A comprehensive geospatial analysis project for monitoring water body extent in Chilika Lake, Odisha using NDWI (Normalized Difference Water Index) and Google Earth Engine.
- Overview
- Features
- Methodology
- Data Sources
- Installation & Setup
- Usage
- Outputs & Exports
- Results
- Technical Details
- Future Enhancements
- Author
- License
This repository presents a NDWI-based water body mapping solution for Chilika Lake, Asia's largest brackish water lagoon. Using Google Earth Engine and Sentinel-2 satellite imagery, this project analyzes water extent dynamics, seasonal variations, and provides detailed geospatial statistics for wetland monitoring and environmental research.
- Location: Odisha, India
- Significance: Ramsar Wetland Site, critical biodiversity hotspot
- Analysis Period: 2023
- Satellite Data: Sentinel-2 Surface Reflectance (10m resolution)
- ✅ Automated Water Detection: NDWI-based thresholding (0.3 threshold)
- ✅ Cloud Filtering: <10% cloud cover for image quality
- ✅ Multi-Format Exports: CSV, GeoTIFF, Shapefile outputs
- ✅ Vector Conversion: Raster-to-polygon with area/perimeter metrics
- ✅ Visual Analysis: Interactive maps with RGB composites and NDWI visualization
- ✅ Statistical Summary: Automated area calculations and detailed reports
- ✅ Legend Integration: Informative map legend for easy interpretation
The Normalized Difference Water Index (NDWI) is calculated using the formula:
NDWI = (Green - NIR) / (Green + NIR)
Where:
- Green: Sentinel-2 Band B3 (560 nm)
- NIR: Sentinel-2 Band B8 (842 nm)
- Water Pixels: NDWI > 0.3
- Non-Water Pixels: NDWI ≤ 0.3
This threshold effectively separates water bodies from land, vegetation, and other surface features.
- Data Acquisition: Load Sentinel-2 SR Harmonized imagery
- Preprocessing: Filter by date, location, and cloud cover
- Composite Generation: Create median composite for 2023
- Index Calculation: Compute NDWI for all pixels
- Classification: Apply threshold to generate water mask
- Vectorization: Convert raster mask to polygon features
- Analysis: Calculate area, perimeter, and other statistics
- Visualization: Generate maps with multiple layers
- Export: Save results in multiple formats
- Sentinel-2 Surface Reflectance Harmonized
- Collection:
COPERNICUS/S2_SR_HARMONIZED - Bands Used: B3 (Green), B4 (Red), B8 (NIR)
- Resolution: 10 meters
- Temporal Range: January 1 - December 31, 2023
- Collection:
- Asset:
projects/lulc-467806/assets/ChilkaSHP - Format: Shapefile
- Coverage: Chilika Lake AOI (Area of Interest)
-
Google Earth Engine Account
- Sign up at https://earthengine.google.com/
- Wait for account approval
-
Access to GEE Code Editor
- Navigate to https://code.earthengine.google.com/
# Clone the repository
git clone https://github.com/satwik-shreshth/NDWI_Chilika.git
# Navigate to project directory
cd NDWI_ChilikaEnsure the Chilika Lake shapefile is uploaded to your GEE assets:
- Path:
projects/lulc-467806/assets/ChilkaSHP - Format: Shapefile with all components (.shp, .shx, .dbf, .prj)
- Open the Google Earth Engine Code Editor
- Copy the contents of
NDWI_Chilika JS.js - Paste into the GEE code editor
- Verify asset path:
"projects/lulc-467806/assets/ChilkaSHP" - Click Run to execute the analysis
The script will display:
- RGB Composite: True color visualization of Chilika Lake
- NDWI Map: Continuous NDWI values (-1 to 1)
- Water/Non-Water Classification: Binary classification map
- Vector Boundaries: Water body polygons
- Statistics Panel: Summary of water extent and metrics
The script includes automated export tasks:
// Exports are automatically queued in the Tasks tab
// Click "Run" on each export task to download- Water Body Area Statistics (CSV)
- Polygon ID
- Area (hectares/sq km)
- Perimeter (meters)
- NDWI statistics
-
NDWI Raster (GeoTIFF)
- Continuous NDWI values
- 10m resolution
- Full AOI coverage
-
Water Mask (GeoTIFF)
- Binary classification (0/1)
- Water = 1, Non-water = 0
- Water Body Polygons (Shapefile)
- Individual water body features
- Area and perimeter attributes
- Compatible with QGIS/ArcGIS
- RGB Composite (GeoTIFF)
- True color image for reference
- Bands: Red, Green, Blue
The analysis provides comprehensive insights into:
- Total water surface area in Chilika Lake
- Seasonal water extent variations
- Identification of distinct water bodies
- Change detection capabilities
- Environmental Monitoring: Track wetland health and changes
- Biodiversity Studies: Support habitat assessment
- Climate Research: Analyze water extent patterns
- Policy Planning: Inform conservation strategies
- Disaster Management: Monitor flooding and drought conditions
// Key functions and operations
- ee.FeatureCollection() // Load AOI
- ee.ImageCollection() // Sentinel-2 data
- .filterBounds() // Spatial filter
- .filterDate() // Temporal filter
- .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10)) // Cloud filter
- .median() // Composite generation
- .normalizedDifference(['B3', 'B8']) // NDWI calculation
- .gt(0.3) // Thresholding
- .reduceToVectors() // Vectorization- Browser: Chrome, Firefox, or Edge (latest versions)
- Internet: Stable connection for GEE processing
- Storage: Adequate Google Drive space for exports
- Cloud filtering reduces processing time
- Median compositing handles missing data
- Vectorization limited to water pixels for efficiency
- Multi-temporal analysis (2020-2025)
- Seasonal comparison (pre-monsoon vs post-monsoon)
- Integration with other indices (MNDWI, AWEI)
- Machine learning classification
- Time-series animation
- Automated change detection
- Real-time monitoring dashboard
- Integration with precipitation data
Satwik Shreshth
- 🎓 MCA Final Year, Sikkim University
- 🔬 Research Focus: IoT, ML, Remote Sensing
- 🌐 Portfolio: satwik-shreshth.github.io
- 📧 Connect via Portfolio Contact
This project is part of my research in geospatial analysis and environmental monitoring. Contributions, suggestions, and feedback are welcome!
© 2024 Satwik Shreshth. All rights reserved.
This project is developed for educational and research purposes. The code is available for academic use with proper attribution.
- Google Earth Engine: For providing the cloud-based platform
- ESA Copernicus Programme: For Sentinel-2 satellite data
- QGIS: For shapefile preparation and validation
- India Space Academy: For academic support
- McFeeters, S. K. (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features.
- Sentinel-2 User Handbook: ESA Standard Document
- Google Earth Engine Documentation: https://developers.google.com/earth-engine
⭐ If you find this project useful, please consider starring the repository!
For questions or collaboration opportunities, feel free to reach out through my portfolio website.