Sapling-NeRF: Geo-Localised Sapling Reconstruction in Forests for Ecological Monitoring

2026-02-26

AI Summary

Existing 3D sensing methods struggle to accurately reconstruct the fine-grained structure of understory saplings and lack repeatable, georeferenced capabilities for long-term monitoring. This work proposes a three-tier representation framework integrating Neural Radiance Fields (NeRF), LiDAR SLAM, and GNSS, achieving— for the first time—an implicit 3D reconstruction with real-world scale and geospatial coordinates. Evaluated in Wytham Woods (UK) and Evo forest (Finland), the method captures stem height, branching architecture, and leaf-to-wood ratios of saplings 0.5–2 m tall with centimeter-level accuracy, significantly outperforming conventional terrestrial laser scanning (TLS). The approach establishes a novel, quantitative paradigm for repeatable, long-term ecological monitoring of forest understory dynamics.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Saplings are key indicators of forest regeneration and overall forest health. However, their fine-scale architectural traits are difficult to capture with existing 3D sensing methods, which make quantitative evaluation difficult. Terrestrial Laser Scanners (TLS), Mobile Laser Scanners (MLS), or traditional photogrammetry approaches poorly reconstruct thin branches, dense foliage, and lack the scale consistency needed for long-term monitoring. Implicit 3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) are promising alternatives, but cannot recover the true scale of a scene and lack any means to be accurately geo-localised. In this paper, we present a pipeline which fuses NeRF, LiDAR SLAM, and GNSS to enable repeatable, geo-localised ecological monitoring of saplings. Our system proposes a three-level representation: (i) coarse Earth-frame localisation using GNSS, (ii) LiDAR-based SLAM for centimetre-accurate localisation and reconstruction, and (iii) NeRF-derived object-centric dense reconstruction of individual saplings. This approach enables repeatable quantitative evaluation and long-term monitoring of sapling traits. Our experiments in forest plots in Wytham Woods (Oxford, UK) and Evo (Finland) show that stem height, branching patterns, and leaf-to-wood ratios can be captured with increased accuracy as compared to TLS. We demonstrate that accurate stem skeletons and leaf distributions can be measured for saplings with heights between 0.5m and 2m in situ, giving ecologists access to richer structural and quantitative data for analysing forest dynamics.

Problem

• sapling reconstruction
• ecological monitoring
• geo-localisation
• 3D sensing
• forest regeneration

Innovation

• Neural Radiance Fields
• LiDAR SLAM
• Geo-localisation
• Sapling reconstruction
• Ecological monitoring