Uncertainty within satellite LiDAR estimations of vegetation and topography
Uncertainty within satellite LiDAR estimations of vegetation and topography
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DOI：
10.1080/01431160903380631
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发表时间：
2010-02
影响因子：
3.4
通讯作者：
J. Rosette;P. North;Juan Suarez;S. Los
中科院分区：
文献类型：
--
作者：
J. Rosette;P. North;Juan Suarez;S. Los
本文演示了在冰云和陆地高程卫星/地球科学激光高度计系统(ICESat/GLAS)波形中识别混合植被和不同地形地区的典型地面高程和植被高度估计的能力。在大足迹光探测和测距(LiDAR)波形中估计植被高度依赖于估计最上面的冠层表面(信号起点)和代表地面的高程的能力，这两者都受到植被特性和地形坡度的影响。我们使用机载激光雷达数据、野外测量和飞行辐射传输模型，研究了从ICESat/GLAS数据估计植被高度的不确定度来源。与独立的10m分辨率数字地形模型相比，对卫星波形进行高斯分解的方法在估计地面高程时的平均偏差为−0.10m。第二种利用波形幅度和地形指数估计植被高度的方法有效地去除了坡度作为误差源，但产生了更大的地表偏移量(−0.83m)。两种估算植被高度的方法与机载LiDAR估计值的相关系数(R2)=0.68，均方根误差(RMSE)=4.4m和R2=0.61，RMSE=4.9m。然而，被截留的植被和斜坡的结构和光学特性之间的复杂相互作用需要进一步了解。像飞行这样的工具提供了一种有用的手段来探索波形对植被特性和地形坡度的敏感性。
This paper demonstrates the ability to identify representative ground elevation and vegetation height estimates within the Ice, Cloud and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) waveforms for an area of mixed vegetation and varied topography. Estimating vegetation height within large-footprint Light Detection and Ranging (LiDAR) waveforms relies on the ability to estimate the uppermost canopy surface (signal beginning) and an elevation representing the ground surface, both of which are influenced by vegetation properties and topographic slope. We examined sources of uncertainty for vegetation height estimation from ICESat/GLAS data using airborne LiDAR data, field measurements and the FLIGHT radiative transfer model. In comparison with an independent 10-m resolution digital terrain model (DTM), a method using Gaussian decomposition of the satellite waveform produced a mean bias of −0.10 m when estimating ground elevation. A second method of estimating vegetation height using waveform extent and a terrain index effectively removed slope as an error source but produced a greater ground surface offset (−0.83 m). The two methods of estimating vegetation height compared well with airborne LiDAR estimates (correlation coefficient (R 2) = 0.68, root mean square error (RMSE) = 4.4 m and R 2 = 0.61, RMSE = 4.9 m, respectively). However, the complex interplay of the structural and optical properties of the intercepted vegetation and slope requires further understanding. A tool such as FLIGHT provides a useful means to explore the sensitivity of the waveform to both vegetation properties and topographic slope.