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<sup>2</sup> 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) allometric equation. An average 80 % of sampled trees were detected from point cloud of the plots. The average of plots values of R<sup>2</sup> and RMSE for manually measured DBHs were 0.95, 2.7 cm respectively. Similarly, the average of plots values of R<sup>2</sup> and RMSE for manually measured trees heights were 0.77, 2.96 m respectively. The average value of AGB/carbon stocks estimated from field measurements and T-LiDAR manually derived DBHs and trees heights were 286 Mg ha-1 and 134 Mg ha<sup>−1</sup>; and 278 M ha-1 and 130 Mg ha<sup>−1</sup> respectively. The R<sup>2</sup> values for the estimated AGB and AGC were both 0.93 and corresponding RMSE values were 42.4 Mg ha<sup>−1</sup> and 19.9 Mg  ha<sup>−1</sup> respectively. AGB and AGC were estimated with 14.8 % accuracy.
Highlights
There is a growing need of accurate and effective methods for estimating forest biomass/carbon stocks to meet the requirements of both Kyoto Protocol and UN-REDD programmes (Castedo et al, 2012)
The use of remote sensing techniques is critical for assessing fine-scale spatial variability of tropical forest biomass/carbon stock over broad spatial extents (Clark et al, 2011)
To assess the accuracy of trees detected from point cloud data, manually detected trees per plot were compared with respect to field observations
Summary
There is a growing need of accurate and effective methods for estimating forest biomass/carbon stocks to meet the requirements of both Kyoto Protocol and UN-REDD programmes (Castedo et al, 2012). Recent advances in T-LiDAR technology have made LiDAR data widely available to study vegetation structure characteristics and forest biomass It may provide an alternative for the permanent sample plot method for ground-based forest inventory. The specific objectives of the study was (i) to detect trees manually from TLiDAR point cloud data; (ii) to manually derive plot inventory parameters (i.e. DBH and tree height); (iii) to compare the accuracy of manually derived parameters and (iv) to estimate above ground biomass (AGB)/carbon and assess accuracy.
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