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Remote Sens. 2019, 11(4), 381; https://doi.org/10.3390/rs11040381

Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features

1
DTIS, ONERA, Université Paris Saclay, 91123 Palaiseau, France
2
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Received: 22 December 2018 / Revised: 30 January 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
(This article belongs to the Section Forest Remote Sensing)
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Abstract

This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy. View Full-Text
Keywords: synthetic aperture radar (SAR); LIDAR; interferometry; polarimetry; machine learning; Above-Ground Biomass; canopy height synthetic aperture radar (SAR); LIDAR; interferometry; polarimetry; machine learning; Above-Ground Biomass; canopy height
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Brigot, G.; Simard, M.; Colin-Koeniguer, E.; Boulch, A. Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features. Remote Sens. 2019, 11, 381.

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