Although remote sensing data have been used to estimate total suspended matter （TSM） in coastal waters, it has limitations when applied to estuary waters in low spatial resolution situations. The spatial resolution ...Although remote sensing data have been used to estimate total suspended matter （TSM） in coastal waters, it has limitations when applied to estuary waters in low spatial resolution situations. The spatial resolution of ocean color satellites such as SeaWiFS and MODIS is usually -1 km, and therefore is not adequate for small, local-scale areas such as the Zhujiang （Pearl） River estuary. In contrast, 30 m-resolution EO-1 Hyperion imagery has potential for studying TSM in localized areas. We measured the surface spectral radiance reflectance of the river estuary water in the visible and near infra-red spectral range. Sensitivity analysis indicated that the ratio of remote sensing reflectance at 813 nm （Rrs（813）） to reflectance at 559 nm （Rrs（559）） could be used to estimate TSM concentration, and a linear relationship was established between the ratio and in-situ TSM concentration. We applied the linear relationship to Hyperion imagery to map TSM concentration in the estuary. The Hyperion imagery provided sufficient spatial resolution to detect spatiotemporal changes in TSM concentrations in the estuary small estuary area. This study demonstrated the usefulness of Hyperion imagery for mapping the distribution of TSM in estuary waters. Keyword： Hyperion; total suspended matter （TSM）; Zhujiang （Pearl） River estuary展开更多
There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling ...There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.展开更多
基金Supported by the National Natural Science Foundation of China(No. 40976106)the Science Foundation Program of Guangdong Ocean University (No. 1012339)the Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics of Second Institute of Oceanography, SOA (No. SODE1203)
文摘Although remote sensing data have been used to estimate total suspended matter （TSM） in coastal waters, it has limitations when applied to estuary waters in low spatial resolution situations. The spatial resolution of ocean color satellites such as SeaWiFS and MODIS is usually -1 km, and therefore is not adequate for small, local-scale areas such as the Zhujiang （Pearl） River estuary. In contrast, 30 m-resolution EO-1 Hyperion imagery has potential for studying TSM in localized areas. We measured the surface spectral radiance reflectance of the river estuary water in the visible and near infra-red spectral range. Sensitivity analysis indicated that the ratio of remote sensing reflectance at 813 nm （Rrs（813）） to reflectance at 559 nm （Rrs（559）） could be used to estimate TSM concentration, and a linear relationship was established between the ratio and in-situ TSM concentration. We applied the linear relationship to Hyperion imagery to map TSM concentration in the estuary. The Hyperion imagery provided sufficient spatial resolution to detect spatiotemporal changes in TSM concentrations in the estuary small estuary area. This study demonstrated the usefulness of Hyperion imagery for mapping the distribution of TSM in estuary waters. Keyword： Hyperion; total suspended matter （TSM）; Zhujiang （Pearl） River estuary
基金supported by the National Natural Science Foundation of China(Grant Nos.41272359&11001019)the Specialized Research Fund for the Doctoral Program of Higher Education(SRFDP)the Fundamental Research Funds for the Central Universities
文摘There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.
