseline PolInSAR (DBPI) method for forest height inversion. This DBPI method follows the classical three-stage inversion method’s idea used in single baseline PolInSAR (SBPI) inversion, but it avoids the assumption of the smallest ground-to-volume amplitude ratio (GVR) by employing an additional baseline to constrain the inversion procedure. In this paper, we present for the first time an assessment of such a method on real PolInSAR data over boreal forest. Additionally, we propose an improvement on the original DBPI method by incorporating the sloped random volume over ground (S-RVoG) model in order to reduce the range terrain slope effect. Therefore, a digital elevation model (DEM) is needed to provide the slope information in the proposed method. Three scenes of P-band airborne PolInSAR data acquired by E-SAR and light detection and ranging (LIDAR) data available in the BioSAR2008 campaign are employed for testing purposes. The performance of the SBPI, DBPI, and modified DBPI methods is compared. The results show that the DBPI method extracts forest heights with an average root mean square error (RMSE) of 4.72 m against LIDAR heights for trees of 18 m height on average. It presents a significant improvement of forest height accuracy over the SBPI method (with a stand-level mean improvement of 42.86%). Concerning the modified DBPI method, it consistently improves the accuracy of forest height inversion over sloped areas. This improvement reaches a stand-level mean of 21.72% improvement (with a mean RMSE of 4.63 m) for slopes greater than 10°.
Sponsor: This work was supported in part by National Nature Science Foundation of China under Grant 41531068, 41371335, 41671356, and 41274010, the Spanish Ministry of Economy and Competitiveness and EU FEDER under Project TIN2014-55413-C2-2-P, China Scholarship Council under Grant 201406370079, and Hunan Provincial Department of Education Science Research Key Project 15A074. The BioSAR2008 campaign data is provided by European Space Agency under the ESA EO Project 14751.
URI: http://hdl.handle.net/10045/68814
ISSN: 2072-4292
DOI: 10.3390/rs9080819
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: http://dx.doi.org/10.3390/rs9080819
Appears in Collections:INV - SST - Artículos de Revistas

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