ABSTRACT:This research was conducted to derive forest sample plot inventory parameters from terrestrial LiDAR (T-LiDAR) for estimating above ground biomass (AGB)/carbon stocks in primary tropical rain forest. Inventory parameters of all sampled trees within circular plots of 500 m 2 were collected from field observations while T-LiDAR data were acquired through multiple scanning using Reigl VZ-400 scanner. Pre-processing and registration of multiple scans were done in RSCAN PRO software. Point cloud constructing individual sampled tree was extracted and tree inventory parameters (diameter at breast height-DBH and tree height) were measured manually. AGB/carbon stocks were estimated using Chave et al., (2005)

Forest inventory parameters, in particular DBH (diameter at breast height) and tree height, can be used to accurately estimate aboveground carbon, which is an important indicator of forest productivity and sequestration of carbon. This study demonstrates and tests a method to automatically and manually derive forest inventory parameters for estimating above-ground carbon (AGC) using a Terrestrial Laser Scanner (TLS). The study was conducted in the Royal Belum State Park of Peninsular Malaysia by establishing 24 circular inventory sample plots of 0.05 ha. A RIEGL VZ-400 TLS system was used to acquire point cloud data of the sample plots through multiple scanning, and these data were further preprocessed and co-registered using the RiSCAN PRO software. The DBH and tree height for each tree within the plots were manually measured using a distance measurement tool in RiSCAN PRO and automatically derived from Computree software using a tree segmentation approach. The inventory parameters derived from TLS were compared with the field measurements for calculating the AGC using an allometric equation. On average, 89% and 90% of the sampled trees were detected from the point cloud data of the plots using the manual and automatic detection methods, respectively. The mean values of R 2 (coefficient of determination) and RMSE (Root Mean Square Error) for manually measured DBH and tree height across the plots were 0.95 and 2.70 cm, and 0.77 and 2.96 m, respectively. We also obtained an average value of 0.86 and 2.47 cm, and 0.51 and 3.15 m for R 2 and RMSE, respectively between field-measured and automatically derived DBH and tree height from TLS data across all the sample plots of the study area. Our method provides clear evidence that TLS has the potential to derive forest inventory parameters, which can be used to estimate above-ground carbon in the tropical rainforest accurately.

ABSTRACT:This research was conducted to derive forest sample plot inventory parameters from terrestrial LiDAR (T-LiDAR) for estimating above ground biomass (AGB)/carbon stocks in primary tropical rain forest. Inventory parameters of all sampled trees within circular plots of 500 m 2 were collected from field observations while T-LiDAR data were acquired through multiple scanning using Reigl VZ-400 scanner. Pre-processing and registration of multiple scans were done in RSCAN PRO software. Point cloud constructing individual sampled tree was extracted and tree inventory parameters (diameter at breast height-DBH and tree height) were measured manually. AGB/carbon stocks were estimated using Chave et al., (2005)

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