利用Sentinel-2多光谱影像构建一种潮滩提取指数

Constructing of Tidal Flat Extraction Index in Coastal Zones Using Sentinel-2 Multispectral Images

潮间带潮滩由于受到潮汐周期性淹没的影响导致难以精准确定其空间分布,因此,迫切需要利用遥感技术了解潮滩受潮汐淹没的光谱变化特征,构建潮滩提取指数,对潮滩的精准解译提供方法及基础数据支持。基于多时相Sentinel-2多光谱影像,通过分析高、低潮影像上不同地物的光谱反射率特征差异,优选出能反映潮滩特征的波段,构建一种海岸带潮滩提取指数。在此基础上,从三方面对已构建潮滩指数的可行性进行论证:(1)将潮滩提取指数应用到3个不同潮滩类型的研究区,研究了潮滩指数的可分离性及对不同潮滩类型区域的适用性,研究结果表明:与其他地类相比,构建的潮滩提取指数对潮滩具有较好的可分离性,并且适用于砂质、泥质不同种类的潮滩;(2)研究了潮滩提取指数对不同分类方法(包括最小距离法、极大似然法、支持向量机)的适用性,研究表明:采用所选取的三种分类方法进行潮滩解译时,其总体精度均大于93%,Kappa系数均大于0.85,潮滩提取指数对不同的分类方法均具有普适性,且可有效提高潮滩的解译精准度;(3)研究了潮滩提取指数对不同数据源的适宜性,采用“珠海一号”数据与本文Sentinel-2多光谱数据解译潮滩并对比结果,研究显示:构建的潮滩提取指数适用于不同数据源,且取得了较好的潮滩分类精度。该方法提高了海岸带潮滩遥感提取的准确度,丰富了潮滩遥感解译理论,对海岸带潮滩生态系统的科学管理与保护提供了理论指导与意义。

It is difficult to accurately determine the spatial distribution of tidal flats in intertidal zones due to periodic tidal inundation.Therefore,it is urgent to use remotely-sensed technology to detect the spectral variation characteristics of tidal flats,construct a tidal flat extraction index,and then provide methods and basic data support for flat tidal interpretation.Based on multi-temporal Sentinel-2 images,this research analyzed the spectral reflectance differences of different land cover types in the high-and low-tide images and then determined the bands that can reflect flat tidal characteristics.Finally,a tidal flat recognition index was proposed by mathematical combination.The proposed tidal flat index is studied:(1)the proposed tidal flat recognition index was applied to three study areas with different tidal flat types,and the tidal flat recognition index’s separability and applicability to different tidal flat types are studied.The results showed that the proposed tidal flat recognition index showed a good performance on tidal flat separability compared with other land cover types and is applicable to different types of sandy and muddy tidal flats;(2)the applicability of the tidal flat recognition index to different classification methods(including minimum distance method,maximum likelihood method and support vector machine)is studied.The results showed that the overall accuracy is greater than 93%,and the kappa coefficient is greater than 0.85 for distinguishing tidal flats.The tidal flat recognition index is universal to different classification methods and can effectively improve the accuracy of distinguishing tidal flats;(3)the suitability of the tidal flat recognition index to different remotely-sensed data sources is studied.Compared the Sentinel-2 images with OHS images,the results showed that the tidal flats are distinguished,and the tidal flat recognition index proposed is applicable to different data sources,achieving a higher classification accuracy.This study improves the accuracy of distinguishing tidal flats using remote sensing data,enriches the theory of flat tidal interpretation,and provides theoretical guidance and significance for the scientific management and protection of the coastal.