LiDAR can be used to create extremely detailed terrain and vegetation models.
This can be applied in planning, resource inventory erosion and biodiversity monitoring and environmental resource management.
LiDAR assists the creation of GIS surfaces of trees and stand attributes and 3D walkthrough and visualisations.
LiDAR Software Applications
Interpine has developed a large toolbox giving us the ability to carry out a large number of LiDAR analysis workflows. These build on our expertise in LAStools, Quick Terrain Modeler, FUSION, Pix4D, ERSI ArcMap Extensions and Arbonaut software tools.
UAV derived structure from motion photogrammetric point cloud analysis.
Development of relationships between LiDAR metrics and tree and stand attributes such as volume and carbon, and building predictive models for stand characteristics.
Implementation of Regression Estimation and Regression Modelling and k-Nearest Neighbour (k-NN)approaches.
Out in Industry
Frequently Asked Questions
What is Plot Imputation and how do you generate Forest Yield Tables
A selection of LiDAR metrics which have the largest impact on the yield estimates is done using the forest yield reference plots and thier respective merics.
Once the predictor metic relationships are established the reference plots are related (imputed) to represent a grid cell across the target impputation grid. this introducing the concept of nearest neighbours (kNN). In the simplest sense this could be thought of as 1 plot is used to represent each grid cell in the network, that being often referred to k=1 in the terminology of kNN. However we can use more than 1 plot to represent a grid cell (k=2,3,4 etc) as a simple average or provide a respective weighting of each of these plots for each grid cell when k>1.
Then it’s just maths to simply select any “Area of Interest” (typically a stand, harvest area or might be an entire forest!) and the respecttive target imputation grid cells that fall in the area, to get a final yield table.
Why is LiDAR Data Quality Assurance is Important
To increase the success of any LiDAR project we must be sure any anomalies are not present in the dataset. Producing a good quality control report increasese the success of LiDAR unventory results