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 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, which consistently outperformed standard cylinder modeling approaches. Our code contributions and forest datasets are freely available at https://github.com/harelab-ucsc/RedwoodNeRF.

Plant phenotyping relevance

NeRFおよびMLSによる森林再構成から樹木DBHを推定し、凸包モデルによる推定法を提案・比較検証しているため、植物形質取得手法が中心です。

Code and data availability

The authors explicitly state their code contributions and forest datasets (SLAM and NeRF reconstructions used for DBH estimation) are freely available in a public GitHub repository. The other URLs are a cited third-party tool (NeRFCapture) and a background reference (USDA aerial survey), neither of which is a paper-own