中亚热带森林单木地上生物量的机载激光雷达估测

以雪峰山武冈林场为研究对象,利用遥感数据和地面实测样地数据,研究机载激光雷达（LiDAR）估测中亚热带森林乔木层单木地上生物量的能力.利用条件随机场和最优化方法实现LiDAR点云的单木分割,以单木尺度为对象提取的植被点云空间结构、回波特征以及地形特征等作为遥感变量,采用回归模型估测乔木层地上生物量.结果表明: 针叶林、阔叶林和针阔混交林的单木识别率分别为93%、86%和60%；多元逐步回归模型的调整决定系数分别为0.83、0.81和0.74,均方根误差分别为28.22、29.79和32.31 t·hm^-2；以冠层体积、树高百分位值、坡度和回波强度值构成的模型精度明显高于以树高为因子的传统回归模型精度.以单木为对象从LiDAR点云中提取的遥感变量有助于提高森林生物量估测精度.

Estimating individual tree aboveground biomass of the mid-subtropical forest using airborne LiDAR technology.

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 (R²_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.