https://github.com/brylie/remote-sensing-analysis
Geospatial analysis of remote observation data.
https://github.com/brylie/remote-sensing-analysis
copernicus earth-engine earth-observation geospatial geospatial-analysis geospatial-data geospatial-data-analysis geospatial-intelligence geospatial-processing geospatial-visualization remote-sensing sentinel-2
Last synced: about 1 month ago
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Geospatial analysis of remote observation data.
- Host: GitHub
- URL: https://github.com/brylie/remote-sensing-analysis
- Owner: brylie
- License: apache-2.0
- Created: 2025-04-21T09:55:40.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-23T07:24:36.000Z (6 months ago)
- Last Synced: 2025-09-01T17:56:51.807Z (about 1 month ago)
- Topics: copernicus, earth-engine, earth-observation, geospatial, geospatial-analysis, geospatial-data, geospatial-data-analysis, geospatial-intelligence, geospatial-processing, geospatial-visualization, remote-sensing, sentinel-2
- Language: Python
- Homepage:
- Size: 17.8 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# Remote Sensing Analysis
A Python pipeline for analyzing remote sensing data using Google Earth Engine, focusing on vegetation and moisture indices.
## Key Features
- **Satellite Data Extraction**: Seamless integration with Google Earth Engine for acquiring Sentinel data
- **Vegetation Indices**: Implementation of multiple vegetation indices (EVI, LAI)
- **Moisture Analysis**: Calculation of Moisture Stress Index (MSI) for water stress assessment
- **Statistical Analysis**: Distribution analysis and comparison between different indices
- **Modular CLI**: Command-line interface for running separate pipeline steps
- **Resource Optimization**: Run specific pipeline stages independently to save computational resources
- **Geospatial Processing**: GeoTIFF output with proper coordinate systems and metadata
- **Visualization**: Generate plots and maps for data interpretation
## About the Project
Remote Sensing Analysis provides tools for analyzing satellite imagery to derive vegetation and moisture indices. The primary objectives are to:
- Extract and process Sentinel satellite data using Google Earth Engine
- Calculate vegetation indices (EVI, LAI) and moisture stress index (MSI)
- Generate statistical analysis of these indices for environmental assessment
- Provide both a command-line interface and notebooks for interactive analysis
## Data Sources
The pipeline works with the following data sources:
### Sentinel-2 MSI
- Used for calculating vegetation and moisture indices
- Accessed through Google Earth Engine
- 10-20m resolution (depending on bands)
### MODIS and Copernicus Products (Optional)
- Can be integrated for comparison with pre-calculated indices
- Available through their respective APIs
## Installation
### Prerequisites
- Python 3.12+
- UV package manager (recommended) or pip
### Setup
```bash
# Clone the repository
git clone https://github.com/yourusername/remote-sensing-analysis.git
cd remote-sensing-analysis
```
```bash
# Set up environment
uv venv
```
```bash
# Activate the environment Mac/Linux
source .venv/bin/activate
```
```bash
# Activate the environment Windows
.venv\Scripts\activate
```
```bash
# Install dependencies
uv sync
```
```bash
# Set up Earth Engine authentication
earthengine authenticate
```
## Usage
The remote sensing analysis pipeline supports modular execution, allowing you to run specific parts of the pipeline independently.
