Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass
Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass
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DOI：
10.1109/tgrs.2011.2176133
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发表时间：
2012-01
影响因子：
8.2
通讯作者：
M. Neumann;S. Saatchi;L. Ulander;J. Fransson
中科院分区：
文献类型：
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作者：
M. Neumann;S. Saatchi;L. Ulander;J. Fransson
对L和P波段极化干涉合成孔径雷达(POLINSAR)数据在北方森林生物量估测中的性能进行了评估。POLinSAR数据分解为地面贡献和体积贡献，反演出垂直森林结构和偏振层特征。分析了生物量对所获得参数的敏感性，并将一组这些参数用于生物量估计，评估了一种参数方法和两种非参数方法：多元线性回归方法、支持向量机方法和随机森林方法。该方法应用于瑞典北部北方森林试验场克里克兰流域的机载合成孔径雷达数据。森林生物量平均为94吨/公顷，林分水平上升到183吨/公顷(小区水平为317吨/公顷)。结果表明，在L波段，HH-VV的强度对生物量的响应更为敏感。在P波段，偏振散射机制类型指标与生物量相关性最强。根据所用参数集的不同，偏振指标与由林高和林分蓄积率组成的估计结构信息相结合，使L波段生物量估测的均方根误差(RMSE)提高了17%-25%，P波段的均方根误差(RMSE)提高了5%-27%。再加上额外的基频和体积偏振特性，L波段的RMSE提高了27%，P波段的RMSE提高了43%。最好的情况下，交叉验证的生物质RMSE减少到20吨/公顷。非参数估计方法没有改善生物量估计的交叉验证RMSE，但可以提供更真实的生物量值分布。
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.