Conference Agenda

We present a residual-aware, multi-sensor 3-D coseismic displacement decomposition applied to the 2025 Mw 7.7 Myanmar earthquake. The workflow combines multi-track Sentinel-1 SAR pixel offsets (range and azimuth) with Sentinel-2 optical pixel offsets, using only the north–south component where the signal clearly exceeds the optical noise level.

The key innovation is to handle sensor- and mosaicking-related residuals within a robust inversion framework rather than as ad hoc preprocessing. Strip-wise and inter-track trends are removed by MAD–Tukey IRLS plane fitting that suppresses long-wavelength orbital and viewing-geometry errors while preserving sharp near-fault steps in overlap zones. A residual-aware weighted least-squares inversion is then performed per pixel to recover east–west, north–south and vertical displacements and their fault-parallel projection. The resulting fields provide spatially continuous, cross-sensor-consistent constraints on fault-parallel slip along this exceptionally long rupture.

In Kolkata, potential land subsidence occurred primarily due to excessive groundwater extraction, which has been one of the major environmental crises, along with rapid urbanization and soft soil characteristics. This study investigates Kolkata's land surface deformation patterns from 2017 to 2023 using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. The study comprehensively analyzes deformation scenarios from 2017 to 2022; additionally, a detailed examination of the 2023 deformation scenario reveals continued trends and localized changes in subsidence patterns. The result shows that the mean ground velocity between 2017 and 2022 varies between -2.8 and -5.5 mm/year, and the area under the subsidence zone shows an increasing trend. Predictive models for 2024 and 2025 are developed based on historical data, providing forecasts of future subsidence trends. The prediction indicates that in 2024, the area under the high deformation class will be relatively higher compared with 2025. Spatial association analyses explore correlations between subsidence patterns of different years in Kolkata. The findings of this study may facilitate the assessment of the possible effects of ground-level movement on resource management, safety, and economics in this densely populated city.

Rapid and accurate detection of landslides after extreme climate events, such as heavy rainfalls or hurricanes, is essential for hazard response and mitigation. Traditional mapping methods rely on manual interpretation or labelled datasets, limiting scalability. This paper presents an integrated workflow combining unsupervised autoencoder-based + KMeans change detection and deep learning semantic segmentation to improve landslide identification in Western North Carolina following Hurricane Helene (September 2024). The approach leverages Planetscope RGB-NIR imagery at 3 m spatial resolution and North Carolina Department of Environmental Quality post-event landslide inventory points. The unsupervised autoencoder extracts latent features and highlights change zones, while segmentation models such as UNet learn spatial–contextual patterns from semi-automated labels. Results demonstrate high detection accuracy with segmentation models achieving strong overlap with ground-truth inventories and minimal false positives with an F1-score of 92%. This hybrid pipeline bridges rapid unsupervised detection and precise pixel-level segmentation, enabling scalable, near-real-time landslide mapping.

Rapid and reliable landslide mapping is critical for post-disaster response, yet Synthetic Aperture Radar (SAR)-based detection remains challenging due to speckle noise, geometric distortions, and complex terrain. This study develops an operational post-event landslide extraction framework using a UNet segmentation architecture with multimodal geospatial data fusion. High-resolution COSMO- SkyMed SAR imagery is combined with terrain representations derived from Digital Elevation Models (DEM), Red Relief Image Maps (RRIM), and rainfall indices to evaluate the contribution of complementary geospatial information to segmentation performance.

Experiments were conducted across three major landslide-triggering events in Japan (Kyushu, Hokkaido, and Kumamoto), comparing SAR-only and multimodal configurations. Results demonstrate that integrating terrain information and rainfall data improves landslide detection performance compared with SAR-only inputs. RRIM consistently outperforms DEM as a topographic descriptor, particularly in steep or heterogeneous terrain, while rainfall information provides moderate gains in recall. Boundary-based metrics further indicate improved geometric fidelity of mapped landslides when multimodal inputs are incorporated. The framework requires only a single post-event SAR acquisition supplemented with publicly available ancillary datasets, enabling rapid and scalable generation of landslide inventories without reliance on pre-disaster imagery. These findings establish a reproducible baseline for SAR-driven landslide segmentation and highlight the potential of multimodal geospatial data fusion for operational disaster response and hazard monitoring.

In this study, we integrated information from the French avalanche database, high-resolution digital elevation models (DEMs), and Sentinel-1 SAR images to model avalanche extents for events occurring across three distinct time periods in three French massifs. The modelled avalanche extents were compared with manually delineated polygons mapped over SAR RGB composites generated using the principle applied in colour-based change detection algorithms. The comparison revealed a strong correspondence between the two independent approaches, with IoU values ranging from 0.42 to 0.47 and F1 scores between 0.58 and 0.63 across the different massifs. We further analyzed the distribution of SAR backscatter values in pre- and post-event images across different zones of the avalanche paths. The results indicated that a fixed 3 dB threshold would most likely be insufficient to capture the complete avalanche extent, as certain zones exhibited backscatter increases of less than 3 dB in post-event SAR imagery. As a result, a multi-threshold approach based on different avalanche zones is recommended. Finally, we assessed the potential of Sentinel-2 optical imagery to detect surface changes and characterize the physical behaviour of avalanche-affected paths following intense avalanche

events. However, the results were inconsistent, exhibiting the expected trends in one study area but nearly opposite patterns in the other, indicating that the integration of optical data for automated avalanche mapping may not always be reliable.

