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首页> 外文期刊>International journal of remote sensing >Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: implications for canopy height retrieval from future ICESat-2 data
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Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: implications for canopy height retrieval from future ICESat-2 data

机译:自动表面检测方法对微脉冲光子计数激光雷达测高仪数据的适用性:从将来的ICESat-2数据检索冠层高度的意义

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摘要

We develop and validate an automated approach to determine canopy height, an important metric for global biomass assessments, from micro-pulse photon-counting lidar data collected over forested ecosystems. Such a lidar system is planned to be launched aboard the National Aeronautics and Space Administration's follow-on Ice, Cloud and land Elevation Satellite mission (ICESat-2) in 2017. For algorithm development purposes in preparation for the mission, the ICESat-2 project team produced simulated ICESat-2 data sets from airborne observations of a commercial micro-pulse lidar instrument (developed by Sigma Space Corporation) over two forests in the eastern USA. The technique derived in this article is based on a multi-step mathematical and statistical signal extraction process which is applied to the simulated ICESat-2 data set. First, ground and canopy surfaces are approximately extracted using the statistical information derived from the histogram of elevations for accumulated photons in 100 footprints. Second, a signal probability metric is generated to help identify the location of ground, canopy-top, and volume-scattered photons. According to the signal probability metric, the ground surface is recovered by locating the lowermost high-photon density clusters in each simulated ICESat-2 footprint. Thereafter, canopy surface is retrieved by finding the elevation at which the 95th percentile of the above-ground photons exists. The remaining noise is reduced by cubic spline interpolation in an iterative manner. We validate the results of the analysis against the full-resolution airborne photon-counting lidar data, digital terrain models (DTMs), and canopy height models (CHMs) for the study areas. With ground surface residuals ranging from 0.2 to 0.5 m and canopy height residuals ranging from 1.6 to 2.2 m, our results indicate that the algorithm performs very well over forested ecosystems of canopy closure of as much as 80%. Given the method's success in the challenging case of canopy height determination, it is readily applicable to retrieval of land ice and sea ice surfaces from micro-pulse lidar altimeter data. These results will advance data processing and analysis methods to help maximize the ability of the ICESat-2 mission to meet its science objectives.
机译:我们开发并验证了一种自动方法,该方法可从森林生态系统中收集的微脉冲光子计数激光雷达数据中确定冠层高度,这是全球生物量评估的重要指标。计划在2017年美国国家航空航天局的后续“冰,云和陆地高空卫星”任务(ICESat-2)上启动这种激光雷达系统。ICESat-2项目用于算法开发,为任务准备研究小组从美国东部两片森林上的商用微脉冲激光雷达仪器(由Sigma Space Corporation开发)的空中观测结果生成了模拟的ICESat-2数据集。本文得出的技术基于多步数学和统计信号提取过程,该过程应用于模拟的ICESat-2数据集。首先,使用从海拔高度直方图中导出的统计信息来近似提取地面和树冠表面,以获取100个足迹中的累积光子。其次,生成信号概率度量以帮助识别地面,树冠顶部和体积散射光子的位置。根据信号概率度量,通过在每个模拟的ICESat-2足迹中定位最低的高光子密度簇来恢复地面。此后,通过找到地上光子的第95个百分位数存在的海拔来检索树冠表面。三次样条插值法以迭代方式减少了剩余的噪声。我们针对研究区域的全分辨率机载光子计数激光雷达数据,数字地形模型(DTM)和树冠高度模型(CHM)验证了分析结果。我们的结果表明,该算法在地表残差为0.2至0.5 m且树冠高度残差为1.6至2.2 m的情况下,在覆盖率高达80%的森林生态系统中表现良好。考虑到该方法在确定树冠高度的挑战性情况下的成功,很容易适用于从微脉冲激光雷达高度计数据中检索陆冰和海冰表面。这些结果将促进数据处理和分析方法的发展,以帮助最大限度地发挥ICESat-2任务实现其科学目标的能力。

著录项

  • 来源
    《International journal of remote sensing》 |2014年第13期|5263-5279|共17页
  • 作者单位

    Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA;

    Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA;

    Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA,National Snow and Ice Data Center (NSIDC), University of Colorado, Boulder, CO, USA;

    Applied Research Laboratories, University of Texas at Austin, Austin, TX, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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