Aims: This study focused on the newest evidence of the relationship between forest environmental exposure and human health and assessed the health efficacy of forest bathing on the human body as well as the methodological quality of a single study, aiming to provide scientific guidance for interdisciplinary integration of forestry and medicine. Method: Through PubMed, Embase, and Cochrane Library, 210 papers from January 1, 2015, to April 1, 2019, were retrieved, and the final 28 papers meeting the inclusion criteria were included in the study. Result: The methodological quality of papers included in the study was assessed quantitatively with the Downs and Black checklist. The methodological quality of papers using randomized controlled trials is significantly higher than that of papers using non-randomized controlled trials (p < 0.05). Papers included in the study were analyzed qualitatively. The results demonstrated that forest bathing activities might have the following merits: remarkably improving cardiovascular function, hemodynamic indexes, neuroendocrine indexes, metabolic indexes, immunity and inflammatory indexes, antioxidant indexes, and electrophysiological indexes; significantly enhancing people's emotional state, attitude, and feelings towards things, physical and psychological recovery, and adaptive behaviors; and obvious alleviation of anxiety and depression. Conclusion: Forest bathing activities may significantly improve people's physical and psychological health. In the future, medical empirical studies of forest bathing should reinforce basic studies and interdisciplinary exchange to enhance the methodological quality of papers while decreasing the risk of bias, thereby raising the grade of paper evidence.

Experiencing nature can induce the perception of happiness because of mental stress alleviation and well-being restoration. The largeness of green space may not always mean the frequency of experiencing greenery. It is arguing about the probability of positive sentiments in response to an experience of interacting with green nature. In this study, 38 green spaces were investigated in Nanchang City, China, where the green space area was evaluated by the largeness of the landscape metrics of the Normalized Vegetation Index (NDVI), and Green View Index (GVI) data were further obtained using Open Street Maps (OSM). The semantic segmentation method was used by machine learning to analyze a total of 1549 panoramic photos taken in field surveys to assess the Panoramic Green View Index (PGVI) proportion. The photos of 2400 people’s facial expressions were obtained from social networks at their check-in visits in green spaces and rated for happy and sad scores using FireFACE software. Split-plot analysis of variance suggested that different categories of NDVI largeness had a significant positive effect on posted positive sentiments. Multivariate linear regression indicated that PGVI was estimated to have a significant contribution to facial expression. Increasing the amount of PGVI promoted happy and PRI scores, while at the same time, neutral sentiments decreased with increasing PGVI. Overall, increasing the PGVI in green spaces, especially in parks with smaller green spaces, can be effective in promoting positive emotions in the visitor experience.

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Urban expansion has been changing the urban thermal environment. Understanding the spatial distribution and temporal trends in the urban thermal environment is important in guiding sustainable urbanization. In this study, we focused on the land use/land cover (LULC) changes and urban expansion in Nanchang city, Jiangxi province, China. The four elements in the remote sensing-based ecological index (RSEI) are heat, greenness, dryness, and wetness, which correspond to the land surface temperature (LST), NDVI, NDBSI, and WET, respectively. According to the synthetic images of the average indices, we conducted temporal trend analysis together with statistical significance test for these images. We conducted partial correlation analyses between LST and NDVI, NDVSI, as well as WET. In addition, we used the LULC maps to analyze the multi-year trends in urban expansion. Then, we superimposed the trends in daytime and nighttime LST in summer on urban expansion area to extract the LST trends at sample locations. The results showed that LULC in Nanchang has substantially changed during the study period. The areas with statistically significant trends in LST coincided with the urban expansion areas. Land cover change was the main reason for LST change in Nanchang. In particular, artificial surfaces showed the greatest increase in LST; for per 100 km2 expansion in artificial surfaces, the daytime and nighttime LST increased by 0.8 °C and 0.7 °C, respectively. Among all the study land cover types, water bodies showed the greatest differences in LST change between the daytime and nighttime. There were statistically significant correlations between increases in LST and increases in NDBSI as well as decreases in NDVI and WET. In view of the considerable impact of urban expansion on the urban thermal environment, we urge local authorities to emphasize on urban greening when carrying out urban planning and construction.

To further explore the specific application of nonlinear system in the construction of landscape architecture and promote the parametric development of landscape design, in this exploration, based on the digital elevation model (DEM) and nonlinear algorithm theory, the road selection scheme using cost distance algorithm and path distance algorithm is explored. The results suggest that there are obvious differences between cost distance algorithm and path distance algorithm in the consideration of parameters in use. The path distance algorithm needs to further consider the influence of surface grid and vertical system parameters. Environmental factors and different classification methods have great influence on road selection. When the limit value is greater than [Formula: see text], there is no obvious change in road selection. However, when SLOPE values are [Formula: see text], [Formula: see text] and [Formula: see text], respectively, the difference in road selection is small, and the application effect is good. This exploration can provide the sufficient reference for the follow-up study of landscape road selection.