Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey

The potential impact of aquaculture on Ildırı Bay, Turkey, was assessed using remote sensing data collected over 37 d between September 2009 and February 2011. The dataset was improved by applying a local empirical neural network (NN) algorithm. Impact was evaluated in terms of total suspended matte...

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Autores principales:	F Bengil, K Can Bizsel
Formato:	article
Lenguaje:	EN
Publicado:	Inter-Research 2014
Materias:	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/b3b4dc9d267440ffa7a271bf5c9f6db2
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id	oai:doaj.org-article:b3b4dc9d267440ffa7a271bf5c9f6db2
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spelling	oai:doaj.org-article:b3b4dc9d267440ffa7a271bf5c9f6db22021-11-16T10:30:13ZAssessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey1869-215X1869-753410.3354/aei00115https://doaj.org/article/b3b4dc9d267440ffa7a271bf5c9f6db22014-12-01T00:00:00Zhttps://www.int-res.com/abstracts/aei/v6/n1/p67-79/https://doaj.org/toc/1869-215Xhttps://doaj.org/toc/1869-7534The potential impact of aquaculture on Ildırı Bay, Turkey, was assessed using remote sensing data collected over 37 d between September 2009 and February 2011. The dataset was improved by applying a local empirical neural network (NN) algorithm. Impact was evaluated in terms of total suspended matter (TSM) and Secchi disk depth (SDD) as effective variables showing changes in underwater light fields in each defined subarea. Subareas were farm sites with their peripheries (impact zones) and the whole study area for 2 different regions within the bay. Real-time datasets of TSM and SDD were obtained for 7 different days within the same period. To create an NN algorithm, the full swath of geo-located products (with 300 m resolution) from the MERIS sensor aboard ENVISAT was used along with in situ data. The NN algorithm showed good performance, with an accuracy of 97.46% for TSM and 99.58% for SDD. No significant (Fs > 0.05) impact on the environment was observed; however, the time series analyses of similarities and anomalies showed that the impact zones have different temporal characteristics compared to the whole bay and vice versa. The highest particle concentrations and lowest light penetration occurred in the spring and summer. Water circulation patterns were identified as the major force determining the distribution and hence the source of particles and were also applied to reflect the particle loads introduced by feeding activity performed in aquaculture facilities. The influence of dissolved organic carbon to TSM and SDD indicates that the contribution of colored dissolved organic matter is another important variable for effective monitoring of aquaculture activity in the bay.F BengilK Can BizselInter-ResearcharticleAquaculture. Fisheries. AnglingSH1-691EcologyQH540-549.5ENAquaculture Environment Interactions, Vol 6, Iss 1, Pp 67-79 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5
spellingShingle	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 F Bengil K Can Bizsel Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey
description	The potential impact of aquaculture on Ildırı Bay, Turkey, was assessed using remote sensing data collected over 37 d between September 2009 and February 2011. The dataset was improved by applying a local empirical neural network (NN) algorithm. Impact was evaluated in terms of total suspended matter (TSM) and Secchi disk depth (SDD) as effective variables showing changes in underwater light fields in each defined subarea. Subareas were farm sites with their peripheries (impact zones) and the whole study area for 2 different regions within the bay. Real-time datasets of TSM and SDD were obtained for 7 different days within the same period. To create an NN algorithm, the full swath of geo-located products (with 300 m resolution) from the MERIS sensor aboard ENVISAT was used along with in situ data. The NN algorithm showed good performance, with an accuracy of 97.46% for TSM and 99.58% for SDD. No significant (Fs > 0.05) impact on the environment was observed; however, the time series analyses of similarities and anomalies showed that the impact zones have different temporal characteristics compared to the whole bay and vice versa. The highest particle concentrations and lowest light penetration occurred in the spring and summer. Water circulation patterns were identified as the major force determining the distribution and hence the source of particles and were also applied to reflect the particle loads introduced by feeding activity performed in aquaculture facilities. The influence of dissolved organic carbon to TSM and SDD indicates that the contribution of colored dissolved organic matter is another important variable for effective monitoring of aquaculture activity in the bay.
format	article
author	F Bengil K Can Bizsel
author_facet	F Bengil K Can Bizsel
author_sort	F Bengil
title	Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey
title_short	Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey
title_full	Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey
title_fullStr	Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey
title_full_unstemmed	Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey
title_sort	assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for ildırı bay, turkey
publisher	Inter-Research
publishDate	2014
url	https://doaj.org/article/b3b4dc9d267440ffa7a271bf5c9f6db2
work_keys_str_mv	AT fbengil assessingtheimpactofaquaculturefarmsusingremotesensinganempiricalneuralnetworkalgorithmforildırıbayturkey AT kcanbizsel assessingtheimpactofaquaculturefarmsusingremotesensinganempiricalneuralnetworkalgorithmforildırıbayturkey
_version_	1718426569641295872

Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey

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