Effect of forest stand density on the estimation of above ground biomass/carbon stock using airborne and terrestrial LIDAR derived tree parameters in tropical rain forest, Malaysia

• Published in: Environmental Systems Research
• Main Author: Wassihun A.N.; Hussin Y.A.; Van Leeuwen L.M.; Latif Z.A.
• Format: Article
• Language: English
• Published: Springer Medizin 2019
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092396838&doi=10.1186%2fs40068-019-0155-z&partnerID=40&md5=4ea704172d2ceee1db886cd19b494a6c

Background: Forest stand density in tropical rainforests is crucial functional and structural variable of forest ecosystems in which above ground biomass can be derived. Currently, there is a growing demand for airborne and terrestrial LIDAR in measuring forest trees parameters for accurate assessment of forest biomass/carbon stock to meet the requirements of UN-REDD + program. Although several studies have been conducted on above ground biomass/carbon stock in tropical rainforest using forest inventory parameters derived from airborne and terrestrial LIDAR, no research was conducted on how the estimation of above ground biomass/carbon stock using airborne and terrestrial LIDAR derived parameters is affected by forest stand density in a tropical rainforest. Therefore, this study aims to analyze and investigate the strength of the relationship between forest stand density and its above ground biomass estimated using airborne and terrestrial LIDAR derived trees parameters. Purposive sampling approach was adopted for the selection of the unit of analysis. Results are based on data collected from 32 sample plots measured and scanned in the field. Airborne LIDAR was used to derive upper canopy trees height, while terrestrial LIDAR was used to derive the height of lower canopy trees and DBH of all lower and upper canopy trees. The DBH measured in the field was used to compute forest stand density and to validate the DBH manually extracted from TLS point cloud data. The DBH manually derived from TLS point cloud data was used to estimate AGB of the sampled plots for both upper and lower canopy trees. Results: Descriptive statistics, linear regression and correlation analysis were used to answer the research questions of this study. Out of 1033 trees measured and scanned in the field, 855 trees (82.7%) were extracted from TLS point cloud data and 178 trees (17.3%) were missed due to occlusion. The Pearson correlation coefficient (r) between a total number of trees measured and scanned in the field and the total number of trees extracted from TLS point cloud data was 0.95. R2 of 0.89 was found to explain the relationship between number of missed trees per plot against a number of trees measured in the field per plot. The strength of the effect of forest stand density on AGB is explained by R2 which is 0.91. Conclusions: Based on the findings, forest stand density have significant effect on above ground biomass at 1% significance level. Since there is a strong relationship between forest stand density and AGB and the measurement of forest stand density from the ground is fast, forest stand density could be recommended as a proxy to estimate above ground biomass. © The Author(s) 2019.