【摘要】 借助机载小光斑LiDAR点云和地面调查的73个样地数据,以亚热带天然次生林为研究对象,首先采用主成分分析法、逐步回归法和贝叶斯模型平均法,分别优化筛选LiDAR提取变量;在此基础上,拟合最优模型估算各森林参数并评价精度;最后基于最优模型进行蓄积量的升尺度制图。结果表明:通过主成分分析法筛选出的最优LiDAR提取变量为平均高度(hmean)、60%冠层返回密度变量(d6)和高度变异系数(hcv),且这3个变量在逐步回归法和贝叶斯模型平均法中多被选中;逐步回归法拟合模型效果最好(R2为0.39~0.84),而贝叶斯模型平均法(R2为0.32~0.77)和主成分分析法(R2为0.26~0.74)次之;就各森林参数而言,Lorey’s树高(R2为0.74~0.84)和优势树高(R2为0.73~0.82)的估算精度最高,胸径(R2为0.48~0.57)和蓄积(R2为0.46~0.55)次之,而株数(R2为0.35~0.44)和胸高断面积(R2为0.29~0.39)最低。更多还原

【Abstract】 Based on the aerial-borne small footprint LiDAR point cloud and 73 sample plots from field inventory,this paper sets the subtropical secondary forests as a research subject. First,the methods of principle component analysis( PCA),stepwise regression and bayesian modeling averaging( BMA) were applied to optimize the extraction of LiDAR-derived metrics; second,the optimized models were used to estimate each forest parameter and then the accuracy evaluation was performed; finally,the volume information was up-scaled to map its spatial distribution. The results demonstrated that the optimized LiDAR-derived metrics selected by PCA were average height( hmean),60% canopy return density( d6)and the coefficiency of height variation( hcv),and these metrics were also selected by stepwise regression and BMA. The stepwise regression method fitted the best model( R2 was 0. 39- 0. 84),while BMA( R2 was 0. 32- 0. 77) and PCA( R2 was 0. 26- 0. 74) performed a little poor. Among each forest parameters,Lorey’s height( R2 was 0. 74- 0. 84) and dominated height( R2 was 0. 73- 0. 82) had the highest accuracy,whereas DBH( R2 was 0. 48- 0. 57) and volume( R2 was 0. 46- 0. 55) were a little lower,and stem number( R2 was 0. 35- 0. 44) and basal area( R2 was 0. 29- 0. 39) were the lowest.更多还原