利用变量投影重要性准则筛选郁闭度估测参数
Selection of parameters for estimating canopy closure density using variable importance of projection criterion
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摘要: 基于森林一类调查数据和相应的遥感影像数据，比较了基于变量投影重要性（VIP）准则与Bootstrap准则所选变量对模型预报精度的影响；利用基于VIP准则所选变量构建偏最小二乘回归模型，并估测了黑龙江省北部塔河地区森林郁闭度情况。结果表明：利用VIP准则所选变量都能通过Bootstrap非参数检验（α=0.05）；利用这些最优变量建立的偏最小二乘回归模型取得了不低于全模型和Bootstrap模型的精度，前者的相对均方误差分别是后两者的99.2%和99.6%，并且提高了样地或像素水平的估测精度和效率。
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关键词:
- 变量投影重要性 /
- 偏最小二乘回归模型 /
- 遥感影像 /
- Bootstrap检验
Abstract: With the forest inventory data and relevant remote sensing data, two approaches of variable selection, i.e. bootstrap method and variable importance of projection (VIP) criterion, were compared in their impacts on prediction accuracy of estimating model.Subsequently, the forest canopy closure density of the study area, Tahe County, was estimated by a simplified PLSR(partial least square regression) model,which has a better prediction accuracy. Here, we defined a model including all variables as full model, a model only including selected variables by Bootstrap method as Bootstrap model, and a model only including selected variables by VIP criterion as VIP model. The results showed that all those variables selected by VIP criterion passed through non-parameters Boostrap test (α=0.05). The VIP model had no lower accuracy than full model and Bootstrap model, and the relative root mean square error of the former was 99.2% and 99.6% of those of the latters, respectively. In addition, the VIP model improved the estimation accuracy at plot or pixel level. -
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