A multi-platform LiDAR dataset for standardized forest inventory measurement at long term ecological monitoring sites

2026-04-16

AI Summary

This study addresses the lack of multi-platform integration and standardization in existing forest LiDAR benchmark datasets, which hinders robust validation of three-dimensional structural measurements, ecological monitoring, and allometric models. Conducted at an ICOS long-term ecological observatory site, this work presents the first marker-free, SLAM-driven co-acquisition of unmanned aerial LiDAR (ULS), terrestrial laser scanning (TLS), and mobile backpack laser scanning (MLS) data, comprehensively capturing the full vertical vegetation profile from canopy to understory while linking to concurrent ecological and flux observations. The resulting dataset includes approximately 333 million high-accuracy TLS points alongside aligned multi-platform data, publicly released in LAZ and E57 formats. This resource establishes a reproducible, highly consistent 3D forest benchmark to support research in point cloud registration, scanning efficiency evaluation, structural modeling, and biomass estimation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

We present a curated multi-platform LiDAR reference dataset from an instrumented ICOS forest plot, explicitly designed to support calibration, benchmarking, and integration of 3D structural data with ecological observations and standard allometric models. The dataset integrates UAV-borne laser scanning (ULS) to measure canopy coverage, terrestrial laser scanning (TLS) for detailed stem mapping, and backpack mobile laser scanning (MLS) with real-time SLAM for efficient sub-canopy acquisition. We focus on the control plot with the most complete and internally consistent registration, where TLS point clouds (~333 million points) are complemented by ULS and MLS data capturing canopy and understory strata. Marker-free, SLAM-aware protocols were used to reduce field and processing time, while manual and automated methods were combined. Final products are available in LAZ and E57 formats with UTM coordinates, together with registration reports for reproducibility. The dataset provides a benchmark for testing registration methods, evaluating scanning efficiency, and linking point clouds with segmentation, quantitative structure models, and allometric biomass estimation. By situating the acquisitions at a long-term ICOS site, it is explicitly linked to 3D structure with decades of ecological and flux measurements. More broadly, it illustrates how TLS, MLS, and ULS can be combined for repeated inventories and digital twins of forest ecosystems.

Problem

• forest inventory
• LiDAR
• ecological monitoring
• 3D structure
• standardization

Innovation

• multi-platform LiDAR
• SLAM-aware registration
• forest inventory
• 3D structural modeling
• digital twin