Due to the increased frequency and intensity of forest damage caused by diseases and pests, effective methods are needed to accurately monitor the damage degree. Unmanned aerial vehicle (UAV)-based hyperspectral imaging is an effective technique for forest health surveying and monitoring. In this study, a framework is proposed for identifying the severity of damage caused by Tomicus spp. (the pine shoot beetle, PSB) to Yunnan pine (Pinus yunnanensis Franch) using UAV-based hyperspectral images. Four sample plots were set up in Shilin, Yunnan Province, China. A total of 80 trees were investigated, and their hyperspectral data were recorded. The spectral data were subjected to a one-way ANOVA. Two sensitive bands and one sensitive parameter were selected using Pearson correlation analysis and stepwise discriminant analysis to establish a diagnostic model of the damage degree. A discriminant rule was established to identify the degree of damage based on the median value between different degrees of damage. The diagnostic model with R₆₉₀ and R₇₉₈ as variables had the highest accuracy (R² = 0.854, RMSE = 0.427), and the test accuracy of the discriminant rule was 87.50%. The results are important for forest damage caused by the PSB.

Dalbergia sissoo is a woody plant with economic and medicinal value. As the pharmacological qualities and properties of the wood from this plant primarily depend on its extractives, in this study, the metabolomic analysis of extractives from its stems was carried out using UPLC-MS/MS. A total of 735 metabolites were detected from two groups of samples, heartwood and sapwood, with the largest number of terpenoids in type and the largest number of flavonoids in quantity. The PCA and cluster analysis showed significant differences in the metabolite composition between the two groups. The differential metabolites were mainly organic oxygen compounds, flavonoids, and isoflavones. Among the 105 differential metabolites, 26 metabolites were significantly higher in relative content in sapwood than in heartwood, while the other 79 metabolites were significantly higher in relative content in heartwood than in sapwood. KEGG metabolic pathway enrichment analysis showed that these differential metabolites were mainly enriched in three metabolic pathways: Flavonoid biosynthesis, isoflavonoid biosynthesis, and flavonoid and flavonol biosynthesis. This study provides a reference for metabolomics studies in Dalbergia and other woody plants.

The Yunnan pine (Pinus yunnanensis Franch.) is one of the primary forest vegetation types in Yunnan Province and has prominent economic value and ecological significance. Monitoring changes in Yunnan pine forests contributes to their health management. The normalized differential vegetation index (NDVI) is an essential indicator for studying regional vegetation change. Landsat images were used to analyze the NDVI changes of Yunnan pine forests in rainy and dry seasons as well as the NDVI changes of Yunnan pine forests from 2009 to 2020. The results showed that the NDVI in Yunnan pine forests showed opposite trends in the rainy and dry seasons, with an increase in the rainy season and a decrease in the dry season. The areas of NDVI decline during the rainy season are mainly located at the edges of the forest area, with very few within the forest area. The main reason for the decline in NDVI within the forest area was pests. From 2009 to 2020, the areas where NDVI increased and decreased were 92.23% and 7.77% of the pine forests, respectively. The decreasing areas were mainly located at the edges of the pine forests, with sporadic distribution within the forest areas. The drought led to a significant decline in NDVI in the forest area in 2010 and 2013. Due to the lack of protective measures, the forest area in Xijiekou Town was severely infested with pests, with complex NDVI changes and strong fluctuations, and areas of severe pest infestation could be detected by NDVI imagery. This study is meaningful for forest protection and explores the potential capability of Landsat imagery for pest detection.