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

Ahmad Zawawi, Azita and Masami, Shiba and Naim Jemali, Nor Janatun (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). pp. 1-11. ISSN 2171-5068; ESSN: 2171-9845

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Official URL: http://revistas.inia.es/index.php/fs/article/view/...

Abstract

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

Item Type:Article
Keyword:Broad-leaved; Inventory; LiDAR; Subtropical; Tree height
Faculty or Institute:Faculty of Engineering
Publisher:Spanish National Institute for Agricultural and Food Research and Technology
DOI Number:10.5424/fs/2015241-05476
Altmetrics:http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.5424/fs/2015241-05476
ID Code:43641
Deposited By: Mohd Hafiz Che Mahasan
Deposited On:22 Jul 2016 10:51
Last Modified:22 Jul 2016 10:51

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