Enhancing aboveground biomass estimation in Moso bamboo forests: the role of on-year and off-year phenomena in remote sensing

Accurate estimation of aboveground biomass (AGB) in Moso bamboo forests (MBFs) has garnered significant attention over the past two decades. However, the remote sensing-based estimation of AGB in MBFs remains challenging because of the limited understanding of the relationship between Moso bamboo gr...

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Main Authors: Nan Li, Mengyi Hu, Jingyi Xie, Linjia Wei, Tianzhen Wu, Wei Zhang, Shuangxi Gu, Longwei Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Forests and Global Change
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Online Access:https://www.frontiersin.org/articles/10.3389/ffgc.2025.1515767/full
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Summary:Accurate estimation of aboveground biomass (AGB) in Moso bamboo forests (MBFs) has garnered significant attention over the past two decades. However, the remote sensing-based estimation of AGB in MBFs remains challenging because of the limited understanding of the relationship between Moso bamboo growth characteristics and remote sensing data, particularly concerning alternating on-year and off-year cycles. In this study, Sentinel-2 remote sensing imagery and plot survey data were selected, a novel change detection algorithm to assess plot level AGB dynamics between 2018 and 2019 was developed, a hierarchical classifier was proposed to map the spatial distributions of on-year and off-year MBFs, and a time series model was developed for estimating the AGB of MBFs to characterize AGB dynamics between November and December. The results indicated that the AGB of the MBFs exhibited a distinct dynamic cycle characterized by the rapid accumulation of new bamboo and sharp reductions due to selective harvesting during the on-year period, alongside a steady accumulation of lignified bamboo during the off-year period. The AGB of the MBFs during the on-year and off-year cycles ranged primarily from 30 to 80 Mg/ha, with the AGB of the on-year MBFs generally exceeding that of the off-year MBFs. This study demonstrated the potential to accurately estimate AGB and its dynamic changes by accounting for on-year and off-year phenomena.
ISSN:2624-893X