LAVA is a tool to calculate the available area in a user defined study region for building renewable energy generators like utility-scale ground-mounted solar PV and wind onshore.
First, all needed data is downloaded and preprocessed to bring it into the right format. Then the land eligibility analysis is done with the help of atlite. Additionally, a suitable analysis can be performed and timeseries data for the available area derived.
The LAVA tool is flexible research software with a limited user-interface. For user-friendly online GIS tools to identify available land have a look at Energy Access Explorer and REZoning.
Find the tool documentation here.
a) clone the repository (using Git Bash):
git clone https://github.com/DEA-GE/LAVA.git
After cloning, navigate to the top-level folder of the repo in your command window.
b) install python dependencies
The Python package requirements to use the LAVA tool are in the requirements.yaml file. You can install these requirements in a new environment using conda:
conda env create -f envs/requirements.yaml
Then activate this new environment using
conda activate lava
You are now ready to run the scripts in this repository.
c) input data setup
In order to run the tool with the default data setup do the following:
- Download the Digital Elevation Model (DEM) for you study region from GEBCO Gridded Bathymetry Data. Use the download tool. Select a larger area around your study region. Set a tick for a GeoTIFF file under "Grid" and download the file from the basket. Put the file into the folder "DEM" (digital elevation model) and name it gebco_cutout.tif. This data provides the elevation in each pixel.
- Create a Copernicus account. In the LAVA-tool, the openEO-connection to ESAworldcover data is implemented. In order to use it, one needs to be registered with the Copernicus Data Space Ecosystem. Follow these instructions to register. When running the LAVA-tool for the first time, you will be asked to authenticate using your Copernicus account. Click on the link printed by the script and login to authenticate. When runnning the tool again, a locally stored refresh token is used for authentication, so you don't have to login again.
The basic workflow identifies suitable areas for solar PV and onshore wind based on user-defined parameters.
- Adjust configuration files (or use default values for test case).
- Data download and pre-processing: run the script
spatial_data_prep.py. You can run from the terminal via
python spatial_data_prep.py - Exclusion of non-suitable land: run the script
exclusion.py. Run from the terminal to select technology and name the scenario via
python exclusion.py --technology solar --scenario test
With the advanced workflow it is also possible to perform a suitability analysis and derive timeseries data for the available area.
| Data name | Data source |
|---|---|
| DEM (Elevation) | GEBCO Gridded Bathymetry Data |
| Landcover | ESA WorldCover via openEO API (Copernicus Data Space) |
| Population raster | worldpop) |
| Spatial features (road, railways, airports, waterbodies, military, substations, power lines, generators) |
OpenStreetMap via overpass or Geofabrik |
| Coastlines | Global Oceans and Seas (Marine Regions) |
| Protected Areas | World Database of Protected Areas (WDPA) – Protected Planet |
| Mean Wind Speeds | Global Wind Atlas |
| Solar Radiation | Global Solar Atlas |
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Terrascope API: not implemented because of limited functionalities (e.g. only downloads tiles, data cannot be clipped to area of interest). API documentation, ESAworldvcover Product,
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Download DEMs in QGIS for a Specified Extent with the OpenTopography DEM Downloader Plugin
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Meadows et al. conclude: "In conclusion, we found FABDEM to be the most accurate DEM overall (especially for forests and low-slope terrain), suggesting that its error correction methodology is effective at reducing large positive errors in particular and that it generalises well to new application sites. Where FABDEM is not an option (given licensing costs for commercial applications), GLO-30 DGED is the clear runner-up under most conditions, with the exception of forests, where NASADEM (re-processed SRTM data) is more accurate." For a more nuanced assessment read the articel (for some applications FABDEM might not be the most accurate one).
- GEDTM30: GEDTM30 is a global 1-arc-second (~30m) Digital Terrain Model (DTM) built using a machine-learning-based data fusion approach. It can be used as an alternative to the GEBCO DEM. GEDTM30 will hopefully integrated with openeo soon.
- Global Lakes and Wetlands Database: comprehensive global map of 33 different types of wetlands around the world.