ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprs-annals-XI-3-2026-237-2026 A Structured Query Language Approach for processing Smartphone-based LiDAR of Understory Vegetation Pittman Rory https://orcid.org/0000-0001-9820-4519 1 Hu Baoxin 1 Dept. of Earth and Space Science and Engineering, York University, 4700 Keele Street, Toronto ON M3J 1P3, Canada 08 07 2026 XI-3-2026 237 244 Copyright: © 2026 Rory Pittman 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://isprs-annals.copernicus.org/articles/XI-3-2026/237/2026/isprs-annals-XI-3-2026-237-2026.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/XI-3-2026/237/2026/isprs-annals-XI-3-2026-237-2026.pdf

LiDAR sensors incorporated within modern smartphone and tablet devices enable relatively quick and inexpensive collection of ground-based LiDAR data applicable for ground truth mapping as needed for modelling understory vegetation. However, this LiDAR data often requires conversion and processing prior to research use. This study presents a workflow with algorithms utilizing structured query language (SQL) to efficiently process detailed rasterized features from LiDAR data collected by an iPhone Pro Max via the ForestScanner app. After transformation of the LiDAR data, SQL has been employed to voxelize the LiDAR data from which rasterized features have been derived. Various cell sizes for voxels and subsequent pixels have been investigated, leading to a recommended spatial resolution of 0.05 m for cell size dimension. SQL provides precise control for advanced querying to process ground-based LiDAR data for vegetational modelling applications.