Retrieving leaf area index using ICESat/GLAS full-waveform data
Retrieving leaf area index using ICESat/GLAS full-waveform data
复制标题
使用 ICESat/GLAS 全波形数据检索叶面积指数
DOI：
10.1080/2150704x.2013.790573
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发表时间：
2013-08-01
影响因子：
2.3
通讯作者：
Xi, Xiaohuan
中科院分区：
文献类型：
--
作者：
Luo, Shezhou;Wang, Cheng;Xi, Xiaohuan
叶面积指数（LAI）是控制与地表植被有关的许多生物和物理过程的重要参数。首先，对GLAS波形进行高斯分解，识别地面峰值，计算地面和冠层回波能量；其次，地面总能量比（E r）计算为每个GLAS足迹的地面返回能量与总波形返回能量之比。第三，基于Beer-Lambert定律建立了E r与实测LAI之间的回归模型。模型的决定系数（r2）为0.81，均方根误差（RMSE）为0.35 （n = 23, p < 0.001）。最后，采用留一交叉验证程序对构建的回归模型进行评估。结果表明，回归模型没有过拟合数据，具有良好的泛化能力。利用其他15个实测LAI对glas预测LAI的精度进行了验证（r2 = 0.84），结果表明glas预测LAI的精度较高（RMSE = 0.31）。
Leaf area index (LAI) is an important parameter controlling many biological and physical processes associated with vegetation on the Earth's surface. In this study, an algorithm for estimating LAI from the ICESat (Ice, Cloud and land Elevation Satellite)/GLAS (Geoscience Laser Altimeter System) data was proposed and applied to a forest area in the Tibetan Plateau. First, Gaussian decomposition of the GLAS waveform was implemented to identify the ground peaks and calculate the ground and canopy return energy. Second, the ground-to-total energy ratio (E r) was computed as the ratio of the ground return energy to the total waveform return energy for each GLAS footprint. Third, a regression model between the E r and the field-measured LAI was established based on the Beer–Lambert law. The coefficient of determination (R 2) of the model was 0.81 and the root mean square error (RMSE) is 0.35 (n = 23, p < 0.001). Finally, the leave-one-out cross-validation procedure was used to assess the constructed regression model. The results indicate that the regression model is not overfitting the data and has a good generalization capability. We validated the accuracy of the GLAS-predicted LAIs using the other 15 field-measured LAIs (R 2 = 0.84), and the result shows that the accuracy of the GLAS-predicted LAI is high (RMSE = 0.31).