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Care to Avoid Loss of Subcanopy LiDAR Data through Noise Classficiation Algorthims

Care to Avoid Loss of Subcanopy LiDAR Data through Noise Classficiation Algorthims

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,...
The Importance of LiDAR Data Quality Assurance

The Importance of LiDAR Data Quality Assurance

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...
LiDAR Derived Canopy Height Model – Practical Tips in Implementing Use

LiDAR Derived Canopy Height Model – Practical Tips in Implementing Use

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...

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