An independent research platform integrating remote sensing, spatial analysis, and environmental science to understand climate vulnerability and adaptive capacity in South Asia.
About the Creator
GlacioAware was developed by Nandita Saha as an interdisciplinary environmental intelligence initiative exploring climate resilience, flood vulnerability, and sustainability-focused technology in climate-sensitive regions.
Growing up in Bangladesh — one of the world's most climate-exposed countries — shaped an early and sustained interest in the intersection of environmental science, geospatial analysis, and community-level adaptation. This platform reflects an ongoing effort to translate satellite data and spatial statistics into knowledge that is accessible, reproducible, and relevant to the populations most affected by climate change.
Research interests span flood vulnerability assessment, remote sensing (SAR and multispectral), spatial autocorrelation analysis, and the development of composite vulnerability frameworks for South Asian delta regions. Current work centers on the IFVIF (Integrated Flood Vulnerability and Impact Framework) — a multi-dimensional model for district-level flood risk characterization in Bangladesh.
This work is informed by the belief that rigorous environmental science, built with human-centered values and a commitment to open data, is among the most meaningful contributions a researcher can make toward global climate adaptation.
Research Projects
Ongoing research integrating satellite imagery, spatial statistics, and vulnerability frameworks across Bangladesh's flood-affected districts.
A composite vulnerability model combining physical flood exposure, socioeconomic sensitivity, and adaptive capacity across Bangladesh's 64 districts. The framework draws on SAR-derived inundation extent, WorldPop population density, infrastructure access indices, and poverty indicators to produce a spatially explicit vulnerability score (0–1) for each administrative unit.
Temporal analysis of Sentinel-1 C-band SAR imagery to map flood inundation extent across nine flood seasons (2015–2024). Threshold-based water detection combined with change detection algorithms produces consistent, cloud-independent inundation maps at 10m resolution. Results are used as primary inputs to the IFVIF exposure sub-index.
Multi-temporal NDVI analysis using Sentinel-2 (10m) to track vegetation stress, post-flood crop recovery, and longer-term land degradation trends across chars and low-lying agricultural zones. Time-series decomposition isolates flood-induced stress from seasonal variation and identifies areas of persistent vegetation decline.
Application of global Moran's I and local Getis-Ord Gi* statistics to IFVIF vulnerability scores to identify whether risk is spatially clustered. Results confirm statistically significant (p<0.01) high-vulnerability clusters in northwestern and coastal districts, informing targeted policy and intervention prioritization.
Environmental Data Visuals
Schematic representations of key analytical outputs from IFVIF, NDVI, and hotspot analyses.
Schematic district-level IFVIF composite score. Northwest and coastal districts exhibit critical vulnerability driven by inundation frequency, population density, and low adaptive capacity.
NDVI derived from Sentinel-2 (10m). Flood-affected riverine chars show a persistent declining trend (approx. −0.12 across the study period), with visible post-flood stress following major inundation years.
IFVIF sub-index decomposition showing relative contribution of each vulnerability dimension. Flood exposure and socioeconomic sensitivity are the dominant drivers in northwestern and coastal clusters.
Local Gi* statistics (p<0.01, FDR-corrected). Statistically significant high-high clusters concentrate in the northwest (Rangpur, Rajshahi divisions) and coastal districts (Barisal, Khulna divisions), confirming spatially structured vulnerability.
Bangladesh Climate Context
Bangladesh is among the world's most compelling cases for climate adaptation research — not as a passive subject, but as a source of hard-won resilience knowledge.
Positioned at the confluence of the Ganges, Brahmaputra, and Meghna deltas, Bangladesh experiences annual monsoon flooding affecting up to 30% of national territory. Extreme events (2017, 2019, 2022) inundated critical agricultural and urban zones.
Rural populations in floodplains and coastal chars face compounding risks: flood exposure, arsenic-contaminated groundwater, saline intrusion, and seasonal agricultural disruption — concentrated among communities with the least adaptive resources.
Existing early warning systems, embankment infrastructure, and community flood management programs have reduced mortality but face limitations in addressing displacement, land loss, and structural poverty that climate change is intensifying.
Community-led adaptation — floating gardens (baira), elevated homesteads, and cyclone-resistant construction — offer transferable models for other delta regions globally. Bangladesh's adaptation experience holds lessons beyond its borders.
Bangladesh represents an urgent and instructive case for environmental research. High exposure combined with a predominantly rural, dense population creates conditions demanding spatially precise intelligence — not five years from now, but in each monsoon season.
The research produced here aims to be usable by district planners, development organizations, and community adaptation programs — not only legible to academic audiences.
Key Indicators
Data Infrastructure
All analyses use open, institutionally maintained datasets — reproducibility and transparency are core commitments of this research.
ESA Copernicus C-band SAR for all-weather flood inundation mapping at 10m resolution.
ESA Copernicus optical imagery for NDVI, land cover classification, and vegetation change detection.
MODIS surface reflectance, SRTM elevation, GRACE water storage, and GPM precipitation records.
Cloud-based geospatial processing environment for planetary-scale satellite time-series analysis.
High-resolution gridded population data disaggregated by age and sex for exposure analysis.
Open-source spatial analysis libraries for autocorrelation, hotspot detection, and spatial regression.
Research Interests
Publications & Work
Future Directions
Integrating SAR-based inundation detection with community alert systems for localized, timely flood early warning in high-risk districts.
Developing longitudinal metrics that track not just exposure, but changing adaptive capacity over time — measuring communities' evolving resilience.
Extending the IFVIF framework to other South and Southeast Asian delta regions to enable systematic cross-national vulnerability comparison.
Exploring interpretable machine learning for flood extent prediction and vegetation stress detection, with emphasis on open access and equity in use.