Marine Science Faculty Publications
On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass
Document Type
Article
Publication Date
2019
Keywords
Ulva prolifera, Remote sensing, Areal coverage, Biomass
Digital Object Identifier (DOI)
https://doi.org/10.1016/j.rse.2019.01.014
Abstract
Since the outbreak of a large-scale Ulva prolifera bloom in the Yellow Sea during the Qingdao Olympic Sailing Competition in summer 2008, Ulva blooms have been a marine hazard every summer. Accurate and timely information on Ulva areal coverage and biomass is of critical importance for governmental responses, decision making, and field studies. Previous studies have shown that satellite remote sensing is the most effective method for this purpose, yet Ulva areal coverage has been estimated in different ways with significantly different results. The objective of this paper is to determine the lower and upper bounds (T0 and T1) of algae-containing pixels in Floating Algae Index images with an objective method that accurately estimates the Ulva areal coverage in individual images, and then converts coverage to biomass using a previously established conversion equation. First, a seawater background image, FAIsw, is constructed to determine T0, which varies for different algae patches. Then, T1 is determined from water tank and in situ measurements as well as radiative transfer simulations to account for different sensor configurations, solar/viewing geometry, and atmospheric conditions. Such determined T1 for MODIS 250-m resolution data is validated using concurrent and collocated 2-m resolution WorldView-2 data. Finally, Ulva areal coverage derived from MODIS data using this method are compared with those from the high-resolution data (OLI/Landsat, WFV/GaoFen-1), with a mean relative difference of 9.6%. Furthermore, an analysis of 17 same-day MODIS/Terra and MODIS/Aqua image pairs shows that large viewing angles, atmospheric turbidity, and sunglint can lead to an underestimation of Ulva coverage of up to 45% under extreme conditions.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Remote Sensing of Environment, v. 223, p. 194-207
Scholar Commons Citation
Hu, Lianbo; Zeng, Kan; Hu, Chuanmin; and He, Ming-Xia, "On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass" (2019). Marine Science Faculty Publications. 2033.
https://digitalcommons.usf.edu/msc_facpub/2033
