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Use of Individual Tree Detection when Quantifying Forest Structure

Determining the success of forest restoration projects requires methodologies that can quantify forest conditions at landscape-scales while simultaneously providing fine-scale metrics (e.g., tree and group attributes). Individual tree detection (ITD) algorithms, as applied to lidar, have displayed the versatility needed to take on this challenge. Several algorithms exist, each with their own nuances. To evaluate ITD algorithms, we estimated basal area and tree density across a range of restoration treatment intensities (i.e., thinning and repeated burning) using three different algorithms.  We then used the most accurate ITD algorithm and patch metrics to quantify structural differences among treatments.  We found applications of the default Li et al. algorithm best explained variation in tree height and tree density.  ITD allowed us to measure tree density per patch which decreased with treatment intensity. Overall, increasing treatment intensity resulted in more open conditions.

Use of Individual Tree Detection when Quantifying Forest Structure
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Email:
rcb326[at]nau[dot]edu
 
 
 
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