A Novel Method for Forest Height Estimation Using PolInSAR Data

Document Type : Original Article

Authors

1 Photogrammetry and Remote Sensing Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology

2 Professor, Photogrammetry and Remote Sensing Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology

3 Associate Professor, Photogrammetry and Remote Sensing Department, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology

Abstract

Monitoring the earth and its biosphere is an essential task in any scale to achieve a sustainable development. Therefore, forests, as an invaluable natural resource, have an important role to control the climate changes and the carbon cycle. For this reason, biomass and consequently forest height have been known as the key information for monitoring the forest and its underlying surface. Several studies, it has been shown that Synthetic Aperture RADAR (SAR) imaging systems can greatly help to this purpose. In this framework, a novel technique called Polarimetric SAR Interferometry (PolInSAR) is an appropriate and an available tool for forest height estimation, due to its sensitivity to location and vertical distribution of the forest structural components. Based on this, from a view point, the methods employed in this field can be divided into two categories: a) based on Random Volume over Ground (RVoG) inversion model, and b) based on model-based decomposition techniques of PolInSAR data. In this study, in order to improve the forest height estimation, a novel method based on the combination of two mentioned categories has been proposed. The performance and the efficiency of the proposed method were demonstrated by four datasets related to the Pine and the deciduous forests which simulated from the PolSARProSim software in L and P bands.

Keywords

References

Aghabalaei, A., Ebadi, H. &Maghsoudi, Y., 2019, Forest height estimation based on the RVoG inversion model and the PolInSAR decomposition technique, International Journal of Remote Sensing 41, 2684-2703.
Ballester-Berman, J.D. & Lopez-Sanchez, J.M., 2010, Applying the Freeman–Durden decomposition concept to polarimetric SAR interferometry, IEEE Transactions on Geoscience and Remote Sensing 48, 466-479.
Cloude, S., 2010, Polarisation: applications in remote sensing, Oxford University Press.
Cloude, S., Papathanassiou, K., 2003, Three-stage inversion process for polarimetric SAR interferometry, IEE Proceedings-Radar, Sonar and Navigation 150, 125-134.
Cloude, S.R., 2005, POL-InSAR training course, Radio Science.
Cloude, S.R., Papathanassiou, K.P., 1998, Polarimetric SAR interferometry, IEEE Transactions on geoscience and remote sensing 36, 1551-1565.
Colin, E., Titin-Schnaider, C., Tabbara, W., 2005, An interferometric coherence optimization method in radar polarimetry for high-resolution imagery, IEEE Transactions on Geoscience and Remote Sensing 44, 167-175.
 
Ferro-Famil, L. & Neumann, M., 2008, Recent advances in the derivation of POL-inSAR statistics: Study and applications, In: Synthetic Aperture Radar (EUSAR), 2008 7th European Conference on, pp. 1-4.
Ferro-Famil, L., Reigber, A., Pottier, E. & Boerner, W.-M., 2003, Scene characterization using subaperture polarimetric SAR data, IEEE Transactions on Geoscience and Remote Sensing 41, 2264-2276.
Freeman, A., 2007, Fitting a two-component scattering model topolarimetric SAR data from forests, IEEE Transactions on Geoscience and Remote Sensing 45, 2583-2592.
Freeman, A. & Durden, S.L., 1998, A three-component scattering model for polarimetric SAR data, IEEE Transactions on Geoscience and Remote Sensing 36, 96-973-3.
Fu, H., Wang, C., Zhu, J., Xie, Q. & Zhao, R., 2015, Inversion of vegetation height from PolInSAR using complex least squares adjustment method, Science China Earth Sciences 58, 1018-1031.
Grant, M., Boyd, S., 2014, CVX: Matlab software for disciplined convex programming, version 2.1.
Hosseini, S.S., Ebadi, H. & Maghsoudi, Y., 2016, Effectiveness of Coherence optimization on improvement of height estimation using PolInSAR techniques, Engineering Journal of Geospatial Information Technology 4, 29-42.
Latrache, H., Souissi, B. & Ouarzeddine, M., 2018, Forest Height Estimation Using Adaptive Decomposition Method of Polinsar Data, In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, pp. 641-644.
Liao, Z., He, B., Quan, X., van Dijk, A.I., Qiu, S. & Yin, C., 2019, Biomass estimation in dense tropical forest using multiple information from single-baseline P-band PolInSAR data, Remote sensing of environment 221, 489-507.
Managhebi, T., Maghsoudi, Y. & Zoej, M.J.V., 2018, An improvedthree-stage inversion algorithm in forest height estimation using single-baseline polarimetric SAR interferometry data, IEEE Geoscience and Remote Sensing Letters 15, 887-891.
Mette, T., Papathanassiou, K., Hajnsek, I., Pretzsch, H. & Biber, P., 2004, Applying a common allometric equation to convert forest height from Pol-InSAR data to forest biomass, In: Geoscience and Remote Sensing Symposium, 2004. IGARSS'04. Proceedings. 2004 IEEE International.
Minh, N.P., Zou, B., Cai, H. & Wang, C., 2014, Forest height estimation from mountain forest areas using general model–based decomposition for polarimetric interferometric synthetic aperture radar images, Journal of Applied Remote Sensing 8, 083676.
Neumann, M., Ferro-Famil, L. & Reigber, A., 2010, Estimation offorest structure, ground, and canopy layer characteristics from multibaseline polarimetric inter-ferometric SAR data, IEEE Transactions on Geoscience and Remote Sensing 48, 1086-1104.
Papathanassiou, K.P. & Cloude, S.R., 2001, Single-baseline polarimetricSAR interferometry, IEEE Transactions on Geoscience and Remote Sensing 39, 2352-2363.
Sun, X., Wang, B., Xiang, M., Fu, X., Zhou, L. & Li, Y., 2019, S-RVoG Model Inversion Based on Time-Frequency Optimization for P-Band Polarimetric SAR Interferometry, Remote Sensing 11, 1033.
Tabb, M., Orrey, J., Flynn, T. & Carande, R., 2002, Phase diversity: A decomposition for vegetation parameter estimation using polarimetric SAR interferometry, In: Proc. EUSAR, pp. 721-724.
Tan, N.N., Nghia, P.M. & Thuy, B.N., 2019, Improved Three-Component Decomposition Technique for Forest Parameters Estimation from PolInSAR Image, REV Journal on Electronics and Communications 8.
Wenxue, F., Huadong, G., Xinwu, L., Bangsen, T. & Zhongchang, S., 2016, Extended three-stage polarimetricSAR interferometry algorithm by dual-polarization data, IEEE Transactions on Geoscience and Remote Sensing 54, 2792-2802.
Yamaguchi, Y., Moriyama, T., Ishido, M. & Yamada, H., 2005, Four-component scattering model for polarimetric SAR image decomposition, IEEE Transactions on Geoscience and Remote Sensing 43, 1699-1706.
Zhang, L., Duan, B. & Zou, B., 2017, Research on Inversion Models for Forest Height Estimation Using Polarimetric SAR Interferometry, ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII, 659-663.
 

Files

Share

How to cite

Statistics