Code for the Principles and Practices of Remote Sensing MSc course. This is an introduction to python and then uses Python to batch process lidar data to make a biomass map.
There are two jupyter notebooks
- python_foundations.ipynb
- batch_process.ipynb
Gives a brief overview of a few aspects of python.
Provides some pre-written functions which can be used to process lidar data to make biomass. The notebook takes students through how to modify the code to batch process a large area and visualise the data.
There is insufficient space on Noteable to store the full-res geotiffs. So we shapp coarsen them to 10 m resolution first. The mean elevation for the DTM and the maximum elevation for the DSM. This uses an Rscript:
In
/geos/netdata/key_methods/week10/ALS/res3m
Move the DTMs and DSMs to a subdirectory with that name.
Run
Rscript /home/shancoc2/teaching/key_methods/2020-21/week10/code/data/scripts/preProcess1.R
/home/shancoc2/teaching/key_methods/2020-21/week10/code/data/scripts/rename1.csh
And then in
/geos/netdata/key_methods/week10/ALS/res10m
Run
Rscript /home/shancoc2/teaching/key_methods/2020-21/week10/code/data/scripts/preProcess2.R
/home/shancoc2/teaching/key_methods/2020-21/week10/code/data/scripts/rename2.csh