Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

  • Author(s): Eunji Kim, Woo-Kyun Lee, Mihae Yoon, Jong-Yeol Lee, Eun Jung Lee, and Jooyeon Moon
  • DOI: 10.3319/TAO.2016.03.29.01(ISRS)
  • Keywords: LiDAR, Forest, Carbon stock, Individual tree detection algorithm, Climate change
  • Tree detection algorithm having advantages over Local Maxima filtering was developed
  • Tree position and height for further forest carbon quantification were analyzed
  • Previous research results were compared based on various forest conditions

Forest carbon is accurately quantified by observing individual tree positions and heights. This paper proposes a novel algorithm for individual tree detection using Light Detection and Ranging (LiDAR) data in the Chollipo arboretum, South Korea. The proposed algorithm does not need to specify a proper window size for operation, taking advantage over the mostly used local maxima (LM) filtering for forest analysis. Four hundred twenty-nine treetops were detected and the average height and standard errors were 12.74 ± 0.24 m. Reference data were collected from two sources for verifying accuracy: field survey and visual interpretation. Overall, the result was overestimated but showed relatively high accuracy. The field survey detected 87% of the trees with a coefficient of determination (R2) and root mean square error (RMSE) of 0.77 and 1.57 m, respectively. The accuracy index (AI), which examines the correspondence between LiDAR detected and visually interpreted trees, was 91%. The average tree height error between on-site and LiDAR derived data was -1.42 ± 0.64 m and between visually interpreted and LiDAR derived data was -0.84 ± 0.10 m. This study emphasized the choice of algorithm and its parameters depending on forest conditions may influence the individual tree detection result. By comparing our work against previous studies, we found the tree location and height identification accuracy could be improved if different algorithms were used for different types of forests, as well as the LiDAR point density with each algorithm. This study suggests that more accurate individual tree detection could be obtained with different applications based on forest conditions.

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