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红树林是潮滩木本植物群落,其光谱和陆生植被极其相似。利用EO-1卫星ALI(advanced land imager)获取的深圳湾区域影像数据,针对处于水分吸收带的波段5P和波段5,提出了这两个波段的角度指数(angle index),分别表示为b1.25和b1.65。以b1.25-b1.65和归一化差值植被指数(normalized difference vegetation index,NDVI)分类特征,采用决策树方法,开展了红树林遥感识别实验。研究结果表明,红树林独特的滨海湿地特点,使得其像元反射率在波段5P和波段5明显低于陆生植被,从而导致红树林的b1.25-b1.65值明显大于陆生植被;通过结合b1.25-b1.65和NDVI分类特征的决策树方法,能够对红树林进行有效识别,其错分率和漏分率分别为4.29%和5.11%。因此,具有众多红外波段的ALI遥感器在红树林识别中能够发挥重要作用。
Mangroves are tidal flat woody plant communities whose spectra are very similar to terrestrial vegetation. Using the image data of Shenzhen Bay acquired by advanced land imager (EO-1) satellite, the angle indices of these two bands are proposed for band 5P and band 5 in the water absorption band, which are denoted as b1. 25 and b1.65. Based on the classification features of b1.25-b1.65 and normalized difference vegetation index (NDVI), a mangrove remote sensing identification experiment was carried out using the decision tree method. The results show that the mangrove unique coastal wetland features that the pixel reflectivity is significantly lower than that of terrestrial vegetation in 5P and 5 bands, resulting in the b1.25-b1.65 value of mangrove forests being significantly larger than that of terrestrial vegetation. The mangroves can be effectively identified by combining the decision tree method of b1.25-b1.65 and NDVI classification, with the misclassification rate and leakage fraction of 4.29% and 5.11% respectively. Therefore, ALI remote sensors with numerous infrared bands can play an important role in mangrove recognition.