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 | Acesso ao texto completo restrito à biblioteca da Embrapa Pesca e Aquicultura. Para informações adicionais entre em contato com cnpaf.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Pesca e Aquicultura. |
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Data corrente: |
08/11/2024 |
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Data da última atualização: |
08/11/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SILVA, B. A.; UMMUS, M. E.; HAYAKAWA, E. H.; BENNERT, A.; ADAMI, M.; TROMBINI, C. B.; FEIDEN, A.; VASCO, K. L.; BRITO, A. G. |
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Afiliação: |
BRUNO APARECIDO SILVA, UNIVERSIDADE ESTADUAL DO OESTE DO PARANÁ; MARTA EICHEMBERGER UMMUS, CNPASA; ERICSON HIDEKI HAYAKAWA, UNIVERSIDADE ESTADUAL DO OESTE DO PARANÁ; ALTAIR BENNERT, UNIVERSIDADE ESTADUAL DO OESTE DO PARANÁ; MARCOS ADAMI, INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS; CAROLINA BALERA TROMBINI, BIOPARK EDUCAÇÃO; ALDI FEIDEN, UNIVERSIDADE ESTADUAL DO OESTE DO PARANÁ; KENNEDY LEOCADIO VASCO, BOLSISTA DO PROJETO AMAZONIA +10; ALECSANDER GOMES BRITO, CONSULTOR DO PROJETO AMAZONIA +10. |
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Título: |
Mapping aquaculture in inland continental areas of Brazil using machine learning on the Google Earth Engine. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Remote Sensing Applications: Society and Environment, v. 36, 101391, 2024. |
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ISSN: |
2352-9385 |
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DOI: |
https://doi.org/10.1016/j.rsase.2024.101391 |
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Idioma: |
Inglês |
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Conteúdo: |
Aquaculture has played a significant and growing role in the provision of food, food security, and job creation, while simultaneously establishing itself as an important agricultural activity subject to increasingly stringent market practices. The generation of spatial information on aquaculture activity in Brazil can support more assertive planning of new enterprises as well as enable the monitoring of this activity over time, fulfilling an agenda of sustainable aquaculture growth in the country. The aim of this article was to map aquaculture areas in the state of Paraná (Brazil) by developing an automated methodology for the extraction of aquaculture ponds. The methodology was implemented on the Google Earth Engine (GEE) platform and utilized satellite images with a spatial resolution of 4.77 m from Norway’s International Climate and Forest Initiative (NICFI) Program – Planet. The process involved pixel-oriented classification of satellite images and the utilization of the Random Forest (RF) algorithm for classification, based on 1200 training samples. The accuracy of the mapping and the algorithm was evaluated using Producer Accuracy (PA), User Accuracy (UA), and Overall Accuracy (OA). The mapping achieved an OA of 0.87, with a PA of 0.89 and a UA of 0.96 for the aquaculture class, and a PA of 0.66 and a UA of 0.41 for the non-aquaculture class. 42,369 aquaculture ponds were identified in the study area, covering a total area of 11,515 ha. The ponds had sizes smaller than 0.7 ha and non-circular shapes (compactness 0.70). Around 40% of the identified aquaculture tanks were concentrated in just two out of the ten analyzed mesoregions, accounting for 40% of the water surface area used for aquaculture in Paraná. Considering that the results obtained show potential to fill the gap in geospatial information about state aquaculture, our methodology would provide a broad understanding of the state’s aquaculture framework, definition of regions with capacity for the expansion of aquaculture ponds, accuracy in estimating the state’s productive space, monitoring the improper expansion of aquaculture areas, as well as establishing locations where the pressure on water resources by aquaculture prevents the expansion of this activity. Therefore, this study could benefit public and private organizations, and other stakeholders, by systematically monitoring aquaculture production in the country’s leading fish farming state, contributing to assertive decisions in regions where aquaculture production needs to expand sustainably. MenosAquaculture has played a significant and growing role in the provision of food, food security, and job creation, while simultaneously establishing itself as an important agricultural activity subject to increasingly stringent market practices. The generation of spatial information on aquaculture activity in Brazil can support more assertive planning of new enterprises as well as enable the monitoring of this activity over time, fulfilling an agenda of sustainable aquaculture growth in the country. The aim of this article was to map aquaculture areas in the state of Paraná (Brazil) by developing an automated methodology for the extraction of aquaculture ponds. The methodology was implemented on the Google Earth Engine (GEE) platform and utilized satellite images with a spatial resolution of 4.77 m from Norway’s International Climate and Forest Initiative (NICFI) Program – Planet. The process involved pixel-oriented classification of satellite images and the utilization of the Random Forest (RF) algorithm for classification, based on 1200 training samples. The accuracy of the mapping and the algorithm was evaluated using Producer Accuracy (PA), User Accuracy (UA), and Overall Accuracy (OA). The mapping achieved an OA of 0.87, with a PA of 0.89 and a UA of 0.96 for the aquaculture class, and a PA of 0.66 and a UA of 0.41 for the non-aquaculture class. 42,369 aquaculture ponds were identified in the study area, covering a total area of 11,515 ha. The ponds had sizes smaller than... Mostrar Tudo |
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Palavras-Chave: |
Floresta aleatória; Geometric attributes; Google earth engine; Índices espectrais; Machine learning. |
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Thesagro: |
Aquicultura; Floresta. |
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Thesaurus Nal: |
Aquaculture. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 03582naa a2200337 a 4500
001 2168969
005 2024-11-08
008 2024 bl uuuu u00u1 u #d
022 $a2352-9385
024 7 $ahttps://doi.org/10.1016/j.rsase.2024.101391$2DOI
100 1 $aSILVA, B. A.
