Urban Heat Island (UHI) Analysis - Bengaluru (2022)

📌 Project Overview

This project analyzes the Urban Heat Island (UHI) effect for Bengaluru city using satellite-derived Land Surface Temperature (LST) data from MODIS. The project demonstrates how to:

• Fetch temperature raster data using Google Earth Engine (GEE).
• Export GeoTIFF for offline analysis.
• Process and visualize UHI intensity using Python (Rasterio, Matplotlib, NumPy).

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🗂 Data Source

• Dataset: MODIS/006/MOD11A1 (Land Surface Temperature - Daytime)
• Year: 2022
• Spatial Resolution: 1 km
• Region of Interest: 30 km bounding box around Bengaluru, India.

Note: This analysis uses a rectangular bounding box (30 km buffer) around Bengaluru instead of official administrative boundaries. Future improvements can include precise shapefile-based masking for higher accuracy.

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🚀 Workflow

1️⃣ Data Collection (Google Earth Engine)

• Script (UHI_Bengaluru_2022.js) loads daily LST data for 2022.
• Filters images by location and time.
• Computes yearly average temperature (converted to °C).
• Exports raster data (GeoTIFF) to Google Drive.

2️⃣ Data Processing & Visualization (Python)

• Script (uhi_analysis.py) reads GeoTIFF.
• Masks no-data values.
• Plots a heatmap of UHI intensity.
• Computes basic statistics: mean, min, max temperature.

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📦 Folder Structure

urban-heat-island-gis/
│
├── README.md                  # Project documentation
├── requirements.txt           # Python dependencies
├── UHI_Bengaluru_2022.js      # GEE script for data export
├── uhi_analysis.py            # Python script for analysis
├── sample_output_map.png      # Example UHI heatmap
└── data_instructions.txt      # How to access/download dataset

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⚙️ Installation

pip install rasterio matplotlib numpy

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▶️ Usage

Step 1: Fetch Data from GEE

1. Open UHI_Bengaluru_2022.js in Google Earth Engine Code Editor.
2. Click Run → Go to Tasks → Run the Export task.
3. Download the generated GeoTIFF from Google Drive.

Step 2: Analyze Data in Python

1. Place the .tif file in the project folder.
2. Run:

python uhi_analysis.py

3. The script will:
   • Display a heatmap of UHI intensity.
   • Print mean, min, max temperatures.

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📝 Sample Output

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📜 License

© 2025 [Ramya MB]. This project is open-source for learning and demonstration purposes. Please provide proper credit if you reuse this code.