An improved water-depth correction algorithm for seagrass mapping using hyperspectral data

Abstract

Abstract Remote detection of seagrass has been limited because of numerous factors including the influence of the water column, which interferes with reflected signals from the seafloor. In a previously published study, a water-depth correction algorithm was developed to improve the detection of underwater vegetation spectral signals. The algorithm successfully corrected laboratory-measured submerged vegetation spectra for water effects, but the water absorption coefficients, derived from the data collected over a white surface, tended to underestimate the actual water absorption when applied to hyperspectral image data. The experimental conditions were modified to reduce the errors associated with the effects of enhanced multi-path scattering, to improve the algorithm using the new empirical data and to apply the algorithm to an airborne hyperspectral image data obtained over Halodule wrightii seagrass beds at Grand Bay National Estuarine Research Reserve, Mississippi, USA. The water absorption and scattering factors (A w and R w) for a water depth of 40 cm (the local water thickness above the seagrass canopy measured in the field) were applied to the image data to obtain the reflectance that is attributed to the water bottom surface including bare sand and seagrass beds. The contrast between the dark Halodule patches and the bright sand increased in the bands between 500 and 800 nm after the correction. The correction algorithm also increased the normalized difference vegetation index (NDVI) values for the seagrass pixels by restoring the upwelling signal in the near-infrared. Acknowledgements This research is supported by the grants from the National Geospatial-Intelligence Agency (Grant Nos. HM1582-07-1-2005 and HM1582-08-1-0049) and the National Oceanic and Atmospheric Administration–Environmental Cooperative Sciences Center (NOAA–ECSC; Grant No. NA17AE1626, Subcontract # 27-0629-017 to Jackson State University). The image data were collected and pre-processed by the Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln, and distributed through NOAA-ECSC. We sincerely thank Dr. John Schalles of Creighton University for sharing the water quality data and the Jackson State University students, Philemon Kirui and Marvin Washington, who assisted with the field and experimental data collection.