摘要
为提高ALOS PALSAR 数据估测森林结构参数的精度,引入代表林分结构复杂程度的调整熵值（ENTadj ）参与估测,以消除林分结构对雷达后向散射系数的干扰。首先利用野外样地实测的树高计算林分的调整熵值,与Landsat8 OLI 第6 波段建立线性回归模型,获得基于像元的调整熵值。一般森林结构参数与ALOS PALSAR 后向散射系数之间的关系可以用对数模型模拟。引入基于像元的调整熵值作为自变量对原始对数模型进行改进,分别对林分平均高、林分平均胸径、林分蓄积量建立了3 种形式的改进模型。利用原始模型和改进模型分别对杉木林、马尾松林、阔叶林和针阔混交林的上述森林结构参数进行估测。最后比较模型拟合精度筛选出3 项森林结构参数在各类森林中的最优模型,共计12 个。结果表明：考虑林分结构干扰后,雷达估测森林结构参数模型的拟合精度R2均得到了提高。马尾松林各项森林结构参数模型的拟合度提高最大。精度检验结果表明：林分平均高估测精度（RMSE 为0.74 -2.51 m）、林分平均胸径估测精度（RMSE 为2.61 - 5.61 cm）和林分蓄积量估测精度（RMSE 为21.71 -30.92 m^3 / hm^2 ）都比较理想。本研究探讨了林分结构信息应用于合成孔径雷达后向散射系数反演森林结构参数方面的潜力,提高了光学数据结合雷达数据估算森林结构参数的能力。
To improve the estimation accuracy of forest structural parameters with ALOS PALSAR data, we introduced adjusted entropy （ENTadj） which represents the complexity of stand structure for the estimation. Thus, the interference of radar backscattering coefficient caused by stand structure could be eliminated. Firstly, the ENTadj of stand was defined by the measured tree heights in sample plots of the field. And then, the ENTadj based on pixels was calculated by linear regression model established with the integration of the ENTadj of stand and Landsat 8 OLI band 6. Commonly, the relationship between stand structural parameters and ALOS PALSAR backscattering coefficient could be simulated by a logarithm regression model. In this research, ENTadj based on pixel was introduced as a new independent variable
to improve the original logarithm model. Three types of improved models were established for stand mean tree height, stand mean DBH and stand stock volume respectively. The original model and three improved models were used to estimate the above stand structural parameters for Cunninghamia lanceolata stand, Pinus massoniana stand, broadleaf stand and mixed stand. Ultimately, optimal estimating models for each stand structural parameter in the four types of stands were selected by comparing R^2 （coefficient of determination） with totally 12 results. The results showed that the R^2 of models in radar estimating stand structural parameters increased after considering the influence of stand structure, and the R^2 of entire optimal stand models for P. massoniana stand increased the most. The results of accuracy examination revealed that there were desired precisions for estimating tree height （RMSE： 0.74 - 2.51
m）, DBH （RMSE： 2.61 - 5.61 cm） and stock volume （RMSE： 21.71 - 30.92 m^3/ha）. This study explored the potential of applying stand structure information in forest structural parameters, and increased the ability to estimate the forest structural parameters by combining the optical and radar remote sensing data.
出处
《北京林业大学学报》
CAS
CSCD
北大核心
2015年第6期61-69,共9页
Journal of Beijing Forestry University
基金
"863"国家高技术研究发展计划项目(2012AA102001)
关键词
林分结构
ALOS
PALSAR
Landsat8
OLI
树高
胸径
蓄积量
杉木
马尾松
stand structure： ALOS PALSAR： Landast8 OLI： tree height
diameter at breast height（DBH）
stock volume
Cunninghamia lanceolata
Pinus massoniana
