耦合多种特征的森林蓄积量反演方法比较——以雅鲁藏布江流域森林为例  

Comparison of Forest Stock Volume Inversion Methods Coupled With Multiple Features——A Case Study of Forest in Yarlung Zangbo River Basin

作　　者：李子朝 毕守东[1] 崔玉环[1] 郝泷 LI Zi-zhao;BI Shou-dong;CUI Yu-huan;HAO Shuang(School of Science,Anhui Agricultural University,Hefei 230036,China)

机构地区：[1]安徽农业大学理学院,安徽合肥230036

出　　处：《光谱学与光谱分析》2022年第10期3263-3268,共6页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41801332)资助。

摘　　要：森林资源遥感监测是遥感的重要应用方向之一。传统的统测方法花费大量的人力、物力,科学的森林资源预测可以提升工作效率并降低测算成本。森林蓄积量是评价森林生态系统质量的重要指标。蓄积量反演模型是用来估测蓄积量的数学模型,具有学习和预测的功能。同样的地物在不同光照或阴影区域有较大的差别,利用波段比值可以在一定程度上减小光照和阴影区域在建模时得出结果的误差。森林蓄积量的预测模型通常选取光谱信息和纹理特征作为主要建模因子,但未充分考虑选取波段比值、植被指数、地形因子等多特征变量时不同模型对预测精度的影响。针对不同模型的精度问题,以西藏自治区米林县为研究区域,以Landsat OLI影像、DEM数据以及森林资源二类调查数据为数据源,对光谱信息、纹理特征和地形因子等进行提取与分析,并建立了三种基于多特征的森林蓄积量的反演模型,分别是多元逐步回归模型、BP神经网络模型和随机森林模型。旨在研究不同模型对森林蓄积量反演的影响。采用可决系数(R^(2))、平均绝对误差(MAE)和均方根误差(RMSE)来对模型进行拟合度和精度的评价。结果显示随机森林模型的拟合度和精度均为最优(R^(2)=0.739,MAE=55.352 m^(3)·ha^(-1),RMSE=63.195 m^(3)·ha^(-1)),高于多元逐步回归模型(R^(2)=0.541,MAE=58.317 m^(3)·ha^(-1),RMSE=71.562 m^(3)·ha^(-1))和BP神经网络模型(R^(2)=0.477,MAE=67.503 m^(3)·ha^(-1),RMSE=73.226 m^(3)·ha^(-1))。模型预测值的范围为121.3~372.8 m^(3)·ha^(-1),与实际值较为接近。结果表明基于多特征的森林蓄积量反演在实际应用中是有效的,且不同的模型对森林蓄积量的反演精度有一定的影响。随机森林回归模型的反演精度最高,能够较好地应用于森林资源的遥感监测中。该研究可以为森林蓄积量反演方法的选取提供参考和借鉴,有助于森林资�Remote sensing monitoring of forest resources is one of the important application directions of remote sensing.Traditional measurement methods cost a lot of workforce and material resources.Scientific forest resource prediction can improve work efficiency and reduce measurement costs.Forest stock volume is an important index to evaluate the quality of forest ecosystems.The forest stock volume inversion model is a mathematical model used to estimate the forest stock volume,which has the functions of learning and prediction.The same ground features are quite different in different light or shadow areas.The band ratio can be used to reduce the error of the results in modeling light and shadow areas to a certain extent.The forest stock volume prediction model usually selects spectral information and texture features as the main modeling factors.It does not fully consider the impact of different models on the prediction accuracy when selecting multi-characteristic variables such as band ratio,vegetation index,and topographic factors.In order to compare the accuracy of different models,this article takes Milin County in Tibet Autonomous Region as the research area,and uses Landsat OLI images,DEM data and forest resource survey data as data sources to extract analyze spectral information,texture features and topographic factors.Three forest volume inversion models based on multi-features are established.The three models are multiple stepwise regression models,BP neural network models and random forest models.The effects of different methods on the inversion of forest stock are studied.The coefficient of determination(R^(2)),mean absolute error(MAE),and root mean square error(RMSE)are used to evaluate the fit and accuracy of the model.The results showed that the fit and accuracy of the random forest model are the best(R^(2)=0.739,MAE=55.352 m^(3)·ha^(-1),RMSE=63.195 m^(3)·ha^(-1)).The result is higher than the multiple stepwise regression model(R^(2)=0.541,MAE=58.317 m^(3)·ha^(-1),RMSE=71.562 m^(3)·ha^(-1))and BP neu

关 键 词：蓄积量反演 多元逐步回归 BP神经网络 随机森林 Landsat OLI 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置]