On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass

Lianbo Hu; Kan Zeng; Chuanmin Hu; Ming-Xia He

doi:10.1016/j.rse.2019.01.014

On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass

Lianbo Hu, Kan Zeng, Chuanmin Hu, Ming-Xia He

College of Marine Science

Research output: Contribution to journal › Article › peer-review

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 <a title="Learn more about satellite remote sensing from ScienceDirect's AI-generated Topic Pages"> satellite remote sensing </a> 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 (T 0 and T 1 ) 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, FAI sw , is constructed to determine T 0 , which varies for different algae patches. Then, T 1 is determined from water tank and <a title="Learn more about in situ measurements from ScienceDirect's AI-generated Topic Pages"> in situ measurements </a> as well as <a title="Learn more about radiative transfer from ScienceDirect's AI-generated Topic Pages"> radiative transfer </a> simulations to account for different sensor configurations, solar/viewing geometry, and atmospheric conditions. Such determined T 1 for <a title="Learn more about MODIS from ScienceDirect's AI-generated Topic Pages"> MODIS </a> 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 <a title="Learn more about turbidity from ScienceDirect's AI-generated Topic Pages"> turbidity </a> , and sunglint can lead to an underestimation of Ulva coverage of up to 45% under extreme conditions.

Original language	American English
Journal	Remote Sensing of Environment
Volume	223
DOIs	https://doi.org/10.1016/j.rse.2019.01.014
State	Published - Jan 1 2019

Keywords

Ulva prolifera
Remote sensing
Areal coverage
Biomass

Disciplines

Life Sciences

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10.1016/j.rse.2019.01.014License: CC BY

https://doi.org/10.1016/j.rse.2019.01.014

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title = "On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass",

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 (T 0 and T 1 ) 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, FAI sw , is constructed to determine T 0 , which varies for different algae patches. Then, T 1 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 T 1 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. ",

keywords = "Ulva prolifera, Remote sensing, Areal coverage, Biomass",

author = "Lianbo Hu and Kan Zeng and Chuanmin Hu and Ming-Xia He",

year = "2019",

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doi = "10.1016/j.rse.2019.01.014",

language = "American English",

volume = "223",

journal = "Remote Sensing of Environment",

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AU - Hu, Lianbo

AU - Zeng, Kan

AU - Hu, Chuanmin

AU - He, Ming-Xia

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 (T 0 and T 1 ) 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, FAI sw , is constructed to determine T 0 , which varies for different algae patches. Then, T 1 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 T 1 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.

AB - 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 (T 0 and T 1 ) 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, FAI sw , is constructed to determine T 0 , which varies for different algae patches. Then, T 1 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 T 1 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.

KW - Ulva prolifera

KW - Remote sensing

KW - Areal coverage

KW - Biomass

UR - https://digitalcommons.usf.edu/msc_facpub/2033

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DO - 10.1016/j.rse.2019.01.014

M3 - Article

VL - 223

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

ER -

On the Remote Estimation of Ulva Prolifera Areal Coverage and Biomass

Abstract

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