林分蓄积量估测是林业遥感的重要研究领域,由于云雾天气和光谱饱和现象等因素限制了光学遥感影像估测林分蓄积量的精度。合成孔径雷达(SAR)具有穿透性强、受云雾影响小等特点,弥补了光学遥感的不足。以江西省龙南县的针叶林为研究对象,结合Landsat 8与PALSAR-2双极化SAR影像数据,在遥感数据预处理基础上,提取了光谱信息、植被指数、纹理信息和后向散射系数等共245个遥感因子。基于Pearson相关系数法和多元逐步回归法,筛选出65个遥感因子参与林分蓄积量估测。以林分郁闭度作为分层因子,分别采用线性、KNN、支持向量机(SVM)、多重感知机(MLP)和随机森林(RF)5种模型估测林分蓄积量,并对估测结果进行精度检验。实验结果表明:1)相比单独使用Landsat 8的光谱和纹理信息,基于郁闭度分级并融合PALSAR-2的后向散射信息明显提高了蓄积量的反演精度;2)对于低郁闭度林分,线性模型精度最高(rRMSE=21.16%),中郁闭度林分,多重感知机模型估测效果最好(rRMSE=30.61%),高郁闭度林分,多重感知机模型估测效果最好(rRMSE=27.53%)。在结合PALSAR-2的后向散射系数的基础上,郁闭度分层能有效改善中高蓄积量区域的反演精度。

Forest stand volume estimation is an important research field of forestry remote sensing.Factors such as cloud and fog weather and spectral saturation have restricted the accuracy of optical remote sensing image estimation.Synthetic Aperture Radar(SAR) images have the characteristics of strong penetrability and are less likely to be affected by cloud and fog,which make up for the deficiencies of optical remote sensing.This study uses the coniferous forest in Longnan county,Jiangxi Province as the study area,combines Landsat 8 and PALSAR-2 dual-polarization SAR image data to extract a total of 245 remote sensing factors such as spectral information,vegetation index,texture information and backscattering coefficient based on remote sensing data preprocessing.Based on the Pearson correlation coefficient method and the multiple stepwise regression method,65 remote sensing factors are selected for the estimation of stand stock.Taking forest stand canopy closure as a stratification factor,the study adopts five models of linear,KNN,support vector machine(SVM),multi-perceptron(MLP) and random forest(RF) to estimate the forest stand volume,and tests the accuracy of the estimated results.The experimental results show that:1) Compared with using the spectrum and texture information of Landsat8 alone,the backscatter information of PALSAR-2 based on the canopy closure classification and fusion significantly improves the inversion accuracy of accumulation 2) In low canopy closure forest stand,the linear model has the highest accuracy(rRMSE=21.16%),in medium canopy closed forest stand,the MLP model has the best estimation effect(rRMSE=30.61%),in high canopy closure forest stand,the MLP model has the best estimation effect(rRMSE=27.53%).Based on the backscattering coefficient of PALSAR-2,the canopy closure stratification can effectively improve the inversion accuracy of the medium and high accumulation areas.