Predicting carob tree physiological parameters under different irrigation systems using Random Forest and Planet satellite images.

Introduction: In the context of climate change, monitoring the spatial and temporal variability of plant physiological parameters has become increasingly important. Remote spectral imaging and GIS software have shown effectiveness in mapping field variability. Additionally, the application of machine learning techniques, essential for processing large data volumes, has seen a significant rise in agricultural applications. This research was focused on carob tree, a drought-resistant tree crop spread through the Mediterranean basin. The study aimed to develop robust models to predict the net assimilation and stomatal conductance of carob trees and to use these models to analyze seasonal variability and the impact of different irrigation systems. Methods: Planet satellite images were acquired on the day of field data measurement. The reflectance values of Planet spectral bands were used as predictors to develop the models. The study employed the Random Forest modeling approach, and its performances were compared with that of traditional multiple linear regression. Results and discussion: The findings reveal that Random Forest, utilizing Planet spectral bands as predictors, achieved high accuracy in predicting net assimilation (R² = 0.81) and stomatal conductance (R² = 0.70), with the yellow and red spectral regions being particularly influential. Furthermore, the research indicates no significant difference in intrinsic water use efficiency between the various irrigation systems and rainfed conditions. This work highlighted the potential of combining satellite remote sensing and machine learning in precision agriculture, with the goal of the efficient monitoring of physiological parameters.

Two- and three-dimensional indicators of green and grey space exposure and psychiatric conditions and medicine use: A longitudinal study in a large population-based Italian cohort.

BACKGROUND: Evidence available on the associations between urban greenness and mental health is mainly based on cross-sectional studies and has relied on 2D indicators of greenness. This longitudinal study aimed at investigating the association between 2D and 3D indicators of green and grey spaces and incident mental health-related outcomes in a large population-based cohort. METHODS: Our study used data from 593,894 Italian adults (≥30 years) from the Rome Longitudinal Study. Mental health outcomes were defined using either drug prescriptions (antidepressants, antipsychotics, lithium and other mood stabilisers, and anxiolytics, hypnotics and sedatives), or hospitalisation records (for schizophrenia spectrum disorder, depression, anxiety, stress-related and somatoform, or substance use disorders). We obtained 2D and 3D indicators of green and grey exposures including Normalized Difference Vegetation Index (NDVI), green volume, grey volume, number of trees, and Normalized Difference Green-Grey Volume Index around participants' homes. Cox proportional hazards regression models were developed to estimate the association of green and grey space exposure and psychiatric conditions and medicine use, adjusted for relevant covariates. RESULTS: We found beneficial associations of NDVI and the number of trees with antipsychotic and lithium and other mood stabiliser drugs. We also observed detrimental associations between grey volume and lithium and other mood stabilisers and anxiolytic, hypnotic and sedative drugs. Finally, we found a protective association of the NDGG with lithium and other mood stabilisers (HR: 0.977; 95% CI: 0.965-0.990) and anxiolytic, hypnotic and sedative drugs (HR: 0.851; 95% CI: 0.762-0.950). The associations for hospitalisation for psychiatric conditions were less consistent and generally not statistically significant. CONCLUSIONS: Findings suggested that higher greenness areas around residential addresses are associated with reduced use of drugs for psychiatric conditions, while the opposite is true for higher grey space exposure. The study highlights the importance of accurately characterising green and grey spaces, using novel exposure indicators.

Objective greenness, connectedness to nature and sunlight levels towards perceived restorativeness in urban nature.

The beneficial effect of exposure to nature and immersion in natural environments on perceived well-being is well established. Nevertheless, we acknowledge an emerging need to disentangle the role of specific environmental features from individual factors that encourage a positive person-environment interaction. This study aimed at evaluating the associations between four buffer distances of greenness and dimensions of perceived restorativeness, with connectedness towards nature (CTN) as a confounder variable, in a sample of 312 visitors to a large urban park. Variables investigating ecosystem services (ES, e.g., thermal comfort) were included as covariates. Results revealed differentiated effects of greenness level, sunlight intensity, and connectedness to nature in the pathways towards dimensions of restorativeness. Greenness level at 300 m was associated with Fascination, Scope, and Being Away, while at 500 m was associated with Coherence, Scope, and Being Away. ES was found to be associated with Coherence, while CTN with the other three dimensions of restorativeness. The moderating effect of sunlight level in the relationship between NDVI buffer distances and the total score of perceived restorativeness was also confirmed. The present work is intended to offer insights on the interplay between environmental features and individual differences for implications in several contexts, including the opportunity to develop tailor-made planning for urban forestry.

