Remote Estimation of Biomass of emUlva prolifera/em Macroalgae in the Yellow Sea

Lianbo Hu; Chuanmin Hu; HE Ming-Xia

doi:10.1016/j.rse.2017.01.037

Remote Estimation of Biomass of emUlva prolifera/em Macroalgae in the Yellow Sea

Lianbo Hu, Chuanmin Hu, HE Ming-Xia

College of Marine Science

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

Abstract

Since 2008, macroalgal blooms of Ulva prolifera (also called green tides) occurred every summer in the Yellow Sea (YS), causing environmental and economic problems. A number of studies have used satellite observations to estimate the severity of the blooms through estimating the bloom size and duration. However, a critical bloom parameter, namely biomass, has never been objectively determined due to lack of measurements. In this study, laboratory experiments were conducted to measure U. prolifera biomass (wet weight) per unit area and the corresponding <a title="Learn more about Spectral Reflectance from ScienceDirect's AI-generated Topic Pages"> spectral reflectance </a> , through which a robust relationship has been established to link biomass per area to the reflectance-based floating algae index (FAI). The lab-based model has been validated with in situ measurements, with an estimated relative uncertainty of < 16% for algae with FAI values < 0.2 (corresponding to ~ 2 kg/m 2 biomass and accounting for > 99.5% of the algae-containing pixels in satellite images). The model was further transferred to <a title="Learn more about MODIS from ScienceDirect's AI-generated Topic Pages"> MODIS </a> Rayleigh-corrected reflectance ( R rc ), where aerosol impacts on the model were simulated under various atmospheric conditions. The simulations showed an average of 6.5% (up to 12.3% for the extreme case) uncertainties in biomass estimates when MODIS R rc data were used as the model inputs. The dry biomass per wet biomass and carbon and nitrogen contents per dry biomass were also determined through lab experiments, thus making their estimation possible from MODIS R rc data. The model was then applied to time-series of MODIS observations over the YS between 2008 and 2015 to determine the inter-annual variability of these critical parameters. Results showed maximum daily biomass of > 1.7 million tons during June 2015 and minimum daily biomass of < 0.09 million tons during 2012. The ability to estimate U. prolifera biomass at given locations from the near real-time MODIS images is expected to significantly enhance the capacity of an existing monitoring system to provide quantitative information for decision making.

Original language	American English
Journal	Remote Sensing of Environment
Volume	192
DOIs	https://doi.org/10.1016/j.rse.2017.01.037
State	Published - Jan 1 2017

Keywords

Ulva prolifera
Green tide
MODIS
Remote sensing
Biomass
Carbon
Nitrogen

Disciplines

Life Sciences

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Remote Estimation of Biomass of em Ulva prolifera em MacroalgaFinal published version, 2.14 MBLicense: CC BY

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@article{d33a6cefdbd348be8ee0bdeadbe2d050,

title = "Remote Estimation of Biomass of emUlva prolifera/em Macroalgae in the Yellow Sea",

abstract = " Since 2008, macroalgal blooms of Ulva prolifera (also called green tides) occurred every summer in the Yellow Sea (YS), causing environmental and economic problems. A number of studies have used satellite observations to estimate the severity of the blooms through estimating the bloom size and duration. However, a critical bloom parameter, namely biomass, has never been objectively determined due to lack of measurements. In this study, laboratory experiments were conducted to measure U. prolifera biomass (wet weight) per unit area and the corresponding spectral reflectance , through which a robust relationship has been established to link biomass per area to the reflectance-based floating algae index (FAI). The lab-based model has been validated with in situ measurements, with an estimated relative uncertainty of < 16% for algae with FAI values < 0.2 (corresponding to ~ 2 kg/m 2 biomass and accounting for > 99.5% of the algae-containing pixels in satellite images). The model was further transferred to MODIS Rayleigh-corrected reflectance ( R rc ), where aerosol impacts on the model were simulated under various atmospheric conditions. The simulations showed an average of 6.5% (up to 12.3% for the extreme case) uncertainties in biomass estimates when MODIS R rc data were used as the model inputs. The dry biomass per wet biomass and carbon and nitrogen contents per dry biomass were also determined through lab experiments, thus making their estimation possible from MODIS R rc data. The model was then applied to time-series of MODIS observations over the YS between 2008 and 2015 to determine the inter-annual variability of these critical parameters. Results showed maximum daily biomass of > 1.7 million tons during June 2015 and minimum daily biomass of < 0.09 million tons during 2012. The ability to estimate U. prolifera biomass at given locations from the near real-time MODIS images is expected to significantly enhance the capacity of an existing monitoring system to provide quantitative information for decision making. ",

keywords = "Ulva prolifera, Green tide, MODIS, Remote sensing, Biomass, Carbon, Nitrogen",

author = "Lianbo Hu and Chuanmin Hu and HE Ming-Xia",

year = "2017",

month = jan,

day = "1",

doi = "10.1016/j.rse.2017.01.037",

language = "American English",

volume = "192",

journal = "Remote Sensing of Environment",

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T1 - Remote Estimation of Biomass of emUlva prolifera/em Macroalgae in the Yellow Sea

