基于实测光谱数据的太湖水华和水生高等植物识别

Identification of algae-bloom and aquatic macrophytes in Lake Taihu from in-situ measured spectra data

水华和水生高等植物的识别对于内陆水质遥感监测至关重要,其分布状况既可以用于表征水生植物的分布状况,从而间接反映水质分布状况,又可以进一步利用非水华和水草的水体遥感数据进行水质参数反演.然而,常用的多光谱遥感数据很难精确识别水华和水草,只有高光谱遥感数据才能够捕捉水华、水草和水体细致的光谱差异,从而对水华和水草进行精确识别.但是目前还缺乏基于高光谱遥感数据的水华和水草识别的系统性研究.以太湖为研究区,于2006年7月和10月开展了2次水面光谱测量实验,获取了水华、浮叶植物、沉水植物和水体的反射率光谱.在光谱分析的基础上,首先建立了4种光谱指数,进而利用这4种光谱指数建立了水华、浮叶植物、沉水植物和水体的判别公式,并利用2006年10月水面实验测量的光谱数据训练得到了判别公式中的阈值.通过2006年7月水面实验测量的光谱数据的检验,证明提出的判别公式能够很好的识别水华和水草,获得了较高的识别精度.

Identification of algae-bloom and aquatic macrophytes plays a significant role in inland water quality monitoring by remote sensing, which can be used to reflect the water quality status indirectly, and then the result of water can be used to retrieve water quality parameters. However, the mostly used multi-spectral remote sensing data cannot accurately identify algae-bloom and water grass. Only hyperspectral remote sensing data, as the data can be distinguished the subtle differences of the spectral characteristics between algae-bloom and water grass, can be used to identify algae-bloom and water grass with high accuracy. Unfortunately, there have been few of profound researches on the identification of algae-bloom and water from hyperspectral remote sensing data. Lake Taihu is selected to be the study area. Two experiments were carried out in Lake Taihu in July and October of 2006. Reflectance spectra of the floating vegetation, submerged vegetation, algae-bloom, and water were measured. Based on the analysis of the measured spectra, four spectral indexes were defined to build up formulas for identification of the four items. Reflectance spectra measured in October 2006 were used to determine the threshold values in the identification formulas, and reflectance spectra measured in July 2006 were used to validate the identification formulas. The identification results were very good.