摘要
为了实现快速、准确、大面积监测马尾松赤枯病，促进高光谱遥感技术在森林病虫害监测中的应用，通过获取不同严重度的马尾松赤枯病冠层高光谱数据，将冠层光谱、一阶微分和病情严重度数据分别进行相关分析，采用单变量线性回归和多变量逐步回归技术建立马尾松赤枯病病情严重度的反演模型。结果表明：随病情严重度的增加，可见光范围的冠层反射率逐渐增加，近红外波段的冠层反射率逐渐降低，其中在红边（680-780nm）区域变化最大，且病情严重度与红边特征参数存在显著线性关系；以红边特征参数为自变量建立的多变量逐步回归模型，比单变量缌性模型反演病情严重度的效果更好，其拟合帮、预测屠和均方根误差分别为0．815、0．778和0．053，说明红边特征参数对马尾松赤枯病病情严重度具有很好的指示作用。
In order to monitor Pestalotiopsis funerea desm fast, accurately and extensively and promote application of hyper-spectral remote sensing in monitoring forest diseases and insect pests, through high spectral data in different severity of P. funerea desm, relevant analysis was performed among canopy spectrum, first derivative data and the disease severity data, the inversion models of the P. funerea desm severity were built by single variable linear regression and multivariate stepwise regression techniques. The results showed that with the increase of severity level, the canopy reflectivity in visible region enhanced gradually, but the reflectivity in near infrared region weakened gently, and the greatest change in the region of 680-780 nm occurred. There was a significant linear relationship between the disease severity and the red edge feature parameters, and the multivariate stepwise regression model established by using the red edge feature parameters as independent variables was better than the single-variable linear model inversing severity level of the P. funereadesm. The fitting R^2, forecasting R^2 and RMSEwas 0.815, 0.778 and 0.053 respectively, which showed that the red edge feature parameters had excellent indication function on severity level.
出处
《中国农学通报》
CSCD
2012年第4期51-57,共7页
Chinese Agricultural Science Bulletin
基金
林业公益性行业科研专项经费项目"南方速生丰产林健康与活力维护技术研究"(200904006
201004014)
关键词
马尾松赤枯病
病情严重度
高光谱
红边特征参数
反演模型
P. funerea desm
severity level
high spectrum
red edge feature parameters
inversion models
