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• W348955564 abstract "This study describes the development of a hyperspectral remote sensing method to mapand monitor submerged aquatic vegetation, meeting examination and assessment criteria foradoption in the European Water Framework Directive. Identifying macrophyte species using objective remote sensing methods can be a consistent and reliable means to map large areas of lakeshores for monitoring purposes, but only if the spectral properties of in situ species are distinct. To determine this, the spectral signatures of eight common aquatic macrophyte species (Potamogeton perfoliatus, P. pectinatus, Chara aspera, C. contraria,C. intermedia,C. tomentosa, Nitellopsis obtusa, Najas marina) were investigated to establish whether or not they contain sufficient information for species differentiation. To assess the range of spectal variability that may be found in each species, reflectance spectra of homogenous patches were measured in situ with a submersible spectroradiometer (RAMSES) in 2003 and 2004 at Lake Constance and Lake Starnberg, Germany. Results show that reflectance spectra of macrophyte species were significantly different for various spectral regions. A genetic algorithm (GALGO) technique was used to determine important waveband combinations ideal for remote sensing identification of substrate types. Statistical tests such as unsupervised classifications (Principal Component Analysis) and distance measure (Jeffries-Matusita) indices were used to confirm species separation. Cross-validation by linear discriminant analysis, a supervised classification approach, confirmed that in situ spectra could be used to discriminate between seven species with > 98% accuracy using as few as four optimally-positioned bands in the visible wavelengths. Classification method was applied to airborne hyperspectral remote sensing data from HyMap, acquired during the HyEurope flight campaign in 2003 and 2004. Images were corrected for atmospheric, air-water interface and water body effects using the physical based Modular Inversion & Processing System (MIP). After pre-processing, the hyperspectral data were classified to bottom cover classes by linear spectral unmixing. The result contains percent cover classes for short-growing macrophytes (e.g. Characeae), tall-growing macrophytes (e.g. P. pectinatus), and bottom sediments. A subsequent classification of pixels more than 70 % vegetation cover was performed on species level, producing a detailed macrophyte distribution map (in 4 × 4 m2 pixel resolution) to 4.5 m water depth. The results of this study demonstrate that it is possible to accurately detect and delineate submerged macrophytes using a hyperspectral remote sensing technique, and that the potential for species separation using advanced data-analysis techniques exists." @default.
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• W348955564 date "2007-01-17" @default.
• W348955564 modified "2023-09-27" @default.
• W348955564 title "A method for mapping submerged macrophytes inlakes using hyperspectral remote sensing" @default.
• W348955564 hasPublicationYear "2007" @default.
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