Vol. 90

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2021-01-14

Electromagnetic Simulation for Estimation of Forest Vertical Structure Using PolSAR Data

By Shimaa Ahmed Megahed Soliman, Khalid Fawzy Ahmed Hussein, and Abd-El-Hadi A. Ammar
Progress In Electromagnetics Research B, Vol. 90, 129-150, 2021
doi:10.2528/PIERB20110802

Abstract

A novel method for the estimation of a forest vertical structure using Polarimetric Synthetic Aperture Radar (PolSAR) data only without the need to interferometry data is proposed in the present paper. Electromagnetic (EM) simulation is used to develop the proposed method, where the SAR pulse is simulated as a plane wave incident in the direction of the side looking angle of the SAR. For this purpose, the forest canopy layer is modeled as clouds of randomly oriented thin straight dipoles which are randomly distributed within an inclined prism volume, whereas the forest soil surface is modeled as a random rough surface. This prism has a horizontal rectangular base and parallelogram sides parallel to the direction of the incident plane wave (side looking angle of the SAR). The proposed method aims to estimate the average height of the canopy layer above the soil surface, the canopy layer thickness and the roughness of the forest ground surface. The proposed method is based on the Radar Vegetation Index (RVI) and the normalized Radar Cross Section (RCS) calculated from the PolSAR data and their relevance to the parameters of the forest vertical structure. Some examples are presented to demonstrate the capability of the proposed method using some PolSAR images obtained through EM simulation of the scattering from forest regions and by applying the theorem of SAR target composition with the Multiple Component Scattering Model (MCSM). The phase differences between the components of scattering obtained from the solution of the SAR target decomposition problem are used in the estimation process. The accuracy of the proposed method is assessed by calculating the percentage error of the estimated vertical structure and ground roughness for each resolution cell of the simulated forest region. It is shown that the percentage errors of the estimated parameters are very low, which reflects the accuracy and efficiency of the proposed method.

Citation


Shimaa Ahmed Megahed Soliman, Khalid Fawzy Ahmed Hussein, and Abd-El-Hadi A. Ammar, "Electromagnetic Simulation for Estimation of Forest Vertical Structure Using PolSAR Data," Progress In Electromagnetics Research B, Vol. 90, 129-150, 2021.
doi:10.2528/PIERB20110802
http://jpier.org/PIERB/pier.php?paper=20110802

References


    1. Miner, R., "Impact of the global forest industry on atmospheric greenhouse gases," Forestry, Food and Agriculture Organization of the United Nations, Rome, 2010.

    2. Brigot, G., M. Simard, E. Colin-Koeniguer, and A. Boulch, "Retrieval of forest vertical structure from PolInSAR data by machine learning using LIDAR-derived features," Remote Sensing, Vol. 11, No. 4, 381, 2019.
    doi:10.3390/rs11040381

    3. Cloude, S. R. and K. P. Papathanassiou, "Three-stage inversion process for polarimetric SAR interferometry," IEE Proceedings — Radar, Sonar and Navigation, Vol. 150, No. 3, 125-134, 2003.
    doi:10.1049/ip-rsn:20030449

    4. Neumann, M., L. Ferro-Famil, and A. Reigber, "Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 3, 1086-1104, 2009.
    doi:10.1109/TGRS.2009.2031101

    5. Mette, T., F. Kugler, K. Papathanassiou, and I. Hajnsek, "Forest and the random volume over ground-nature and effect of 3 possible error types," European Conference on Synthetic Aperture Radar (EUSAR), 1-4, VDE Verlag GmbH, 2006.

    6. Zhou, Y.-S., W. Hong, and F. Cao, "An improvement of vegetation height estimation using multi-baseline polarimetric interferometric SAR data," PIERS Online, Vol. 5, No. 1, 6-10, 2009.
    doi:10.2529/PIERS080907033305

    7. Kim, Y. and J. Zyl, "Comparison of forest estimation techniques using SAR data," Proc. IEEE IGARSS Conf., 1395-1397, 2001.

    8. Kim, Y., T. Jackson, R. Bindlish, S. Hong, G. Jung, and K. Lee, "Retrieval of wheat growth parameters with radar vegetation indices," IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 4, 808-812, 2014.
    doi:10.1109/LGRS.2013.2279255

    9. Huang, Y., J. P. Walker, Y. Gao, X. Wu, and A. Monerris, "Estimation of vegetation water content from the radar vegetation index at L-band," IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 2, 981-989, 2016.
    doi:10.1109/TGRS.2015.2471803

    10. Szigarski, T. Jagdhuber, M. Bau, C. Thiel, M. Parrens, J. Wignero, M. Piles, and D. Entekhabi, "Analysis of the radar vegetation index and potential improvements," Remote Sensing, Vol. 10, No. 11, 1776, 2018.
    doi:10.3390/rs10111776

    11. Soliman, S. A. M., K. F. A. Hussein, and A.-E.-H. A. Ammar, "Electromagnetic resonances of natural grasslands and their effects on radar vegetation index," Progress In Electromagnetics Research B, Vol. 86, 19-38, 2020.
    doi:10.2528/PIERB19080702

    12. Papathanassiou, K. P. and S. R. Cloude, "Single-baseline polarimetric SAR interferometry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 39, No. 11, 2352-2363, 2001.
    doi:10.1109/36.964971

    13. Brandfass, M., C. Hofmann, J. C. Mura, J. R. Moreira, and K. P. Papathanassiou, "Parameter estimation of rain forest vegetation via polarimetric radar interferometric data," SAR Image Analysis, Modeling, and Techniques IV, Vol. 4543, 169-179, International Society for Optics and Photonics, January 200.

    14. Cloude, S. R., K. P. Papathanassiou, I. Woodhouse, J. Hope, J. C. Suarez Minguez, P. E. Osborne, and G. Wright, "The Glen Affric radar project: Forest mapping using polarimetric interferometry," IGARSS 2001, Scanning the Present and Resolving the Future, Proceedings, IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217), 2001.

    15. Sarabandi, K. and Y. C. Lin, "Simulation of interferometric SAR response for characterizing the scattering phase center statistics of forest canopies," IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 1, 115-125, 2000.
    doi:10.1109/36.823906

    16. Yang, H., D. Liu, G. Sun, Z. Guo, and Z. Zhang, "Simulation of interferometric SAR response for characterizing forest successional dynamics," IEEE Geoscience and Remote Sensing Letters, Vol. 11, No. 9, 1529-1533, 2014.
    doi:10.1109/LGRS.2014.2298431

    17. Freeman, A. and S. L. Durden, "A three-component scattering model for polarimetric SAR data," IEEE Transactions on Geoscience and Remote Sensing, Vol. 36, No. 3, 963-973, 1998.
    doi:10.1109/36.673687

    18. Yamaguchi, Y., T. Moriyama, M. Ishido, and H. Yamada, "Four-component scattering model for polarimetric SAR image decomposition," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, No. 8, 1699-1706, 2005.
    doi:10.1109/TGRS.2005.852084

    19. Zhang, L., B. Zou, H. Cai, and Y. Zhang, "Multiple-component scattering model for polarimetric SAR image decomposition," IEEE Geoscience and Remote Sensing Letters, Vol. 5, No. 4, 603-607, 2008.
    doi:10.1109/LGRS.2008.2000795

    20. Cloude, S. R. and E. Pottier, "A review of target decomposition theorems in radar polarimetry," IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, No. 2, 498-518, Mar. 1996.
    doi:10.1109/36.485127

    21. Maıtre, H., Processing of Synthetic Aperture Radar (SAR) Images, John Wiley & Sons, 2013.