Development of DTM for use in Harvesting Planning, Roading Planning and Environamental Surveys.
Creation of GIS surfaces of trees and stand attributes
3D walkthrough and visualisations
LiDAR Software Applications
Ability to carry out a large number of LiDAR analysis workflows which build on our expertise in LAStools, Quick Terrain Modeler, FUSION, Pix4D, ERSI ArcMap Extensions and Arbonaut software tools
Implementation of Regression Estimation and Regression Modelling and k-Nearest Neighbour (k-NN)approaches.
Development of relationships between LiDAR metrics and tree and stand attributes.
Out in Industry
Frequently Asked Questions
What is Plot Imputation and how do you generate Forest Yield Tables
Interpine uses YTGEN forest yield calculation combined with LiDAR data to produce forest attributes. LiDAR metrics are computed across the area of interest (target imputation grid) and for all the reference plots. These reference plots being a ground measured plot where known assessments of volume and grade mix have been conducted, and a yield table is developed in software such as YTGEN.
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
Interpine undertakes a LiDAR quality assurance audit in every LiDAR inventory project. The main objective is to ensure the data produced is accurate and meets acceptable standards. A workflow process is applied to identify issues in the dataset, if these issues are not corrected on time it could create downstream problems.
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