Exploring the ‘dark’ side of forest therapy and recreation: A critical review and future directions

This scoping review aims to outline key discoveries, gaps in knowledge, and potential future directions in the field of forest therapy research in Europe. Clinical studies investigating the effects of forest therapy sessions in Europe were systematically screened through PubMed, Embase, Cochrane Library, the Directory of Open Access Journals (DOAJ), and Google Scholar (search date: January 2024). This review encompasses 26 selected studies, collectively engaging 2775 participants across various European countries, including Italy, Poland, Spain, Germany, Finland, Hungary, Sweden, Iceland, and Switzerland. These studies predominantly measured psychological outcomes such as mood states, affect, attention levels, subjective wellbeing, and quality of life. Additionally, physiological assessments were conducted, covering vital signs, salivary cortisol, body composition, and blood count. Notably, most of the investigation sites were covered by evergreen forests. Forest therapy practices in Europe demonstrated significant benefits in psychological aspects, including relaxation, mood enhancement, and improved wellbeing, accompanied by physiological improvements, particularly in asthma patients. While a fair-to-good methodological quality was identified in randomized controlled trials, the acknowledged weaknesses point towards a need for more rigorous and standardized research approaches. In conclusion, this comprehensive scoping review provides valuable insights into the current landscape of forest therapy research in Europe, highlighting its potential to positively impact both physical and mental wellbeing. The findings underscore the need for further research addressing methodological limitations and exploring gaps in understanding, thereby contributing to the continued advancement of forest therapy as a holistic wellbeing intervention within the European context.

Section: Introductionmentioning

confidence: 99%

Forest Therapy Research in Europe: A Scoping Review of the Scientific Literature

Mazzoleni,

Donelli,

Zabini

et al. 2024

“…Forests are a crucial ecological resource. However, fire, which is one of the primary threats to forests, poses a significant danger to both forest ecosystems and human life and property [1][2][3]. To manage forests effectively and establish safe areas after a fire, it is essential to have information about where a fire is likely to stop.…”

Section: Introductionmentioning

confidence: 99%

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

Sun

Zhang

et al. 2023

Forest fires create burned and unburned areas on a spatial scale, with the boundary between these areas known as the fire boundary. Following an analysis of forest fire boundaries in the northern region of Yangyuan County, located in the Liangshan Yi Autonomous Prefecture of Sichuan Province, China, several key factors influencing the formation of fire boundaries were identified. These factors include the topography, vegetation, climate, and human activity. To explore the impact of these factors in different spaces on potential results, we varied the distances between matched sample points and built six fire environment models with different sampling distances. We constructed a matched case-control conditional light gradient boosting machine (MCC CLightGBM) to model these environment models and analyzed the factors influencing fire boundary formation and the spatial locations of the predicted boundaries. Our results show that the MCC CLightGBM model performs better when points on the selected boundaries are paired with points within the burned areas, specifically between 120 m and 480 m away from the boundaries. By using the MCC CLightGBM model to predict the probability of boundary formation under six environmental models at different distances, we found that fire boundaries are most likely to form near roads and populated areas. Boundary formation is also influenced by areas with significant topographic relief. It should be noted explicitly that this conclusion is only applicable to this study region and has not been validated for other different regions. Finally, the matched case-control conditional random forest (MCC CRF) model was constructed for comparison experiments. The MCC CLightGBM model demonstrates potential in predicting fire boundaries and fills a gap in research on fire boundary predictions in this area which can be useful in future forest fire management, allowing for a quick and intuitive assessment of where a fire has stopped.

Spatial Layout and Driving Factors of Forest Therapy Bases in Fujian Province, Southern China

Wang,

Chen,

et al. 2024

In recent years, as civilization and human society have progressed, the potential and innovative capacity of various sectors of forest therapy have increasingly been recognized. However, the landscape of forest therapy is characterized by significant disparities in its distribution and uneven development patterns. Therefore, a comprehensive analysis of the factors influencing the distribution of forest therapy bases is crucial for optimizing the organization and allocation of resources within this industry, thereby promoting the growth of the forest therapy bases. This research delves into the spatial arrangement of forest therapy bases within Fujian Province, southern China. This study employs the nearest neighbor index, geographic concentration index, kernel density index, scale index, spatial autocorrelation analysis, and redundancy analysis to identify the primary factors influencing the geographical distribution of the bases. The study reveals three key findings about the spatial distribution of forest therapy bases in Fujian Province: (1) Centers are predominantly located in Nanping and Sanming, with a development pattern moving eastward and southward from Jianning and Taining in Sanming. (2) An imbalance is evident in the distribution, where areas with higher center concentrations exhibit a stronger spatial autocorrelation, characterized by high-density clusters. (3) Economic and environmental variables substantially affect center placement. At the municipal level, GDP, number of tourists, and forest coverage are significant. Conversely, at the district or county level, determinants include forest coverage, number of primary and secondary school students, forest land area, and GDP. Thus, it is suggested that the selection of bases for future forest therapy and the development of related industries should take into account local economic, environmental, and social factors. It aims to offer a scientific basis for planning forest therapy, potentially spreading its benefits to more areas.