LeWoS <br/>

<br/>

GCMex file (became invalide for matlab 2020a and later) for windows64 is updated

Unsupervised tree leaf-wood classification from point cloud data (for plot-scale data and single trees) <br/> --- (An upgraded version is coming soon!) --- <br/>

Usage<br/>

There are many ways to use this tool.<br/>

(a) if you have Matlab installed:<br/> Option 1. Call the entry level funtion "RecursiveSegmentation_release.m" as:<br/> “[BiLabel, BiLabel_Regu] = RecursiveSegmentation_release(points, ft_threshold, paral, plot);”<br/> Inputs:<br/> % points: this is your nx3 data matrix.<br/> % ft_threshold: feature threshold. suggest using 0.125 or so <br/> % paral: if shut down parallel pool after segmentation (1 or other). <br/> % plot: if plot results in the end (1 or other)<br/> Outputs:<br/> % BiLabel: point label without regularization<br/> % BiLabel_Regu: point label with regularization<br/>

Option 2. Type "LeWoS_RS" in Matlab workspace. This will open up an interface by calling the classdef "LeWoS_RS.m". This classdef file defines the interface.<br/>

Option 3. Drag "LeWoS.mlappinstall" into Matlab workspace. This will install a Matlab App for you. <br/>

(b) if you don't have Matlab installed, and don't want to install it:<br/> Run "LeWoS_installer.exe" for win64. If you need an excutable for other systems (Linux and Mac), please contact me.<br/> (PS: Matlab Runtime 2019b (freely available at https://se.mathworks.com/products/compiler/matlab-runtime.html) is required. You can either install it in advance or do it during the installation of LeWoS.)

--------------------------<br/> *Note that if you load an ascii point cloud with the interface, only space delimiter is supported (without header). Currently, these formats are supported: .las; .mat; .xyz; .txt; .ply; .pcd (recommend to use more generic formats for point clouds, such as las, ply, and pcd) <br/> *This method does not implement any post-processing filters. Users can design and apply post-processing steps to [potentially] further improve the results.

Examples

Plot-level separation<br/> Inside a crown Very thin branches are difficult to detect

Acknowledgement

This repo contains code from Loic Landrieu's repo on point-cloud-regularization (https://github.com/loicland/point-cloud-regularization), and Inverse Tampere's repo on TreeQSM (https://github.com/InverseTampere/TreeQSM).

Bibtex

@article{doi:10.1111/2041-210X.13342,<br/> author = {Wang, Di and Momo Takoudjou, Stéphane and Casella, Eric},<br/> title = {LeWoS: A universal leaf-wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR},<br/> journal = {Methods in Ecology and Evolution},<br/> volume = {11},<br/> number = {3},<br/> pages = {376-389},<br/> keywords = {biomass estimation, component separation, leaf-wood separation, LeWoS, point cloud, terrestrial LiDAR, TreeQSM, tropical forest trees},<br/> doi = {10.1111/2041-210X.13342},<br/> url = {https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13342 },<br/> year = {2020}<br/> }<br/> (Current code is a slightly updated version of the one used in publication. With current one, the results are further improved a bit. e.g., 0.925 ± 0.035 vs 0.91 ± 0.03 in the paper.)

Contact

Di Wang<br/> di-wang@foxmail.com