Conference Agenda

Estimating carbon stock is important for understanding ecosystem dynamics and mitigating climate change. However, biomass mapping in boreal forests faces challenges due to harsh conditions and limited ground truth data for large scale studies. This study presents a parametric model for accurate biomass estimation in the Acadia and Taiga Forest using GEDI Level 4A, Sentinel-2, and SRTM DEM data. We integrated these datasets, and developed the parametric model consisting of spectral bands, vegetation indices, and topographic information with regression techniques, Random Forest and K-nearest neighbour. Results showcase performance of the parametric model with relative weights of variables for accurate Aboveground Biomass Density (AGBD) predictions for the two forest sites. With an average RMSE ranging between 9 Mg/ha to 31 Mg/ha and R^2 values of 0.54 to 0.60, the study reveals the importance of variables like slope, aspect and specific vegetation indices along with raw bands of Sentinel-2 data. Results also demonstrate potential and accuracy limitations of the proposed model with for biomass estimation with high resolution open-source satellite data without ground control. Further research include assessing the model robustness across diverse ecosystems and geographical settings, contributing to sustainable resource management practices.

Accurate estimation of aboveground biomass (AGB) is essential for quantifying forest carbon stocks, monitoring ecosystem change, and supporting greenhouse-gas reporting frameworks. While field measurements remain the benchmark, their limited spatial coverage has driven increasing reliance on remote sensing. Existing global AGB products such as ESA CCI Biomass and GEDI represent major advances but still suffer from signal saturation, sparse sampling, and limited ability to resolve fine-scale structural variation, highlighting an ongoing gap in effectively fusing information from different sensors. Recent studies combining Sentinel-1 (S1) SAR and Sentinel-2 (S2) multispectral imagery have demonstrated improvement in biomass modelling, with machine learning and deep learning achieving R² values from 0.57 to 0.80. However, most work focuses on tropical or broadleaf forests, leaving boreal mixedwood systems underrepresented, and models often rely on short-term composites that overlook multi-year temporal dynamics important for distinguishing long-term growth from seasonal phenology. This study addresses these gaps by developing a Transformer-based deep learning framework that integrates multi-temporal S1 and S2 time series and incorporates Growth & Yield (G&Y) variables as temporal constraints. By leveraging complementary radar–optical interactions and long-range temporal dependencies, the model is designed to reduce signal saturation, enhance structural sensitivity, and improve generalizability across heterogeneous boreal forest conditions.

Fuel Moisture Content (FMC) serves as a vital indicator for monitoring vegetation health and predicting wildfire risk. While existing approaches have largely emphasized temporal variations in FMC, they frequently overlook the inherent spatial clustering patterns of vegetation, leading to compromised spatial prediction accuracy. To overcome this limitation, we introduce a Transformer-based Spatio-Temporal Estimation Framework (TSTEF) that preserves sensitivity to temporal dynamics while incorporating spatial aggregation mechanisms to achieve robust and spatiotemporally consistent FMC estimates. The framework combines spatial autocorrelation analysis with gated recurrent unit (GRU)-based temporal modeling to effectively capture spatiotemporal dependencies, and utilizes Triangular Topology Aggregation Optimization (TTAO) for hyperparameter calibration. The proposed framework was validated using Sentinel-1/2 imagery and MODIS products in California, USA, where it demonstrated: (1) outstanding performance with an average R² > 0.8, MAE < 9%, and relative RMSE of 12.35%; (2) strong agreement between estimated FMC distributions and ground observations, with wildfire burned areas significantly expanding when FMC fell below the 120% critical threshold; and (3) excellent generalization ability during cross-regional validation, achieving relative RMSE values of 20.46% in France, 25.62% in Spain, and 20.76% in Colorado. This study provides a reliable analytical framework for wildfire risk early warning and contributes meaningful insights for ecosystem management amid global environmental change.

In the United States (U.S.), aggressive suppression policies in the 20th century reduced wildfires in the short term, but accumulated fuels contributed to increased wildfire risk in the long term. Land managers are slowly reintroducing the use of fuel treatments, including prescribed (Rx) fires, to remove fuels and mitigate future wildfires. To date, efforts to systematically quantify the efficacy of fuel treatments for wildfire mitigation have been limited in spatial and temporal scope or rely on proxies, such as low-intensity wildfires. Here we use the 30-m Harmonized Landsat and Sentinel-2 (HLS) dataset to analyze timeseries of vegetation “greenness” indices, such as the Normalized Burn Ratio (NBR), with observations up to every 2-3 days. We apply a causal inference approach, difference-in-differences (DID), on HLS-derived timeseries of NBR to compare outcomes in treated and surrounding control areas in three different time phases: 1) post-treatment fuel reduction, 2) wildfire-induced burn severity, and 3) post-wildfire vegetation recovery. As a case study, we targeted 37 Rx fires that preceded and intersected the 2024 Park Fire, a large wildfire in northern California. We show statistical evidence that Rx fires reduce fuel loads (12 Rx fires), wildfire burn severity (12 Rx fires), and post-wildfire vegetation recovery (14 Rx fires). Our approach requires only the spatial footprint and timing of the fuel treatments, thus enabling regional to nationwide analyses using a large number of fuel treatments to quantify the general efficacy of fuel treatments across a variety of treatment types, fuel types, and topography.

Ecosystem extent has been selected as an indicator of biodiversity under the Kunming-Montreal Global Biodiversity Framework. With wildfires on the rise in Canada and around the World there is a need to understand their impact on ecosystem extent for Framework reporting requirements. In Canada’s Arctic and Sub-arctic the growing season is much shorter than at lower latitudes, resulting in few cloud-free optical images and making it a challenge to monitor the impacts of fire and recovery at fine temporal resolutions. Synthetic Aperture Radar (SAR), particularly winter coherence, can be an indicator of ecosystem extent in the sub-arctic because burned areas will exhibit patterns that reflect more dynamic freeze-thaw cycles in the winter period than non-burned areas. Winter coherence and phase was calculated for 39 Sentinel 1 images over the time periods of 2017-2018, 2018-2019, 2019-2020, 2020-2021 in Northern Manitoba, Canada. When relating these values to known fire areas it was found that there is a distinct difference between areas recently burned as opposed to those burned further in the past. These data will be used as predictor variables in a Random Forest ecosystem classifier with outputs of overall accuracy and Shapley feature importance assessed.

Accurate and frequently updated land cover maps are vital for various scientific communities, as well as for public and regional authorities, supporting decision-making, planning, sustainable development, and natural resources management. Regular monitoring and mapping also play a crucial role for agricultural areas, particularly considering the projected population growth and shifting dietary patterns in many of the fastest growing regions of the world, that pose significant challenges for humanity. Over the past decade, the availability of Sentinel-1 and Sentinel-2 data has significantly increased the potential for high spatial resolution land cover mapping using dense time series. However, mapping croplands and distinguishing between crop types remains a more complex task, often requiring data of higher spatial, spectral, and temporal resolution. In this context, this study aims to evaluate the synergistic use of multi-temporal data from Sentinel-1, Sentinel-2, and EnMAP data for detailed land cover and crop type mapping in agriculural regions of western Greece.