Urban residents have a higher risk of hypertension and psychological stress than rural residents. Aside from medical interventions, understanding how to control hypertension and alleviate the stress of urban populations has become a public concern. As an alternative medical measure, forest therapy has shown the effects of normalizing blood pressure (BP) and reducing stress increasingly in recent literature, but this is still inconclusive. In order to systematically verify forest therapy’s effects on the BP and mental stress of urban residents, we conducted meta-analyses to assess the changes in systolic blood pressure (SBP), diastolic blood pressure (DBP), and salivary cortisol concentration (SCC; a stress biomarker) between the forest therapy group and urban control group. We searched 4 online databases, and 21 studies on BP involving 2270 participants and 13 studies on SCC involving 1786 participants were included. Through the meta-analysis, the mean difference and confidence interval (CI) estimates indicated that forest therapy significantly reduced participants’ SBP −3.44 (95% CI −5.74, −1.14), DBP −3.07 (95% CI −5.59, −0.54), and SCC −0.07 (95% CI −0.10, −0.04), as compared with urban control. Yet, there was substantial heterogeneity (I² = 72.87–88.59%) among these studies. We also found that each prediction interval (PI) for SBP (95% PI −13.30, 6.42), DBP (95% PI −15.54, 9.41), and SCC (95% PI −0.18, 0.05) were all much wider than the CIs and contained values above 0. This means that forest therapy may reduce SBP, DBP, and SCC on average, but may not exclude adverse results. Meta-regression analyses showed that participants’ age and baseline SBP levels determined the heterogeneity among SBP studies. After forest therapy, older participants and those with higher baseline SBP levels had greater SBP-lowering effects. Among DBP studies, the primary source of heterogeneity was participants’ baseline DBP levels; participants with higher baseline DBP levels had greater DBP reduction. In subgroup analyses, we discovered that longer-term forest therapy programs (≥20 min) resulted in a greater reduction in BP and SCC than shorter-term forest therapy programs (<20 min). Additionally, seated viewing, walking, or multi-session programs in forests were observed to have similar effects on reducing BP and SCC. Despite extensive analyses, the study did not identify any sources of heterogeneity among forest therapy programs for lowering SCC levels. Overall, we conclude that forest therapy programs have beneficial therapeutic effects on urban residents’ physio-psychological health through lowering BP and relieving stress (reducing SCC). This finding provides solid evidence of the contribution of forest therapy to urban residents’ health and wellbeing.

Due to improved data collection and processing techniques, forestry surveys are now more efficient and accurate, generating large amounts of forestry data. Forestry Big Data (FBD) has become a critical component of the forestry inventory investigation system. In this study, publications on FBD were identified via the Web of Science database, and a comprehensive bibliometric analysis, network analysis, and analysis of major research streams were conducted to present an overview of the FBD field. The results show that FBD research only began nearly a decade ago but has undergone an upswing since 2016. The studies were mainly conducted by China and the US, and collaboration among authors is relatively fragmented. FBD research involved interdisciplinary integration. Among all the keywords, data acquisition (data mining and remote sensing) and data processing (machine learning and deep learning) received more attention, while FBD applications (forecasting, biodiversity, and climate change) have only recently received attention. Our research reveals that the FBD research is still in the infancy stage but has grown rapidly in recent years. Data acquisition and data processing are the main research fields, whereas FBD applications have gradually emerged and may become the next focus.

With the concept of sustainable management of plantations, individual trees with excellent characteristics in plantations have received attention from breeders. To improve and maintain long-term productivity, accurate and high-throughput access to phenotypic characteristics is essential when establishing breeding strategies. Meanwhile, genetic diversity is also an important issue that must be considered, especially for plantations without seed source information. This study was carried out in a ginkgo timber plantation. We used simple sequence repeat (SSR) markers for genetic background analysis and high-density terrestrial laser scanning for growth structural characteristic extraction, aiming to provide a possibility of applying remote sensing approaches for forest breeding. First, we analyzed the genetic diversity and population structure, and grouped individual trees according to the genetic distance. Then, the growth structural characteristics (height, diameter at breast height, crown width, crown area, crown volume, height to living crown, trunk volume, biomass of all components) were extracted. Finally, individual trees in each group were comprehensively evaluated and the best-performing ones were selected. Results illustrate that terrestrial laser scanning (TLS) point cloud data can provide nondestructive estimates of the growth structural characteristics at fine scale. From the ginkgo plantation containing high genetic diversity (average polymorphism information content index was 0.719) and high variation in growth structural characteristics (coefficient of variation ranged from 21.822% to 85.477%), 11 excellent individual trees with superior growth were determined. Our study guides the scientific management of plantations and also provides a potential for applying remote sensing technologies to accelerate forest breeding.

Forests are susceptible to sudden disturbances, particularly those induced by wind, which can cause ecological and economic losses. Researchers have published a large number of articles from different disciplines and perspectives, leading to multidisciplinary intersections and increased literature in this field, which reduces the efficiency of traditional literature review. Employing quantitative techniques, bibliometric analysis has a great advantage in analyzing large amounts of literature and providing a visualized overview of the development and trends of the research field. This study conducted a comprehensive bibliometric analysis to elucidate the evolving landscape of research on forest wind disturbance. The methodology involved a systematic data collection process from the Web of Science Core Collection, resulting in the identification of 839 relevant publications for bibliometric analysis. The results show that there has been a consistent and steady growth in publications, with a distinct spike corresponding to Hurricane Hugo. Publications have mostly come from the United States, which also possesses the leadership in international collaboration. The standout topics include windthrow, tree motion during windstorms, European forests, wind damage risk estimation, hurricanes' impact on forests, and the long-term impacts of wind disturbances. Furthermore, four prospective directions for future research are identified, including studies into hurricanes, forest structure, climate change, and typhoon-related impacts. Our research collectively contributes to a comprehensive understanding of the dynamic landscape of forest wind disturbance research, providing a foundation for future research and strategic planning in this critical field.