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Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band

National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA), University of Montpellier, Land Environment Remote Sensing and Spatial Information (TETIS), 34090 Montpellier, France
National Center for Remote Sensing, National Council for Scientific Research (CNRS), Riad al Soloh, 1107 2260 Beirut, Lebanon
Doctoral School of Sciences and Technologies, Lebanese University (LU), 1003 Beirut, Lebanon
Laboratory of Informatics, Robotics, and Microelectronics of Montpellier (LIRMM), University of Montpellier, 34090 Montpellier, France
National Aeronautics and Space Administration (NASA), Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA 91125, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1934;
Received: 17 July 2019 / Revised: 14 August 2019 / Accepted: 16 August 2019 / Published: 19 August 2019
Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities. View Full-Text
Keywords: tomography SAR; AfriSAR; TropiSAR; LiDAR LVIS tomography SAR; AfriSAR; TropiSAR; LiDAR LVIS
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MDPI and ACS Style

El Moussawi, I.; Ho Tong Minh, D.; Baghdadi, N.; Abdallah, C.; Jomaah, J.; Strauss, O.; Lavalle, M.; Ngo, Y.-N. Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band. Remote Sens. 2019, 11, 1934.

AMA Style

El Moussawi I, Ho Tong Minh D, Baghdadi N, Abdallah C, Jomaah J, Strauss O, Lavalle M, Ngo Y-N. Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band. Remote Sensing. 2019; 11(16):1934.

Chicago/Turabian Style

El Moussawi, Ibrahim, Dinh Ho Tong Minh, Nicolas Baghdadi, Chadi Abdallah, Jalal Jomaah, Olivier Strauss, Marco Lavalle, and Yen-Nhi Ngo. 2019. "Monitoring Tropical Forest Structure Using SAR Tomography at L- and P-Band" Remote Sensing 11, no. 16: 1934.

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