1. Dr Samantha Lavender and Davide Mainas,
Integrating QGIS functionality into a data workflow through both automated processing and a plugin
Dr Samantha Lavender and Davide Mainas,
Pixalytics Ltd
Plymouth Science Park, Plymouth

2. Topics being discussed
Workflow to create Landsat 8 mosaics:
Started with code running within QGIS
Transitioned to code running from a web frontend
First project developing a QGIS plugin
Some initial thoughts / lessons learnt

3. Landsat 8 Mosaicing Why write our own mosaicing tool?
Larger areas are covered by more than one Landsat scene
Scene sensing date flexibility e.g. summer vs spring
Reducing scene-to-scene visual differences, including option of applying a full atmospheric correction
Convenient web interface for customers

4. Landsat 8 Mosaicing: Steps
Select the scenes: convert select areas to WRS-2 Paths and Rows
Seasonal matching: pick images of around the same day of year (and ideally year) to minimise on-ground differences
Atmospheric normalisation / correction
Single band clipping: extra sections that are needed
Removing no data values: black (fill) areas around Landsat image
Merging single bands and create RGB composite
What’s next:
Cloud removal / hole filling
Further work on improving the colour and contrast homogenization

5. Example: variation within vegetation

6. Landsat 8 Mosaic: QGIS
Running python script from QGIS, from the Python Console
Mosaic definition: centre point latitude and longitude and distance from center
QGIS Integration using QGIS Python API:
Pros:
Real-time visual feedback of processing by displaying in QGIS
Cons:
Slower
Unstable

7. Landsat 8 Mosaic: Python module
Python module using GDAL and OGR utilities and libraries
Mosaic definition: SW and NE Lat/Lon of bounding box
Pros:
Standalone
Easy integration with web interface
Cleaner and overall less verbose code
Cons:
Vector/Raster Layer display currently missing – will look at loading layers into WebGIS when processing completes
Previous cloud removal algorithm removed for now as used QGIS functionality – will re-develop

8. Recent experience with plugin
Developing the front-end is relatively straightforward with the use of plugin: Plugin Builder
Use the following to build and deploy code from development location to QGIS folder: pb_tool and Plugin Reloader
Performing the processing is proving to be more complex:
Dealing with files that have a Custom Coordinate Reference System (CRS), and then reprojection to another CRS
Currently trying a combination of gdal to write GeoTIFF (with Custom CRS) and then gdalogr:warpreproject
warpreproject parameters vary with QGIS version (> 15) so need backward compatibility in code
Using temporary raster files for intermediate steps, as couldn’t see how to ‘just process in memory’ when using gdalogr

9. Conclusions
More and more code is being developed in Python, so by choosing this language we can use functionality from several communities.
Most of our ‘QGIS code’ is actually using gdal functionality, and so the automated workflow just uses this.
Online forums have been very helpful alongside reading code from other developers, including the QGIS underpinning code.
There is difficulty is knowing whether answers are still valid for the current QGIS version, so also using processing.alghelp(‘xxx') can help to check the current format of functions
Although the plugin is for a client (and can’t be shared) we’ll aim to share our ‘experience learnt’, how best to do this?

10. Landsat-8 data courtesy of the USGS/NASA.
Thank You
Landsat-8 data courtesy of the USGS/NASA.