Accurate estimation of aboveground biomass in Moso bamboo (Phyllostachys edulis) forests under Pantana phyllostachysae Chao stress using UAV multispectral remote sensing and self-establish allometric equations.

Journal: Pest management science
Published Date: Jun 24, 2025

Abstract

BACKGROUND: Moso bamboo (Phyllostachys edulis) plays a pivotal role in the global carbon cycle because of its rapid growth and significant ecological benefits. Accurate estimation of its aboveground biomass (AGB) is therefore essential for effective carbon management. However, the influence of its primary leaf-feeding pest, Pantana phyllostachysae Chao (P. phyllostachysae), on AGB remains poorly understood, potentially compromising estimation accuracy. This study aims to develop allometric equations and integrate them with machine learning algorithms to accurately estimate the AGB of Moso bamboo forests under varying levels of pest stress.

Authors

  • Anqi He
    Department of General Surgery, Tianjin Medical University General Hospital, 154 Anshan Road, Tianjin, 300052, People's Republic of China.
  • Zhanghua Xu
    College of Environment and Safety Engineering, Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou, China.
  • Guantong Li
    Institute of Logistics Science & Engineering, Shanghai Maritime University, Shanghai, China.
  • Lingyan Chen
    Affiliated Hospital of Jiaxing University, The First Hospital of Jiaxing, Jiaxing, Zhejiang, China.
  • Huafeng Zhang
    Department of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China.
  • Bin Li
    Department of Magnetic Resonance Imaging (MRI), Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Yifan Li
    College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA.
  • Xiaoyu Guo
    Johns Hopkins University, Baltimore, MD, USA.
  • Zenglu Li
    Network Center (Information Construction Office), Sanming University, Sanming, Fujian, China.
  • Fengying Guan
    International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, China.

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