基于高光谱遥感的沉香冠层重要参数估测研究

Estimation of Important Canopy Parameters of Agarwood Based on Hyperspectral Remote Sensing

利用高光谱遥感手段构建沉香叶绿素含量和叶面积指数反演模型,是实现沉香树长势及健康状况精准诊断的关键。以沉香试验小区为基础,测定了6a生沉香树冠层光谱反射率及其对应的叶绿素含量和叶面积指数。结果表明:1)沉香树冠层光谱反射率与沉香树叶绿素含量和叶面积指数之间存在一定相关性,相关性因参数不同而存在差异;2)相关性分析表明,叶绿素含量与红外波段(760,761,759,765,764nm)处的光谱反射率关系最为密切,叶面积指数与红外波段(778,777,779,776,782nm)处的光谱反射率关系最为密切;3)相较于植被指数和特征波段拟合效果,得出基于BP神经网络建立的叶绿素含量和叶面积指数回归模型预测效果最好。总体认为高光谱技术结合BP神经网络法可以很好地监测沉香叶绿素含量和叶面积指数等参数值动态变化。

The use of hyperspectral remote sensing to construct an inversion model of agarwood chlorophyll content and leaf area index is the key to accurate diagnosis of agarwood tree growth and health.Based on the experimental plot of agarwood, the spectral reflectance of the canopy of 6-year-old agarwood and its corresponding chlorophyll content and leaf area index were measured.The result shows: 1)There was a certain correlation between the spectral reflectance of agarwood canopy and the chlorophyll content and leaf area index of agarwood leaves, and the correlation varied with different parameters;2)Correlation analysis showed that the chlorophyll content was the most closely related to the spectral reflectance in the infrared bands(760,761,759,765,764 nm),and the leaf area index was closely related to the spectral reflectance in the infrared band(778,777,779,776,782 nm);3)Compared with the fitting effect of vegetation index and characteristic band, it was concluded that the regression model of chlorophyll content and leaf area index based on neural network had the best prediction effect.Therefore, it is believed that hyperspectral technology combined with BP neural network method can monitor the dynamic changes of parameters well, such as chlorophyll content and leaf area index in agarwood.