A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions

• Published in: Ocean dynamics 2017-02, Vol.67 (2), p.299-316
• Main Authors: Ibrahim, Elsy, Kim, Wonkook, Crawford, Melba, Monbaliu, Jaak
• Format: Article
• Language: English
• Subjects: Airborne sensing; Atmospheric Sciences; Belgium 7-11 September 2015; Earth and Environmental Science; Earth Sciences; Fluid- and Aerodynamics; Geophysics/Geodesy; Intertidal environment; Marine; Moisture content; Monitoring/Environmental Analysis; Oceanography; Organic matter; Principal components analysis; Regression analysis; Remote sensing; Sediments; Soil moisture; Topical Collection on the 13th International Conference on Cohesive Sediment Transport in Leuven
• ISSN: 1616-7341; 1616-7228
• DOI: 10.1007/s10236-016-1024-1

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.