Lidar sampling for large-area forest characterization: A review
Lidar sampling for large-area forest characterization: A review
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DOI：
10.1016/j.rse.2012.02.001
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发表时间：
2012-06
影响因子：
13.5
通讯作者：
M. Wulder;Joanne C. White;R. Nelson;E. Næsset;H. Ørka;N. Coops;T. Hilker;C. Bater;T. Gobakken-
中科院分区：
文献类型：
--
作者：
M. Wulder;Joanne C. White;R. Nelson;E. Næsset;H. Ørka;N. Coops;T. Hilker;C. Bater;T. Gobakken-
利用数字遥感数据进行森林调查的能力往往受到措施性质的限制，除多角度或立体观测外，这些措施基本上对垂直分布的属性不敏感。因此，当信号饱和时，通常进行经验估计来表征诸如高度、体积或生物量等属性，并具有已知的渐近关系。激光雷达(光探测和测距)已经成为一种收集并随后表征垂直分布属性的可靠手段。激光雷达已被确定为森林调查的适当数据来源；然而，由于涉及的后勤、成本和数据量，使用激光雷达进行大面积监测和测绘活动仍然具有挑战性。使用激光雷达作为大面积估计的抽样工具，可能会缓解部分或全部这些问题。许多因素推动并普遍使用机载剖面图、机载扫描和星载激光雷达系统作为抽样工具，测量和监测面积从数万到数百万平方公里的地区的森林资源。在这一交流中，我们提出了激光雷达采样作为一种手段，以实现及时和稳健的大面积表征。我们简要概述了不同激光雷达系统和数据的性质，然后介绍了激光雷达采样的理论和统计学基础。介绍了目前的应用情况，并展望了激光雷达在大面积生态系统特征描述和监测的综合采样框架中的应用前景。我们还包括关于统计、激光雷达采样方案、应用程序(包括数据整合和分层)以及后续信息生成的建议。
The ability to use digital remotely sensed data for forest inventory is often limited by the nature of the measures, which, with the exception of multi-angular or stereo observations, are largely insensitive to vertically distributed attributes. As a result, empirical estimates are typically made to characterize attributes such as height, volume, or biomass, with known asymptotic relationships as signal saturation occurs. Lidar (light detection and ranging) has emerged as a robust means to collect and subsequently characterize vertically distributed attributes. Lidar has been established as an appropriate data source for forest inventory purposes; however, large area monitoring and mapping activities with lidar remain challenging due to the logistics, costs, and data volumes involved. The use of lidar as a sampling tool for large-area estimation may mitigate some or all of these problems. A number of factors drive, and are common to, the use of airborne profiling, airborne scanning, and spaceborne lidar systems as sampling tools for measuring and monitoring forest resources across areas that range in size from tens of thousands to millions of square kilometers. In this communication, we present the case for lidar sampling as a means to enable timely and robust large-area characterizations. We briefly outline the nature of different lidar systems and data, followed by the theoretical and statistical underpinnings for lidar sampling. Current applications are presented and the future potential of using lidar in an integrated sampling framework for large area ecosystem characterization and monitoring is presented. We also include recommendations regarding statistics, lidar sampling schemes, applications (including data integration and stratification), and subsequent information generation.