LiDAR-derived forest inventory data to map and quantify ecologically important large trees across large spatial...

Area of Science:

• Forest Ecology
• Remote Sensing
• Biodiversity Conservation

Background:

• Large old trees are crucial for forest biodiversity.
• Their decline is a growing concern, necessitating effective inventory methods.
• Existing methods for large tree quantification are often limited in scope or flexibility.

Purpose of the Study:

• To develop and present a methodology for mapping and quantifying large trees using airborne LiDAR data.
• To create a flexible and efficient solution integrated within the R statistical software.
• To address the need for large-scale inventory of ecologically important large trees.

Main Methods:

• Utilized airborne LiDAR data and canopy height models for mapping.
• Developed an R-based algorithm integrating existing packages.
• Calibrated tree counting using local height and crown diameter data.
• Defined height-dbh curves from inventory plots to use height as a surrogate for dbh thresholds.

Main Results:

• Successfully mapped and quantified large tree distributions across 1.65 million ha in New Brunswick, Canada.
• Estimated over 37 million large trees broadly distributed within the study area.
• Presented spatial maps and frequency statistics according to provincial forest management guidelines.

Conclusions:

• The developed methodology efficiently maps and quantifies large tree distributions over extensive forest areas.
• This approach serves as a metric for assessing biodiversity maintenance at the landscape level.
• The methodology is adaptable to various forest-specific definitions of large trees based on height or dbh.