【作者】 刘永新；

【导师】 姜琦刚；

【作者基本信息】 吉林大学 ， 地图制图学与地理信息工程， 2021， 硕士

【摘要】 森林蓄积量作为评价森林资源的重要指标,准确地调查森林蓄积量分布情况对合理评估林业资源开发利用现状及规划部署至关重要。传统森林蓄积量调查方法存在调查周期长、工作量大、调查精度低等不足。随着国民经济的快速发展,在短期内对森林蓄积量分布情况进行准确调查的需求越来越迫切。利用遥感技术可以在布设少量样地的情况下,在相对较短的时间内对森林蓄积量的分布情况进行调查。本文以北京市为研究区,以Sentinel-2号卫星影像和全国森林蓄积量调查结果为数据源。通过卫星影像提取单波段光谱反射率、植被指数、纹理因子等三大类61个特征因子,对所有特征因子与森林蓄积量值进行相关性分析,选取其中的18个相关性最高的特征因子作为自变量。以实地调查样地蓄积量为因变量,通过偏最小二乘法、支持向量机、随机森林、人工神经网络等方法构建森林蓄积量反演模型,分析各模型的回归精度,选定合适的方法构建森林蓄积量反演模型反演研究区森林蓄积量值。结合地形特征与外业调查观察结果,对森林蓄积量的空间分布情况进行了简要分析。本研究的主要成果如下:(1)在进行定量遥感反演之前,对参与建模的多景影像进行相对辐射定标工作,可以减少因传感器等因素造成的误差,提高最终模型反演精度。(2)在提取的三类特征因子中,以植被指数与森林蓄积量值的相关性最高,最高可达0.44,单波段反射率次之,纹理因子特征与森林蓄积量值相关性相对较低。纹理因子与森林蓄积量的相关性随着窗口大小的增大而增大。(3)对比用于建模反演的四种方法,以人工神经网络方法构建的模型精度最高,均方根误差为1.19m~3,平均绝对误差0.96m~3,判定系数为0.61。最终利用人工神经网络方法构建的反演模型,对北京市森林蓄积量进行了反演,估测北京市2019年森林蓄积量2731万m~3,通过与北京市第九次森林资源清查结果(2016年)对比,模型估测精度87.9%,与预测北京市2019年森林蓄积量值相比较,模型估测精度可达93.3%,证明该模型可以总体预测北京市森林蓄积量值及分布情况。(4)结合野外调查经验,通过分析森林蓄积量在不同坡向分布情况,得出森林蓄积量在背阴坡向分布的数量普遍高于向阳坡向的结论,背阴坡向森林蓄积量值高出向阳坡向森林蓄积量值达21%。其原因主要是背阴坡面受太阳辐射较少,有利于水分和其他营养成分的富集,从而利用林木生长,增大森林蓄积量分布。更多还原

【Abstract】 Forest stock volume is one of the crucial stand parameters to evaluate forest resources.Accurately investigating the distribution of forest stock volume is very important to reasonably evaluate the development and utilization status and planning deployment of forest resources.The traditional forest stock volume survey method has the shortcomings of long survey cycle,heavy workload and low survey accuracy.With the rapid development of the national economy,it is increasingly urgent to accurately investigate the distribution of forest volume in the short term.Remote sensing technology can be used to investigate the distribution of forest volume in a relatively short period of time when a small number of plots are set.This paper selects the Beijing as the study area.Sentinel-2 images and the national forest stock volume inventory data were used as data source.61 feature factors of Band reflectance,vegetation index,texture factor was selected from satellite images.The correlation analysis of feature factors and forest stock volume were analyzed,and 18feature factors with the highest correlation are selected as independent variables.With the forest stock volume of a single sample plot as the dependent variable,the estimation model of forest stock volume was established by partial least squares method,support vector machine,random forest,artificial neural network.By analyzing the regression accuracy of each model,the appropriate method to build forest stock volume inversion model was Selected.Combined with the topographic characteristics and field survey results,the spatial distribution of forest volume was briefly analyzed.The main results of this study are as follows:(1)Before quantitative remote sensing inversion,the relative radiometric calibration of multi-view images involved in modeling can reduce the error caused by sensors and other factors,and improve the accuracy of the final model inversion.(2)Among the three feature factors extracted,the correlation between vegetation index and forest stock value is the highest,up to 0.44,followed by single-band reflectance,and the correlation between texture factor features and forest stock value is relatively low.The correlation between texture factor and forest volume increases with the increase of window size.(3)Compared with the four methods used for modeling inversion,the artificial neural network method has the highest accuracy,the root mean square error is 1.18 m~3,and the average absolute error is 0.96 m~3.The determination coefficient is 0.61.Finally,the inversion model constructed by neural network method was used to invert the forest volume of Beijing,and the forest volume of Beijing in 2019 was estimated to be 27.31million m~3.Compared with the results of the ninth forest resource inventory in Beijing(2016),the estimation accuracy of the model was 87.9%.Compared with the predicted forest volume of Beijing in 2019,the estimation accuracy of the model was 93.3%,which proved that the model can predict the value and distribution of forest volume in Beijing.(4)Combined with field investigation experience,by analyzing the distribution of forest volume in different slope directions,it is concluded that the distribution of forest volume in the back shady slope direction is generally higher than that in the sunny slope direction,and the forest volume value in the back shady slope direction is higher than that in the sunny slope direction,reaching 21%.The main reason is that the back shady slope is less affected by solar radiation,which is conducive to the enrichment of water and other nutrients,so as to use forest growth and increase forest volume distribution.更多还原