Prova et al. (2026) Application of radar remote sensing for cyclone damage mapping in Bangladesh: a coherence-based approach

Identification

• Journal: Natural hazards and earth system sciences
• Year: 2026
• Date: 2026-10-19
• Authors: Shadmin Islam Prova, Md. Shahoriar Sarker, Rosdania Islam, Ridad Ibne Mahfuz
• DOI: 10.5194/nhess-26-5103-2026

Research Groups

• Department of Disaster Science and Climate Resilience, University of Dhaka, Dhaka-1000, Bangladesh
• Institute of Disaster Management and Vulnerability Studies, University of Dhaka, Dhaka-1000, Bangladesh
• Institute of Modern Languages, University of Dhaka, Dhaka-1000, Bangladesh

Short Summary

This study presents a coherence-based Synthetic Aperture Radar (SAR) framework for identifying and quantifying cyclone-induced surface changes in the coastal districts of Bangladesh. The framework, validated with high accuracy (94%), offers a scalable, cloud-independent tool for rapid post-cyclone damage assessment, enhancing disaster monitoring and response.

Objective

• To develop and validate a multi-temporal SAR coherence-based framework for rapid, cloud-independent, and scalable assessment of cyclone-induced surface damage in the vulnerable coastal districts of Bangladesh, focusing on five major cyclones (Amphan, Sitrang, Mocha, Hamoon, Remal).

Study Configuration

• Spatial Scale: Coastal districts of Bangladesh, including Khulna, Satkhira, Barguna, Patuakhali, Bhola, Cox's Bazar, and Chattogram. Specific upazilas such as Kalapara, Dacope, Koyra, Kutubdia, Pekua, Maheshkhali, and Chakaria.
• Temporal Scale: Five major cyclone events from 2020 to 2024 (Amphan (2020), Sitrang (2022), Mocha (2023), Hamoon (2023), and Remal (2024)). Multi-temporal Sentinel-1 data with short temporal baselines (typically ≤ 15 days) for image pairs.

Methodology and Data

• Models used: Interferometric Synthetic Aperture Radar (InSAR) coherence-based change detection (CCD) framework. Rule-based adaptive cyclone-specific thresholding for damage mapping.
• Data sources: Multi-temporal Sentinel-1 C-band Single Look Complex (SLC) SAR data (VV-polarized, Interferometric Wide Swath (IW) mode, ascending orbit) from the Copernicus Sentinel-1 mission. SRTM 1 arcsec (approximately 30 meters) Digital Elevation Model. Field observations (for Cyclone Remal 2024) and secondary damage datasets (for other cyclones) for validation. Sentinel-1 VV-polarized GRD imagery for inundation mapping in Google Earth Engine (GEE).

Main Results

• A consistent V-shaped decline in mean coherence values was observed during each cyclone event, followed by gradual post-event recovery, indicating surface disturbance.
• Areas of significant coherence loss spatially corresponded to zones affected by flooding, vegetation damage, and structural disruption.
• Estimated damage extents (areas of coherence loss): Remal (2024) showed the largest affected area (approximately 601 km²), followed by Mocha (approximately 474 km²), Amphan (approximately 288 km²), Sitrang (approximately 122 km²), and Hamoon (approximately 117 km²).
• Validation yielded an overall classification accuracy of 94% and a Kappa coefficient of 0.88 for the coherence-based damage detection (field-validated for Cyclone Remal 2024).
• Inundation mapping achieved an overall classification accuracy of 94.12% with a Kappa coefficient of 0.87.
• Integration of damage and inundation scenarios showed that 78.85% of pixels within mapped inundation areas were classified as coherence-derived surface disturbance, while 44.90% of pixels outside inundation were also classified as disturbed, demonstrating that coherence captures a broader spectrum of cyclone impacts beyond flood extent alone (e.g., wind-induced damage and geomorphic changes).

Contributions

• Establishes multi-temporal SAR coherence analysis as a scalable, cloud-independent, and operationally effective tool for rapid post-cyclone damage assessment in complex coastal environments.
• Enhances the reliability of disaster monitoring and offers strong potential for integration into near-real-time disaster response systems.
• Demonstrates that a common multi-temporal coherence framework can capture both inundation-related and non-inundation-related surface disturbances across multiple cyclone events within a complex deltaic environment.
• Provides a consistent and scalable framework for mapping cyclone-induced surface changes, improving understanding of coherence behavior in complex deltaic environments and contributing to more effective disaster assessment and resilience planning.

Funding

• Department of Disaster Science and Climate Resilience at the University of Dhaka (research facilities).
• ESA Copernicus Data Space Ecosystem Open Access Hub (SAR imagery).

Citation

@article{Prova2026Application,
  author = {Prova, Shadmin Islam and Sarker, Md. Shahoriar and Islam, Rosdania and Mahfuz, Ridad Ibne},
  title = {Application of radar remote sensing for cyclone damage mapping in Bangladesh: a coherence-based approach},
  journal = {Natural hazards and earth system sciences},
  year = {2026},
  doi = {10.5194/nhess-26-5103-2026},
  url = {https://doi.org/10.5194/nhess-26-5103-2026}
}