摘要
以马尾松针叶野外高光谱数据为基础,分析了马尾松光谱变化,构建或借助不同光谱特征参数,在理论和实践分析的基础上,建立了马尾松针叶叶绿素含量与光谱反射率及9个特征参数之间的关系。研究结果表明:(1)马尾松叶绿素含量在527,703,1364及1640nm四个波长附近,与其反射率具有较好的线性关系,为马尾松遥感监测在波段选择上提供了依据;(2)红边位置、红边平均反射率、红边位置附近平均反射率、红边斜率、红边面积、红谷吸收深度、绿峰反射高度、红边归一化植被指数、红边植被胁迫指数等9个马尾松反射光谱特征参数均与叶绿素含量间存在指数函数关系,相关系数绝对值在0.5～0.7之间;(3)采用9个光谱特征参数建立了马尾松针叶叶绿素含量预测模型,且所建立的基于高斯核函数变换的偏最小二乘回归模型对叶绿素含量的预测精度远远大于传统线性回归模型,模型的均方误差为0.0088,平均绝对百分误差为0.7617%。
In the present study,the authors built the relationships between the total chlorophyll and hyperspectral features of P. massoniana. The research results showed that （1） chlorophyll content has a good linear relationship with spectral reflectance around 527,703,1 364 and 1 640 nm,and this result is helpful for us to select some important bands when monitoring P. massoniana by remote sensing image; （2） all of the nine kinds of spectral feature parameters including red edge position,mean reflectance of red edge,mean reflectance around red edge position,red edge slope,red edge area,absorption depth of red band,green peak height,red edge normalized difference vegetation index and red edge vegetation stress index,have exponential function relationship （r=0.5-0.7） with the total chlorophyll; （3） the total chlorophyll content can be predicted by multivariate model by the nine spectral feature parameters,and partial least-squares regression model have higher prediction accuracy than the traditional multivariate linear model. The model＇s root mean square （RMS） is 0.008 8,and mean absolute percentage error is 0.761 7%. During the growth of vegetation,biochemical parameters such as chlorophyll have vital function,for example,it can indicate the health status or pathological feature. So,the models mentioned just above will help us understand the ecological process of P. massoniana forest and provide valuable reference for monitoring P. massoniana and pine wood nematode disease by remote sensing technique.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2009年第11期3033-3037,共5页
Spectroscopy and Spectral Analysis
基金
国家"863"专题课题项目(2006AA12Z109)
浙江省林业厅项目(07A16)资助
