Progresses on SAR remote sensing of tropical forests: forest biomass retrieval and analysis of changing weather conditions

TEBLDINI Stefano; YANG Xinwei; BAI Yu; MARIOTTI D′ALESSANDRO Mauro; LIAO Mingsheng; YANG Wen

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Progresses on SAR remote sensing of tropical forests: forest biomass retrieval and analysis of changing weather conditions
Vol. 24, Issue S1, Pages: 167-172(2020)
Received：21 October 2019，

Accepted：16 April 2020，

Published：07 June 2020
DOI：
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DOI：

Tebldini S, Yang X W, Bai Y, Mariotti D′Alessandro M, Liao M S and Yang W. 2020. Progresses on SAR remote sensing of tropical forests: forest biomass retrieval and analysis of changing weather conditions. Journal of Remote Sensing(Chinese). 24(S1): 167-172 DOI：

摘要

Abstract

This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation (ID: 32278)

sub-project Multi-baseline SAR Processing for 3D/4D reconstruction (ID: 32278_2). The work here reported focuses on two important aspects of SAR remote sensing of tropical forests

namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions. Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest AGB (Above Ground Biomass). However

the two parameters that determine this linear relationship might vary for different tropical forest sites. For purpose of solving this problem

we investigate the possibility of using LiDAR derived AGB to help training the two parameters. Experimental results obtained by processing data from the TropiSAR campaign support the feasibility of the proposed concept. This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging

for which we simulate BIOMASS repeat pass tomography using ground-based TropiSCAT data with revisit time of 3 days and rainy days included. The resulting backscattered power variation at 30 m are within 1.5 dB. For this forest site

this error is translated into an AGB error of about 50-80 ton/ha

which is 20% or less of forest AGB.

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Keywords

references

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