Awesome

forestlas

Python code for generating metrics of forest vertical structure from airborne LiDAR data. This code was developed as part of my PhD (completed in 2016, can be viewed <a href=https://www.researchgate.net/publication/290436021_Assessment_of_forest_canopy_vertical_structure_with_multi-scale_remote_sensing_from_the_plot_to_the_large_area>here</a>) and was developed over the forests of Victoria, Australia. The aim was to develop a suite of metrics that are robust to forest type i.e. can be applied without prior information of forest structure.

There are a number of methods available, check this <a href=https://github.com/philwilkes/forestlas/blob/master/forestlas_intro.ipynb> Jupyter notebook</a> for an introduction. Functions include reading .las files to numpy array, writing to .las as well as a number of methods to dice, slice and tile LiDAR data. The main set of functions found in forestlas.canopyComplexity. These allow you to derive metrics of vertical canopy structure such as <i>Pgap</i> and also estimate number of canopy layers. More information can be found in this paper <a href=https://doi.org/10.1111/2041-210X.12510>Wilkes, P. et al. (2016). Using discrete-return airborne laser scanning to quantify number of canopy strata across diverse forest types. Methods in Ecology and Evolution, 7(6), 700–712</a>.

Funding

This research was funded by the Australian Postgraduate Award, Cooperative Research Centre for Spatial Information under Project 2.07, TERN/AusCover and Commonwealth Scientific and IndustrialResearch Organisation (CSIRO) Postgraduate Scholarship.