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 ecosystem services (ES) concept is embedded in all new European directives but its integration in the Water Framework Directive (WFD) is absent, despite the latter being the major legislative tool. The research aims to assess the knowledge of ES that lies within the River Basin Management Plans (RBMPs) in Greece so as to further depict their representation in the relevant planning tools, to support policy making and express the implementation experience to Member States for assisting EU reloading processes. The information on the ES’ status was extracted by the official RBMPs, processed and grouped following Common International Classification of Ecosystem Services (CICES) classification. The prioritization of ES included in the programs of measures (PoMs) further showed the targeting for each River Basin District (RBD). The results were not homogenous for the RBDs, revealing different needs in measures and indicating there is a lack in “communication” between the relevant EU and national regulations. Moreover, a wide suite of water-related ES is hindered in addressing multiple benefits coming from provisioning, regulating and cultural ES. For a proper WFD reload and continuation, the infusion of the ES concept and prerequisites in its objective, and the reviewing of the RBMPs’ target and the suggestion of integrated PoMs are necessary steps that could deliver added value in such legislation.

Historic land changes reflect human pressure in aquatic ecosystems. The value of littoral and riparian zones for lakes is of outmost importance for their hydrology, ecology and support of ecosystem services. The last 75 years altered the landscape mosaic of Lake Pamvotis affecting those habitats. In such an ancient, urban, Mediterranean shallow lake, historic black and white aerial photographs and satellite images were screened in 25 years timestep, from 1945 for extracting land use/land cover changes. The classification took place in two buffer zones (50 m and 500 m). The changes are assessed with respect to their possible impact in water-related ecosystem services. Ecosystem services values were further calculated historically along with landscape ecology indices. The research revealed an extreme landscape makeover until 1995 with detrimental effects in the riparian habitats, with augmented pressure in the 0-50 m buffer. Artificial areas covered 21% in 1995 when in 1945 they occupied only 9%. Total area ecosystem services values rised at the expense of some provisioning and regulating services. As for landscape homogenization and fragmentation, it was already affected by human. Through this research, new ways of studying water bodies can be developed, by following the pressures’ and alterations’ evolution, aiming at better urban and management planning.

Eutrophication caused by nutrient enrichment is a predominant stressor leading to lake degradation and, thus, the set-up of boundaries that support good ecological status, the Water Framework Directive’s main target, is a necessity. Greece is one of the Member States that have recorded delays in complying with the coherent management goals of European legislation. A wide range of different statistical approaches has been proposed in the Best Practice Guide for determining appropriate nutrient thresholds. To determine the nutrient thresholds supporting the good status of natural Greek lakes, the phytoplankton dataset gathered from the national monitoring programme (2015–2020) was used for shallow and deep natural lakes. The regression analyses were sufficient and robust in order to derive total phosphorus thresholds that ranged from 20 to 41 μg/L in shallow and 15–32 μg/L in deep natural lake types. Nutrient boundaries that encompass the stressors these lakes are subject to, are essential in proper lake management design.

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.