Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data

Abstract

This study explores the utility of small-footprint, discrete return lidar data in deriving important forest structural attributes with the primary objective of estimating plot-level mean tree height, dominant height, and volume of Eucalyptus grandis plantations. The secondary objectives of the study were related to investigating the effect of lidar point densities (1 point/m2, 3 points/m2, and 5 points/m2) on height and volume estimates. Tree tops were located by applying local maxima (LM) filtering to canopy height surfaces created at each density level, followed by buffering using circular polygons. Maximum and mean height values of the original lidar points falling within each tree polygon were used to generate lidar mean and dominant heights. Lidar mean value was superior to the maximum lidar value approach in estimating mean plot height (R2∼0.95; RMSE∼7%), while the maximum height approach resulted in superior estimates for dominant plot height (R2 ∼0.95; RMSE∼5%). These observations were similar across all lidar point density levels. Plot-level volume was calculated using approaches based on lidar-derived height variables and stems per hectare, as well as stand age. The level of association between estimated and observed volume was relatively high (R2=0.82—0.94) with non-significant differences among estimates at high lidar point densities and field observation. Nearly all estimates, however, exhibited negative biases and RMSE ranging in the order of 20—43%. Overall, the results of the study demonstrate the potential of lidar-based approaches for forest structural assessment in commercial plantations, even though further research is required on improving stems per hectare (SPHA) estimation.

Comparing image-based point clouds and airborne laser scanning data for estimating forest heights

“…The same parameters at single tree level can also be derived directly (i.e., tree height and crown area) or indirectly (i.e., volume, basal area and mean DBH) from CHM by separation of individual crown using the individual tree detection approach (ITDBrandtberg 1999, Hyyppä & Inkinen 1999, Koch et al 2006. CHM as well as height metrics derived from Airborne Laser Scanning (ALS), also called Light detection and ranging (LiDAR) data, were successfully applied in many case studies for the assessment of forest structure information, as ALS has the capability for extraction of both the digital terrain model (DTM), which represents the forest floor, and the digital elevation model (DEM), which represents the entire canopy of forest (Nelson et al 1984, Nilsson 1996, Hyyppä et al 2008, Packalén et al 2008, Tesfamichael et al 2010, White et al 2015a, Yamamoto et al 2017.…”

Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar

“…( [17][18][19][20][21][22][23][24][25]). Lidar based estimation of forest biophysical parameters has been implemented at the grid level [26,27], stand level [28][29][30], sub-stand segment level [31], plot level [32][33][34], tree cluster level [35], and individual tree level [36][37][38]. Only a few parameters, such as height and crown diameter, can be measured directly from the lidar data (e.g., [39,40]).…”

Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR

“…The extraction of biophysical parameters in many forest types, including deciduous [28], conifer [29] and hemiboreal [30], pine [31,32], eucalyptus [33], and urban forests [34] from LiDAR data both at the plot and individual tree level is possible. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction.…”