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Notebook","category":"Biosphere","sub_category":"Forest Remote Sensing","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# forestlas\n[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)\n\n![LiDAR derived vertical profiles](http://www2.geog.ucl.ac.uk/~ucfaptv/3_PLOTS_geo_trees_bar_spectra_v5.png)\nPython code for generating metrics of forest vertical structure from airborne LiDAR data. This code was developed as \npart of my PhD (completed in 2016, can be viewed \n\u003ca 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\u003ehere\u003c/a\u003e) \nand was developed over the forests of Victoria, Australia.\nThe aim was to develop a suite of metrics that are robust to forest type i.e. can be applied without prior information of \nforest structure.\n\nThere are a number of methods available, check this \u003ca href=https://github.com/philwilkes/forestlas/blob/master/forestlas_intro.ipynb\u003e\nJupyter notebook\u003c/a\u003e for an introduction.\nFunctions include reading `.las` files to numpy array, writing to `.las` as well as a number of methods to dice, slice and tile \nLiDAR data.\nThe main set of functions found in `forestlas.canopyComplexity`.\nThese allow you to derive metrics of vertical canopy structure such as \u003ci\u003ePgap\u003c/i\u003e and also estimate number of canopy layers.\nMore information can be found in this paper \u003ca href=https://doi.org/10.1111/2041-210X.12510\u003eWilkes, P. et al. (2016). Using discrete-return airborne laser scanning to \nquantify number of canopy strata across diverse forest types. 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