Edge Effect Issue on Hyperspectral LiDAR Point Cloud Data: Generation Principle and Framework of Detection and Filtering

The edge effect, caused by partial illumination of leaf boundaries, leads to significant hyperspectral LiDAR (HSL) echo-intensity loss and severely restricts precise expression of the spectral characteristics and retrieval accuracy of biochemical components in leaf-edge area. Despite being one of the major radiometric effects in LiDAR sensing, edge-effect has not been systematically investigated for hyperspectral intensity data. Along these lines, this study presents the first comprehensive exploration, detection, and filtering of edge effects in HSL-collected data. We analyze the physical mechanisms responsible for multi-layer edge-effect formation and propose a new integrated detection and filtering framework, incorporating histogram of intensity distribution, edge detection, and spherical spatial filtering (HIDEDaSF). Results obtained from HSL system on broadleaf plant demonstrate that our framework robustly detects edge-affected spectral points across 32 wavelengths. After filtering and correction, the standard deviation and coefficient of variation of edge-region intensities are reduced by 22.68% and 28.30%, respectively, and the mean ratio of coefficient of variation is 0.7288, less than 1, confirming again the stability and effectiveness of the proposed algorithm framework. The HIDEDaSF framework preserves valid spectral information and significantly improves the consistency and quantitative reliability of hyperspectral intensity data at leaf edge zones. Although tailored for the HSL system, this framework is also adaptable to other multi- and hyperspectral LiDAR platforms. This study enriches the fundamental knowledge of edge-effect radiometric behaviors and expands the methodology foundation for fine-scale hyperspectral LiDAR vegetation remote sensing. We are willing to make our codes freely available via https://github.com/Jie-Bai/HIDEDaSF.