### JupyterLab Environment
The project includes Jupyter notebooks for interactive analysis and visualization:
```bash
# Start JupyterLab
uv run jupyter lab
```
This will open JupyterLab in your browser, where you can access:
- `notebooks/lai_finland.ipynb`: Example notebook for analyzing LAI and MSI for Finland
- Create new notebooks to explore other regions or indices
### Command Line Interface
```bash
# Show help and available commands
python scripts/run_pipeline.py --help
# Run the full pipeline
python scripts/run_pipeline.py full --config config/pipeline.yaml
# Only extract and process data (skip statistics)
python scripts/run_pipeline.py extract --area finland --start-date 2024-04-01
# Generate statistics from existing GeoTIFF files
python scripts/run_pipeline.py statistics --input-dir data/output --output-dir data/output/statistics
```
### Command Options
#### Common Options
All commands support the following options:
- `--config`, `-c`: Path to the configuration file (default: `config/pipeline.yaml`)
- `--log-level`, `-l`: Log level (DEBUG, INFO, WARNING, ERROR)
#### Full Pipeline Command
```bash
python scripts/run_pipeline.py full [OPTIONS]
```
Options:
- `--area`, `-a`: Area of interest (overrides config)
- `--start-date`, `-s`: Start date (YYYY-MM-DD, overrides config)
#### Extract Data Command
```bash
python scripts/run_pipeline.py extract [OPTIONS]
```
Options:
- `--area`, `-a`: Area of interest (overrides config)
- `--start-date`, `-s`: Start date (YYYY-MM-DD, overrides config)
#### Generate Statistics Command
```bash
python scripts/run_pipeline.py statistics [OPTIONS]
```
Options:
- `--input-dir`, `-i`: Directory containing GeoTIFF files
- `--output-dir`, `-o`: Directory to save statistics
- `--pattern`, `-p`: Glob pattern to match GeoTIFF files (default: `*.tif`)
## Configuration
The pipeline is configured through a YAML file. Example configuration:
```yaml
# Area of interest
area: "finland"
# Time range for data extraction
start_date: "2024-04-01"
end_date: "2024-04-30"
# Metrics to calculate
metrics:
- "EVI"
- "LAI"
- "MSI"
# Output settings
output:
directory: "data/output"
prefix: "rs_metrics_"
# Statistics settings
statistics:
enabled: true
output_directory: "data/output/statistics"
```
## Testing
The project includes comprehensive unit tests for all modules. Run tests using pytest:
```bash
# Run all tests
uv run pytest
# Run tests with verbose output
uv run pytest -v
# Run tests for a specific module
uv run pytest tests/processors/
# Run a specific test file
uv run pytest tests/processors/test_preprocessing.py
```
### Test Coverage
You can check test coverage using pytest-cov:
```bash
# Run tests with coverage report
uv run pytest --cov=src
# Generate HTML coverage report
uv run pytest --cov=src --cov-report=html
# View coverage for specific modules
uv run pytest --cov=src.processors --cov=src.statistics
```
After generating the HTML report, open `htmlcov/index.html` in your browser to view detailed coverage information.
## Results and Output Files
The pipeline generates the following outputs:
1. **GeoTIFF Files**: Raster files for each calculated index
- Example: `data/output/rs_metrics_finland_20240401_EVI.tif`
2. **Statistical Analysis**: CSV files with summary statistics and distribution plots
- Example: `data/output/statistics/rs_metrics_finland_20240401_EVI_statistics.csv`
3. **Comparison Plots**: Visualizations comparing different indices
- Example: `data/output/statistics/index_comparison.png`
## Implemented Metrics
### Enhanced Vegetation Index (EVI)
- Improved vegetation index with soil and atmospheric corrections
- Formula: `EVI = 2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))`
### Leaf Area Index (LAI)
- Quantifies leaf material in an ecosystem
- Formula: `LAI = 3.618 * EVI - 0.118`
### Moisture Stress Index (MSI)
- Assessment of water stress in vegetation
- Formula: `MSI = SWIR / NIR`
## Contributing
For information on contributing to this project, please see [CONTRIBUTING.md](CONTRIBUTING.md).
## License
This project is licensed under the [Apache License Version 2.0](LICENSE).
## Contributors ✨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind are welcome!
## Code of Conduct
We are committed to fostering an open and welcoming environment. By participating in this project, you agree to abide by our [Code of Conduct](CODE_OF_CONDUCT.md).
## Community Support
- **Issues**: Please use [GitHub Issues](https://github.com/brylie/remote-sensing-analysis/issues) for bug reports, feature requests, and discussions.
- **Discussions**: For questions and general discussions, use [GitHub Discussions](https://github.com/brylie/remote-sensing-analysis/discussions).
We welcome contributions from developers of all skill levels. If you're new to the project or to remote sensing in general, look for issues labeled `good first issue` to get started.
