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http://dx.doi.org/10.7780/kjrs.2015.31.2.7

Estimation of Stand-level Above Ground Biomass in Intact Tropical Rain Forests of Brunei using Airborne LiDAR data  

Yoon, Mihae (Division of Environment Science and Ecological Engineering, Korea University)
Kim, Eunji (Department of Climate Environment, Graduate School of Life & Environmental Sciences, Korea University)
Kwak, Doo-Ahn (Korea Forest Conservation Association)
Lee, Woo-Kyun (Division of Environment Science and Ecological Engineering, Korea University)
Lee, Jong-Yeol (Division of Environment Science and Ecological Engineering, Korea University)
Kim, Moon-Il (Division of Environment Science and Ecological Engineering, Korea University)
Lee, Sohye (Division of Environment Science and Ecological Engineering, Korea University)
Son, Yowhan (Division of Environment Science and Ecological Engineering, Korea University)
Salim, Kamariah Abu (Biology Department, Kuala Belalong Field Studies Centre, Universiti Brunei Darussalam)
Publication Information
Korean Journal of Remote Sensing / v.31, no.2, 2015 , pp. 127-136 More about this Journal
Abstract
This study aims to quantify the stand-level above ground biomass in intact tropical rain forest of Brunei using airborne LiDAR data. Twenty four sub-plots with the size of 0.09ha ($30m{\times}30m$) were located in the 25ha study area along the altitudinal gradients. Field investigated data (Diameter at Breast Height (DBH) and individual tree position data) in sub-plots were used. Digital Surface Model (DSM), Digital Terrain Model (DTM) and Canopy Height Model (CHM) were constructed using airborne LiDAR data. CHM was divided into 24 sub-plots and 12 LiDAR height metrics were built. Multiple regression equation between the variables extracted from the LiDAR data and biomass calculated by using a allometric equation was derived. Stand-level biomass estimated from LiDAR data were distributed from 155.81 Mg/ha to 597.21 Mg/ha with the mean value of 366.48 Mg/ha. R-square value of the verification analysis was 0.84.
Keywords
Airborne LiDAR; Tropical Rain Forest; Above Ground Biomass;
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