Allometric model based estimation of biomass and carbon stock for individual and overlapping trees using terrestrial LiDAR

Above ground biomass (AGB) and carbon stock are key parameters to understand the carbon sequestration in trees which plays a major role in reducing the CO2 emissions in the atmosphere and thereby reducing the global climate change. With the latest technology of Light detection and ranging (LiDAR), it is now possible to estimate the AGB and Carbon stock at tree level. Studies on the use of terrestrial LiDAR for AGB and carbon stock estimation have mostly used the individual trees as it is easy to extract such standalone trees from LiDAR point cloud data. However, trees which are adjacent and overlapping that are common in forests are difficult to extract and the present study considered such a plot within our university campus and showed how both point cloud data and photos can be utilized to calculate the AGB & Carbon stock without extracting the trees. Terrestrial LiDAR survey of 37 scans was performed at 7 locations covering 144 trees of 21 types which has a mix of both individual and overlapping trees with varying diameter, height, green cover, etc. The LiDAR derived diameter at breast height (DBH) and height of 144 trees were compared with the actuals and it was found that the LiDAR measurements were highly accurate as the resultant mean absolute percentage error (MAPE) was less than 5. The extracted DBH and height were used in a generic allometric model to calculate the AGB and carbon stock and was calculated as 67.893 Mg and 34 Mg respectively. A slanting S-shaped curve was found to fit well between AGB and the input parameters of DBH and height with R2 of 0.87 and 0.96 respectively. The model results revealed that instead of using both DBH and height to estimate biomass and carbon stock, any one parameter also can be used to estimate the AGB as the results are comparable.