A Generalized Guideline for Airborne LiDAR Data Quality Assessment: An Indian Perspective

Abstract. Airborne LiDAR is a widely adopted remote sensing technology for generating high-resolution three-dimensional (3D) geospatial data. However, ensuring the application-specific reliability and usability of LiDAR datasets requires precise quality assessment. Internationally, the airborne LiDAR data quality assessment guidelines and standard are very few and none of them from India. Thus, it is crucial to address the distinctive challenges and requirements that arise from India's geographical and climatic conditions. This study presents a structured framework and suggested guidelines for evaluating the quality of airborne LiDAR data that align with India's specific characteristics. The methodology begins with the selection of representative test sites across different land cover types, including both vegetated and non-vegetated areas. High-precision Ground Control Points (GCPs) and independent checkpoints are collected using a range of surveying techniques, including RTK, RTN, static GNSS, and total station surveys. The vertical, horizontal, and 3D positional accuracy of the LiDAR data are statistically evaluated using RMSE metrics. In cases where the LiDAR returns do not coincide directly with checkpoints, interpolation techniques such as Triangulated Irregular Networks (TIN), Inverse Distance Weighting (IDW), and Kriging are applied to estimate elevation values. Beyond absolute accuracy, internal dataset consistency is evaluated by analyzing Nominal Pulse Spacing (NPS), Nominal Pulse Density (NPD), swath overlaps, point spacing uniformity, and identifying data voids. The study presents practical and comprehensive guidelines for data quality reporting, offering clarity to both data producers and end-users.