Conference Agenda

This study introduces an automated decision-support tool implemented on Google Earth Engine for mapping vegetated shorelines using Sentinel-2 NDVI. The tool enables reproducible diachronic coastline extraction, rapid processing of large datasets, and supports coastal change monitoring and management applications.

Average rates of shoreline change are key indicators for assessing coastal evolution. The study area is located in Bragança, on the northeast coast of Pará, Brazil, covering urban, estuarine and natural areas. Between 1984 and 2024, despite a general trend of increasing coastline, areas with increasing human occupation experienced significant coastal erosion, causing building retreat, partial loss of homes, and damage to beach access roads. Using the Digital Shoreline Analysis System (DSAS) and time series of dense satellite images processed in Google Earth Engine, the coastline was analyzed in the study area. As a result, the average linear rate of variation showed a slight general retreat of the coastline, accompanied by high morphodynamic variability and low statistical consistency in linear trends. Urbanized sectors exposed to ocean forces were the most vulnerable to erosion, while estuarine and mangrove areas were more stable. The high supply of sediments from the estuaries contributed positively to the addition of the coastline in several regions. These findings emphasize the importance of strategic coastal management considering natural and human influences on shoreline dynamics.

This study proposes a practical pipeline for cross-sensor harmonization and short-term temporal estimation of mangrove leaf reflectance using multi-platform hyperspectral data. We combine laboratory (HySpex VNIR-1800; Days 1/3/7), field (Specim IQ; Day 1), and UAV (Cubert X20 Plus; Day 1) measurements over 400–900 nm for three species (Ceriops tagal, Avicennia marina, A. germinans). Field and UAV spectra are interpolated to the HySpex grid, and multiplicative change factors derived from HySpex Day-1→Day-3/7 trends are used to estimate later-day reflectance for non-lab sensors. Accuracy is assessed via RMSE and Pearson’s r, with focus on chlorophyll-sensitive regions (~450, 680, 720–750 nm). Systematic platform effects appear: in-field spectra exceed HySpex by ~2.5% (A. germinans), ~5.7% (A. marina), and ~11.5% (C. tagal), while HySpex exceeds UAV by ~4.38%, ~7.89%, and ~11.5%, respectively. After harmonization, temporal consistency is strong for A. germinans (RMSE ≈0.047–0.050; r ≈0.958–0.981) and solid for A. marina (Specim RMSE ≈0.066–0.081; r ≈0.943–0.970), with higher UAV variability. Spectral trajectories track post-harvest stress: ~15–20% decline near 680 nm for C. tagal and ~10% for A. germinans, alongside expected green and red-edge/NIR shifts. The workflow enables comparable, temporally resolved spectra across instruments, supporting scalable vegetation phenotyping and long-term mangrove monitoring where single-sensor continuity is limited.

Coastal vegetation provides essential protection against shoreline erosion, wave action, storm surge, and supports biodiversity in low-lying tidal environments. This research discusses methods of using UAS based hyperspectral and multispectral sensors and a deterministic Spectral Information Divergence approach to monitor and preserve the ecosystem in coastal environments. The work focusses on implementing the methodology for monitoring different species of mangrove in a protected natural area located in Florida, USA. The achieved accuracy of 90% proves the ability of UAS based remote sensing system to support a resilience-based restoration and long-term monitoring.

Deforestation remains a critical global challenge with profound implications for food security, ecosystem resilience, and disaster risk reduction. In Sierra Leone, the Western Area Peninsula National Park (WAPNP), one of the country’s last remaining tracts of primary tropical moist forest, faces increasing pressures from illegal logging, mining, and land encroachment despite legal protection since 2012. These activities threaten essential ecosystem services, including water provision, fertile soils, and local climate regulation, while exacerbating vulnerability to floods, landslides, and droughts.

This study evaluates the extent of WAPNP’s closed-canopy forest cover using Sentinel-2 imagery from 2020 to 2024, complemented by very-high-resolution (VHR) data and ground-truth observations for validation. The analysis identifies the main human drivers of forest loss and maps the spatial distribution and remaining extent of forest cover within the park. The results highlight the power of combining Copernicus Sentinel-2 imagery with open-access forest datasets to provide a reproducible, and cost-effective monitoring of forest cover in data-limited tropical regions, offering a valuable tool for conservation planning and management.

The expansion of irrigated crops such as almond and pistachio in arid and semi-arid regions poses a challenge in a context of water resource scarcity. Understanding crop water requirements across large areas has become feasible thanks to remote sensing techniques and the growing availability of satellite imagery with increasingly higher spatial and temporal resolution. However, models have shortcomings that lead to uncertainties in their estimates. In this study, we introduce the model ensemble technique as a method to constrain uncertainty in crop water requirements, with a particular focus on woody crops. This study is centered in the province of Albacete, for the period 2022–2024, and combines two surface energy balance models, METRIC and SenET_TSEB, with a water balance model asssited by NDVI imagery to obtain time series of daily actual crop evapotranspiration (ETa), with a spatial resolution of 20–30 meters. Comparison with in situ measurements recorded at two eddy-covariance towers located in almond and pistachio orchards shows better correlation of the results using the ensemble. At a weekly scale, an average error of 4.9 mm d⁻¹ and 2.8 mm d⁻¹ are obtained for the almond and pistachio crops. Accumulated ETa values over the growing season are consistent and provide confidence to assist in irrigation scheduling, detect stress conditions, and/or quantify water needs at a plot scale. These results reinforce the role of satellite remote sensing in water resources management, in particularly relevant crops for our region such as almond and pistachio orchards.

This study verifies the effectiveness and advantages of spaceborne GNSS-R technology for VWC retrieval, and clarifies that the intercept feature of vegetation observations and Γpeak reflectivity are the core components for constructing high-precision models. The proposed method provides a new technical means for large-scale and efficient VWC monitoring, and has positive significance for improving the assessment of vegetation health and disaster risks.