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利用样木收获法收集了34个样地中长白落叶松林分地上部分生物量信息,选取其中29个样地生物量信息分别与样地林分因子信息和TM遥感影像信息拟合建立生物量模型,利用其余5个样地的生物量信息进行模型精度检验和误差分析.结果表明:长白落叶松地上部分生物量均可用林分因子和遥感因子进行线性拟合;林分因子线性模型对长白落叶松中幼林地上生物量的估测精度较高(林分P=94.33%,遥感P=92.32%),且检验误差较小(林分MRE=6%,遥感MRE=31%),模型模拟效果较好;若只考虑长白落叶松中龄林,这2种模型的估测效果相当(林分模型和遥感模型的误差分别为329.9和313.6t).整体而言,林分因子模型估测长白落叶松树皮、干材和总生物量的效果优于遥感因子模型,对于中龄林来说,遥感模型估测叶花果、树枝和树冠生物量的效果较好.
The biomass information of above-ground part of Larix sylvestris var.mongolica collected from 34 plots was collected by the sample tree harvesting method. The biomass of 29 sample plots was selected to establish the biomass model by fitting with the forest stand factor information and TM remote sensing image information respectively , And biomass information of the remaining five plots was used to test the accuracy of the model and the error analysis.The results showed that the aboveground biomass of Larix olgensis was linear fit with stand factors and remote sensing factors.The linear regression model of stand factors The estimation accuracy of aboveground biomass of Pinus massoniana plantation was higher (P = 94.33%, P = 92.32%), and the test error was smaller (MRE = 6% for forest and MRE = 31% for remote sensing) The results of these two models are equivalent (the errors of forest model and remote sensing model are 329.9 and 313.6t, respectively) .At the whole, the stand factor model estimates the growth of Larix olgensis The effects of skins, stems and total biomass were better than those of remote sensing factors. For the middle-aged forest, remote sensing model estimated the biomass of leaves, branches and canopies better.