AU - Hu, Lianbo

AU - Hu, Chuanmin

AU - Ming-Xia, HE

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Since 2008, macroalgal blooms of Ulva prolifera (also called green tides) occurred every summer in the Yellow Sea (YS), causing environmental and economic problems. A number of studies have used satellite observations to estimate the severity of the blooms through estimating the bloom size and duration. However, a critical bloom parameter, namely biomass, has never been objectively determined due to lack of measurements. In this study, laboratory experiments were conducted to measure U. prolifera biomass (wet weight) per unit area and the corresponding spectral reflectance , through which a robust relationship has been established to link biomass per area to the reflectance-based floating algae index (FAI). The lab-based model has been validated with in situ measurements, with an estimated relative uncertainty of < 16% for algae with FAI values < 0.2 (corresponding to ~ 2 kg/m 2 biomass and accounting for > 99.5% of the algae-containing pixels in satellite images). The model was further transferred to MODIS Rayleigh-corrected reflectance ( R rc ), where aerosol impacts on the model were simulated under various atmospheric conditions. The simulations showed an average of 6.5% (up to 12.3% for the extreme case) uncertainties in biomass estimates when MODIS R rc data were used as the model inputs. The dry biomass per wet biomass and carbon and nitrogen contents per dry biomass were also determined through lab experiments, thus making their estimation possible from MODIS R rc data. The model was then applied to time-series of MODIS observations over the YS between 2008 and 2015 to determine the inter-annual variability of these critical parameters. Results showed maximum daily biomass of > 1.7 million tons during June 2015 and minimum daily biomass of < 0.09 million tons during 2012. The ability to estimate U. prolifera biomass at given locations from the near real-time MODIS images is expected to significantly enhance the capacity of an existing monitoring system to provide quantitative information for decision making.

AB - Since 2008, macroalgal blooms of Ulva prolifera (also called green tides) occurred every summer in the Yellow Sea (YS), causing environmental and economic problems. A number of studies have used satellite observations to estimate the severity of the blooms through estimating the bloom size and duration. However, a critical bloom parameter, namely biomass, has never been objectively determined due to lack of measurements. In this study, laboratory experiments were conducted to measure U. prolifera biomass (wet weight) per unit area and the corresponding spectral reflectance , through which a robust relationship has been established to link biomass per area to the reflectance-based floating algae index (FAI). The lab-based model has been validated with in situ measurements, with an estimated relative uncertainty of < 16% for algae with FAI values < 0.2 (corresponding to ~ 2 kg/m 2 biomass and accounting for > 99.5% of the algae-containing pixels in satellite images). The model was further transferred to MODIS Rayleigh-corrected reflectance ( R rc ), where aerosol impacts on the model were simulated under various atmospheric conditions. The simulations showed an average of 6.5% (up to 12.3% for the extreme case) uncertainties in biomass estimates when MODIS R rc data were used as the model inputs. The dry biomass per wet biomass and carbon and nitrogen contents per dry biomass were also determined through lab experiments, thus making their estimation possible from MODIS R rc data. The model was then applied to time-series of MODIS observations over the YS between 2008 and 2015 to determine the inter-annual variability of these critical parameters. Results showed maximum daily biomass of > 1.7 million tons during June 2015 and minimum daily biomass of < 0.09 million tons during 2012. The ability to estimate U. prolifera biomass at given locations from the near real-time MODIS images is expected to significantly enhance the capacity of an existing monitoring system to provide quantitative information for decision making.

KW - Ulva prolifera

KW - Green tide

KW - MODIS

KW - Remote sensing

KW - Biomass

KW - Carbon

KW - Nitrogen

UR - https://digitalcommons.usf.edu/msc_facpub/1967

U2 - 10.1016/j.rse.2017.01.037

DO - 10.1016/j.rse.2017.01.037

M3 - Article

VL - 192

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

ER -

Remote Estimation of Biomass of emUlva prolifera/em Macroalgae in the Yellow Sea

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