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Parametric and non-parametric forest biomass estimation from PolInSAR data

M. Neumann ; S. S. Saatchi ; Lars M. H. Ulander (Institutionen för rymd- och geovetenskap, Radarfjärranalys) ; J. E. S. Fransson
IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011. Vancouver, 24-29 July 2011 p. 420-423. (2011)
[Konferensbidrag, refereegranskat]

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.

Nyckelord: Forest biomass, polarimetric SAR interferometry, random forest, regression, support vector machine

Denna post skapades 2011-12-20. Senast ändrad 2016-05-24.
CPL Pubid: 150512


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Institutioner (Chalmers)

Institutionen för rymd- och geovetenskap, Radarfjärranalys (2010-2017)


Elektroteknik och elektronik

Chalmers infrastruktur