Characterizing forest structure using LiDAR and multi-frequency SAR remote sensing

Describing forest structure is fundamental to understanding forest ecology and calculating biomass estimations. To enable its characterization with large spatial coverage, we investigate data recorded by airborne LiDAR and three different radar frequencies over a deciduous broadleaf forest at the Hainich National Park in central Germany. This study aims at distilling the microwave frequencies and polarisations that most closely relate to structural metrics extracted from the LiDAR point clouds, and are therefore most promising for extending spatial or temporal coverage.

The LiDAR point clouds, which are provided openly by the Thuringian State Office for Land Management and Geoinformation, were processed to five structural metrics at 25 m x 25 m pixel size. These metrics comprise an estimation of fractional cover based on vegetation return numbers,  an intensity-based fractional cover approach (Hopkinson & Chasmer 2009), the skewness and standard deviation of the height distribution, as well as the the vertical complexity index as defined by van Ewijk (2011). These metrics were compared to terrain-corrected backscatter of phenologically matching scenes from three different sensor frequencies: an X Band scene from DLR TerraSAR-X, C Band from Copernicus Sentinel-1, and L Band from JAXA ALOS-2. 

The scenes represent leaf-off conditions. To reduce misleading factors, the analysis was limited to areas with moderate slope angles below 10 degrees. Subsequently, regression models between the lidar metrics and backscatter intensities were built.
First results from bivariate correlations indicate the best match between ALOS-2 HV and fractional cover (r²=0.41) as well as standard deviation (r²= 0.43). Among the metrics, fractional cover is associated most closely with backscatter in all frequencies: the highest correlation coefficients amount to 0.37 for X Band (VV), 0.22 for C Band (VH), and 0.41 for L Band (HV), respectively. In general, C Band exhibits the lowest pairwise correlations with most density metrics, compared to L- and X Band. 
The poster will show the results of multivariate regression models and discuss which combination of frequencies and polarizations is best suited for the derivation of specific forest structure parameters at larger scales.

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Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113(1), 275–288. DOI:10.1016/j.rse.2008.09.012

van Ewijk, K. Y., Treitz, P. M., & Scott, N. A. (2011). Characterizing Forest Succession in Central Ontario using Lidar-derived Indices. Photogrammetric Engineering & Remote Sensing, 77(3), 261–269. DOI: 10.14358/PERS.77.3.261