The unrelenting global expansion of the urban heat island over the last century.

The past century has seen dramatic increases in global temperatures and mounting urbanization. As a result of these events, the urban heat island (UHI) effect has received growing attention in scientific research worldwide. A global search was initially conducted using a scientific literature database to collect all available relevant publications to understand how the UHI has been expanding worldwide and affecting more cities across different latitudes and altitudes. Subsequently, a semantic analysis was performed to extract city names. The literature search and analysis combined resulted in 6078 publications in which UHI was investigated in 1726 cities worldwide in the 1901 to 2022 time period. The cities were grouped into 'first appearance' and 'recurrent appearance'. Results show that UHI was studied in only 134 cities during the 90-year period from 1901 to 1992, with a remarkable growth over time in the number of cities where interest in UHI increased. Interestingly, the number of first appearances was always notably higher than the number of recurrent appearances. The Shannon evenness index was employed to identify the spatial locations (hotspots) across the globe where UHI-related research has been concentrated in multiple cities over the last 120 years. Finally, Europe was selected as a testbed for conducting an analysis to shed light on how economic, demographic, and environmental factors can impact UHI. Our study is unique for having demonstrated not only the rapid growth of cities affected by UHI globally but also the increasing and unrelenting expansion of UHI occurrences across different latitudes and altitudes over time. These novel findings will undoubtedly be of interest to scientists investigating the UHI phenomenon and its trends. Stakeholders will acquire a broader perspective and deeper understanding of UHI in order to engage in more effective urban planning to offset and mitigate the phenomenon's adverse effects in the context of increasing climate change and urbanization.

Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe.

The impacts and threats posed by wildfires are dramatically increasing due to climate change. In recent years, the wildfire community has attempted to estimate wildfire occurrence with machine learning models. However, to fully exploit the potential of these models, it is of paramount importance to make their predictions interpretable and intelligible. This study is a first attempt to provide an eXplainable artificial intelligence (XAI) framework for estimating wildfire occurrence using a Random Forest model with Shapley values for interpretation. Our findings accurately detected regions with a high presence of wildfires (area under the curve 81.3%) and outlined the drivers empowering occurrence, such as the Fire Weather Index and Normalized Difference Vegetation Index. Furthermore, our analysis suggests the presence of anomalous hotspots. In contexts where human and natural spheres constantly intermingle and interact, the XAI framework, suitably integrated into decision support systems, could support forest managers to prevent and mitigate future wildfire disasters and develop strategies for effective fire management, response, recovery, and resilience.

Optimal ranges of social-environmental drivers and their impacts on vegetation dynamics in Kazakhstan.

Kazakhstan is part of the Eurasian Steppes, the world's largest contiguous grassland system. Kazakh grassland systems are largely understudied despite being historically important for agropastoral practices. These grasslands are considered vulnerable to anthropogenic activities and climatic variability. Few studies have examined vegetation dynamics in Central Asia owing to the complex impacts of moisture, climatic and anthropogenic forcings. A comprehensive analysis of spatiotemporal changes of vegetation and its driving factors will help elucidate the causes of grassland degradation. Here, we investigated the individual and pairwise interactive influences of various social-environmental system (SES) drivers on greenness dynamics in Kazakhstan. We sought to examine whether there is a relationship between peak season greenness and its drivers - spring drought, preceding winter freeze-thaw cycles, percent snow cover and snow depth - for Kazakhstan during 2000-2016. As hypothesized, snow depth and spring drought accounted for 60 % and 52 % of the variance in the satellite-derived normalized difference vegetation index (NDVI) in Kazakhstan. The freeze-thaw process accounted for 50 % of NDVI variance across the country. In addition, continuous thawing during the winter increased vegetation greenness. We also found that moisture and topographic factors impacted NDVI more significantly than socioeconomic factors. However, the impacts of socioeconomic drivers on vegetation growth were amplified when they interacted with environmental drivers. Terrain slope and soil moisture had the highest q-values or power of determinant, accounting for ~70 % of the variance in NDVI across the country. Socioeconomic drivers, such as crop production (59 %), population density (48 %), and livestock density (26 %), had significant impacts on vegetation dynamics in Kazakhstan. We found that most of the pairwise interactive influences of the drivers exhibited bi-factor enhancement, and the interaction between soil moisture and elevation was the largest (q = 0.92). Our study revealed the optimal ranges and tipping points of SES drivers and quantified the impacts of various driving factors on NDVI. These findings can help us identify the factors causing grassland degradation and provide a scientific basis for ecological protection in semiarid regions.

