Talk:HyMap: Hyperspectral seafloor mapping and direct bathymetry calculation in littoral zones
Job Dronkers (august 2020):
The hyperspectral imaging method described in the article is based on the reflectance spectrum of sunlight (wavelength range of 400–2500 nm). Each underwater object has a particular reflectance spectrum ("optical fingerprint"). Hyperspectral sensors record the full spectrum of reflected light, in each pixel of the scanned seafloor. The reflectance spectrum is compared to reference spectra obtained from, e.g. a spectral library or field samples. Characteristics of underwater objects can be derived in this way. By collecting and analyzing the reflectence spectra a map of the seafloor coverage can be established with a resolution related to the pixel dimensions. Because sunlight is used as a source, this method is called passive hyperspectral imaging. Passive hyperspectral imaging methods are limited to coastal areas with shallow water depth, usually less than 10 m and less than 50 m in very clear water (Fearns et al., 2011[1]).
By using an artificial light source, hyperspectral sensors mounted on a remotely operated vehicle can provide maps of the seafloor at much greater depths. This is a promising method for environmental monitoring and habitat mapping in coastal waters where the penetration of sunlight is limited by depth and turbidity (Foglini et al., 2019[2]). A recent experiment shows that active hyperspectral imaging methods can be used to detect manganese nodules on the seafloor at depths of over 4 kilometers (Dumke et al., 2018[3]). Underwater hyperspectral imaging provides a new way to produce high-quality descriptions of underwater sediments, minerals, benthic habitats, and heritage sites; a review of equipment, methods and applications is presented in Liu et al. 2020[4]).
See also
Wikipedia article Hyperspectral imaging
References
- ↑ Fearns P.R.C., Klonowski W., Babcock R.C., England P., Phillips J. 2011. Shallow water substrate mapping using hyperspectral remote sensing. Cont. Shelf Res.v31: 1249–1259
- ↑ Foglini, F., Grande, V., Marchese, F., Bracchi, V. A., Prampolini, M., Angeletti, L., Castellan, G., Chimienti, G., Hansen, I. M., Gudmundsen, M., Meroni, A. N., Mercorella, A., Vertino, A., Badalamenti, F., Corselli, C., Erdal, I., Martorelli, E., Savini, A., & Taviani, M. 2019. Application of Hyperspectral Imaging to Underwater Habitat Mapping, Southern Adriatic Sea. Sensors 19(10), 2261
- ↑ Dumke, I., Norner, S.M., Purser, ., Marcon, Y., Ludvigsen, M., Ellefmod, S.L., Johnsen, G. and Soereida, F. 2018. First hyperspectral imaging survey of the deep seafloor: High-resolution mapping of manganese nodules. Remote Sensing of Environment 209: 19–30
- ↑ Liu, B., Liu, Z., Men, S., Li, Y., Ding, Z., He, J. and Zhao, Z. 2020. Underwater Hyperspectral Imaging Technology and Its Applications for Detecting and Mapping the Seafloor: A Review. Sensors 20, 4962. doi:10.3390/s20174962. Open access https://www.mdpi.com/1424-8220/20/17/4962
