In conjunction with the ForestTECH 2017 series this year, Interpine will be running a LiDAR Analysis workshop the day after each event – Friday 17 November, Rotorua and in Melbourne, Thursday 23 November. It will teach delegates how to manipulate, process and visualize LiDAR datasets, with a specific focus on forestry derived forestry outputs. These include terrain, vegetation surfaces and forest yield related calculation pathways.
In New Zealand, the course includes hands-on labs and presentations and will be held at the computer facility at Toi Ohomai in Rotorua.
In Australia, participants will bring along their own computers. The workshop will be run at the ForestTECH 2017 conference venue in Melbourne.
Presenters: Dr. Martin Isenburg and the Interpine Team (David Herries, Peter Auge, Susana Gonzalez and Sarah Pitcher-Campbell).
Example LiDAR Workshop Datasets
Riegl LiDAR Scanner – VUX1-UAV Radiata Pine Post T2 PHI
Riegl LiDAR Scanner – VUX1-UAV Radiata Pine Old Archive 1972 Stand
Riegl LiDAR Scanner – Wide Area LiDAR – Radiata Pine PHI Stand
Nearest Neighbour – Normal Stocked Area
Nearest Neighbour – Partially Stocked Area
Nearest Neighbour – High Stocked Young Area
Nearest Neighbour – Tall unthinnged Area
Nearest Neighbour – Broken Tops within Plots
Nearest Neighbour – Uneven Forest Edge
These are time limited so grab them within the next 30 days.
LASTools version for workshop use only (time limited)
Recent ForestTech Paper and Presentations
Comparing LiDAR based Yield Estimates with Weighbridge and Harvester Data
Integrating LiDAR and others data sources into Operational, Strategic and Tactical Planning
Application of LiDAR in FCNSW Softwoods
Results from Ground Plots and Harvest Reconciliations of LiDAR Data
Plots to Stands: Producing LiDAR Vegetation Metrics for Imputation Calculations
Density and Spacing of LiDAR
Airborne LiDAR Sensor Technology Update in New Zealand
Generating Spike-Free Digital Surface Models from LiDAR
Beautiful Full-Waveform LiDAR of a Tropical Rainforest
Rasterizing Perfect Canopy Height Models from LiDAR
Discriminating Vegetation from Buildings
The Importance of LiDAR quality assurance
Optimising Nearest Neighbour Information—a simple, efficient sampling strategy for forestry plot imputation using remotely sensed data
Forest Inventory Sample Designs – Quasi-random Sampling