Nitrogen fixation is a biogeochemical process that plays a vital role in river ecology and can occur in sediments and soils. The present investigation was carried out over 50 km to reach the Padma River of Bangladesh, downstream of the confluence of the Ganges and Brahmaputra rivers. The study area is highly dynamic, with various land-use–land-cover (LULC) types such as cropland (CL), natural vegetation (NV), dry, bare land (DBL) and land with water (LW). A field study was carried out during the low flow (dry/winter) season to measure the nitrogen fixation rate (NFR) using the acetylene reduction method for each type of LULC. Linear regression using a mixed-modelling approach showed that NFR was highly related to sediment bulk density (SBD) and moisture across all the LULC types. Sentinel-2 data were then used to develop relationships between band 11 and SBD. Later, NFR was upscaled in different seasons based on the random forest algorithm, which relied solely on Sentinel-2 products. The results showed that seasonal changes in the surface area and number of LULC types could alter NFR in the study reach. This satellite-based estimation of the spatial and temporal distribution of NFR can more precisely demonstrate the role of NFR as an ecosystem service. Moreover, to better estimate the nitrogen budget of large rivers, consideration of NFR is essential, especially in the case of large lowland rivers where seasonal inundation occurs frequently.

Informal settlements present a significant urban challenge in Valparaíso, driven by socioeconomic pressures, migration, and geographical constraints. This study utilizes open geospatial data and advanced remote sensing techniques to detect, map, and analyze the temporal and spatial dynamics of these settlements between 2017 and 2022.
Satellite imagery from Sentinel-2 and Landsat-8, combined with topographic and socioeconomic data, was processed using machine learning models for land-cover classification. Random Forest emerged as the most accurate algorithm, effectively mapping slum areas and revealing patterns of expansion and contraction. The study also incorporated terrain metrics such as slope and elevation, critical in Valparaíso’s topography, to assess their influence on settlement distribution.
The results indicate that slum areas fluctuated over the study period, from 1.14 km² in 2017 to 0.83 km² in 2022, reflecting dynamic land-use patterns influenced by migration, housing market pressures, and policy decisions. The findings underscore the role of demographic and economic factors in shaping informal urban growth, exacerbated by inadequate formal housing options and socio-spatial marginalization.

Direct and indirect factors, such as habitat loss, landscape degradation, fire, soil erosion, anthropogenic activities, demographics, and climate change threaten and lead to a decline in biodiversity levels. Cost-efficient biodiversity monitoring systems are therefore needed for identifying the location and magnitude of changes in order to prevent or mitigate losses and enhance the resilience capacity of protected areas through appropriate conservation measures.

The aim of this work is the development of a cloud-based Earth Observation Data Cube (EODC) for monitoring anthropogenic changes and pressures on Natura 2000 sites (EO Natura EODCs), serving as a key tool for their rational management and protection. EODCs have emerged as a promising solution for the efficient management of Earth Observation Big Data generated by satellites, which are provided through open access from various data repositories.

The EO Natura EODC is built using the Open Data Cube (ODC) framework, exploiting the availability of no-cost Sentinel-2 imagery. The EO Natura EODC is provided using Amazon Web Services (AWSs) cloud-based services and infrastructure, e.g. Virtual Private Cloud (VPC), Amazon Relational Database Service (RDS), EC2 Instances, S3 Buckets, Application Load Balancer (ALB), Amazon Cognito. Specialized Python scripts using the Open Data Cube (ODC) and the xcube open-source software packages/toolkits have been developed to provide EO data in an analysis-ready form to users, simplifying access, processing, and visualization of EO data. Moreover, flexible containerized applications utilize open-source platforms like Docker and Apache Airflow to serve user-friendly and secure interfaces to end users and administrators, facilitating the authoring, scheduling, and monitoring of necessary workflows. Two information extraction pipelines based on Change Vector Analysis for exploring the EO Natura EODC infrastructure have been developed and validated for monitoring open quarry activity and wildfire perimeters at a regional scale.

Natural disasters, such as volcanic eruptions, pose significant threats to human life and ecosystems. Remote sensing technology offers a valuable tool for monitoring these events and assessing their impacts. This study utilizes high-resolution imagery from the Planetscope constellation to investigate the 2021 volcanic eruption on La Palma Island, Spain, and its effects on surrounding forest areas. By analyzing a time series of satellite images, we aim to evaluate the sensor's capability to track the eruption's progression, focusing on monitoring lava flow growth, quantifying the area covered by volcanic material, and assessing the impact on infrastructure and settlements.

