Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

编号 030026004

推送时间 20201012

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

来源期刊 REMOTE SENSING

期 第260期

发表时间 20191218

关键词 radiative transfer model;  Lidar;  airborne laser scan;  point cloud;  reflectance;  leaf area index;  gap probability;  clumping;  Gaussian decomposition;  waveform; 

摘要 Airborne?lidar?point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the?lidar?point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability or leaf area index (LAI) from small-footprint?airborne?laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various?lidar?instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible?lidar?point intensity, including radiometric quantities?derived?from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact as footprint size increases. By contrast, we verified that physical methods using?lidar?point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from?lidar?instrument vendors.

服务人员 付贺龙

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