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.

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.

Association between indoor-outdoor green features and psychological health during the COVID-19 lockdown in Italy: A cross-sectional nationwide study.

Exposure to public green spaces was shown to be associated with psychological health. Nonetheless, evidence is lacking on the role of different green features within and/or surrounding the home environment when public green spaces are inaccessible or not usable. The overarching goal of this study is to shed light on the associations between the presence of greenness within the home and in the surrounding environment and the detrimental effects of quarantine on psychological health during the COVID-19 pandemic lockdown in Italy. A cross-sectional nationwide study involving an online survey was conducted of an Italian population-based sample of 3886 respondents on the association of indoor and outdoor green features (i.e., presence of plant pots, sunlight, green view and accessibility of private green space and natural outdoor environment) with self-reported increases in anxiety, anger, fear, confusion, moodiness, boredom, irritability, recurrent thoughts and/or dreams, poor concentration and sleep disturbance during the COVID-19 lockdown. Single-exposure regression models were performed to estimate associations between single green features and each psychological health outcome adjusted for relevant covariates. In the adjusted models, the presence of plant pots at home was associated with a lower self-reported increase in anxiety, anger, fear, irritability, and sleep disturbance. A greater amount of sunlight in the home was associated with a lower increase in anger, fear, confusion, moodiness, boredom, irritability, poor concentration, and sleep disturbance. A greater amount of green view and access to private green spaces were both associated with a lower increase in each of the psychological health outcomes except for green view and recurrent thought and/or dreams. Natural outdoor environment was associated with anxiety, fear, boredom, irritability, and sleep disturbance. Significant associations remained robust when adjusted for number of confirmed COVID-19 cases. Insights on future investigations are provided.

Green spaces, quality of life, and citizen perception in European cities.

Urban green spaces generate a number of perceived benefits toward human health and well-being, including an overall improvement in the quality of life. To date, processes underlying these benefits are mostly investigated at city level, while very little research has been conducted at a larger scale, such as the European level. Evidence is lacking on the association between green spaces and citizens' perceived benefits based on variations among cities with different socio-economic conditions. This study aims to disentangle the relationship between greenness, citizens' perceived quality of life, the environment, social inclusion and urban management in 51 European cities, as well as the role of per capita income in moderating the effects of greenness on overall perceived quality of life. Through structural equation modeling, the effect of greenness was tested on citizens' perceived overall quality of life (i.e., on the environment, social inclusion, and urban management). The role of metropolitan gross domestic product per capita in moderating the relationship between greenness and citizens' perceptions on overall quality of life was investigated. Our findings confirm the influence of greenness on citizens' quality of life. More importantly, the influence of greenness on the overall perception of quality of life was considerably more marked in lower-income cities. This study represents one of the first attempts at exploring the complex mechanisms underlying the association between green space and citizens' well-being at continent level. Practical implications for European urban planners and policy makers are discussed.

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.

Human Health-Environment Interaction Science: An emerging research paradigm.

