[general]
name=LAS to ENVI-met Tree Converter
qgisMinimumVersion=3.10
description=Converts LAS/LAZ point clouds into ENVI‑met tree models with Leaf Area Density (LAD) calculation and A‑gs physiological parameterisation
version=1.1
author=Peer Schöneberger
email=P.Schoeneberger@geo.uni-mainz.de

supportsQt6=True

about=This plugin provides a complete workflow to convert LiDAR point clouds (LAS/LAZ format) into 3D tree models suitable for the microclimate simulation software ENVI‑met. Users can interactively select a trunk point and define a region of interest, apply percentile‑based filtering to isolate tree points, and then scale, rotate, or translate the point cloud to match desired dimensions. The plugin voxelises the data at 1 m resolution and calculates Leaf Area Density (LAD) values using user‑selected reference voxels. Further refinements such as crown factor adjustment, trunk enhancement, LAD thresholding, and ground clearing are available. Physiological A‑gs parameters can be chosen from species presets or entered manually. The final tree can be exported as an ENVI‑met plant file (.txt) and optionally inserted directly into an existing project database. Designed for researchers and urban planners, the plugin bridges the gap between high‑resolution LiDAR data and microclimate modelling in ENVI‑met. Some of the tools in las2ENVImet require the Python libraries laspy, lazrs and matplotlib, which are not included in the default installation of QGIS. See the "Dependencies" section in the README file.


tracker=https://github.com/peer-schoeneberger/las2ENVImet/issues
repository=https://github.com/peer-schoeneberger/las2ENVImet
# End of mandatory metadata

# Recommended items:

hasProcessingProvider=no
# Uncomment the following line and add your changelog:
# changelog=

# Tags are comma separated with spaces allowed
tags=LiDAR,LAS,LAD,ENVI-met,envimet,voxel,tree,urban forestry,microclimate,point cloud,vegetation,urban climate

homepage=https://github.com/peer-schoeneberger/las2ENVImet#readme
category=Plugins
icon=icon.png
# experimental flag
experimental=False

# deprecated flag (applies to the whole plugin, not just a single version)
deprecated=False

# Python dependencies: laspy, numpy (and matplotlib for faster polygon point-in-test) – please install via pip
# plugin_dependencies=

Category of the plugin: Raster, Vector, Database or Web
#category=Analysis

# If the plugin can run on QGIS Server.
server=False

