Xarray-Spatial is a spatial processing toolbox for GIS professionals. It is an open source GIS Python library for raster analysis that is easy to install and extend to apply to different GIS scenarios and use cases.
In the GIS world, rasters represent continuous phenomena such as elevation, rainfall and distance, directly as numerical values or as RGB images created for humans to view.
Why We Made it Open Source?
At makepath, we believe open source software propels innovation in the industry.
Open source tools are free, community-driven, and encourage innovation by empowering users and contributors to suggest products and features.
Below is an overview of a few use cases for Xarray-Spatial.
What Can We Learn from Satellite Data?
With the multispectral tool, users can analyze the wavelengths of images with different multispectral band indices (packaged in the xarray.DataArray format).
Some examples include:
- Evaluating Normalized Difference Vegetation Index (NDVI) patterns, which indicate areas with high red-band radiation reflectance and therefore high vegetation density.
- Calculating Normalized Burn Ratio (NBR), which highlights burned areas and measures burn severity.
- Calculating Normalized Difference Moisture Index (NDMI), which spotlights areas with high vegetation based on water content.
The hillshade tool allows users to analyze terrain by assigning an illumination value to each cell, based on a specific azimuth and altitude. A simple Digital Elevation Model (DEM) can be transformed into a shaded relief map to highlight the actual shape of a terrain.
With the proximity tool, users can analyze raster data to determine allocation, direction, and proximity. Some examples includes:
- allocation() allows the user to show which pixels are closest to each source pixel.
- direction() allows the user to show the direction to the closest source pixel for each pixel.
- proximity() allows the user to show which pixels are within a given range of each source pixel.
Xarray-Spatial can be extended with Numba and scaled with Dask, both of which are open source libraries. Such integration capabilities allow users to exponentially increase the computing power and scalability of their work for faster results.
To learn more, visit the Xarray-Spatial homepage and GitHub page.
Have any questions about Xarray-Spatial or want to learn how you can become a contributor? Drop us a line at firstname.lastname@example.org.
Stay tuned for more open source spotlights!