During recent decades, the growing interactions between human well-being and the environment have led to the development of new research paradigms. A number of disciplines have recognized the importance of the human-environment relationship in all aspects of human life from an economic, ecological, social and political perspective. In conformity with this trend, we conducted a bibliometric review of scientific publications on the interactions between two main research domains: human-centered and environmental-centered sciences. The aim is to provide a temporal and spatial perspective on how these research domains have paired up and co-evolved along a common pathway towards a new research paradigm, named Human Health-Environment Interaction Science (HHEIS). Our results revealed a constant growth over time in the scientific production concerning HHEIS. The network and cluster analyses showed a progressive overlapping of keywords among studies published in environmental and human health journals to a match of principal keywords. As a statistical indicator of this trend, the similarity index showed an increase in the number of keywords for both research domains. In terms of country productivity, the US, UK and China are in the lead, while EU countries are the most interconnected. Our review demonstrates the existence of HEIS as a comprehensive paradigm encompassing research findings with implications for human and environmental interaction. An overview of the history of HHEIS and the implications of current EU research-funding initiatives have been provided.

Co-creating urban green infrastructure connecting people and nature: A guiding framework and approach.

Urban green infrastructure (UGI) and nature-based solutions are increasingly recognized as instruments to address urban sustainability challenges, yet rely on a good understanding of complex social-ecological system (SES) to function adequately. Adaptive co-management (ACM), engaging a broad variety of stakeholders in collaborative learning, is an effective strategy to improve the resilience of a SES. However, ACM studies have been criticized for neglecting the urban context, while also offering little clarity on process objectives and outcomes. To address these knowledge gaps, while also drawing attention to the important issue of socially inclusive UGI development, we present a guiding framework and approach to encourage the ACM of UGI featuring two main components. Firstly, a Learning Alliance (LA) serves as an instrument for collaborative learning and experimentation across different scales. To facilitate upscaling, we propose to establish a complementary Urban Learning Lab (ULL) to facilitate a regular exchange between the LA and legitimate peripheral networks and stakeholders in the city region. Secondly, a stepwise approach to SES analysis serves to engage a representative group of stakeholders in the LAs and ULLs, and support the processes of setting LA objectives and monitoring of adaptive capacity. We illustrate our approach to the ACM of UGI with a case study of LivadaLAB in Ljubljana, Slovenia. Applying the framework and approach, we demonstrate increased adaptive capacity of the SES around UGI as indicated by: 1) improved overall stakeholder salience, in particular for previously disempowered actor groups, 2) increased number and strength of connections between stakeholders, and 3) the consideration of a broader range of sustainable development objectives by stakeholders in their daily practice.

Modeling the influence of alternative forest management scenarios on wood production and carbon storage: A case study in the Mediterranean region.

Forest ecosystems are fundamental for the terrestrial biosphere as they deliver multiple essential ecosystem services (ES). In environmental management, understanding ES distribution and interactions and assessing the economic value of forest ES represent future challenges. In this study, we developed a spatially explicit method based on a multi-scale approach (MiMoSe-Multiscale Mapping of ecoSystem services) to assess the current and future potential of a given forest area to provide ES. To do this we modified and improved the InVEST model in order to adapt input data and simulations to the context of Mediterranean forest ecosystems. Specifically, we integrated a GIS-based model, scenario model, and economic valuation to investigate two ES (wood production and carbon sequestration) and their trade-offs in a test area located in Molise region (Central Italy). Spatial information and trade-off analyses were used to assess the influence of alternative forest management scenarios on investigated services. Scenario A was designed to describe the current Business as Usual approach. Two alternative scenarios were designed to describe management approaches oriented towards nature protection (scenario B) or wood production (scenario C) and compared to scenario A. Management scenarios were simulated at the scale of forest management units over a 20-year time period. Our results show that forest management influenced ES provision and associated benefits at the regional scale. In the test area, the Total Ecosystem Services Value of the investigated ES increases 85% in scenario B and decreases 82% in scenario C, when compared to scenario A. Our study contributes to the ongoing debate about trade-offs and synergies between carbon sequestration and wood production benefits associated with socio-ecological systems. The MiMoSe approach can be replicated in other contexts with similar characteristics, thus providing a useful basis for the projection of benefits from forest ecosystems over the future.