摘要
基于微波的后向散射系数估计森林地上生物量(AGB)易受后向散射系数饱和的影响,而利用森林高度,根据生长方程估计AGB,却没有考虑和AGB密切相关的林分密度、树种组成、林层垂直分布等空间结构特征的作用,针对这些问题,提出一种基于极化相干层析(Polarization Coherence Tomography,PCT)技术的AGB估计方法。基于德国宇航局(DLR)机载SAR系统(ESAR)获取的特劳斯坦(Traunstein)试验区L-波段极化干涉SAR(PolInSAR)数据,通过对具有不同AGB水平的典型林分的相对反射率函数曲线的分析,定义了9个与AGB具有相关性的特征参数。然后基于20个林分的实测AGB数据,以林分尺度上这9个特征参数的平均值为自变量,以实测林分平均AGB为因变量,采用逐步回归分析法构建了AGB估测模型,并对该模型进行评价,对影响模型估计精度的因素进行分析,结果表明,由PCT提取的相对反射率函数特征参数对AGB很敏感,充分利用相对反射率函数信息可提高AGB估计精度。
Forest above ground biomass （AGB） estimation using microwave backscattering coefficient is normally limited to low level AGB because of the ＂saturation＂ problem in backscattering coefficient. In addition, forest height may be used to estimate AGB by allometric equation, but the changing conditions of the forest in terms of density, tree species composition etc. limit the accuracy and performance of the method. In order to overcome the above disadvantages and improve the estimation accuracy, a method for AGB estimation is proposed in this paper, which is based on polarization coherence tomography （PCT） technology. Using repeat pass ESAR L-band PoIInSAR data collected by DLR at the Traunstein test site, the radar relative reflectivity function of each pixel is reconstructed using PCT, from which the average relative reflectivity profiles for the 20 validation stands are computed. Then 9 profile characteristic parameters closely related to biomass are defined and extracted for each forest stand. The natural logarithms of these 9 profile parameters are taken as independent variables for multivariate linear regression analysis with the natural logarithm of the field-measured AGB as dependent variable using stepwise regression method. Forest AGB estimation model is established and evaluated, and the factors possibly affecting the performance of the AGB estimation model are also analyzed. The results show that these parameters, which are extracted from the average relative reflectivity function inversed with PCT, are sensitive to forest AGB. The accuracy of AGB estimation can be improved if we make full use of the information contained in the relative reflectivity function.
出处
《遥感学报》
EI
CSCD
北大核心
2011年第6期1138-1155,共18页
NATIONAL REMOTE SENSING BULLETIN
基金
国家重点基础研究重点计划(973计划)(编号:2007CB714404)
国家自然科学基金(编号:60890074)~~
关键词
遥感技术
应用
理论
图像处理
polorimetric interferometric SAR, polarization coherence tomography, stepwise regression analysis method, forestabove ground biomass
