Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy
Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy
复制标题
DOI：
10.1016/j.rse.2015.04.013
复制
发表时间：
2015-07
影响因子：
13.5
通讯作者：
S. Hancock;J. Armston;Zhan Li;R. Gaulton;Philip Lewis;M. Disney;F. Danson;A. Strahler;C. Schaaf;K. Anderson;K. Gaston
中科院分区：
文献类型：
--
作者：
S. Hancock;J. Armston;Zhan Li;R. Gaulton;Philip Lewis;M. Disney;F. Danson;A. Strahler;C. Schaaf;K. Anderson;K. Gaston
全波激光雷达具有比其他任何实用方法都更详细地探测植被的独特能力。由激光雷达回波能量计算得到的反射率是一个重要的参数，因此准确地提取反射率是至关重要的。已经提出了15种不同的方法来提取返回能量（从目标反向散射的光量），从简单到数学复杂，但相对精度尚未得到评估。本文使用一个模拟器比较各种方法在广泛的目标和激光雷达系统参数。对于硬目标，最简单的方法（窗口求和，峰值和二次）给出了最一致的估计。它们的精度不高，但低标准差表明它们可以校准以提供准确的能量。这可能是一些商业激光雷达开发人员使用它们的原因，主要兴趣是测量固体物体。然而，模拟结果表明，这些方法是不合适的植被。广泛使用的高斯拟合在硬目标上表现良好（0.24%均方根误差，RMSE），求和和样条方法也是如此（0.30% RMSE）。在植被，大足迹（15米）的系统，高斯拟合表现最好（12.2%RMSE）紧随其后的总和和样条（均为12.7%RMSE）。对于植被上较小的足迹（33 cm和1 cm），相对精度相反（总和和样条的RMSE为0.56%，高斯拟合为1.37%）。高斯拟合需要重平滑（卷积与8米高斯），而没有需要的总和和样条。这些更简单的方法对噪声的鲁棒性也更强，并且比高斯拟合的计算成本低得多。因此，得出的结论是，总和和样条是最准确的提取返回能量从波形激光雷达植被，除了大的足迹（15米），高斯拟合是稍微更准确。这些结果表明，使用高斯拟合或专有算法的小足迹（约15米）激光雷达系统可能会报告不准确的能量，从而反映植被。此外，系统的脉冲长度，采样间隔和噪声对不同目标的精度的影响进行了评估，这对传感器的设计有影响。
Full waveform lidar has a unique capability to characterise vegetation in more detail than any other practical method. The reflectance, calculated from the energy of lidar returns, is a key parameter for a wide range of applications and so it is vital to extract it accurately. Fifteen separate methods have been proposed to extract return energy (the amount of light backscattered from a target), ranging from simple to mathematically complex, but the relative accuracies have not yet been assessed. This paper uses a simulator to compare all methods over a wide range of targets and lidar system parameters. For hard targets the simplest methods (windowed sum, peak and quadratic) gave the most consistent estimates. They did not have high accuracies, but low standard deviations show that they could be calibrated to give accurate energy. This may be why some commercial lidar developers use them, where the primary interest is in surveying solid objects. However, simulations showed that these methods are not appropriate over vegetation. The widely used Gaussian fitting performed well over hard targets (0.24% root mean square error, RMSE), as did the sum and spline methods (0.30% RMSE). Over vegetation, for large footprint (15 m) systems, Gaussian fitting performed the best (12.2% RMSE) followed closely by the sum and spline (both 12.7% RMSE). For smaller footprints (33 cm and 1 cm) over vegetation, the relative accuracies were reversed (0.56% RMSE for the sum and spline and 1.37% for Gaussian fitting). Gaussian fitting required heavy smoothing (convolution with an 8 m Gaussian) whereas none was needed for the sum and spline. These simpler methods were also more robust to noise and far less computationally expensive than Gaussian fitting. Therefore it was concluded that the sum and spline were the most accurate for extracting return energy from waveform lidar over vegetation, except for large footprint (15 m), where Gaussian fitting was slightly more accurate. These results suggest that small footprint (≪ 15 m) lidar systems that use Gaussian fitting or proprietary algorithms may report inaccurate energies, and thus reflectances, over vegetation. In addition the effect of system pulse length, sampling interval and noise on accuracy for different targets was assessed, which has implications for sensor design.