245 $aMapping aquaculture in inland continental areas of Brazil using machine learning on the Google Earth Engine.$h[electronic resource]
260 $c2024
520 $aAquaculture has played a significant and growing role in the provision of food, food security, and job creation, while simultaneously establishing itself as an important agricultural activity subject to increasingly stringent market practices. The generation of spatial information on aquaculture activity in Brazil can support more assertive planning of new enterprises as well as enable the monitoring of this activity over time, fulfilling an agenda of sustainable aquaculture growth in the country. The aim of this article was to map aquaculture areas in the state of Paraná (Brazil) by developing an automated methodology for the extraction of aquaculture ponds. The methodology was implemented on the Google Earth Engine (GEE) platform and utilized satellite images with a spatial resolution of 4.77 m from Norway’s International Climate and Forest Initiative (NICFI) Program – Planet. The process involved pixel-oriented classification of satellite images and the utilization of the Random Forest (RF) algorithm for classification, based on 1200 training samples. The accuracy of the mapping and the algorithm was evaluated using Producer Accuracy (PA), User Accuracy (UA), and Overall Accuracy (OA). The mapping achieved an OA of 0.87, with a PA of 0.89 and a UA of 0.96 for the aquaculture class, and a PA of 0.66 and a UA of 0.41 for the non-aquaculture class. 42,369 aquaculture ponds were identified in the study area, covering a total area of 11,515 ha. The ponds had sizes smaller than 0.7 ha and non-circular shapes (compactness 0.70). Around 40% of the identified aquaculture tanks were concentrated in just two out of the ten analyzed mesoregions, accounting for 40% of the water surface area used for aquaculture in Paraná. Considering that the results obtained show potential to fill the gap in geospatial information about state aquaculture, our methodology would provide a broad understanding of the state’s aquaculture framework, definition of regions with capacity for the expansion of aquaculture ponds, accuracy in estimating the state’s productive space, monitoring the improper expansion of aquaculture areas, as well as establishing locations where the pressure on water resources by aquaculture prevents the expansion of this activity. Therefore, this study could benefit public and private organizations, and other stakeholders, by systematically monitoring aquaculture production in the country’s leading fish farming state, contributing to assertive decisions in regions where aquaculture production needs to expand sustainably.
650 $aAquaculture
650 $aAquicultura
650 $aFloresta
653 $aFloresta aleatória
653 $aGeometric attributes
653 $aGoogle earth engine
653 $aÍndices espectrais
653 $aMachine learning
700 1 $aUMMUS, M. E.
700 1 $aHAYAKAWA, E. H.
700 1 $aBENNERT, A.
700 1 $aADAMI, M.
700 1 $aTROMBINI, C. B.
700 1 $aFEIDEN, A.
700 1 $aVASCO, K. L.
700 1 $aBRITO, A. G.
773 $tRemote Sensing Applications: Society and Environment$gv. 36, 101391, 2024.
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Embrapa Pesca e Aquicultura (CNPASA) |
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| 1. |  | RODIGHERI, G.; FONTANA, D. C.; SCHAPARINI, L. P.; DALMAGO, G. A.; SCHIRMBECK, J. Net primary productivity and dry matter in soybean cultivation utilizing datas of ndvi multi-sensors. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3/W12-2020, IEEE Latin American GRSS & ISPRS Remote Sensing Conference (LAGIRS 2020), 22?26 March 2020, Santiago, Chile, 2020.| Tipo: Artigo em Anais de Congresso |
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