Speaker
Description
Synthetic Aperture Radar (SAR) has been used for multiple forest applications like forest cover or biomass estimation. More advances techniques like Polarimetric SAR interferometry (Pol-InSAR) further increases the potential of radar as remote sensing tool for forest applications. Pol-InSAR is able to combine different polarizations of the electromagnetic wave with interferometric techniques to determine vertical structure information of the forest [1]. However, the results are limited to forest height [2] or the estimation of vertical structure with limited accuracy [3]. In this context, SAR tomography (TomoSAR) aims to get a more accurate estimation of the vertical structure of the forest by combining multiple SAR acquisitions over the same area [4]. Whit the condition of using a frequency low enough to penetrate and interact with the forest, the result of TomoSAR provides a three dimensional (3-D) image of the forest until the ground, represented by the backscattering of the elements contained in the forest. This 3-D capability makes TomoSAR a powerful tool to be taken it into account for the characterization of forests.
The direct result from TomoSAR is the 3-D radar reflectivity and has not a straightforward ecological meaning that can be directly used for the forestry community. Further interpretation and processing of the results need to be done to extract forest information. This interpretation is challenging, the TomoSAR result depends on the configuration of the system (e.g. frequency, polarisation or the number and distribution of acquisitions), the dielectric properties of the scene (e.g. moisture content) or the algorithm used to get the 3-D backscattering of the forest. Therefore it is not easy to extract forest information from the radar signal and this topic is still under investigation [5].
In this presentation, we discuss how we can relate TomoSAR results with physical 3-D forest structure, i.e. the arrangement of trees in the 3-D space, by means of the local maxima obtained from the vertical radar reflectivity profiles. More in detail, two indices to define the forest structure will be presented, one for the horizontal structure, which represents the canopy density in the upper part of the forest and another one for the vertical structure, which reflects the distribution of trees in the vertical direction [6].In order to evaluate this concept, three tomographic SAR datasets (in 2008, 2012 and 2016) over a managed temperate forest in the south of Germany together with Lidar and ground measurements are available. The results will show on a first step how we can differentiate different forest structure types and then how we can use the horizontal and vertical indices to monitor changes on the structure of the forest over time [7].
References
[1] A. Moreira, P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek and K. P. Papathanassiou, "A tutorial on synthetic aperture radar," in IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 1, pp. 6-43, March 2013.
[2] K. P. Papathanassiou and S. R. Cloude, "Single-baseline polarimetric SAR interferometry," in IEEE Transactions on Geoscience and Remote Sensing, vol. 39, no. 11, pp. 2352-2363, Nov. 2001.
[3] S. Cloude, “Polarization Coherence Tomography,” Radio Science, vol. 41, RS4017, Sep. 2006.
[4] A. Reigber, A. Moreira, “First demonstration of airborne SAR tomography using multibaseline L-band data,” IEEE Trans. Geosci. Remote Sens. 2000, 38, 2142–4152,
[5] O. Frey, E. Meier, “Analyzing Tomographic SAR Data of a Forest With Respect to Frequency, Polarization, and Focusing Technique,” IEEE Trans. on Geosc. and Remote Sensing, vol. 49, No. 10, 2011.
[6] M. Tello, V. Cazcarra-Bes, M. Pardini and K. Papathanassiou, "Forest Structure Characterization From SAR Tomography at L-Band," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi: 10.1109/JSTARS.2018.2859050
[7] V. Cazcarra-Bes, M. Tello-Alonso, R. Fischer, M. Heym, and K. Papathanassiou, “Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography,” Remote Sensing, vol. 9, no. 12, p. 1229, Nov. 2017.
