Urban Heat Island Model QGIS Plugin

This QGIS plugin implements a comprehensive toolset for detecting, forecasting, and predicting Urban Heat Islands (UHI) using Landsat 8 satellite imagery. It features direct integration with the USGS Earth Explorer M2M API for seamless data acquisition.

Key Features

1. Data Acquisition (M2M API)

• Direct Search: Search for Landsat scenes directly within QGIS using your M2M API credentials.
• Criteria: Filter by Map Canvas Extent, Date Range, and Cloud Cover.
• Auto-Download: Automatically downloads and organizes the required bands (B4, B5, B6, B10) for analysis.

2. Analysis Modes

• Detection (Spectral Indices):
  • Uses Red, NIR, SWIR, and Thermal bands.
  • Calculates NDVI, NDBI, and UI to compute a weighted UHI Index.
• Forecast (Scenario-Based):
  • Simulates urban growth scenarios (e.g., +10% urbanization).
  • Adjusts NDBI values to model future heat distribution.
• Prediction (Machine Learning):
  • Random Forest: Runs a lightweight regression model to predict thermal anomalies.
  • Deep Learning (U-Net): (Requires TensorFlow) Uses a trained U-Net model for advanced spatial pattern recognition.

3. Visualization

• Automatically loads results as new raster layers in QGIS.
• Exports results to GeoTIFF for further processing.

Installation

Prerequisites

• QGIS 3.x
• Python 3.8+ (bundled with QGIS or external)
• USGS Earth Explorer Account (for data download)

Python Dependencies

The plugin requires specific Python packages. Open the OSGeo4W Shell (Windows) or your terminal and run:

pip install requests scikit-image scikit-learn numpy
# Optional: For Deep Learning mode
pip install tensorflow

Plugin Installation

1. Navigate to your QGIS plugins folder:
   • Windows: C:\Users\<YourUser>\AppData\Roaming\QGIS\QGIS3\profiles\default\python\plugins
   • Mac/Linux: ~/.local/share/QGIS/QGIS3/profiles/default/python/plugins
2. Clone this repository into the plugins folder:

   git clone https://github.com/Mercy14846/Urban-Heat-Island-Model.git uhi_qgis_plugin

   (Note: Ensure the folder is named uhi_qgis_plugin so QGIS recognizes it.)
3. Restart QGIS.
4. Go to Plugins > Manage and Install Plugins > Installed and check "Urban Heat Island Model".

Setup & Configuration

USGS Credentials

To use the search and download features, you need a USGS account with M2M API access requested.

1. In Plugin: Enter your Username and Password directly in the "Data Acquisition" tab.
2. Config File: Alternatively, edit config.py in the plugin directory to set default credentials:

   EARTHEXPLORER_USERNAME = "YourUsername"
   M2M_API_TOKEN = "YourToken" # Optional: Use Token directly

Usage Workflow

1. Open Plugin: Click the UHI icon in the QGIS toolbar.
2. Search Data:
   • Go to Data Acquisition tab.
   • Zoom to your area of interest on the map.
   • Click Use Map Canvas Extent.
   • Set Date Range and Cloud Cover.
   • Click Search Scenes.
3. Download:
   • Select a scene from the results table.
   • Click Download Data.
4. Run Analysis:
   • Go to Analysis tab.
   • Select Mode: Detection, Forecast, or Prediction.
   • Select Bands: Map the downloaded files (Red, NIR, SWIR, Thermal).
   • Output: Choose a save location.
   • Click Run Analysis.

Troubleshooting

• "Python not found": Ensure your environment variables point to the QGIS Python interpreter.
• "Missing Dependencies": If tensorflow is missing, the Deep Learning mode will be disabled. Install it via OSGeo4W Shell.
• Auth Errors: Verify your USGS password or M2M Token. Tokens expire periodically.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT