NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest

Abstract

Forest mapping provides critical observational data needed to understand the dynamics of forest environments. Notably, tree diameter at breast height (DBH) is a metric used to estimate forest biomass and carbon dioxide (CO2) sequestration. Manual methods of forest mapping are labor intensive and time consuming, a bottleneck for large-scale mapping efforts. Automated mapping relies on acquiring dense forest reconstructions, typically in the form of point clouds. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) generate point clouds using expensive LiDAR sensing and have been used successfully to estimate tree diameter. Neural radiance fields (NeRFs) are an emergent technology enabling photorealistic, vision-based reconstruction by training a neural network on a sparse set of input views. In this paper, we present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. In addition, we propose an improved DBH-estimation method using convex-hull modeling. Using this approach, we achieved 1.68 cm RMSE (2.81%), which consistently outperformed standard cylinder modeling approaches.

Plant phenotyping relevance

森林内の樹木DBHという個体形態形質を、NeRF・MLS再構成と凸包モデルで推定し、手法比較と精度評価を行っているため、植物形質取得法が中心です。

Code and data availability

The authors explicitly state that their forest datasets (SLAM and NeRF reconstructions, imagery) and TreeTool modeling code contributions are freely available on their public GitHub repository, which is listed in the allowed URLs.