LAUSR

Abstract Moso bamboo is the most widely distributed bamboo forest type in tropical and subtropical areas. Increasing trends in both expansion and logging of Moso bamboo have occurred over the past four decades due to its rapid growth rate and short harvest rotation period. The spatial and temporal distribution of bamboo forest is the basis for the study of the carbon cycle in bamboo forest ecosystems. Previous studies have consistently focused on different influences of scale, data and algorithms for bamboo forest mapping; however, there is still considerable uncertainty in bamboo forest mapping due to the unique on-year and off-year phenomena and their significant differences in spectral characteristics. Northwestern Zhejiang Province has the most widely distributed bamboo forest and was selected as the study area. Based on Sentinel-2 and Landsat 8 time-series data, changes in the spectral characteristics during the growth cycle of bamboo forest were analyzed and the optimum time window for the classification of the bamboo forest was determined. The seasonal index of the bamboo forest based on spectral differences was the proposed approach, and on-year and off-year bamboo forest mapping was conducted seasonally using Landsat 8 and Sentinel-2 data. The results show that (1) the best period to distinguish bamboo forest from other forest types is April–May, followed by December–February, and the best month for distinguishing between on-year and off-year bamboo forests is May; (2) the spectral differences between bamboo forest and other forest types are mainly reflected in the red-edge 3 near-infrared (NIR) and NIR narrow bands (740–865 nm); (3) Sentinel-2 data have obvious advantages over Landsat 8 data in distinguishing the bamboo forest because of better spectral, temporal and spatial resolutions; and (4) the multi-temporal bamboo forest index has a better overall classification accuracy (OA = 91.2) for distinguishing on-year and off-year bamboo forests than the mono-temporal index and can be applied to other regions.