LULC Processor with Python (Jupyter Notebook)

A Python script for processing Land Use and Land Cover (LULC) data using Jupyter Notebook. This tool is designed to analyze, visualize, and process LULC datasets efficiently.

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Table of Contents

1. Overview
2. Features
3. Installation
4. Usage
5. Data Requirements
6. Output
7. Contributing
8. License
9. Acknowledgments

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Overview

This project provides a Python-based solution for processing Land Use and Land Cover (LULC) data. It is implemented as a Jupyter Notebook, making it easy to visualize and interact with the data. The script includes functionalities for data preprocessing, analysis, and visualization.

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Features

• Automated LULC Processing: Extracts LULC information from multiple raster files.
• Batch Processing: Processes all LULC TIFF files in a folder automatically.
• Data Preprocessing: Reads and cleans LULC data using shapefiles for region selection.
• Visualization: Generates time-series plots and maps to display LULC changes.
• Statistical Analysis: Calculates land cover changes, impervious surface values, and runoff coefficients.
• Excel Output: Saves LULC analysis results in a structured Excel file.
• Image Output: Produces summary plots showing year-by-year LULC trends.

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Installation

To use this script, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/LULC-Processor-Python-Script.git
   

2. Navigate to the project folder:

   cd LULC-Processor-Python-Script
   

3. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   venv\Scripts\activate     # On Windows
   

4. Install the required dependencies:

   pip install -r requirements.txt
   

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Usage

1. Open Jupyter Notebook or Visual Studio Code Editor.
2. Open LULC_Processor.ipynb in Jupyter.
3. Run the cells step by step to:
   • Load and preprocess LULC data.
   • Perform analysis and extract statistics.
   • Visualize results.
   • Export Excel and image outputs.

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Data Requirements

1. LULC Raster Files: TIFF format (2001-2023) stored in a folder.
2. Shapefile: Defines the study area for spatial selection.
3. Projection: Ensure raster files have a common coordinate system.

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Output

1. Excel File (LULC_Analysis.xlsx)
2. LULC Change Plot (LULC_Change.png): A multi-year comparison plot showing land use transitions.
3. Maps: Color-coded maps highlighting LULC changes over time.

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Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (feature-branch).
3. Commit your changes and push.
4. Open a Pull Request.

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License

This project is licensed under the MIT License.

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Acknowledgments

Special thanks to open-source geospatial libraries such as GeoPandas, Rasterio, NumPy, Matplotlib, for making this possible.

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