Editorial: The Benefits of Nature-Based Solutions to Psychological Health

Nature-based solutions (NBS) have been defined by the European Commission as actions aiming to provide environmental, social, and economic benefits through the inclusion of natural features in the urban environment. The exposure to natural environments, including NBS in urban contexts, has been associated with a large number of health benefits (Ulrich et al., 1991; Berman et al., 2008; Spano et al., 2020), particularly mental health and well-being among those most studied. Earlier studies on such benefits have been mainly experimental, investigating the short-term effects of brief exposure to natural environments on stress reduction and cognitive restoration (Kaplan and Kaplan, 1989; Berto, 2005; Nilsson et al., 2010; Carrus et al., 2017). More recently, large-scale epidemiological studies have provided further evidence of the long-term effects of sustained exposure to green spaces on mental health and well-being throughout the life course (Hartig et al., 2014; Gascon et al., 2015; McCormick, 2017; de Keijzer et al., 2020). Several dimensions characterize the human–nature interaction. In this sense, the present Research Topic was intended to provide an overview of studies focusing on the association of exposure to natural environments in urban, peri-urban, and rural settings with psychological well-being and mental health from different perspectives

Nature reappraisers, benefits for the environment: A model linking cognitive reappraisal, the “being away” dimension of restorativeness and eco-friendly behavior

In the last decades, an increasingly prominent role has been given to the motivational factors that can promote pro-environmental behavior. In this contribution, we focus on the role of the individual’s ability to shape the emotions originating from nature in engaging in pro-environmental behavior. In particular, we expect that an emotion regulation strategy as cognitive reappraisal should positively predict pro-environmental behavior, through enhanced perceived restorativeness attributed to the natural environment in terms of the experience of “being away.” One-hundred and fifteen visitors to an urban park (Parco Nord Milano) filled out a questionnaire including measures of cognitive reappraisal, the experience of “being away,” and pro-environmental behaviour while in the park. Results confirmed that cognitive reappraisal was positively and significantly related to pro-environmental behavior. Importantly, the indirect effect of cognitive reappraisal on pro-environmental behavior through the experience of “being away” was significant. Findings suggest the importance of implementing interventions aimed at promoting the habitual use of cognitive reappraisal to enhance the experience of “being away” and, thus, sustain pro-environmental behavior

The wildland-urban interface map of Italy: A nationwide dataset for wildfire risk management

A wildland-urban interface (WUI) raster map was created for the Italian peninsula with a resolution of 30 m per pixel. The map creation process consisted of three fundamental steps: (1) selection of buildings within the wildland-urban interface areas and subsequent classification of these into isolated, scattered, and clustered buildings; (2) creation of the tree canopy cover layer; (3) generation of WUI map by the intersection of two previous products. According to the WUI map, more than half of the total area of Italy is occupied by interface areas. Areas with buildings classified as clustered (24.61%) and scattered (19.15%) predominate on the territory compared to isolated buildings (14.93%). Most of the buildings are located in areas with a tree cover canopy between up to 64%. This map is functional to the implementation of forest fire prevention plans and to the identification of buildings that are close to fire risk areas such as forests, grasslands, and pastures

Machine learning techniques for fine dead fuel load estimation using multi‐source remote sensing data

Fine dead fuel load is one of the most significant components of wildfires without which ignition would fail. Several studies have previously investigated 1‐h fuel load using standard fuel parameters or site‐specific fuel parameters estimated ad hoc for the landscape. On the one hand, these methods have a large margin of error, while on the other their production times and costs are high. In response to this gap, a set of models was developed combining multi‐source remote sensing data, field data and machine learning techniques to quantitatively estimate fine dead fuel load and understand its determining factors. Therefore, the objectives of the study were to: (1) estimate 1‐h fuel loads using remote sensing predictors and machine learning techniques; (2) evaluate the performance of each machine learning technique compared to traditional linear regression models; (3) assess the importance of each remote sensing predictor; and (4) map the 1‐h fuel load in a pilot area of the Apulia region (southern Italy). In pursuit of the above, fine dead fuel load estimation was performed by the integration of field inventory data (251 plots), Synthetic Aperture Radar (SAR, Sentinel‐1), optical (Sentinel‐2), and Light Detection and Ranging (LIDAR) data applying three different algorithms: Multiple Linear regression (MLR), Random Forest (RF), and Support Vector Machine (SVM). Model performances were evaluated using Root Mean Squared Error (RMSE), Mean Squared Error (MSE), the coefficient of determination (R2) and Pearson’s correlation coefficient (r). The results showed that RF (RMSE: 0.09; MSE: 0.01; r: 0.71; R2: 0.50) had more predictive power compared to the other models, while SVM (RMSE: 0.10; MSE: 0.01; r: 0.63; R2: 0.39) and MLR (RMSE: 0.11; MSE: 0.01; r: 0.63; R2: 0.40) showed similar performances. LIDAR variables (Canopy Height Model and Canopy cover) were more important in fuel estimation than optical and radar variables. In fact, the results highlighted a positive relationship between 1‐h fuel load and the presence of the tree component. Conversely, the geomorphological variables appeared to have lower predictive power. Overall, the 1‐h fuel load map developed by the RF model can be a valuable tool to support decision making and can be used in regional wildfire risk management

