Land management using suitable thinning intensities can promote the yield of Moso bamboo (Phyllostachys edulis) forests and alter the nutrient content and stoichiometric characteristics within the soil. However, the effects of different thinning intensities on soil carbon (C), nitrogen (N), and phosphorus (P) stoichiometry of P. edulis forests have not been comprehensively studied. Here, we evaluated the effects of three thinning intensities—no thinning control (NT), moderate thinning (annual removal of 15% of mature bamboo, MT), and heavy thinning (annual removal of 33% of mature bamboo, IT)—on the soil organic C (SOC), total N (TN), and total P (TP) stocks and their stoichiometry characteristics of a P. edulis forest located in the subtropical zone. The results showed that SOC, TN, and TP stocks decreased with increasing soil depth after three years of thinning. The SOC, TN, and TP stocks and the ecological stoichiometry ratios were varied with the change of thinning intensity and SOC stocks declining with the increased thinning intensity, but TN and TP stocks for the MT treatment were enhanced. The order of TN and TP stocks was MT > NT > IT. In comparison to CK, TN and TP stocks for MT increased significantly by 18.8 and 37.3%, while SOC, TN, and TP stocks for IT decreased by 31.0%, 7.2%, and 21.4%, respectively. The C:N and C:P ratios of MT decreased by 32.8% and 39.0%, and those of IT decreased by 26.5% and 15.6%, respectively. In summary, we conclude that the MT is an effective management strategy to promote soil nutrient cycling and provides a reference for formulating management strategies in subtropical Moso bamboo forests.

Low consumer-grade cameras attached to small unmanned aerial vehicles (UAV) can easily acquire high spatial resolution images, leading to convenient forest monitoring at small-scales for forest managers. However, most studies were carried out in the low canopy density and flat ground plantations to detect individual trees. We selected overlapping canopy plantation in mountainous area in the eastern of China and acquired high spatial resolution UAV RGB images to detect individual trees. A total of 402 reference trees were located in three rectangle plots (900 m²). To enhance the confidence of the tested individual tree detection method, clear-cutting and Real-Time Kinematic (RTK) were used to obtain the truth values in the plots. A novel method for semi-automatic individual tree detection was proposed based on a local-maximum algorithm and UAV-derived DSM data (LAD) in this study. The detection accuracy of LAD was compared with commonly used methods based on UAV-derived orthophoto images, local-maximum algorithm (LAO), object-oriented feature segmentation (OFS), multiscale segmentation technique (MST) and manual visual interpretation (MVI). The overall accuracy (OA (%) decreased in the order of LAD (84.5%) > MST (69.1%) > OFS (65.1%) > MVI (64.1%) > LAO (59.1%). LAD had only 15.5%s omission errors (OM (%), which was less than half of the other four methods in comparison. It was noteworthy that MVI had 35.9% OM %, which revealed that MVI should be used carefully as the truth value. LAD showed similar repeated detection error (RP (%) and completely wrong detection (CW (%), while the other four methods had obviously higher CW % than the RP %. From our results, it can be concluded that the proposed LAD method may help improving the accuracy of individual tree detection to an acceptable accuracy (>80%) in dense mountain forests, and has practical advantages in future research direction to assess tree attributes from UAV RGB image.