Forest Height Estimation Using MultiBaseline Low-Frequency PolInSAR Data Affected by Temporal Decorrelation

For repeat-pass interferometric systems, temporal decorrelation (TD) is inevitable and cannot be ignored, and can lead to significant bias in the forest height estimation. The TD random volume over ground (TD + RVoG) model has been found to be a reasonable way to describe the scattering process over...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Zhang, Bing, Fu, Haiqiang, Zhu, Jianjun, Peng, Xing, Lin, Dongfang, Xie, Qinghua, Hu, Jun
Format: Article
Language:English
Subjects:
Biological system modeling
Coherence
Data models
Decorrelation
Estimation
Forest height
Forestry
Forests
Height
Interferometry
Modelling
multibaseline
polarimetric synthetic aperture radar interferometry (PolInSAR)
Polarimetry
Radar
Radar data
SAR (radar)
Synthetic aperture radar
Synthetic aperture radar interferometry
temporal decorrelation (TD)
temporal decorrelation random volume over ground (TD + RVoG)
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Summary:For repeat-pass interferometric systems, temporal decorrelation (TD) is inevitable and cannot be ignored, and can lead to significant bias in the forest height estimation. The TD random volume over ground (TD + RVoG) model has been found to be a reasonable way to describe the scattering process over forest areas. In this letter, based on the TD + RVoG model, a new forest height estimation method is proposed for use with multibaseline polarimetric synthetic aperture radar interferometry (PolInSAR) data. First, the correlation between the ground-to-volume ratios (GVRs) associated with the different polarizations is parameterized according to the geometric interpretation of the RVoG model. An interferometric pair that is assumed to have no TD is then selected based on the eccentricity of the polarimetric coherence region, and the other interferometric pairs are fitted by the TD + RVoG model. E-synthetic aperture radar (E-SAR) P -band PolInSAR data sets affected by TD are used to prove the effectiveness of the proposed method. The experimental results show that the forest height results are improved by 25.90% when compared to the RVoG-based method.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3052727