Specifically, we examine the impact on forests to assess the rate and pattern of vegetation changes, aiming to identify influencing factors such as soil fertility, climate conditions, and human intervention. Integrating remote sensing data with field observations and other complementary data sources will enable a comprehensive understanding of the volcanic eruption's impact and its long-term ecological consequences. This research contributes to the development of early warning systems, disaster response strategies, and sustainable land management practices in volcanic regions.

The expansion of offshore renewable energy projects presents opportunities for sustainable energy generation but raises concerns about potential impacts on marine ecosystems. This study focuses on assessing the environmental effects of offshore wind energy exploitation using satellite imagery. The image collection is carried out 20 km off the coast of Viana do Castelo, using as a reference point the WindFloat Atlantic project in Portugal, as well as in other comparable locations. By analyzing satellite data from August 2018, 2021, and 2024, this research evaluates possible changes in marine conditions, particularly chlorophyll concentrations, as an indicator of primary productivity and ecosystem health. Data werecollected using Sentinel-2 imagery, leveraging its high spatial resolution and spectral bands suitable for marine monitoring. As one of the main variables, chlorophyll analysis was conducted using the OceanColor software suite and validated through indices such as the Normalized Difference Chlorophyll Index (NDCI), mapped and compared across the selected years. The results revealed spatial and temporal variations in chlorophyll concentration around the project site, providing evidence of localized ecosystem shifts potentially linked to the operation of offshore turbines. These findings highlight the utility of remote sensing as a cost-effective, non-invasive tool for environmental monitoring in offshore energy developments. This approach demonstrates how geospatial technology can support sustainable marine planning, align with ecosystem protection goals, and contribute to achieving environmental sustainability objectives. The research provides a replicable methodology applicable to other offshore energy sites worldwide, offering valuable insights for stakeholders in marine resource management.

Monitoring live fences in agroforestry landscapes is crucial for understanding ecosystem connectivity and biodiversity conservation, yet traditional detection methods struggle with their complex spatial–spectral characteristics. Building upon our previous work on multi-stream deep learning for live fence detection, which achieved over 83% accuracy, we propose a novel approach integrating foundation models to enhance scene-level classification capabilities. Our framework combines specialized vegetation detection features with pre-trained visual knowledge through a dual-stream architecture while leveraging optimal spectral band configurations. The methodology utilizes NIR–Green–Blue bands and NDVI integration, enhanced by self-attention mechanisms for improved contextual understanding. We evaluated our approach using multi-temporal PlanetScope imagery from three distinct agroforestry sites in Ecuador, capturing both dry and rainy seasons. This research advances automated live fence monitoring by combining specialized spectral analysis with the robust feature learning capabilities of foundation models, offering an improved solution for sustainable landscape management. The proposed approach aims to enhance detection accuracy while maintaining computational efficiency and supporting practical applications in conservation planning and policy implementation.

Most new satellite platforms, from evolved cubesats to sophisticated large spacecrafts, such as those in the Sentinel series, are equipped with high-resolution cameras that produce detailed images. These images, if properly utilized, represent crucial sources of information for a wide range of applications. However, the volumes of data generated by these space missions require efficient processing methods, making it essential to develop techniques for automatic analysis and interpretation. The use of advanced object detection techniques, such as those based on deep learning, has proven to be key. Among the most promising techniques is the YOLO (You Only Look Once) model, which enables the rapid detection and segmentation of features in images. YOLOv8, in particular, has shown significant performance improvements, increasing detection consistency and reliability. Thanks to its efficient design and real-time processing capabilities, its architecture proves to be well suited for rapid and precise detection, even on large-scale satellite images. This study explores the application of YOLOv8 for the detection and segmentation of geophysical features (mainly lakes) in high-resolution Sentinel-2 satellite images in Italy. The approach demonstrated high precision and robustness, even under partial visibility conditions due to cloud coverage hiding part of the scene, thanks to the use of extended training (from 60 to 100 epochs). Different metrics have been computed and reported to confirm these findings. YOLOv8 also proved to be efficient, suggesting its possible application onboard the imaging spacecraft itself, opening new possibilities in autonomous operations. Overall, the adoption of these deep learning models for automatic satellite image processing offers great potential, improving data management efficiency.

