01-05 December 2025
INCOIS, Hyderabad, India.
| Abstract Submission No. | ABS-01-0280 |
| Title of Abstract | Coastal Change Monitoring through Automated Shoreline Extraction on Google Earth Engine and validation with RTK GPS |
| Authors | Kumarapu Kumar*, P S N Acharyulu, K Venkateswararao, B Shiviah |
| Organisation | INOCIS, Hyderabad |
| Address | 12-69, Achiyyapalem Village, Vadalakunta (PO), Gopalapuram (M), West Godavari (Dt), Andhra Pradesh Rajahmundry, West Godavari (Dt),, Andhra Pradesh, India Pincode: 534316 E-mail: k.kumar-p@incois.gov.in |
| Country | India |
| Presentation | Poster |
| Abstract | Coastal zones are dynamic environments constantly influenced by natural processes such as erosion, accretion, sea-level rise, and anthropogenic interventions. Monitoring shoreline dynamics is vital for effective coastal management, disaster preparedness, and sustainable development. India, with an originally reported coastline of 7,516 km, now spans 11,098 km when including island territories, highlighting the increasing extent and complexity of coastal monitoring requirements. Traditional shoreline mapping methods are often laborious, time-consuming, and constrained by data availability. Recent advances in cloud computing and remote sensing, particularly through the Google Earth Engine (GEE), have enabled efficient large-scale analysis of multi-temporal satellite imagery. This study presents an approach for the automatic extraction of shoreline changes using GEE, leveraging freely available Landsat and Sentinel datasets. Spectral indices such as the Normalized Difference Water Index (NDWI) and Modified NDWI were employed, followed by classification and edge-detection techniques to delineate the dynamic land, water interface. Multi-year analysis revealed significant spatial variations along India coastline, with erosion hotspots in eastern deltas and accretion zones along western coasts. Validation against high-resolution imagery demonstrated reliable accuracy of the automated method. The study confirms that GEE provides a scalable, and reproducible framework for shoreline change monitoring across vast Indian coastline. These insights are crucial for policymakers, researchers, and coastal planners to identify vulnerable areas, strengthen adaptation strategies, and ensure sustainable coastal development. Future enhancements could integrate higher-resolution imagery and machine learning models for improved shoreline detection accuracy. |
| Are you part of IIOE-2 endorsed project | no |
| Keywords | Remote Sensing, Coastal Monitoring, Shoreline changes detection, Google Earth Engine, |
| For Awards | yes |
| Date Of Birth | 03-08-1991 |
| ECSN Registration Number | IIOE2-ECSN-0170 |