Conference Agenda

This research addresses the need for accurate updates to the seabed datasets in coastal areas under environmental and human pressure. It uses Google Earth Engine (GEE) to develop a cloud-based application for Satellite-Derived Bathymetry (SDB) of the Irish bays using Sentinel-2 and Landsat-8 imagery. For the validation, the OPW Pilot Coastal Monitoring and INFOMAR datasets were used. The research refines semi-empirical algorithms and introduces an Earth Engine App (EEA) using the JavaScript API specifically tailored for and non-specialist public use. The methodology employed included pre-selecting high-quality satellite images based on the higher R-squared and lower RMSE to ensure reliability and better performance. In the initial phase, 18 bays were assessed, and the results showed that five bays (Dublin, Dungarvan, Portrane, Rosses, and Tramore) performed better across the evaluated metrics. he development and use of this application support a wide range of marine applications, especially for capacity building, as part of the pilot research led by Maynooth University and Geological Survey Ireland (GSI).

Accurate estimation of methane fluxes across Arctic wetlands is essential for understanding carbon–climate feedbacks, yet remains difficult due to sparse ground measurements, strong spatial heterogeneity, and the coarse resolution of most existing bottom-up inventories. To address these limitations, we develop a high-resolution methane flux modeling framework that integrates multisensor Earth observation data with a geofoundational deep-learning approach. The study leverages 30 m Harmonized Landsat–Sentinel (HLS) imagery, together with environmental predictors from SMAP and ERA5, and daily eddy-covariance methane flux measurements from Arctic sites after 2015. Following data filtering and quality control, the dataset comprises more than 7,600 daily observations from 45 wetland sites across northern high latitudes. A hybrid model architecture is constructed by combining the Prithvi geospatial foundation model for HLS feature extraction with a lightweight feature-wise attention encoder processing 48 auxiliary environmental variables. Fused latent representations are used to predict daily methane flux at 30 m resolution. The model demonstrates strong performance on an independent test set, capturing key spatial and temporal patterns of methane emissions. By enabling fine-scale flux estimation far beyond the resolution of conventional 0.1°–0.5° inventories, the framework offers new opportunities for detailed Arctic methane monitoring and improved characterization of wetland-driven emissions.

To plan nature restoration of fluvial corridors on a national level an inventory of existing man-made levees is mandatory. We suggest an automatic method for a river-wise extraction of levees from a high resolution terrain model based on profiles perpendicular to the river axis. In this course we present a method to cover corridors with non overlapping profiles with a given maximum distance. Levee detection is based on a mathematical formulation of the protective function of levees. In an evaluation of 150 km river length distributed over nine different rivers in Austria the method detected 98% of manually extracted levees, and 68% of their length.

Temperatures in urban areas are rising due to the climate change. Together with increasing urbanization and densification reducing cooling green spaces in cities this leads to so called urban heat islands (UHI) with increased surface- and air-temperatures in urban areas relatively to the surrounding areas. Since high temperatures are the reason for many exceed deaths municipalities are forced to protect their citizens. Satellite earth observation allows to monitor the development of urban heat islands to warn inhabitants early from dangerous heat. An other important way is increasing the resilience of cities to heat waves. For this we developed a simple but efficient method for the simulation of urban surface- and air-temperatures from single very high resolution stereo satellite images. In this paper we present the improved workflow for the simulation of urban temperatures together with the calibration and validation. Further we compare the results to in-situ-measurements in the city of Memmingen in southern Germany, to LandSat thermal mapper imagery and existing works on urban heat islands. Additionally we show how modifying the digital twin e.g. by adding trees or water areas allow the simulation of different scenarios to support decision-makers on their path towards resilient cities.

Scandinavia is facing climate change, with mean temperatures projected to rise by 2-4°C. To prepare Swedish forests for this challenge, the Swedish tree breeding program aims to selects trees adapted to a range of biotic and abiotic conditions. Key variables in this selection process include spring phenology, damage, and overall tree vitality.

Traditionally, these data have been collected through manual field assessments, a resource-intensive approach that constrains both the number of trees that can be evaluated and the frequency of measurements. Remote sensing offers an alternative: high-resolution multispectral drone imaging enables the scoring of greater numbers of trees in less time, captures multiple data points across the growing season, and reduces the risk of human error through algorithmic measurement.

This project aims to develop methods suitable for integration into the Swedish tree breeding program by using multispectral drone imagery to assess spring phenology, shoot damage, and vitality in young Norway Spruce. Field campaigns were conducted during spring 2023 and 2024. Bud flush is modeled from spectral values of tree crowns, using manual assessments of a subset of trees as training data. To capture the full progression of bud flush at high temporal resolution, images were acquired before the vegetation season and up to twice weekly during the period of most rapid development. The same modeling framework is applied to assess damage and vitality.

Antarctic ice shelves regulate ice sheet mass balance through their "buttressing effect", with major implications for global sea level rise. This study focuses on the Nansen Ice Shelf in Victoria Land, East Antarctica, which exhibits complex topography and sensitivity to environmental changes. Previous research has primarily centered on its significant collapse event in 2016; however, systematic evolutionary patterns over longer timescales remain unclear. This study integrates multi-source remote sensing observations from 1948 to 2025 to systematically reconstruct changes in the Nansen Ice Shelf's geometric characteristics (crevasse width, area) and dynamic parameters (ice flow velocity). Findings reveal distinct activity differences between the northern and southern regions of the ice shelf, closely linked to their respective boundary conditions and structural features.