Forest Height Estimation Using a Single-Pass Airborne L-Band Polarimetric and Interferometric SAR System and Tomographic Techniques

This paper addresses forest height estimation for boreal forests at the test site of Edson in Alberta, Canada, using dual-baseline PolInSAR dataset measured by Intermap’s single-pass system. This particular dataset is acquired by using both ping-pong and non-ping-pong modes, which permit forming a dual-baseline TomoSAR configuration, i.e., an extreme configuration for tomographic processing. A tomographic approach, based on polarimetric Capon and MUSIC estimators, is proposed to estimate the elevation of tree top and of underlying ground, and hence forest height is estimated. The resulting forest DTM and DSM over the test site are validated against LiDAR-derived estimates, demonstrating the undeniable capability of the single-pass L-band PolInSAR system for forest monitoring.

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Forest height estimation using single-pass dual-baseline L-Band PolInSAR data

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6351946 ◽

2012 ◽

Cited By ~ 4

Author(s):

Qiaoping Zhang ◽

Yue Huang ◽

Marcus Schwaebisch ◽

Bryan Mercer ◽

Ming Wei

Keyword(s):

Height Estimation ◽

Single Pass ◽

Forest Height ◽

L Band

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Forest height estimation using single-pass polarimetric SAR tomography at L-Band

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6947200 ◽

2014 ◽

Author(s):

Yue Huang ◽

Qiaoping Zhang ◽

Marcus Schwaebisch ◽

Ming Wei ◽

Bryan Mercer

Keyword(s):

Polarimetric Sar ◽

Height Estimation ◽

Single Pass ◽

Forest Height ◽

L Band

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Tree height estimation using an airborne L-band polarimetric interferometric SAR

IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217) ◽

10.1109/igarss.2001.976868 ◽

2002 ◽

Author(s):

M. Shimada ◽

Q. Muhtar ◽

T. Tadono ◽

H. Wakabayashi

Keyword(s):

Tree Height ◽

Height Estimation ◽

L Band ◽

Interferometric Sar

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Slope three-layer scattering model for forest height estimation over mountain forest areas from L-band single-baseline PolInSAR data

Journal of Applied Remote Sensing ◽

10.1117/1.jrs.12.025008 ◽

2018 ◽

Vol 12 (02) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Nghia Pham Minh

Keyword(s):

Mountain Forest ◽

Height Estimation ◽

Scattering Model ◽

Forest Height ◽

L Band ◽

Single Baseline

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S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry

Remote Sensing ◽

10.3390/rs11091033 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1033 ◽

Cited By ~ 3

Author(s):

Xiaofan Sun ◽

Bingnan Wang ◽

Maosheng Xiang ◽

Xikai Fu ◽

Liangjiang Zhou ◽

...

Keyword(s):

Boreal Forests ◽

Incidence Angle ◽

Sar Interferometry ◽

Negative Slope ◽

Height Estimation ◽

Model Inversion ◽

Time Frequency ◽

Forest Height ◽

Terrain Slope ◽

Frequency Optimization

This paper investigates the potential of the time-frequency optimization on the basis of the sublook decomposition for forest height estimation. The optimization is deemed to be capable of extracting a relatively accurate volume contribution when P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) systems are adopted to observe forest-covered areas. The highest and the lowest phase centers acquired by the time-frequency optimization modify the conventional three-stage inversion process. This paper presents, for the first time, a performance assessment of the time-frequency optimization on P-band Pol-InSAR data over boreal forests. Simultaneously, to alleviate the model inversion errors caused by topographic fluctuations, forest height is estimated based on the sloped Random Volume over Ground (S-RVoG) model in which the incidence angle is corrected with the terrain slope. The E-SAR P-band Pol-InSAR data acquired during the BIOSAR 2008 campaign in Northern Sweden is utilized to evaluate the performance of the proposed method. From the results of the forest height estimation preprocessed with time-frequency optimization, the root mean square error (RMSE) of Random Volume over Ground (RVoG) and S-RVoG model on negative slope are 5.09 m and 4.71 m, respectively. It is concluded that the time-frequency processing and negative terrain slope compensation improve the inversion performance by 41 . 49 % and 11 . 96 % , respectively.

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Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-band

Remote Sensing ◽

10.3390/rs13112147 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2147

Author(s):

Xing Peng ◽

Xinwu Li ◽

Yanan Du ◽

Qinghua Xie

Keyword(s):

Covariance Matrix ◽

Input Parameter ◽

Forest Biomass ◽

Three Dimensional ◽

Estimation Accuracy ◽

Biomass Estimation ◽

Height Estimation ◽

Matrix Estimation ◽

Forest Height ◽

L Band

Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m.

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