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

Nghia Pham Minh

doi:10.1117/1.JRS.12.025008

11 May 2018 Slope three-layer scattering model for forest height estimation over mountain forest areas from L-band single-baseline PolInSAR data

Nghia Pham Minh

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 2, 025008 (May 2018). https://doi.org/10.1117/1.JRS.12.025008

Abstract

A slope three-layer scattering model (STSM) for retrieving forest height in mountain forest region using L-band polarimetric synthetic aperture radar interferometry (PolInSAR) data is proposed in this paper. The proposed model separates the vertical structure of forest into three layers: canopy, tree trunk, and ground layer, which account for the effect of topography for forest height calculation in a sloping forest area. Compared to the conventional two-layer random volume over ground model, the STSM improves substantial for modeling of actual mountain forest, allowing better understanding of microwave scattering process in sloping forest area. The STSM not only enables the accuracy improvement of the forest height estimation in sloping forest area but also provides the potential to isolate more accurately the direct scattering, double-bounce ground trunk interaction, and volume contribution, which usually cannot be achieved in the previous forest height estimation methods. The STSM performance is evaluated with simulated data from PolSARProSim software and ALOS/PALSAR L-band spaceborne PolInSAR data over the Kalimantan areas, Indonesia. The experimental results indicate that forest height could be effectively extracted by the proposed STSM.

Citation Download Citation

Nghia Pham Minh "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 12(2), 025008 (11 May 2018). https://doi.org/10.1117/1.JRS.12.025008

Received: 21 October 2017; Accepted: 10 April 2018; Published: 11 May 2018

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