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A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds
Earth Surface Processes and Landforms ( IF 2.8 ) Pub Date : 2020-08-29 , DOI: 10.1002/esp.4992 Daniele Pinton 1 , Alberto Canestrelli 2 , Benjamin Wilkinson 3 , Peter Ifju 4 , Andrew Ortega 4
Earth Surface Processes and Landforms ( IF 2.8 ) Pub Date : 2020-08-29 , DOI: 10.1002/esp.4992 Daniele Pinton 1 , Alberto Canestrelli 2 , Benjamin Wilkinson 3 , Peter Ifju 4 , Andrew Ortega 4
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Salt marshes are transitional zones between ocean and land, which act as natural buffers against coastal hazards. The survival of salt marshes is governed by the rate of organic and inorganic deposition, which strongly depends on vegetation characteristics, such as height and density. Vegetation also favours the dissipation of wind waves and storm surges. For these reasons, an accurate description of both ground elevation and vegetation characteristics in salt marshes is critical for their management and conservation. For this purpose, airborne LiDAR (light detection and ranging) laser scanning has become an accessible and cost‐effective tool to map salt marshes quickly. However, the limited horizontal resolution (~1 m) of airborne‐derived point clouds prevents the direct extraction of ground elevation, vegetation height and vegetation density without the coupling with imagery datasets. Instead, due to the lower flight altitude, UAV (unmanned aerial vehicle)‐borne laser scanners provide point clouds with much higher resolution (~5 cm). Although methods for estimating ground level and vegetation characteristics from UAV LiDAR have been proposed for flat ground, we demonstrate that a sloping ground increases prediction errors. Here we derive a new formulation that improves the estimation by employing a correction based on a LiDAR‐derived estimate of local ground slope. Our method directly converts the 3D distribution of UAV LiDAR‐derived points into vegetation density and height, as well as ground elevation, without the support of additional datasets. The proposed formulation is calibrated by using measured density and height of Spartina alterniflora in a marsh in Sapelo Island, Georgia, USA, and successfully tested on an independent dataset. Our method produces high‐resolution (40 × 40 cm2) maps of ground elevation and vegetation characteristics, thus capturing the large gradients in the proximity of tidal creeks. © 2020 John Wiley & Sons, Ltd.
中文翻译：
一种基于高分辨率无人机的LiDAR点云估算沿海盐沼地面海拔和植被特征的新算法
盐沼是海洋和陆地之间的过渡区，是抵御沿海灾害的天然缓冲区。盐沼的生存取决于有机和无机沉积的速率，这在很大程度上取决于植被特征，例如高度和密度。植被还有利于风浪和风暴潮的消散。由于这些原因，准确描述盐沼中的地面海拔和植被特征对于其管理和保护至关重要。为此，机载LiDAR（光检测和测距）激光扫描已成为一种快速绘制盐沼地图的便捷且经济高效的工具。但是，机载点云的水平分辨率有限（〜1 m）阻止了直接提取地面标高，植被高度和植被密度，而无需与影像数据集耦合。取而代之的是，由于较低的飞行高度，无人机（无人机）提供的激光扫描仪可提供分辨率更高（约5厘米）的点云。尽管已经提出了从无人机LiDAR估算地面水平和植被特征的方法，但我们证明了倾斜的地面会增加预测误差。在这里，我们得出了一个新的公式，该公式通过基于LiDAR得出的局部地面坡度估算值进行校正来改进估算值。我们的方法无需额外的数据集支持，即可将无人机LiDAR衍生的点的3D分布直接转换为植被密度和高度以及地面标高。建议的配方通过使用测得的密度和高度进行校准在美国佐治亚州萨佩洛岛的沼泽地上的互花米草，并在独立的数据集上成功进行了测试。我们的方法可以生成高分辨率的（40×40 cm 2）地面海拔和植被特征图，从而捕获潮汐小溪附近的大坡度。分级为4 +©2020 John Wiley＆Sons，Ltd.
更新日期：2020-11-03
中文翻译：
一种基于高分辨率无人机的LiDAR点云估算沿海盐沼地面海拔和植被特征的新算法
盐沼是海洋和陆地之间的过渡区，是抵御沿海灾害的天然缓冲区。盐沼的生存取决于有机和无机沉积的速率，这在很大程度上取决于植被特征，例如高度和密度。植被还有利于风浪和风暴潮的消散。由于这些原因，准确描述盐沼中的地面海拔和植被特征对于其管理和保护至关重要。为此，机载LiDAR（光检测和测距）激光扫描已成为一种快速绘制盐沼地图的便捷且经济高效的工具。但是，机载点云的水平分辨率有限（〜1 m）阻止了直接提取地面标高，植被高度和植被密度，而无需与影像数据集耦合。取而代之的是，由于较低的飞行高度，无人机（无人机）提供的激光扫描仪可提供分辨率更高（约5厘米）的点云。尽管已经提出了从无人机LiDAR估算地面水平和植被特征的方法，但我们证明了倾斜的地面会增加预测误差。在这里，我们得出了一个新的公式，该公式通过基于LiDAR得出的局部地面坡度估算值进行校正来改进估算值。我们的方法无需额外的数据集支持，即可将无人机LiDAR衍生的点的3D分布直接转换为植被密度和高度以及地面标高。建议的配方通过使用测得的密度和高度进行校准在美国佐治亚州萨佩洛岛的沼泽地上的互花米草，并在独立的数据集上成功进行了测试。我们的方法可以生成高分辨率的（40×40 cm 2）地面海拔和植被特征图，从而捕获潮汐小溪附近的大坡度。分级为4 +©2020 John Wiley＆Sons，Ltd.
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