LiDAR Acquisition
Our team can arrange the acquisition of LiDAR for your forest or land. We are the industry experts when it comes to LiDAR and can tailor the specifications to suit your requirements.
LiDAR acquisition can range from small sub-canopy areas, to forest stands surveyed using our UAV-based LiDAR systems, through to millions of hectares captured by multiple suppliers.
Data Analysis
Interpine are experienced in analysing a range of remote sensing data types. We offer end to end LiDAR capture and analysis, with commmon outputs including terrain and vegetation models. These can be used for harvest planning, resource inventory and environmental management.
We also provide Machine Learning analysis on UAV and aerial imagery. Common targets include woody debris (slash), windthrow and early age tree crops.
Predictive Modelling
Interpine has pioneered the development of predictive modelling using the relationships between LiDAR and tree or stand attributes. This is known as imputation and is a powerful tool for understanding forest metrics such as volume yield and carbon sequestration.
We use multiple approaches, including 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
Once the predictor metric relationships are established the reference plots are related (imputed) to represent a grid cell across the target imputation grid. This introduces 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 respective target imputation grid cells that fall in the area, to get a final yield table.
Why is LiDAR Data Quality Assurance 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 increases the success of LiDAR inventory results.
If we didn't answer all of your questions, feel free to drop us a line anytime.