Stem volume of tropical forests from polarimetric radar
Stem volume of tropical forests from polarimetric radar
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DOI：
10.1080/01431160903475217
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发表时间：
2011-01-01
影响因子：
3.4
通讯作者：
Treuhaft, R. N.
中科院分区：
文献类型：
--
作者：
Goncalves, F. G.;Santos, J. R.;Treuhaft, R. N.
在这项研究中，我们探讨了潜在的极化合成孔径雷达（PolSAR）数据的树干体积估计在热带森林。我们使用校准的L波段，高入射角的数据从机载系统SAR-R99 B，在塔帕霍斯国家森林，帕拉，巴西的实验区获得。为了评估PolSAR数据在此应用中的潜力，我们使用了回归分析，其中一阶模型适合预测每公顷干体积，从现场测量确定。与以往在热带森林的研究不同，这套潜在的解释变量包括一系列基于相位信息的PolSAR属性，以及功率测量。模型选择技术的决定系数（R2）和均方误差（MSE）的基础上确定了几个有用的子集的解释变量干体积估计，包括后向散射系数HH极化，交叉极化比，HH-VV相位差，极化相干性，和体积散射分量的弗里曼分解。对选定模型的评价表明，PolSAR数据可用于量化研究地点的树干体积，均方根误差（RMSE）约为20-29 m3 ha-1，相当于平均树干体积的8-12%。使用独立数据的外部验证显示，平均预测误差小于14%。在308 m3 ha-1（生物量为357 Mg ha-1）的体积下，未观察到实测体积与模拟体积的饱和效应。然而，由于数据集体积范围的限制，无法对饱和度进行正式评估。
In this study, we investigated the potential of polarimetric synthetic aperture radar (PolSAR) data for the estimation of stem volume in tropical forests. We used calibrated L-band, high incidence angle data from the airborne system SAR-R99B, acquired over an experimental area in the Tapajos National Forest, Para, Brazil. To evaluate the potential of PolSAR data for this application we used regression analysis, in which first-order models were fit to predict stem volume per hectare, as determined from field measurements. Unlike previous studies in tropical forests, the set of potential explanatory variables included a series of PolSAR attributes based on phase information, in addition to power measurements. Model selection techniques based on coefficient of determination (R2) and mean square error (MSE) identified several useful subsets of explanatory variables for stem volume estimation, including backscattering coefficient in HH polarization, cross-polarized ratio, HH-VV phase difference, polarimetric coherence, and the volume scatter component of the Freeman decomposition. Evaluation of the selected models indicated that PolSAR data can be used to quantify stem volume in the study site with a root mean square error (RMSE) of about 20-29 m3 ha-1, corresponding to 8-12% of the mean stem volume. External validation using independent data showed average prediction errors of less than 14%. Saturation effects in measured versus modelled volume were not observed up to volumes of 308 m3 ha-1 (biomasses of 357 Mg ha-1). However, no formal assessment of saturation was possible due to limitations of the volume range of the dataset.