Rural abandonment and associated rapid urbanization, agricultural intensification, and climate change have been key factors transforming terrestrial landscapes, with significant impacts on the environmental quality and the ecosystem services (ES) provided to human welfare. In this study, two understudied rural river basins located in Thrace, North Greece, were selected to assess changes in landscape pattern, composition, and eco-environmental quality and ecosystem services values (ESV). Cloud-based remote sensing (RS) analyses of multitemporal Landsat imagery in the Google Earth Engine (GEE) platform were applied for multitemporal land use/land cover changes (LULCC) quantification between 1984–2021, along with landscape pattern, eco-environmental quality, and ES assessment. Although ecosystem changes observed were not extensive over this period, eco-environmental quality appeared to be affected due to fragmentation. Preserving the ecosystem’s naturalness can enhance cultural ES to avoid further values loss originating from provisioning ES exploitation. This study highlights the strong connection between landscape configuration and eco-environmental quality, emphasizing the strong impact that anthropogenic activities have on the environment. The monitoring of the effects of LULCC on ecosystem health and the economic value of ES is crucial for the introduction of spatial planning and restoration policies.

The global rapid urbanization intensifies environmental challenges related to climate change, such as air pollution and the urban heat island (UHI) effect in built environments. The need to optimize nature-based solutions (NBSs) is imperative to mitigate climate change and adapt to extreme weather phenomena. Against this background, this review offers an analysis regarding the integration of vertical greenery systems (VGSs) into urban environments so as to capitalize on their environmental, social, and economic benefits. Key aspects of the review include the positive role of VGSs in UHI mitigation, air quality improvement, stormwater management, and biodiversity enhancement, while examining social aspects (i.e., improved well-being and mental health, noise reduction, and urban built aesthetics). Finally, parameters related to economic benefits and energy efficiency are assessed. The submission further analyses the significant challenges that VGSs face, such as high maintenance costs, structural risks, plant health issues, fire hazards, and other limitations (legislative and technical). The crucial need for interdisciplinary collaborations among urban planners, architects, environmental engineers, and stakeholders is highlighted, in order to successfully integrate VGSs into urban buildings. Thus, this paper aims to identify key strategies for optimizing VGSs’ implementation and provide valuable insights for policymakers and researchers aiming to enhance urban sustainability through vertical greening.

Freshwater ecosystems provide an array of provisioning, regulating/maintenance, and cultural ecosystem services. Despite their crucial role, freshwater ecosystems are exceptionally vulnerable due to changes driven by both natural and human factors. Water quality is essential for assessing the condition and ecological health of freshwater ecosystems, and its evaluation involves various water quality parameters. Remote sensing has become an efficient approach for retrieving and mapping these parameters, even in optically complex waters such as small rivers. This study specifically focuses on modelling two non-optically active water quality parameters, dissolved oxygen (DO) and electrical conductivity (EC), by integrating 3 m PlanetScope satellite imagery with data from real-time in situ remote monitoring sensors across two small rivers in Thrace, Northeast Greece. We employed three different experimental setups using a support vector regression (SVR) algorithm: ‘Multi-seasonal by Individual Sensor’ (M-I-S) for individual sensor analysis across two seasons, ‘Multi-seasonal—All Sensors’ (M-A-S) integrating data across all seasons and sensors, and ‘Seasonal—All Sensors’ (S-A-S) focusing on per-season sensor data. The models incorporating multiple seasons and all in situ sensors resulted in R² values of 0.549 and 0.657 for DO and EC, respectively. A multi-seasonal approach per in situ sensor resulted in R² values of 0.885 for DO and 0.849 for EC. Meanwhile, the seasonal approach, using all in situ sensors, achieved R² values of 0.805 for DO and 0.911 for EC. These results underscore the significant potential of combining PlanetScope data and machine learning to model these parameters and monitor the condition of ecosystems over small river surfaces.

Water quality monitoring is essential for assessing a freshwater ecosystem’s status. This knowledge is indispensable for selecting restoration measures to ensure the provision of ecosystem services and sustainable growth of human communities. Remote sensing (RS) has proven to be effective for this purpose, offering broad coverage and high temporal and spatial resolution, which is particularly important for small water bodies. In this study, UAV-based multispectral imagery is employed to estimate key water quality parameters, namely, Chlorophyll-a (Chl-a) and turbidity, which are relevant to global and national legislation and policies. Machine learning models were developed using the support vector regression (SVR) algorithm. The Chl-a model resulted in an R² value of 0.49 and an RMSE of 0.24 μg/L, while the turbidity model resulted in an R² value of 0.70 and an RMSE of 0.38 Formazin Nephelometric Unit (FNU). These models enabled the generation of detailed spatial distribution maps of water quality indicators for the studied river. The proposed approach provides valuable information that supports monitoring for both pressure and restoration impacts, promoting the sustainability of freshwater ecosystems.

The European building stock is aging and needs renovation. Holistic renovation approaches, including Vertical Forest (VF) systems, are emerging as sustainable alternatives to demolition and reconstruction. This paper reviews and defines missing reliable damage and hazard intensity measures for the holistic renovation of existing reinforced concrete (RC) buildings with VF systems. Based on an extensive literature review and preliminary studies, including empirical multiparametric system evaluation assessments, Monte Carlo simulations, and System-Theoretic Process Analysis (STPA), combined structural, non-structural, vegetation, and human comfort components are examined. Key damage indicators are identified, including interstory drift ratio, residual deformation, concrete and reinforcement strains/stresses, and energy dissipation, and their applicability to VF-integrated structures are evaluated. Green modifications are found to have higher risk profiles than traditional RC buildings (mean scores from Monte Carlo method: 9.72/15–11.41/15 vs. 9.47/15), with moisture management and structural integrity as critical concerns. The paper advances the understanding of hazard intensity measures for seismic, wind, and rainfall impacts. The importance of AI-driven vegetation monitoring systems with 80–99% detection accuracy is highlighted. It is concluded that successful VF renovation requires specialized design codes, integrated monitoring systems, standardized maintenance protocols, and enhanced control systems to ensure structural stability, environmental efficiency, and occupant safety.

Forest ecosystems are major providers of multiple ecosystem services, yet they are increasingly impacted by various natural and human-induced disturbances, as well as by climate change, particularly evident in the territory of the Mediterranean basin. As a result, forests are under significant pressure, leading to their severe degradation and loss. Therefore, the systematic monitoring of forests and the retrieval of their attributes are crucial for effective forest management and conservation planning. In this study we produced a national scale tree canopy cover map for Greece, at 20 m resolution, incorporating visually interpreted sample plots, Sentinel-2 satellite data, elevation data, canopy height, and an ecosystem type map. The tree canopy cover model was developed using a stacked ensemble of four machine learning algorithms; Gradient Boosting Machines, Distributed Random Forest, Generalized Linear Model, and Deep Learning (Neural Networks). For the model's assessment, maps depicting per pixel prediction uncertainties were generated. Tree canopy cover ensemble models were developed for six forest types of Greece. According to the results, the tree canopy cover model, achieved an R² value of 0.72 and RMSE of 18.846%. For the individual tree canopy models developed per forest ecosystem type, performance varied, with R² value ranging from 0.165 to 0.535 and RMSE from 19.811% to 25.767%. The study highlights the potential of integrating advanced machine learning techniques with earth observation data to enhance forest mapping and provide a standartised approach for forest monitoring and accounting, simultaneously supporting sustainable forest management practices, toward the implementation of national and EU nature conservation, restoration and adaptation policies.