Parametric and non-parametric forest biomass estimation from PolInSAR data
Paper i proceeding, 2011

Biomass estimation performance from model-based polarimetric SAR interferometry (PolInSAR) using generic parametric and non-parametric regression methods is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data is decomposed into ground and volume contributions, estimating vertical forest structure, and using a set of obtained parameters for biomass regression. The considered estimation methods include multiple linear regression, support vector machine and random forest. The biomass estimation performance is evaluated on DLR's airborne SAR data at L- and P-bands over Krycklan Catchment, a boreal forest test site in Northern Sweden. The combination of polarimetric indicators and estimated structure information has improved the root mean square error (RMSE) of biomass estimation up to 28% at L-band and up to 46% at P-band. The cross-validated biomass RMSE was reduced to 20 tons/ha.

polarimetric SAR interferometry

random forest

Forest biomass


support vector machine


M. Neumann

Jet Propulsion Laboratory, California Institute of Technology

S. S. Saatchi

Jet Propulsion Laboratory, California Institute of Technology

Lars Ulander

Chalmers, Rymd- och geovetenskap, Radarfjärranalys

J. E. S. Fransson

Sveriges lantbruksuniversitet (SLU)

IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. Vancouver, 24-29 July 2011

420-423 6049154
978-145771005-6 (ISBN)


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