Sun-induced chlorophyll fluorescence (SIF) and vegetation-reflected radiance will be essential Level 2 operational products provided by the upcoming European Space Agency (ESA) FLuorescence EXplorer-Sentinel-3 (FLEX-S3) mission. Ensuring the accuracy of these products requires a validation process that compares satellite data with ground-reference (fiducial) measurements. To meet ESA's stringent uncertainty standards for Level 2 products, the SpaFLEX project, supported by the Spanish Ministry of Science and Innovation, aims to design and implement a robust calibration and validation (Cal/Val) strategy for the FLEX-S3 mission in Spain. This strategy will establish Cal/Val test sites; characterize ground-based, UAV, and airborne instruments; define reference measurement protocols and sampling methods; and develop uncertainty budgets for Level 2 product validation.

As a first step towards addressing these goals, in this work, we present the design and execution of a field campaign carried out in two Holm Oak forest areas of different tree densities in Sarrión - Manzanera (Teruel, Northeast Spain). In both areas, two CO2 flux towers and two fluorescence measurement systems (FloX) are installed. We analyzed the spatial heterogeneity of Sentinel-2 images in order to determine the minimum number of Environmental Sampling Units (ESUs) to carry out intensive spectroradiometric measurements with a Field-Portable ASD, quantify leaf fluorescence with a Fluowat device, and overfly with the Piccolo Fluorescence sensor onboard a UAV. For both areas, we also conducted a UAV flight campaign with the Cubert S185 hyperspectral camera for subsequent data scaling. These results are being used to define upscaling procedures towards the FLEX-S3 300 m nominal pixel.

Land degradation poses significant challenges to sustainable agriculture and ecosystem resilience in Ghana’s agroecological zones. These regions—critical for crops like cocoa, which underpin national food security and economic stability—are increasingly threatened by unsustainable practices and climatic pressures. Leveraging the potential of remote sensing, this study employs Trends.Earth, a geospatial platform aligned with SDG 15.3, to monitor land degradation.
This methodology integrates multiple remote sensing datasets to evaluate land productivity, land cover changes, and soil organic carbon (SOC). Productivity is calculated using MODIS NDVI data (250 m resolution, 2001–2020) to analyze long-term trends, current states, and relative performance against ecological benchmarks. Land cover dynamics are derived from ESA CCI Land Cover data (300 m resolution, 1992–2020), reclassified into seven standardized categories. SOC estimates are based on SoilGrids (250 m resolution), providing insights into carbon dynamics in the top 30 cm of soil. By combining these indicators through a one-out all-out approach, this multi-source framework provides a detailed spatial and temporal assessment of degradation patterns, highlighting priority areas for intervention.
The key findings reveal that forest zones are severely impacted by deforestation and declining soil health, savannah areas suffer from productivity losses, and transition zones face complex interactions between climatic and anthropogenic pressures. Preliminary analyses focus on areas of cocoa presence, revealing that many of these zones overlap with regions affected by land degradation, which may pose significant risks to agricultural productivity and sustainability.
This study offers actionable insights for targeted interventions, such as implementing climate-smart agriculture and sustainable land management practices. This research underscores the critical role of advanced remote sensing tools in achieving SDG 15.3 and informing adaptive strategies to combat land degradation. It demonstrates how integrating high-resolution geospatial data with policy-oriented analysis can guide sustainable resource management, ensuring resilient ecosystems and livelihoods.

This work is focused on applications of Remote Sensing (RS) and Geographic Information Systems (GIS) for forest area analyses and research on the inter-relationship between the state of forest stands, soils, and soil microbial communities. A common database for working in the GIS environment was created for the territory of Vitosha Natural Park (Bulgaria). Differentiation of relatively homogeneous territorial units was achieved via the GIS tools using several base criteria such as the slope, altitude, exposure, soil type, basic rock, and tree composition. Study sites were selected for each territorial unit for field mensuration of the forest stands in sample plots, as well as for taking soil profiles and samples for microbiological analysis. The GIS database for territorial units was supplemented with specialized information on the forest stands from the Vitosha Natural Park management plan. The research of the selected territory with remote sensing was performed by means of automatic classification of satellite data from Sentinel-2. Multiple specially selected vegetation and soil indices were applied for this purpose. The analysis of the field measurements and results from remote sensing for the forest stands indicators show a relation between the soil fertility, soil type, and total microbial count. The studied soil types were classified according to the World Reference Base classification system. The complex analysis proves the inter-relation between the soil type, microbial abundance, and tree species, which are also strongly influenced by the altitude, exposure, and terrain slope.