Conference Agenda

Climate change is intensifying droughts, creating an urgent need to understand these events and take necessary mitigation actions. This work focuses on the Bundelkhand region of Uttar Pradesh, India, an area that frequently experiences severe water stress and is highly susceptible to drought. We used multi-source remote sensing datasets to monitor drought conditions through established drought indices. The analysis period spans from 2000 to 2021. CHIRPS data were used to calculate SPI and RAI, while PKU GIMMS NDVI data were used to calculate VCI. ERA-5 Land was used for soil moisture data to derive SMCI. To track how drought propagates, we performed a correlation analysis between the indices representing meteorological and agricultural drought. The results show that the datasets and the methods are suitable for identifying droughts in the region. Historical drought episodes were accurately detected, and the analysis of the 2015 drought revealed its onset from June to September, which aligns with the monsoon season in Bundelkhand. The datasets and indices used provide a practical and reliable output for sparse ground-based observations for regional drought monitoring and management.

The Near-global Combined Drought Monitoring (NEC-DROMO) dataset provides a comprehensive depiction of drought conditions by integrating multiple agro-climatological variables across global land areas. Spanning 2002–2021 at a monthly time step and 0.25° spatial resolution, NEC-DROMO combines soil moisture, vegetation water content (VWC), rainfall, and temperature to capture both agricultural and meteorological drought signals. A key feature of the dataset is the use of Principal Component Analysis (PCA) to derive dynamic, month-specific weights for each variable, allowing the Combined Drought Indicator (CDI) to reflect seasonal and regional variability in drought drivers.

The dataset is built primarily on inputs from the ECoHydrological Land Reanalysis (ECHLA), which provides soil moisture, VWC, and temperature derived from passive microwave observations assimilated through a land–vegetation model. Rainfall fields are obtained from the ERA5 reanalysis, ensuring consistency across atmospheric and land-surface conditions. Validation against satellite-based indicators, ground observations, and event-based disaster datasets demonstrates NEC-DROMO’s strong capability to reproduce observed drought patterns globally.

With its multi-variable foundation and long-term coverage, NEC-DROMO serves as a valuable resource for drought monitoring, climate analysis, food-security assessment, and agricultural risk management. It supports detailed historical analyses and offers an integrated perspective for users seeking reliable, spatially consistent drought information.

Tropical terrestrial vegetation is critical to the global carbon cycle but faces escalating drought threats. Traditional assessments using fixed climate thresholds often ignore actual physiological responses and non-moisture disturbances. To address this, we developed a novel framework that isolates the true physiological impacts of atmospheric and soil moisture (SM) deficits to identify growing-season vegetation droughts (1982–2019). Results reveal pantropical increases in drought intensity, with tropical forests experiencing significantly sharper intensifications than other biomes. Regionally, African forests exhibit the most severe expansions in drought intensity and area. Interpretable machine learning attributes this intensifying drought predominantly to declining SM (NDVI: 52.1%; LAI: 53%). Finally, while reliable historical reconstruction is vital for future projections, CMIP6 models fail to reproduce these observed trends. These findings highlight mounting drought pressures on tropical forests and underscore the critical need for improved climate models to inform mitigation strategies.

Extreme heat events (EHEs) pose growing risks to densely populated subtropical cities such as Hong Kong, yet there remains a need for lightweight, interpretable tools that can provide multi-day forecasts based on readily available observations. This study develops a multi-source data driven framework that integrates aerosol optical depth (AOD), land surface temperature (LST), precipitable water (PW), and precipitation (Precip), together with ARIMA-based anomaly features, to predict EHEs over Hong Kong. Using a seven-day sliding window, independent XGBoost classifiers are trained to forecast daily EHE occurrence probabilities for the next 1–5 days over ten climate years (March 2015–February 2025). A lead-specific threshold optimization on a validation subset is applied to maximize F1-score. Test results show that AUC values for Lead 1–Lead 5 remain between 0.935 and 0.883, with F1-scores between 0.738 and 0.639, indicating robust predictability up to five days in advance. A process-scale duration inference method based on the leading continuous segment of the predicted sequence achieves 67.08% exact-match accuracy, 77.69% accuracy within ±1 day, and a mean absolute error of 0.75 days. The proposed framework is computationally efficient and operationally relevant, offering practical support for urban heat early warning and risk management.

Anthropogenic climate change and land use transformations are interactively reshaping environmental risks. This study investigates the critical feedback between Land Use/Land Cover (LULC) change and shifts in Köppen-Geiger (KG) climate zones in Taiwan from 2001–2020, and their combined impact on disaster hotspots.

Using MODIS and CHIRPS data alongside a comprehensive disaster inventory, we quantified the spatial co-occurrence of LULC change and climate zone transitions. Our preliminary results reveal a significant climatic shift, with over 10,500 km² transitioning from tropical monsoon (Am) to a drier tropical savanna (Aw) climate, alongside substantial wetland loss and urban expansion. We hypothesize that these dynamic "climate transition zones" are emerging fronts of heightened disaster risk.

Our analysis tests whether areas undergoing active climate reclassification concentrate a disproportionate share of historical landslides and floods. The expected outcome is a novel, dynamic risk assessment framework that moves beyond static models. By identifying these emerging hotspots, this research provides a critical tool for proactive land-use planning and climate-resilient disaster risk reduction, with methodologies applicable to other complex, hazard-prone regions.

Climate change is amplifying the occurrence and intensity of Extreme Event Induced Disasters (EEID), such as floods and wildfires, which increasingly threaten societies and ecosystems. Fast and accurate monitoring tools are therefore essential for damage assessment and emergency response. Remotely sensed data, particularly from the Copernicus Sentinel-2 mission, provide valuable multispectral information for large-scale environmental monitoring, but their manual analysis remains time-consuming.

Recent advances in Deep Learning (DL) have enhanced classification, segmentation, and change detection of geospatial data. New multimodal Prompt-Based (PB) architectures integrate image and text inputs via Text Encoders (TEs), enabling zero-shot detection of previously unseen objects. These models promise flexible, prompt-driven analysis but often underperform compared to Object-Specific (OS) models optimized for particular tasks.

In Earth Observation (EO), foundation models such as Prithvi-EO and TerraFM mark a major step forward, offering generalized pre-training across vast multi-sensor datasets to support downstream OS tasks with limited data.

While DL traditionally requires coding expertise, commercial GIS platforms now integrate DL tools accessible through Graphical User Interfaces (GUIs), allowing inference and limited fine-tuning of pre-trained models. This democratizes DL access for GIS users but shifts expertise toward model evaluation and interpretability.

This study systematically compares PB and OS models executed through both GUI-based and Python environments using Sentinel-2 flood and wildfire imagery, assessing accuracy, flexibility, and processing efficiency to evaluate the balance between accessibility and performance in the democratization of DL for EEID monitoring.