🌿 GreenSetu: GIS-Based Mangrove Analysis System

The project focuses on understanding the environmental impact of rapid urbanization and supporting sustainable urban planning through data-driven insights.

Objectives

• Analyze mangrove cover change from 2015 to 2025
• Estimate carbon stock reduction due to mangrove loss
• Compare mangrove decline with urban expansion
• Develop an interactive web-based dashboard

Data Sources

• Global Mangrove Watch
• Government & environmental reports

Project Structure

GreenSetu/
├── dashboard_app.py                 # Main Streamlit dashboard application
├── graphs_plotty.ipynb              # Jupyter notebook for plotting graphs
├── Mumbai_NDVI_CSV.csv              # NDVI data for Mumbai
├── README.md                        # Project documentation
├── satellite data.qgz               # QGIS project file for satellite data
├── data/                            # Directory for datasets
│   ├── gee_export.csv               # General Earth Engine export
│   ├── mangrove_2020_2025_final.csv # Final cleaned mangrove data (2020-2025)
│   ├── mangrove_to_urban_2020_2025_final.csv # Mangrove to urban conversion data
│   ├── mangrove_to_urban_gee_export_v2.csv # Version 2 of mangrove to urban export
│   └── mumbai_mangrove_geometry.geojson # Real mangrove geometry for map rendering (optional)
├── gee/                             # Google Earth Engine scripts
│   ├── mumbai_mangrove_analysis.js  # Script for Mumbai mangrove analysis
│   ├── mumbai_mangrove_geometry_export.js # Export real mangrove polygons for the dashboard
│   ├── mumbai_mangrove_to_urban_analysis.js # Script for mangrove to urban analysis
│   ├── mumbai_urban_growth_analysis.js # Script for urban growth analysis
│   └── thane_creek_mangrove_2020_2025.js # Script for Thane Creek mangrove analysis
└── scripts/                         # Python scripts for data cleaning
    ├── clean_mangrove_export.py     # Script to clean mangrove export data
    ├── clean_mangrove_to_urban_export.py # Script to clean mangrove to urban export
    ├── clean_urban_export.py        # Script to clean urban export data
    └── gee_table_to_geojson.py      # Convert GEE CSV geometry exports into GeoJSON

How to Run

This project includes a Streamlit dashboard in dashboard_app.py.

1. Create and activate a virtual environment

python3 -m venv .venv
source .venv/bin/activate

2. Install dependencies

pip install streamlit pandas numpy plotly scikit-learn

3. Run the dashboard

python3 -m streamlit run dashboard_app.py

4. Open it in your browser

After Streamlit starts, open the local URL shown in the terminal. It is usually:

http://localhost:8501

Notes

• The dashboard reads its datasets from the data/ folder automatically.
• For the real mangrove map, place mumbai_mangrove_geometry.geojson or mumbai_mangrove_geometry.csv inside data/.
• Run the command from the project root directory.

SDG Alignment

• SDG 11: Sustainable Cities & Communities
• SDG 13: Climate Action
• SDG 15: Life on Land