基于无人机与卫星图像的大型绿藻孔石莼(Ulva pertusa)遥感估算研究

Remote sensing estimation of green macroalgae Ulva pertusa based on unmanned aerial vehicle and satellite image

卫星影像是监测海面漂浮绿藻的重要数据源,但是混合像元的存在使得绿藻提取存在一定的误差。想要实现近海区域底栖绿藻的精细监测,需要解决绿藻亚像素覆盖度的问题。本文以厘米级分辨率无人机数据的绿藻提取结果为基准,通过分析Landsat卫星影像绿藻光谱,建立绿藻亚像素覆盖度与多种植被指数和多个特征波段反射率的反演模型。结果表明,蓝、绿、红波段反射率与绿藻亚像素覆盖度呈现较好的线性关系,随着绿藻亚像素覆盖度递增,蓝、绿、红波段反射率的值均递减。将蓝、绿、红波段的三种绿藻亚像素覆盖模型进行验证,发现绿波段反射率所建立的反演模型具有更高的准确性,决定系数、均方根误差、平均相对误差分别为0.92%、0.07%、10.85%。本文所建立的模型可以估算大型绿藻亚像素覆盖度,实现Landsat卫星影像对大型绿藻的精细监测。

Satellite images are valuable data sources for monitoring floating green macroalgae on the sea surface. However,there are large errors in green macroalgae coverage derived on mixed pixels. It is thus important to solve the problem of sub-pixel coverage of green macroalgae, for precise monitoring of benthic green macroalgae in coastal area. In this paper,retrieval models were established to link sub-pixel coverage of green macroalgae with vegetation indexes and reflectance of characteristic bands by analyzing spectral characteristics of green macroalgae from the Landsat images, based on the results of green macroalgae coverage derived from unmanned aerial vehicle(UAV). The results show excellent linear relationships between the reflectance of blue, green, and red bands and the sub-pixel coverage of green macroalgae, and the reflectance decreases monotonically with increasing sub-pixel coverage. These three models were verified, and the results show that the model based on the reflectance of green band was more accuracy than the other indexes or index combination, with the highest coefficient of determination(R2), root mean square error(RMSE), and mean relative error(MRE) values of 0.92, 0.07, and 10.85%, respectively. Hence, we provide a model that can estimate the sub-pixel coverage of green macroalgae, and realize the precise monitoring of the coverage of green macroalgae extracted from Landsat images.