title: [Estimating individual tree aboveground biomass of the mid-subtropical forest using airborne LiDAR technology].
abstract: Taking Wugang forest farm in Xuefeng Mountain as the research object, using the airborne light detection and ranging (LiDAR) data under leaf-on condition and field data of concomitant plots, this paper assessed the ability of using LiDAR technology to estimate aboveground biomass of the mid-subtropical forest. A semi-automated individual tree LiDAR cloud point segmentation was obtained by using condition random fields and optimization methods. Spatial structure, waveform characteristics and topography were calculated as LiDAR metrics from the segmented objects. Then statistical models between aboveground biomass from field data and these LiDAR metrics were built. The individual tree recognition rates were 93%, 86% and 60% for coniferous, broadleaf and mixed forests, respectively. The adjusted coefficients of determination (R(2)adj) and the root mean squared errors (RMSE) for the three types of forest were 0.83, 0.81 and 0.74, and 28.22, 29.79 and 32.31 t · hm(-2), respectively. The estimation capability of model based on canopy geometric volume, tree percentile height, slope and waveform characteristics was much better than that of traditional regression model based on tree height. Therefore, LiDAR metrics from individual tree could facilitate better performance in biomass estimation.
authors.forename: Feng
authors.initials: F
authors.lastname: Liu
authors.forename: Chang
authors.initials: C
authors.lastname: Tan
authors.forename: Pi-Feng
authors.initials: PF
authors.lastname: Lei
mesh_terms: D018533:Biomass; D065928:Forests; D008027:Light; D008962:Models, Theoretical; D058998:Remote Sensing Technology; D014197:Trees / Q000254:growth & development*