A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data
A Ground Elevation and Vegetation Height Retrieval Algorithm Using Micro-Pulse Photon-Counting Lidar Data
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使用微脉冲光子计数激光雷达数据的地面高程和植被高度反演算法
DOI：
10.3390/rs10121962
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发表时间：
2018-12
期刊：
影响因子：
5
通讯作者：
huzhenyue
中科院分区：
文献类型：
--
作者：
朱笑笑;聂胜;王成;习晓环;huzhenyue
冰、云和陆地高程卫星2号(ICESat-2)任务使用微脉冲光子计数激光雷达系统绘制和监测大面积陆地生态系统的生物量和碳。为ICESat-2数据处理和应用做准备，本文旨在开发和验证一种有效的算法，以便更好地从光子计数激光雷达数据中估计地面高程和植被高度。我们提出的新算法由三个关键步骤组成。首先，利用基于局域统计分析的去噪算法滤除噪声光子。其次，通过建立高程频率直方图、经验模式分解(EMD)和逐步增密等一系列操作，将信号光子分为冠层光子和地面光子。同时，我们还利用百分位数统计方法从冠层光子中识别出了冠层顶部(TOC)光子。之后，分别由地面光子和TOC光子通过三次样条插值法生成地面和TOC曲面。最后，利用反演的地表和总有机碳地表估算了地表高程和植被高度。结果表明，该去噪算法在识别背景噪声和保留信号光子方面是有效的。反演地高程的精度高于反演植被高度，夜间数据的结果好于相应的白天数据。具体而言，地面高程估计的均方根误差(RMSE)值白天为2.25-6.45m，夜间为2.03-6.03m。植被高度估计的均方根值白天为4.63~8.92m，夜间为4.55~8.65m。由于RMSE值较低，我们的算法在估计地面高程和植被高度方面比以前的算法有更好的表现。此外，实验结果还表明，光子分类算法有效地降低了坡度和植被覆盖度的负面影响。总之，本文为利用微脉冲光子计数激光雷达数据估算地面高程和植被高度提供了一种有效的解决方案。
The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission employs a micro-pulse photon-counting LiDAR system for mapping and monitoring the biomass and carbon of terrestrial ecosystems over large areas. In preparation for ICESat-2 data processing and applications, this paper aimed to develop and validate an effective algorithm for better estimating ground elevation and vegetation height from photon-counting LiDAR data. Our new proposed algorithm consists of three key steps. Firstly, the noise photons were filtered out using a noise removal algorithm based on localized statistical analysis. Secondly, we classified the signal photons into canopy photons and ground photons by conducting a series of operations, including elevation frequency histogram building, empirical mode decomposition (EMD), and progressive densification. At the same time, we also identified the top of canopy (TOC) photons from canopy photons by percentile statistics method. Thereafter, the ground and TOC surfaces were generated from ground photons and TOC photons by cubic spline interpolation, respectively. Finally, the ground elevation and vegetation height were estimated by retrieved ground and TOC surfaces. The results indicate that the noise removal algorithm is effective in identifying background noise and preserving signal photons. The retrieved ground elevation is more accurate than the retrieved vegetation height, and the results of nighttime data are better than those of the corresponding daytime data. Specifically, the root-mean-square error (RMSE) values of ground elevation estimates range from 2.25 to 6.45 m for daytime data and 2.03 to 6.03 m for nighttime data. The RMSE values of vegetation height estimates range from 4.63 to 8.92 m for daytime data and 4.55 to 8.65 m for nighttime data. Our algorithm performs better than the previous algorithms in estimating ground elevation and vegetation height due to lower RMSE values. Additionally, the results also illuminate that the photon classification algorithm effectively reduces the negative effects of slope and vegetation coverage. Overall, our paper provides an effective solution for estimating ground elevation and vegetation height from micro-pulse photon-counting LiDAR data.
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发表时间：
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期刊：
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影响因子：
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