🌊 Flood Mapping in SNNPR, Ethiopia (2017–2024)

This project combines Google Earth Engine (GEE) and Python (Google Colab) to map and assess flooding in the Southern Nations, Nationalities, and Peoples' Region (SNNPR) using Sentinel-1 SAR data, ESA WorldCover land use, and GHSL population datasets.

Both the GEE Code Editor and Python (Earth Engine Python API via Google Colab) were used for spatial analysis, visualization, and data export.

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📌 Objectives

• Map flood extent for each year from 2017 to 2024
• Quantify flooded area per year using Sentinel-1 SAR imagery
• Identify land use/land cover (LULC) types affected by flooding
• Estimate population exposed to flooding annually
• Generate and visualize a flood hazard index map

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🛰️ Data Sources

Dataset	Source
Sentinel-1 SAR (VV)	COPERNICUS/S1_GRD
Land Cover (2020)	ESA/WorldCover/v100
Population (2015 baseline)	JRC/GHSL/P2016/POP_GPW_GLOBE_V1
Administrative Boundaries	FAO/GAUL/2015/level1, level2
Region	SNNPR, Ethiopia

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🖥️ Tools Used

Tool/Platform	Purpose
GEE Code Editor	Initial development, visualization, export
Python (Colab)	Batch processing, automation, result export
geemap	Interactive mapping in Python notebooks
earthengine-api	Accessing Earth Engine in Python

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🗂️ Project Files

File/Folder	Description
flood_mapping_script.js	GEE JavaScript code for flood mapping
flood_analysis_colab.ipynb	Python Colab notebook using Earth Engine API
results/	Output images/maps (PNG, GeoTIFF)
data/	Exported CSV files with yearly stats
LICENSE	MIT License
.gitignore	Optional ignored files (e.g., .DS_Store)

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📊 Results

• Flood Area by Year (2017–2024): data/flood_area_by_year.csv
• Population Exposed by Year: data/population_exposed.csv
• LULC Impact Analysis: data/lulc_flood_impact.csv
• Flood Hazard Map: results/flood_hazard_map.png (index of flood frequency)
• Permanent Water: Visualized separately using Sentinel-1 composites

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📈 Sample Visuals

![Flood Area](results/flood_area_by_year.png)
![LULC Affected](results/lulc_affected_by_year.png)
![Population Exposure](results/population_exposed.png)
🚀 How to Reproduce
🧪 Option 1: Google Earth Engine (Code Editor)
Open code.earthengine.google.com

Paste the contents of flood_mapping_script.js

Modify the region or years if needed

Run the script and export maps/statistics

🐍 Option 2: Python in Google Colab
Open the flood_analysis_colab.ipynb notebook

Authenticate with Earth Engine (ee.Authenticate())

Run each cell step-by-step

View charts and export CSVs (flood area, population exposed, etc.)

🔓 License
This project is licensed under the MIT License — see the LICENSE file for details.

👥 Authors
1️⃣ Mohammed Abdulahi
PhD Candidate, Haramaya University
📧 mohammed.mussa@haramaya.edu.et

2️⃣ Zinabu Bora (Ph.D.)
Postdoctoral Researcher – Environmental Ecological Construction
National Engineering Technology Research Center for Desert-Oasis Ecological Construction
Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences
818 South Beijing Road, Urumqi, 830011, Xinjiang, China
📧 zinabubora@ms.xjb.ac.cn | zinabu_bora@yahoo.com

Project Year: 2025