by Susana Gonzalez | May 6, 2026 | Log Measurement, Log Scaling
Accurate measurement of log volumes remains one of the most persistent challenges in post-harvest forestry operations. Traditional stack-based scaling methods rely on geometric assumptions and conversion factors that can struggle with real-world conditions such as...
by Susana Gonzalez | Feb 13, 2026 | LiDAR, UAV's / Drones
The Interpine team currently utilises the DJI Zenmuse L3 LiDAR sensor as part of its operational drone services, reflecting a continued focus on improving data quality and operational capability. For those interested in understanding how the L3 compares with the...
by Susana Gonzalez | Dec 16, 2025 | Deep Learning / Ai, Environmental, Slash Assessment
Interpine Innovation Leads AI Detection for Woody Debris Assessment, Driving Top Honours for Tairāwhiti Cyclone Recovery Project. Our team recently deployed advanced AI detection systems to support a comprehensive assessment of large woody debris across the Tairāwhiti...
by Susana Gonzalez | Aug 6, 2025 | Deep Learning / Ai, LiDAR, Uncategorised
Integrating spatial intelligence in real time using machine learning / AI within the forest sector Summer Internship Position – 2025-2026 10-week internship available to start in November 2025 The Company: Interpine is committed to “shaping the forestry...
by Susana Gonzalez | Jul 5, 2019 | LiDAR
The capture and use of high density LiDAR data across forest plantations requires a good understanding of the final output. Sometimes this output is only a digital terrain model and or other times it is to quantify forest attributes, such as mean top height, DBH,...
by Susana Gonzalez | Mar 10, 2016 | GIS and Mapping, LiDAR
Interpine undertakes a LiDAR quality assurance audit in every LiDAR inventory project, to ensure the data produced meets acceptable standards. An audit is initiated within 30 days of final data delivery, the main objective is to ensure the data produced is accurate. A...
by Susana Gonzalez | Mar 10, 2015 | Forest Inventory, GIS and Mapping, LiDAR
Canopy Height Model (CHM) is a difference between Digital Surface Model (DSM), being developed from first returns and Digital Terrain Model (DTM) created from the ground returns. Figure 1 – CHM image with trees between 0 and 65 meters of height. The lowest...
