Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Forest height estimation using DCP mode of compact PolInSAR data

Document Type : Research Paper

Authors
Amir Aghabalaei 1
Hamid Ebadi 2
Yasser Maghsoudi 3
1 Ph.D. Student, Faculty of geodesy and geomatics engineering, K. N. Toosi University of Technology
2 Professor, Faculty of geodesy and geomatics engineering, K. N. Toosi University of Technology
3 Associate Professor, Faculty of geodesy and geomatics engineering, K. N. Toosi University of Technology
10.22131/sepehr.2019.37503
Abstract
Extended Abstract
Introduction
Monitoring and assessment of the biosphere are two essential tasks at any scale. Based on this, forests play an important role in controlling the climate and the global carbon cycle. For this reason, biomass and consequently forest height are known as the key information for forest monitoring. In the recent decade, several studies have shown that the Synthetic Aperture RADAR (SAR) imaging systems in Compact Polarimetry (CP) mode can overcome the disadvantages of Full Polarimetric (FP) SAR imaging systems and provide a good performance in various remote sensing applications such as monitoring and managing the important natural resources like forests. In this regard, a novel technique named Polarimetric Interferometry SAR (PolInSAR) has been further considered as a powerful tool for forest height estimation.
 
Materials & Methods
In this research, the performance of the Compact PolInSAR (C-PolInSAR) data in Dual Circular Polarization (DCP) mode has been investigated in order to retrieve the forest height. For this reason, the common methods which are used for forest height estimation including Digital Elevation Model (DEM) differential method, coherence amplitude inversion, and phase & coherence inversion methods were applied and implemented on these data. In all of the aforementioned methods, LL+RR and LR polarizations were considered as the selected channels for estimating the volumetric and ground coherences, respectively. Then, the estimated coherences were considered as the input parameters for all of the mentioned methods.
 
Results & Discussion
To evaluate the performance and the efficiency of C-PolInSAR data in DCP mode, the results obtained from these data were compared with those obtained from Full PolInSAR (F-PolInSAR) data. The results obtained in this study in two datasets simulated from PolSARProSim software in both L and P bands showed that the C-PolInSAR data in DCP mode yielded a similar result compared to the F-PolInSAR data for forest height estimation (when the HH+VV polarization is adopted as the ground backscattering), because, in this case the LL+RR and the LR polarizations are equal to the HV and the HH+VV polarizations, respectively, particularly, the C-PolInSAR data in DCP mode yielded 0.78 m and 0.55 m improvements for forest height estimation in L and P bands, respectively. In addition, all of the employed methods provided better and closer results compared to the real forest height (i.e. 18 m) in L band compared to P band, because the electromagnetic (EM) waves have a more penetration into the canopy in L band compared to P band. Thus, the attenuation of these waves is low and consequently the height estimation is more accurate. Without considering the used bands, the DEM method provided the lowest precision compared to other methods, because the HV (or LL+RR) phase center can lie anywhere between half the tree height and top of the canopy. The exact location of this phase depends on two vegetation parameters which are the wave mean attenuation and the vertical canopy structure variations. In this case, the trees have very thin canopies, and consequently, the attenuation is small, but the phase center is high due to the structure. In other words, when the canopy extends over the entire forest height, then the phase center can be at half the true height for low density (low attenuation), through to the top of the canopy for dense vegetation (high attenuation). This ambiguity is inherent in single baseline methods, and in order to overcome this, model-based correction methods need to be employed.
It was also observed that the coherence amplitude method is among the weak algorithms due to ignoring the phase and its sensitivity to the attenuation and structural variations but it can be used as a backup solution when other approaches fail. Finally, the phase and the coherence inversion method had better results than two aforementioned methods for the forest height estimation. In this method, selecting the factor ‘’ is very important and it should be selected in a way to be strong towards the attenuation changes. In this study, = 0.4 was adopted to maintain the height error variations.
 