Public perceptions of forests across Italy: An exploratory national survey

In a context of progressive expansion of the Italian forest area, we present the results of a national survey exploring public perception of forests across different geographical scales in Italy. Perceptions of forests are assessed in rela-tion to popular beliefs on relevant environmental issues such as countering climate change, protecting biodiversity, and promoting social cohesion and environmental education. Participants (N = 1059) living in five different regions of Northern (Trentino-Alto Adige/Südtirol, Piemonte), Central (Lazio, Molise) and Southern Italy (Puglia), were recruited in the survey and completed a paper-and-pencil questionnaire. Survey questions regarded the estimated percentage of forest cover, the perceived importance of different environmental issues and of different material and non-material forest products, as well as partici-pants’ perceptions regarding connectedness to nature. Results revealed a gen-eralized tendency to overestimate the extension of forest surface area in the participants’ region, in Italy, and in the European Union. Results also showed high scores for participants’ perceived importance of environmental issues, such as climate change and biodiversity protection, and in their belief that forests could play a positive role in addressing these issues and providing im-portant outcomes and benefits for the quality of human life, such as health and well-being or social cohesion

Is experience the best teacher? Knowledge, perceptions, and awareness of wildfire risk

Wildfires represent a natural phenomenon with detrimental effects on natural resources and human health. A better knowledge, perception, and awareness of wildfire risk may help communities at risk of exposure to prevent future events and safeguard their own lives. The aim of this study is to explore differences between individuals with and without previous wildfire experience, in terms of (1) subjective and advanced wildfire knowledge, (2) self-reported perceptions, (3) level of information, (4) self-protection measures, and (5) importance of community involvement. As a second step, we investigated differences in the same variables, focusing more deeply on a group of individuals with previous wildfire experience, classifying them according to fire-related employment (fire-related workers vs. non-workers) and wildland–urban interface (WUI) proximity (WUI residents vs. non-WUI residents). The Kruskal–Wallis test was applied to establish differences between the pairs of subsamples. Our results partially confirmed our hypothesis, that direct experience leads individuals to have a greater preparedness on the topic of wildfires. Perception of knowledge is reflected only at a shallow level of expertise, and, therefore, no relevant within-group differences related to fire-related employment or to WUI proximity were detected. Moreover, available information was perceived to be insufficient, thus we report a strong need for developing effective communication to high-risk groups, such as homeowners and fire-related workers

Are community gardening and horticultural interventions beneficial for psychosocial well-being? A meta-analysis

Recent literature has revealed the positive effect of gardening on human health; however, empirical evidence on the effects of gardening-based programs on psychosocial well-being is scant. This meta-analysis aims to examine the scientific literature on the effect of community gardening or horticultural interventions on a variety of outcomes related to psychosocial well-being, such as social cohesion, networking, social support, and trust. From 383 bibliographic records retrieved (from 1975 to 2019), seven studies with a total of 22 effect sizes were selected on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Meta-analytic findings on 11 comparisons indicate a positive and moderate effect of horticultural or gardening interventions on psychosocial well-being. Moderation analysis shows a greater effect size in individualistic than collectivistic cultures. A greater effect size was also observed in studies involving community gardening compared to horticultural intervention. Nevertheless, an effect of publication bias and study heterogeneity has been detected. Despite the presence of a large number of qualitative studies on the effect of horticulture/gardening on psychosocial well-being, quantitative studies are lacking. There is a strong need to advance into further high-quality studies on this research topic given that gardening has promising applied implications for human health, the community, and sustainable city management

Modeling fire ignition probability and frequency using Hurdle models: a cross-regional study in Southern Europe

Background: Wildfires play a key role in shaping Mediterranean landscapes and ecosystems and in impacting species dynamics. Numerous studies have investigated the wildfire occurrences and the influence of their drivers in many countries of the Mediterranean Basin. However, in this regard, no studies have attempted to compare different Mediterranean regions, which may appear similar under many aspects. In response to this gap, climatic, topographic, anthropic, and landscape drivers were analyzed and compared to assess the patterns of fire ignition points in terms of fire occurrence and frequency in Catalonia (Spain), Sardinia, and Apulia (Italy). Therefore, the objectives of the study were to (1) assess fire ignition occurrence in terms of probability and frequency, (2) compare the main drivers affecting fire occurrence, and (3) produce fire probability and frequency maps for each region. Results: In pursuit of the above, the probability of fire ignition occurrence and frequency was mapped using Negative Binomial Hurdle models, while the models’ performances were evaluated using several metrics (AUC, prediction accuracy, RMSE, and the Pearson correlation coefficient). The results showed an inverse correlation between distance from infrastructures (i.e., urban roads and areas) and the occurrence of fires in all three study regions. This relationship became more significant when the frequency of fire ignition points was assessed. Moreover, a positive correlation was found between fire occurrence and landscape drivers according to region. The land cover classes more significantly affected were forest, agriculture, and grassland for Catalonia, Sardinia, and Apulia, respectively. Conclusions: Compared to the climatic, topographic, and landscape drivers, anthropic activity significantly influences fire ignition and frequency in all three regions. When the distance from urban roads and areas decreases, the probability of fire ignition occurrence and frequency increases. Consequently, it is essential to implement long- to medium-term intervention plans to reduce the proximity between potential ignition points and fuels. In this perspective, the present study provides an applicable decision-making tool to improve wildfire prevention strategies at the European level in an area like the Mediterranean Basin where a profuse number of wildfires take place