Geo_Coral is a Python ETL pipeline that automatically identifies coral reef polygons most at risk of bleaching in Southeast Asia. The system fetches daily NOAA ocean temperature measurements, processes the data into GIS-ready formats, and updates a PostgreSQL database with risk assessments. This project demonstrates operational geospatial workflows and supports environmental decision-making.
- Fetches daily NOAA ocean temperature and Degree Heat Week (DHW) data
- Processes and transforms measurements into GeoTIFFs and GIS-ready formats
- Updates PostgreSQL database with tables: 'coral' and 'temperature'
- Automatically flags coral polygons with higher bleaching risk
- Generates interactive HTML maps for visualization of at-risk reefs
- Implements Python-based ETL pipeline and quality control for operational data
Python, PostgreSQL, GeoTIFF, Raster/Vector GIS processing, NOAA APIs, HTML mapping
- Clone the repository:
git clone https://github.com/GPSProgramming2324/Geo_Coral.git - Install Python dependencies:
pip install -r requirements.txt - Create PostgreSQL database
geocoraland runDatabase/SQL_create_tables.SQL - Run ETL script:
python ETL_database.py
Geo_Coral demonstrates hands-on experience with satellite and Earth observation data workflows, operational decision-making for environmental monitoring, and Python-based automation — key skills for satellite tasking and collection planning.