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Forskningsartikel - Refereegranskat, 2012

Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass

Neumann, Maxim; Saatchi, S.S.; Ulander, L.M.H.; Fransson, Johan


Biomass estimation performance using polarimetric interferometric synthetic aperture radar (PolInSAR) data is evaluated at L- and P-band frequencies over boreal forest. PolInSAR data are decomposed into ground and volume contributions, retrieving vertical forest structure and polarimetric layer characteristics. The sensitivity of biomass to the obtained parameters is analyzed, and a set of these parameters is used for biomass estimation, evaluating one parametric and two non-parametric methodologies: multiple linear regression, support vector machine, and random forest. The methodology is applied to airborne SAR data over the Krycklan Catchment, a boreal forest test site in northern Sweden. The average forest biomass is 94 tons/ha and goes up to 183 tons/ha at forest stand level (317 tons/ha at plot level). The results indicate that the intensity at HH-VV is more sensitive to biomass than any other polarization at L-band. At P-band, polarimetric scattering mechanism type indicators are the most correlated with biomass. The combination of polarimetric indicators and estimated structure information, which consists of forest height and ground-volume ratio, improved the root mean square error (rmse) of biomass estimation by 17%-25% at L-band and 5%-27% at P-band, depending on the used parameter set. Together with additional ground and volume polarimetric characteristics, the rmse was improved up to 27% at L-band and 43% at P-band. The cross-validated biomass rmse was reduced to 20 tons/ha in the best case. Non-parametric estimation methods did not improve the cross-validated rmse of biomass estimation, but could provide a more realistic distribution of biomass values.


Biomass estimation; boreal forest; interferometry; linear regression (LR); polarimetry; random forest (RF); support vector machine (SVM); synthetic aperture radar (SAR)

Publicerad i

IEEE Transactions on Geoscience and Remote Sensing
2012, Volym: 50, nummer: 3, sidor: 714-726

    UKÄ forskningsämne

    Environmental Sciences related to Agriculture and Land-use
    Forest Science

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