Conference Agenda

This study aims to quantify the micro-scale proximal cooling footprint of urban green infrastructure on surrounding environments. Using high-resolution satellite imagery and airborne Land Surface Temperature data, we classified land cover into low- and high-density vegetation areas. Following emissivity corrections, we assessed the cooling effect of high-density vegetation on uniform target areas of low-density cover. This was achieved using exclusive annular buffers compared against a fixed control group. Non-parametric inferential statistics, integrating effect sizes for large raster datasets, were combined with landscape metrics. Results revealed a significant but highly localized cooling effect, with LST reductions exceeding 1.5°C in the immediate proximity (5m) of high-density vegetation, followed by a sharp attenuation as distance increased. Conversely, patch shape and size metrics showed low correlations with cooling intensity, highlighting proximity as the primary driver. In conclusion, high-resolution analyses are crucial for capturing the rapid distance-decay of proximal cooling. These findings demonstrate that an evenly distributed network of dense green elements is strictly necessary for widespread urban heat mitigation, as cooling dissipates quickly over short distances.

The recognized capacity of Green Infrastructure (GI) to support ecosystem services and biodiversity has made it a cornerstone of contemporary spatial planning. This study evaluates the effectiveness of a GIS-based automated workflow, developed within Model Builder functions, to model green infrastructure components through the integration and geospatial analysis of multi-source thematic layers. Results indicate that approximately 24% of the Friuli Venezia Giulia region possesses the characteristics necessary to be classified as green infrastructure. While this network appears well-interconnected across Alpine, sub-Alpine, and coastal areas, the morphology in the central plain is more fragmented, with connectivity relying almost exclusively on river corridors. The approach successfully enabled the integration of strategic areas for environmental protection and ecological connectivity, including both areas of high naturalness and intensely anthropized environments. This distinguishes a structural backbone for the GI from fringe or transformation zones where planning can actively intervene. The framework proves a highly transferable tool for regional-scale spatial planning.

The environmental quality of built-up territories and inhabitants’ health are increasingly challenged due to artificialization, thermal stress, and inequality in access to green. Effective planning of nature-based solutions is a crucial component of climate change mitigation and adaptation and fundamental to increase environmental quality. This paper presents a research focused on satellite data analysis, multi-criteria hotspot detection and environmental modelling in a GIS environment, to address three specific goals: understanding relationships between green cover and thermal stress; mapping target areas where green infrastructure planning is priority based on heat island intensity and social exposure; predicting the expected microclimatic improvement allowed by green infrastructure. The research was carried out using a multiscale approach, from the regional to the city/neighbourhood level, on Italian case studies, to enable evidence-based green infrastructure implementation, throughout the land analysis, planning and design process. Machine learning analysis allowed to find that vegetation is negatively correlated with land surface temperature and becomes the most significant variable in certain years. Environmental hotspots were detected across the region and downscaled at municipal level. Microclimatic modelling of greening scenarios on pilot areas highlighted a potential decrease of mean radiant temperature of 15 degrees or more over large areas in critical days.

Urban overheating has become a critical challenge in Mediterranean cities, affecting environmental quality and outdoor thermal comfort. This study investigates how alternative urban greening strategies can improve microclimatic conditions within an urban regeneration project located in Imola, Italy. A simulation-based approach was adopted using the ENVI-met microclimate model to evaluate multiple vegetation design scenarios characterized by different tree densities and spatial configurations. Simulations were conducted under extreme summer conditions, and thermal comfort was assessed using the Physiological Equivalent Temperature (PET) index. The results indicate that vegetation significantly contributes to reducing outdoor heat stress, although the effectiveness varies depending on vegetation density and spatial distribution. During morning hours, medium-density greening scenarios achieved the greatest reduction in PET (approximately 2 °C), suggesting an optimal balance between shading and ventilation. Under peak afternoon heat conditions, distributed high-density vegetation provided the strongest mitigation effect, reducing PET by up to 2 °C in shaded areas. The findings highlight the importance of considering not only vegetation quantity but also spatial configuration when designing urban green infrastructure. The proposed simulation framework provides a useful decision-support tool for optimizing evidence-based green planning strategies in climate-responsive urban regeneration projects.

Thermal conditions within cities often vary between neighborhoods due to differences in land cover and urban structure. Understanding these spatial variations is important for designing targeted greening strategies to mitigate urban heat. This study examines the distribution and drivers of summer land surface temperature (LST) in Bologna, Italy, using a fine-scale analytical framework. Satellite-derived LST was statistically downscaled to 10 m resolution and combined with indicators describing urban form and demographic characteristics, including built surface fraction, vegetation cover, population density, and land-use types within 200×200 m grids. To investigate spatial variability in surface temperature, an unsupervised clustering approach was applied to group areas with similar thermal characteristics, revealing contrasts between dense built-up districts and greener parts of city. An explainable deep learning model based on a convolutional neural network (CNN) was then used to examine how landscape characteristics influence LST variability. Model performance was compared with several conventional machine-learning techniques. The CNN achieved highest predictive accuracy and captured nonlinear interactions between predictors and temperature. Results indicate that higher proportions of impervious surfaces are strongly associated with elevated LST. These findings highlight the potential of explainable artificial intelligence (XAI) to support high-resolution urban heat analysis and guide targeted green infrastructure planning.

Sustainable Drainage Systems (SuDS) are Nature Based Solutions designed to manage stormwater close to its source, helping to restore more natural drainage processes. Beyond their hydraulic function, SuDS, like all green infrastructures, deliver multiple Ecosystem Services (ESs), defined as the benefits people obtain from ecosystems. In this context, a series of rain gardens, infiltration trenches and bioretention basins were created along a 1 km section of road in Bovisio Masciago (Italy). This study aims to evaluate the additional ESs provided by this system: (i) temperature regulation, (ii) increased biodiversity, and (iii) landscape enhancement. To this end, the study combines various techniques to arrive at a comprehensive assessment of ESs.

The effect on temperatures was investigated through remote sensing (satellite and UAV) and field measurements. Biodiversity was assessed through floristic and ecological analyses of vegetation, while cultural ESs were assessed by engaging the population through questionnaires investigating perceived environmental quality, preferences, and psychological restoration.

The methods and techniques used allowed for a comprehensive assessment of the ESs, revealing the contribution of SUDS (in addition to stormwater management) to improve environmental quality. They also allowed for the identification of aspects to be considered during the design phase to emphasize the multiple benefits.