RAVI - Remote Analysis of Vegetation Indices
RAVI is a QGIS plugin designed to seamlessly integrate with Google Earth Engine (GEE), enabling efficient processing and visualization of geospatial data. Utilizing the Sentinel-2 harmonized surface reflectance catalog, the plugin supports vegetation index calculations and easy download of multispectral imagery. These features make it a viable tool for students, researchers, farmers, and GIS professionals working in agriculture, land monitoring, or environmental management.
Key Features
- Integration with Earth Engine: Easy authentication, direct processing, and immediate visualization of Sentinel-2 data in QGIS.
- Vegetation Index Calculations: Support for indices such as NDVI, EVI, SAVI, and GNDVI, on specific dates or in composite images.
- Time Series Analysis: Generation of time series, integration of NASA POWER precipitation data, and smoothing with Savitzky-Golay filters.
- Interactive Visualization: Creation of interactive charts, raster styling, and export options.
Installation
To install RAVI:
- Open QGIS and navigate to the Plugins menu.
- Select Manage and Install Plugins.
- In the "All" tab, search for "RAVI", select it, and click Install.
- Ensure that RAVI is checked in the installed plugins list.
- IMPORTANT: Restart QGIS
Usage
Step 1: Authentication
- If you don't have a Google Earth Engine account, you can create one and register a project here.
- Open the Google Earth Engine Code Editor to find your project ID.
- You can also find the project ID in the Google Cloud Console under the "Project Info" section.
- After entering your project ID, click Setup Authentication.
- Authorize the plugin to access your Earth Engine account.
Step 2: Select output folder
Choose the folder where you want to save the output files.
Step 3: Load and select the area of interest (AOI)
- Select an AOI by loading a shapefile or GeoJSON file.
- Ensure the geometry is valid (Polygon or MultiPolygon only).
- Add a Google Maps layer to the QGIS canvas for better visualization and context.
- Use the Build AOI from Canvas Extent button to generate a new AOI based on the current canvas extent.
Step 4: Define time range for imagery search
Set a custom time range or select a suggested one.
Step 5: Select vegetation index for time series analysis
Choose the vegetation index you want to analyze from the dropdown menu.
Step 6: Imagery Overlap filter
Set the specified filter according to your analysis requirements.
Step 7: AOI Buffer
Set the Buffer according to your analysis requirements.
Step 8: Cloud pixel percentage filter
Set the specified filter according to your analysis requirements.
Step 9: Valid pixel percentage filter
Set the specified filter according to your analysis requirements.
Panel
1. Time Series Plot
- Visualize the time series of the selected vegetation index over the defined AOI.
- Hover over the plot to see specific values and dates for detailed analysis.
2. Load RGB Layer (Focus on a Day)
Load and display an RGB layer for a specific date to analyze the visual appearance of the area. All spectral bands are downloaded, and the band numbers correspond to the Sentinel-2 bands as listed below:
| Sentinel-2 Band Name | QGIS Band Number | Wavelength (nm) | Spatial Resolution (m) |
|---|---|---|---|
| Band 1 (Coastal aerosol) | 1 | 443 | 60 |
| Band 2 (Blue) | 2 | 490 | 10 |
| Band 3 (Green) | 3 | 560 | 10 |
| Band 4 (Red) | 4 | 665 | 10 |
| Band 5 (Vegetation Red Edge) | 5 | 705 | 20 |
| Band 6 (Vegetation Red Edge) | 6 | 740 | 20 |
| Band 7 (Vegetation Red Edge) | 7 | 783 | 20 |
| Band 8 (NIR) | 8 | 842 | 10 |
| Band 8A (Vegetation Red Edge) | 9 | 865 | 20 |
| Band 9 (Water Vapour) | 10 | 945 | 60 |
| Band 10 (SWIR - Cirrus) | 11 | 1375 | 60 |
| Band 11 (SWIR) | 12 | 1610 | 20 |
| Band 12 (SWIR) | 13 | 2190 | 20 |
3. Load Index Layer (Focus on a Day)
Load and display a vegetation index layer for a specific date.
4. Load Index Layer (Composite)
Generate and display a composite image based on the selected vegetation index and the user-defined metric. The composite image will include all images within the current date selection. Use the date selection tool to filter out specific dates.
5. Date Selection Tool
- Use the date selection tool to filter and select specific dates for analysis.
- The tool updates the time series plot.
- Composite images are based on all selected dates.
6. Savitzky-Golay Filter
Apply the Savitzky-Golay filter to smooth the time series data for enhanced trend analysis. Adjust the polynomial order and window length as needed.
7. Save Options
Save the time series data in CSV format. To save the series as an image, open it in your browser to enable the download option.
8. NASA POWER Precipitation
Add monthly precipitation data from NASA POWER for cross-variable comparisons. Save the precipitation data in CSV format.
9. Quickly Run New Time Series
Quickly run a new time series analysis by changing the Area of Interest (AOI), vegetation index, or time range.
10. Clear All Layers
Clear all loaded layers from the QGIS layer panel to start a new analysis or to declutter your workspace.
Troubleshooting
- Earth Engine Authentication Failed: Ensure you meet all requirements as explained in the authentication section.
- AOI Errors: Verify the AOI file is valid and uses a proper CRS (EPSG:4326 is preferred).
Reporting Issues
If you encounter any issues or have suggestions for improvements, please open an issue in the GitHub Issues section.
Contribute to the Project
Contributions are welcome! Please visit the GitHub repository to get started.
License
RAVI is licensed under the GNU General Public License v2.0 or later. Refer to the LICENSE file for details.
Supervisors
This project was supervised by:
- Prof. Dr. Lucas Rios do Amaral - More about the supervisor
- M.Sc. Isabella Alves da Cunha - More about the co-supervisor
