Forest aboveground biomass (AGB) is an important research topic in the field of forestry, with implications for carbon cycles and carbon sinks. Malania oleifera Chun et S. K. Lee (M. oleifera) is a valuable plant species that is listed on the National Second-Class Protected Plant checklist and has received global attention for its conservation and resource utilization. To obtain accurate AGB of individual M. oleifera trees in a fast, low-finance-cost and low-labor-cost way, this study first attempted to estimate individual M. oleifera tree AGB by combining the centimeter-level resolution RGB imagery derived from unmanned aerial vehicles (UAVs) and the deep learning model of Mask R-CNN. Firstly, canopy area (CA) was obtained from the 3.5 cm high-resolution UAV-RGB imagery using the Mask R-CNN; secondly, to establish an allometric growth model between the diameter at breast height (DBH) and CA, the correlation analysis of both was conducted; thirdly, the AGB estimation method of individual M. oleifera trees was presented based on an empirical equation. The study showed that: (1) The deep learning model of Mask R-CNN achieved an average segmentation accuracy of 90% in the mixed forests to the extraction of the canopy of M. oleifera trees from UAV-RGB imagery. (2) The correlation between the extracted CA and field-measured DBH reached an R² of 0.755 (n = 96). (3) The t-test method was used to verify the predicted and observed values of the CA-DBH model presented in this study, and the difference in deviation was not significant (p > 0.05). (4) AGB of individual M. oleifera was estimated for the first time. This study provides a reference method for the estimation of individual tree AGB of M. oleifera based on centimeter-level resolution UAV-RGB images and the Mask R-CNN deep learning.

The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods.

This study aimed to evaluate the functional activity and phytochemical composition in the flower petals of Paeonia delavayi (P. delavayi) in different colors. P. delavayi petal extracts were prepared by maceration in methanol, including purple petal extract (PPE), red petal extract (RPE), and yellow petal extract (YPE), and their antioxidant activity and α-glucosidase and acetylcholinesterase inhibition activities were evaluated. To correlate these measured activities to phytochemicals in the petals, an ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)-based metabolomics method was applied to profile the compositions in the petals of different colors. Finally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways database was used to identify the related metabolic pathways that are responsible for the production of these polyphenolic phytochemicals in the petals. The results showed that PPE had the highest total phenolic content, total flavonoid content, and the strongest 2,2ʹ-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) radical scavenging ability, ferric reducing antioxidant power, and acetylcholinesterase inhibition ability in all three samples, while YPE showed the strongest 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity and α-glucosidase inhibition ability. A total of 232 metabolites were detected in the metabolomic analysis, 198 of which were flavonoids, chalcones, flavonols, and anthocyanins. Correlation analysis indicated that peonidin-3-O-arabinoside and cyanidin-3-O-arabinoside were the major contributors to the antioxidant activity. Principal component analysis showed a clear separation among these three petals. In addition, a total of 38, 98, and 96 differential metabolites were identified in PPE, RPE, and YPE, respectively. Pathway enrichment revealed 6 KEGG pathways that displayed significant enrichment differences, of which the anthocyanin biosynthesis, flavone and flavonol biosynthesis were the most enriched signaling pathways, revealing a potential reason for the differences in metabolic and functional levels among different colors of P. delavayi petal. P. delavayi petals in different colors have different metabolite contents and functional activities, of which the anthocyanin, flavone, and flavonol metabolites are critical in its functional activities, suggesting the anthocyanin biosynthesis, flavone and flavonol biosynthesis pathways are the key pathways responsible for both petal color and bioactive phytochemicals in P. delavayi flowers.

Unique plateau wetlands in China provide essential ecosystem functions and services and influence the health, environment and security of the downstream regions. In recent years, these plateau wetlands have experienced significant anthropogenic disturbance, but studies that evaluate the effects of such disturbance on ecological stability are rare. Our study tested how three typical types of human-related activities affect plant richness and ecological stability in Napahai plateau wetland, Shangri-La, China. The results showed that the anthropogenic disturbance had a direct effect on richness, and an indirect effect on stability mediated by richness. Anthropogenic disturbance did not alter the positive relationship between plant richness and community stability, and the stabilizing effect of richness could be explained by statistical averaging, overyielding effect, and component population stability. Our study complements previous studies that tested the richness-stability relationships in synthesized assemblages with richness specifically manipulated and studies that introduced mowing treatment to mimic real anthropogenic disturbance. The results further suggest that necessary steps, such as anthropogenic disturbance mitigation and plant richness conservation, are urgently required for maintaining healthy plateau wetlands and for sustaining their ecosystem functions and services.