Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan

  • Azita Ahmad Zawawi 1. The United Graduate School of Agricultural Sciences, Kagoshima University. 2. Faculty of Forestry, University Putra Malaysia.
  • Masami Shiba Faculty of Agriculture, University of The Ryukyus, Okinawa.
  • Noor Janatun Naim Jemali The United Graduate School of Agricultural Sciences, Kagoshima University.


Aim of study: To present an approach for estimating tree heights, stand density and crown patches using LiDAR data in a subtropical broad-leaved forest.

Area of study: The study was conducted within the Yambaru subtropical evergreen broad-leaved forest, Okinawa main island, Japan.

Materials and methods: A digital canopy height model (CHM) was extracted from the LiDAR data for tree height estimation and a watershed segmentation method was applied for the individual crown delineation. Dominant tree canopy layers were estimated using multi-scale filtering and local maxima detection. The LiDAR estimation results were then compared to the ground inventory data and a high resolution orthophoto image for accuracy assessment.

Main results: A Wilcoxon matched pair test suggests that LiDAR data is highly capable of  estimating tree height in a subtropical forest (z = 4.0, p = 0.345), but has limitation to detect small understory trees and a single tree delineation. The results show that there is a statistically significant different type of crown detection from LiDAR data over forest inventory (z = 0, p = 0.043). We also found that LiDAR computation results underestimated the stand density and overestimated the crown size.

Research highlights: Most studies involving crown detection and tree height estimation have focused on the analysis of plantations, boreal forests and temperate forests, and less was conducted on tropical and/or subtropical forests. Our study tested the capability of LiDAR as an effective application for analyzing a highly dense forest.

Key words: Broad-leaved; inventory; LiDAR; subtropical; tree height.

Abbreviations: DBH: Diameter at Breast Height, CHM: Canopy Height Model, DEM: Digital Elevation Model, DSM: Digital Surface Model, LiDAR: Light Detection and Ranging, YFA: Yambaru Forest Area.


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Author Biography

Masami Shiba, Faculty of Agriculture, University of The Ryukyus, Okinawa.
Subtropical Field Science Education and Research Centre



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How to Cite
Ahmad ZawawiA., ShibaM., & JemaliN. J. N. (2015). Accuracy of LiDAR-based tree height estimation and crown recognition in a subtropical evergreen broad-leaved forest in Okinawa, Japan. Forest Systems, 24(1), e002.
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