This study presents an integrated remote sensing and deep learning approach for large-scale detection of asbestos-cement roofing in urban environments. Asbestos remains a major environmental health concern across Europe, where asbestos-cement materials persist in the built environment despite regulatory bans. Accurate identification and quantification of these materials are critical for effective remediation planning and equitable health protection.

The research focused on Poland’s two largest metropolitan areas—Warsaw and Kraków—which differ markedly in morphology and historical development, providing contrasting case studies for model validation. High-resolution orthophotomaps (5 cm and 25 cm) from 2023–2024, combined with national building footprint datasets and field-verified information, were used to train and validate a convolutional neural network (CNN) for binary classification of asbestos and non-asbestos roofs.

The highest producer accuracy (90.4%) and overall accuracy (92.9%) were achieved using 128×128-pixel image windows, confirming that broader spatial context enhances classification precision in dense urban settings. The CNN model demonstrated consistent performance across both cities, highlighting its robustness and scalability.

By integrating open orthophotos with open-source analytical frameworks, the method supports the creation of spatially detailed asbestos inventories aligned with the EU INSPIRE Directive and the 2023 Asbestos Directive (EU 2023/2668). The approach enables cost-effective, standardized monitoring applicable to metropolitan and smaller urban contexts alike.

This study advances data-driven environmental health management by demonstrating that deep learning applied to national aerial imagery can deliver operational tools for mapping asbestos exposure risks and informing sustainable, equitable remediation strategies across Europe.

Urban greenspace is essential for ecological resilience, climate regulation, and human well-being, yet long-term, fine-scale assessments of its spatiotemporal dynamics and the extent to which residents benefit from green exposure remain limited. This study develops a 30-m resolution Fractional Vegetation Cover (FVC) dataset to monitor interannual and seasonal variations in urban greenspace across twelve representative Chinese cities from 2000 to 2020. To capture temporal exposure, we introduce the “greendays” metric, defined as the number of days per year that residents experience visible greenery. A population-weighted exposure model was applied to quantify both the magnitude and equality of greenspace exposure. Results show that greenspace increased across all cities over the two decades, with peri-urban areas exhibiting the most substantial gains due to ecological restoration and park development, while core urban areas experienced moderate but consistent improvements linked to renewal and localized greening efforts. Greendays displayed a slight upward trend, indicating an extended duration of annual greenery exposure for residents. Exposure equality remained high and improved in most cities, suggesting that greening initiatives increasingly benefited diverse population groups. Overall, this study provides a robust and scalable remote-sensing-based framework for tracking urban greenspace and exposure equity, offering critical evidence to support nature-based solutions, environmental justice, and sustainable urban planning in alignment with global development goals.

This study develops a generalized method of moments (GMM) framework to quantitatively assess the integrated relationships among climate, vegetation, and lake water volume. Using GSOD precipitation data, SSEBop evapotranspiration, Nino3.4 and MEI indices, and NDVI, we analyzed monthly variations of climatic and vegetation conditions in the Lake Victoria basin from 2000 to 2020. The associations between these factors and lake water-volume changes were first examined, and dynamic GMM was then applied to remove mutual influences among climate variables, allowing for a more reliable attribution of dominant drivers.Results show that precipitation is the primary driver of seasonal to interannual water-volume variations, while evapotranspiration imposes a consistent negative effect on lake storage. ENSO significantly modulates multi-year water anomalies. Vegetation dynamics respond to both climatic variability and lake water-volume changes, with water-level fluctuations providing additional positive feedback after controlling for climate effects.

Posidonia oceanica (Linnaeus) Delile (referred from now on also as P. oceanica) is a marine flowering plant endemic to the Mediterranean Sea, forming extensive underwater meadows that play vital ecological roles, especially as blue carbon reservoirs. Its distribution spans from Gibraltar to Turkey and North Africa to the Adriatic down to 40-50 m of depth (Cocozza et al., 2024). Human impacts, such as pollution, urbanization, and global warming, have reduced its extent by up to 56% in some regions (Robello et al., 2024). Monitoring these meadows is essential, and remote sensing data such as Neo-Pléiades satellite imagery enable their accurate mapping and health assessment. This study applies object-based classification to orthorectified Neo-Pléiades images to evaluate Posidonia oceanica distribution along Sardinia’s eastern coast.

Soil is a crucial component of the ecosystem, affected by climate change, and is often overlooked by remote sensing experts and insufficiently considered while discussing sustainable development projects. To enhance the use of soil related datasets based on earth observation during the planning phase of participatory processes, a specific analysis workflow was piloted during community consultations in Dodoma, Central Tanzania. In order to enhance the integration of the soil conditions during the design of a new community development plan Landsat 8 data from 2023 and 2024 was processed and prepared to make soil information more accessible to non-technical staff and the local communities in Chamwino district. Results confirm the suitability of the SBI as soil indicator thanks to its high resolution, easy interpretability, and context specificity. Preprocessing through experts was identified as viable solution for preparing the data. In addition, field truthing exercises and conversations with the local community members further confirm the accuracy of this dataset for highlighting areas affected by soil salinity or fertility loss and for the final use during participatory planning processes.

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.