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Marker-controlled watershed algorithm is very simple and effective. LiDAR point cloud is converted into 2D canopy height model (raster model) and if you can imagine inverting the raster model. The depression of the surface model can be identified as tree top and the edge of the derived watershed boundary can be identified as tree crown boundary. The biggest challenges for this method is to overcome the assumption that one tree hold a single tree top and the fine the proper “scale” for segmenting the watershed. It’s sometimes managed by train-and-error approach for finding those parameters. I’m currently looking for ways to overcome these challenges.
I have been using LAStools for years, mostly because it is open source. Over the weekend I have automatically classified some airborne LiDAR data using their “on-the-fly” buffer techniques, the whole process took me less than an hour for approximately 11GB of data. Not the most accurate, but a good start for sure.
Maps that I made, and it’s published!
The 4th biennial GIS in Education and Research Conference will be held on March 4 and 5, 2020, at Hart House, University of Toronto.
Since 2013, I have attended this conference a few times, it is a great way to connect to students, present current GIS work, and talk to others!
I will talk about urban tree detection and mapping using airborne LiDAR.
I am interested in using LiDAR (Light Detection and Ranging) for tree genera / species classification; urban and non-urban scenes.