三种森林生物量估测模型的比较分析

Comparison of three models of forest biomass estimation

森林生物量的定量估算为全球碳储量、碳循环研究提供了重要的参考依据。该研究采用黑龙江长白山地区的TM影像和133块森林资源一类清查样地的数据,选取地学参数、遥感反演参数等71个自变量分别构建多元逐步回归模型、传统BP(back propagation)神经网络模型和基于高斯误差函数的BP神经网络改进模型(Gaussian error function,Erf-BP),进而估算该地区的森林生物量,并进行比较分析。结果表明,多元逐步回归模型估测的森林生物量预测精度为75%,均方根误差为26.87t·m-2;传统BP神经网络模型估测森林生物量的预测精度为80.92%,均方根误差为21.44t·m-2;Erf-BP估测森林生物量的预测精度为82.22%,均方根误差为20.83t·m-2。可见,改进后的Erf-BP能更好地模拟生物量与各个因子之间的关系,估算精度更高。

Aims Quantitative estimation of forest biomass is significant to studies of global carbon storage and carbon cy-cle.Our objective is to develop models to estimate forest biomass accurately.Methods Multi-stepwise regression model,traditional back propagation （BP） neutral network model and BP neutral network model based on Gaussian error function （Erf-BP） were developed to estimate forest biomass in Changbai Mountain of Heilongjiang,China according to TM imagery and 133 plots of forest inventory data.There were 71 dependent variables of geoscience and remote sensing.Important findings The precisions and root mean square errors of multi-stepwise regression model,traditional BP neutral network model and Erf-BP were 75%,26.87 t？m-2;80.92%,21.44 t？m-2 and 82.22%,20.83 t？m-2,re-spectively.Therefore,the relations between forest biomass and various factors can be better modeled and de-scribed by the improved Erf-BP.