Abstract
In this study, full-waveform LiDAR data were exploited to detect weak returns backscattered by the bare terrain underneath vegetation canopies and thus improve the generation of a digital terrain model (DTM). Building on the methods of progressive generation of triangulation irregular network (TIN) model reported in the literature, we proposed an integrated approach where echo detection, terrain identification, and TIN generation were carried out iteratively. The proposed method was tested on a dataset collected by a Riegl LMS Q-560 scanner over a study area near Sault Ste. Marie, Ontario, Canada (46°33′56′′N, 83°25′18′′W). The results demonstrated that more terrain points under shrubs could be identified, and the generated DTMs exhibited more details in the terrain than those obtained using the progressive TIN method. In addition, 1275 points across this study area were surveyed on the ground and used to validate the proposed approach. The estimated elevations were shown to have a strong linear relationship with the measured ones, with R2 values above 0.98, and the RMSEs (Root Mean Squared Errors) between them were less than 0.15 m even for areas with hilly terrains underneath vegetation canopies.
Highlights
An accurate digital terrain model (DTM) is critical to many applications ranging from transportation planning and landform monitoring to forest and water resource management [1,2]. technologies, such as aerial photogrammetry, have been available in the past to generateDTMs, the use of airborne discrete LiDAR (Light Detection and Ranging) data revolutionizes the generation of the digital representation of a terrain surface in terms of accuracy and resolution [2,3].A discrete LiDAR instrument can record more than one echo backscattered from a surface object and measure its 3D coordinates together with on-board position and navigation sensors [4]
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This increase in the number of the terrain points was contributed by the detected weak echoes that were back-scattered by the terrain underneath vegetation
Summary
An accurate digital terrain model (DTM) is critical to many applications ranging from transportation planning and landform monitoring to forest and water resource management [1,2]. technologies, such as aerial photogrammetry, have been available in the past to generateDTMs, the use of airborne discrete LiDAR (Light Detection and Ranging) data revolutionizes the generation of the digital representation of a terrain surface in terms of accuracy and resolution [2,3].A discrete LiDAR instrument can record more than one echo backscattered from a surface object and measure its 3D coordinates together with on-board position and navigation sensors [4]. An accurate digital terrain model (DTM) is critical to many applications ranging from transportation planning and landform monitoring to forest and water resource management [1,2]. Technologies, such as aerial photogrammetry, have been available in the past to generate. DTMs, the use of airborne discrete LiDAR (Light Detection and Ranging) data revolutionizes the generation of the digital representation of a terrain surface in terms of accuracy and resolution [2,3]. An adaptive approach to employ different interpolation methods based on the complexity of local terrain was designed in Maguya et al (2013) [9]. A novel energy function balanced by adaptive ground saliency was used to adapt to steep slopes, discontinuous terrains, and complex objects in the filtering process to identify ground
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