Conclusion
As the final result, the C-PolInSAR data can be an efficient strategy due to its performance, when the full polarimetric imaging systems are either limited or not available. Moreover, utilizing these data in long wavelengths (e.g. P band) is more appropriate due to the effect of the Faraday rotation on the transmitted polarization.
Keywords
Compact PolInSAR
Dual circular polarization
Forest height estimation
Digital Elevation Model (DEM) differential method
Coherence amplitude inversion method
Phase and coherence inversion method
1. Arnaubec, A., Roueff, A., Dubois-Fernandez, P. C., & Réfrégier, P. (2014). Vegetation height estimation precision with compact PolInSAR and homogeneous random volume over ground model. IEEE Transactions on Geoscience and Remote Sensing, 52(3), 1879-1891.
2. Askne, J. I., Dammert, P. B., Ulander, L. M., & Smith, G. (1997). C-band repeat-pass interferometric SAR observations of the forest. IEEE Transactions on Geoscience and Remote Sensing, 35(1), 25-35.
3. Boularbah, S., Ouarzeddine, M., & Belhadj-Aissa, A. (2012). Investigation of the capability of the Compact Polarimetry mode to Reconstruct Full Polarimetry mode using RADARSAT2 data. Advanced Electromagnetics, 1(1), 19-28.
4. Cloude, S., Corr, D., & Williams, M. (2004). Target detection beneath foliage using polarimetric synthetic aperture radar interferometry. Waves in Random Media, 14(2), S393-S414.
5. Cloude, S., & Papathanassiou, K. (2003). Three-stage inversion process for polarimetric SAR interferometry. IEE Proceedings-Radar, Sonar and Navigation, 150(3), 125-134.
6. Cloude, S., Papathanassiou, K., & Reigber, A. (2000). Polarimetric SAR interferometry at P-band for vegetation structure extraction. Paper presented at the EUSAR 2000.
7. Cloude, S. R., Krieger, G., & Papathanassiou, K. P. (2005). A framework for investigating space-borne polarimetric interferometry using the ALOS-PALSAR sensor. Paper presented at the Geoscience and Remote Sensing Symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE International.
8. Cloude, S. R., & Papathanassiou, K. P. (1998). Polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1551-1565.
9. Dubois-Fernandez, P., Angelliaume, S., Souyris, J.-C., & Garestier, C. (2007). The specificity of P band PolInSAR data over vegetation. Paper presented at the Science and Applications of SAR Polarimetry and Polarimetric Interferometry.
10. Guo, S., Li, Y., Yin, Q., Chen, H., Richardson, A., & Hong, W. (2014). Applying the Freeman-Durden decomposition tocompact polarimetric SAR Interferometry. Paper presented at the Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International.
11. Hagberg, J. O., Ulander, L. M., & Askne, J. (1995). Repeat-pass SAR interferometry over forested terrain. IEEE Transactions on Geoscience and Remote Sensing, 33(2), 331-340.
12. Han, K., Jiang, M., Wang, M., & Liu, G. (2018). Compact Polarimetric SAR Interferometry Target Decomposition With the Freeman–Durden Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2847-2861.
13. Lavalle, M., Solimini, D., Pottier, E., & Desnos, Y.-L. (2008a). Investigation on the performance of compact polarimetry in SAR interferometry. Paper presented at the Synthetic Aperture Radar (EUSAR), 2008 7th European Conference on.
14. Lavalle, M., Solimini, D., Pottier, E., & Desnos, Y.-L. (2008b). PolInSAR for Forest Biomass Retrieval: PALSAR Observations and Model Analysis. Paper presented at the IGARSS (3).
15. Liu, Y., Yang, W., Xu, X., & Liao, M. (2011). Investigation on compact polarimetry in SAR interferometry using CETC38 airborne data. Paper presented at the Radar (Radar), 2011 IEEE CIE International Conference on.
16. Papathanassiou, K., Cloude, S., Liseno, A., Mette, T., & Pretzsch, H. (2005). Forest height estimation by means of polarimetric SAR interferometry: actual status and perspectives. Paper presented at the Proceedings of 2nd ESA POLInSAR Workshop, Frascati, Italy.
17. Papathanassiou, K. P., & Cloude, S. R. (2001). Single-baseline polarimetric SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(11), 2352-2363.
18. Ramachandran, N., & Dikshit, O. (2018). Experimental Validation of Compact Tomosar for Vegetation Characterization. Paper presented at the IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium.
19. Raney, R. K. (2007). Hybrid-polarity SAR architecture. Geoscience and Remote Sensing, IEEE Transactions on, 45(11), 3397-3404.
20. Reigber, A., Papathanassiou, K., Cloude, S., & Moreira, A. (2001). SAR tomography and interferometry for the remote sensing of forested terrain. Frequenz, 55(3-4), 119-122.
21. Souyris, J.-C., Imbo, P., Fjortoft, R., Mingot, S., & Lee, J.-S. (2005). Compact polarimetry based on symmetry properties of geophysical media: The/spl pi//4 mode. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 634-646.
22. Touzi, R., Lopes, A., Bruniquel, J., & Vachon, P. W. (1999). Coherence estimation for SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 37(1), 135-149.
23.Truong-Loi, M.-L., & Dubois-Fernandez, P. (2010). An optimized method to estimate forest height with compact polarimetric SAR interferometry. Paper presented at the Synthetic Aperture Radar (EUSAR), 2010 8th European Conference on.
24. Williams, M. L., Pottier, E., Ferro-Famil, L., Allain, S., Cloude, S., Hajnsek, I., . . . Desnos, Y. (2007). Forest coherent sar simulator within polsarpro: an educational toolbox for PolSAR and PolInSAR data processing. Paper presented at the Asian Conference on Remote Sensing, Kuala Lumpur, Malaysia.
25. Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes. IEEE transactions on geoscience and remote sensing, 30(5), 950-959.