Sensitivity of P- and L-band SAR Tomography to Above-Ground Biomass in a Hilly Temperate Forest
Journal article, 2024

Tomographic Synthetic Aperture Radar (TomoSAR) is a promising technique for estimation of forest Above-Ground Biomass (AGB), but knowledge gaps still remain concerning the effects of forest type and ground topography. The paper presents new results at P- and L-band based on data acquired during the TomoSense campaign. The study area is a temperate forest, predominantly beech and spruce, with ground slopes ranging up to 40°. Analysis of vertical reflectivity profiles show distinct differences for spruce and beech. Three AGB retrieval methods are analysed, i.e. total vertical backscatter Itot, canopy backscatter from a height layer Ic, and the ratio Icr=Ic/Itot. All three methods show sensitivity to AGB for spruce, whereas for beech this is only true for the two latter methods. For P-band, a significant ground slope effect is observed, while less so for L-band. The highest R2 is obtained for spruce with HV-polarisation, Ic and ground slopes less than 10°, i.e. R2 = 0.86 and RMSE = 15.6% for P-band and R2 = 0.75 and RMSE = 12.5% for L-band. Corresponding results by including all forest types are R2 = 0.77 and RMSE = 11.4% for P-band and R2 = 0.54 and RMSE = 12.0% for L-band. Moreover, performance of using Icr is similar to that of Ic. The ratio Icr can be determined without absolute radiometric calibration which relaxes system requirements. This paper reinforces the potential of TomoSAR for forest AGB estimation and draws attention to important effects of tree species and ground slope.

AGB retrieval

ground slope

L-band

TomoSAR

P-band

temperate forest

Author

Patrik Bennet

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Lars Ulander

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Mauro Mariotti d'Alessandro

Polytechnic University of Milan

S. Tebaldini

Polytechnic University of Milan

IEEE Transactions on Geoscience and Remote Sensing

0196-2892 (ISSN) 15580644 (eISSN)

Vol. 62 4413519

Subject Categories

Remote Sensing

Forest Science

Geophysics

DOI

10.1109/TGRS.2024.3455790

More information

Latest update

10/25/2024