On-year and off-year phenomena is an unique spatiotemporal characteristics of Moso bamboo forests (MBFs), the combination of multiple sources data to assess the impact of on-/off-year phenomena and human activities is a new perspective to better understand MBFs. There is an urgent need to explore how to characterize the spatial variability of MBFs, whether there is a pattern in the spatial distribution of MBFs boundary lines, and whether the causes of the boundary lines are directly related to human activities. In this paper, Anji County, with a large MBF area, was selected as the study area. Based on Sentinel-2 time-series data, topography and impermeability data, the on-year/off-year status and boundary lines of MBFs were determined, and the Integrated Moso bamboo On-off year Differentiation Index (IMODI) was proposed to evaluate spatial differentiation. Spatial heterogeneity, the shape pattern of boundary lines, and the impact of human activities on the MBFs were quantitatively analyzed, and 631.5 km² of MBFs and 140 on-off boundaries were accurately obtained. The on-year and off-year MBFs and boundaries were mainly distributed in mountainous areas, especially at elevations of 100–400 m and on slopes of 10–35°. The patterns in elevation can be categorized as stable, single-change, ascending-descending and descending-ascending patterns. The patterns in slope can be summarized as stable, single-change, and trapezoidal patterns. The newly proposed IMODI shows the best performance in reflecting the spatial differentiation of MBFs, and the on-year and off-year phenomenon of MBFs was closely related to human activities because the boundary lines were mostly concentrated within 2 km of villages. The results of this paper provide a new perspective to understand landscape of MBFs and the integrated management of carbon sequestration and sinks.

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.

The accurate estimation of aboveground biomass (AGB) in Moso bamboo forests is critical for assessing their carbon sequestration potential and supporting sustainable management. Satellite-based approaches are often constrained by signal saturation and mixed-pixel effects, whereas Unmanned Aerial Vehicle (UAV) imagery enables precise individual tree detection, overcoming these limitations. In this study, we propose a stand density (SD)-driven AGB estimation framework using high-resolution UAV RGB imagery. Individual bamboo positions were extracted using the Revised Local Maximum (RLM) algorithm, which achieved an optimal accuracy at a 2.5 m sampling interval (OA = 82.20%). Using 85 ground-truth plots, we developed six SD-AGB models and evaluated them via 10-fold cross-validation and independent UAV validation (10 plots). The Artificial Neural Network (ANN) model outperformed others, with strong calibration (R² = 0.94, RMSE = 3.78 Mg/ha), robust cross-validation (R² = 0.84 ± 0.06, RMSE = 5.24 ± 0.67 Mg/ha), and reliable independent validation (R² = 0.87, RMSE = 4.56 Mg/ha). Spatial mapping revealed a total of 14,190 bamboo plants with an average AGB of 32.80 Mg/ha. This UAV-based SD-AGB framework provides a robust, scalable, and cost-effective tool for precise biomass estimation, supporting sustainable bamboo forest management and carbon sequestration strategies and progress towards SDG 15.