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.
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.
- 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.
To use this script, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/LULC-Processor-Python-Script.git
- Navigate to the project folder:
cd LULC-Processor-Python-Script
- 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
- Install the required dependencies:
pip install -r requirements.txt
- Open Jupyter Notebook or Visual Studio Code Editor.
- Open LULC_Processor.ipynb in Jupyter.
- Run the cells step by step to:
- Load and preprocess LULC data.
- Perform analysis and extract statistics.
- Visualize results.
- Export Excel and image outputs.
- LULC Raster Files: TIFF format (2001-2023) stored in a folder.
- Shapefile: Defines the study area for spatial selection.
- Projection: Ensure raster files have a common coordinate system.
- Excel File (LULC_Analysis.xlsx)
- LULC Change Plot (LULC_Change.png): A multi-year comparison plot showing land use transitions.
- Maps: Color-coded maps highlighting LULC changes over time.
Contributions are welcome! To contribute:
- Fork the repository.
- Create a new branch (feature-branch).
- Commit your changes and push.
- Open a Pull Request.
This project is licensed under the MIT License.
Special thanks to open-source geospatial libraries such as GeoPandas, Rasterio, NumPy, Matplotlib, for making this possible.