Volume 9 Issue 3
Jun.  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(3): 569-577
TAN Pengyuan, ZHU Jianjun, FU Haiqiang, et al. Inversion of forest height based on ALOS-2 PARSAR-2 multi-baseline polarimetric SAR interferometry data[J]. Journal of Radars, 2020, 9(3): 569–577. doi: 10.12000/JR20030
Citation: TAN Pengyuan, ZHU Jianjun, FU Haiqiang, et al. Inversion of forest height based on ALOS-2 PARSAR-2 multi-baseline polarimetric SAR interferometry data[J]. Journal of Radars, 2020, 9(3): 569–577. doi: 10.12000/JR20030
shu

Inversion of Forest Height Based on ALOS-2 PARSAR-2 Multi-baseline Polarimetric SAR Interferometry Data

DOI: 10.12000/JR20030
  • 1.

    School of Geociences and Info-Physics, Central South University, Changsha 410083, China

  • 2.

    Research Center of Forestry Remote Sensing & Information Engineering Central South University & Technology, Changsha 410004, China

Funds:  The National Natural Science Foundation of China (41531068, 41820104005), Opening Foundation of Hunan Engineering and Research Center of Natural Resource Investigation and Monitoring (2020-1), The Innovation Foundation for Postgraduate of Central South University (2019zzts656)
More Information
  • Corresponding author: ZHU Jianjun, zjj@csu.edu.cn
  • Received Date: 2020-04-02
  • Rev Recd Date: 2020-05-31
    • Available Online: 2020-06-13
  • Publish Date: 2020-06-01
    Abstract
  • To compensate for the limitations of insufficient observation information and simplistic geometric structure of single-baseline InSAR, this study proposes a new method for extracting forest height from ALOS-2 PARSAR-2 multi-baseline PolInSAR datas. Firstly, the Maximum Coherence Difference (MCD) algorithm is introduced to determine the polarization; this algorithm is very sensitive to volume scattering in the polarization space. Then, with the aid of a small amount of externally known forest height data, the coherence amplitude of the polarization is used to solve the temporal decorrelation semi-empirical scattering model. In addition, multi-baseline datas are further fused to increase the diversity of observation geometry and improve the reliability of the inversion results. To verify the effectiveness of the proposed method, we selected Huangfengqiao Forestry Center in Hunan, China as the study area and used three pairs of ALOS-2 PALSAR-2 interferometric images with 14-day temporal baseline for the experimental analysis. The experimental results showed that the method proposed in this study effectively improved the assumptions and addressed the limitation of the existing method that is only applicable to single-baseline interferometric data. Thus, the inversion accuracy can be improved by at least 40%.

     

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