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Registro Completo |
Biblioteca(s): |
Embrapa Amapá. |
Data corrente: |
22/11/2018 |
Data da última atualização: |
16/06/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
BORGES, W. F.; OLIVEIRA, M. S. B. de; SANTOS, G. G.; TAVARES-DIAS, M. |
Afiliação: |
WILLIAM FELIX BORGES, UEAP; MARCOS SIDNEY BRITO DE OLIVEIRA, UNIFAP; GRACIENHE GOMES SANTOS, FAMA; MARCOS TAVARES DIAS, CPAF-AP. |
Título: |
Parasites in Loricariidae from Brazil: checklist and new records for fish from the Brazilian Amazon. |
Ano de publicação: |
2018 |
Fonte/Imprenta: |
Acta Scientiarum. Biological Sciences, v. 40, e40621, p. 1-9, 2018. |
DOI: |
10.4025/actascibiolsci.v40i1.40621 |
Idioma: |
Inglês |
Conteúdo: |
The aim of this study was to investigate the parasites fauna of Ancistrus leucostictus, Hypostomus ventromaculatus, Ancistrus sp. and Hemiancistrus sp. from the Igarapé Fortaleza River (Amapá State, Brazil), besides making a checklist of the parasite species in Loricariidae from Brazil. A total of 53 fishes were collected from November 2013 to August 2014. In the hosts, a total of 1,559 parasites of seven taxa were collected: Unilatus unilatus, Trinigyrus mourei, undetermined metacercariae, Genarchella gernachella, Raphidascaris (Sprentascaris) sp., Gorythocephalus elongorchis and Proteocephalus sp. Ectoparasite species were frequent in the examined Loricariidae species, which also had larval stages of endoparasites. The hosts with the highest sampled number, H. ventromaculatus and Ancistrus sp., had the highest parasite species richness. Loricariidae species from Brazil are parasitized by species of Protozoa, Monogenea, Nematoda, Digenea, Acantocephala, Cestoda, Crustacea and Hirudinea, but monogeneans, digeneans and nematodes were the predominant taxa. |
Palavras-Chave: |
Ectoparasite; Ectoparasito. |
Thesagro: |
Bagre; Hospedeiro Animal; Parasito de Animal; Peixe de Água Doce. |
Thesaurus Nal: |
Catfish; Freshwater fish; Hosts. |
Categoria do assunto: |
S Ciências Biológicas |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/186730/1/CPAF-AP-2018-Parasites-in-Loricariidae.pdf
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Marc: |
LEADER 01906naa a2200277 a 4500 001 2099852 005 2023-06-16 008 2018 bl uuuu u00u1 u #d 024 7 $a10.4025/actascibiolsci.v40i1.40621$2DOI 100 1 $aBORGES, W. F. 245 $aParasites in Loricariidae from Brazil$bchecklist and new records for fish from the Brazilian Amazon.$h[electronic resource] 260 $c2018 520 $aThe aim of this study was to investigate the parasites fauna of Ancistrus leucostictus, Hypostomus ventromaculatus, Ancistrus sp. and Hemiancistrus sp. from the Igarapé Fortaleza River (Amapá State, Brazil), besides making a checklist of the parasite species in Loricariidae from Brazil. A total of 53 fishes were collected from November 2013 to August 2014. In the hosts, a total of 1,559 parasites of seven taxa were collected: Unilatus unilatus, Trinigyrus mourei, undetermined metacercariae, Genarchella gernachella, Raphidascaris (Sprentascaris) sp., Gorythocephalus elongorchis and Proteocephalus sp. Ectoparasite species were frequent in the examined Loricariidae species, which also had larval stages of endoparasites. The hosts with the highest sampled number, H. ventromaculatus and Ancistrus sp., had the highest parasite species richness. Loricariidae species from Brazil are parasitized by species of Protozoa, Monogenea, Nematoda, Digenea, Acantocephala, Cestoda, Crustacea and Hirudinea, but monogeneans, digeneans and nematodes were the predominant taxa. 650 $aCatfish 650 $aFreshwater fish 650 $aHosts 650 $aBagre 650 $aHospedeiro Animal 650 $aParasito de Animal 650 $aPeixe de Água Doce 653 $aEctoparasite 653 $aEctoparasito 700 1 $aOLIVEIRA, M. S. B. de 700 1 $aSANTOS, G. G. 700 1 $aTAVARES-DIAS, M. 773 $tActa Scientiarum. Biological Sciences$gv. 40, e40621, p. 1-9, 2018.
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Registro original: |
Embrapa Amapá (CPAF-AP) |
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Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
22/06/2021 |
Data da última atualização: |
03/03/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 3 |
Autoria: |
PEREIRA, P. R. M.; COSTA, F. W. D.; BOLFE, E. L.; MACARRINGE, L.; BOTELHO, A. C. |
Afiliação: |
Unicamp; Unesp; EDSON LUIS BOLFE, Unicamp, CNPTIA; Unicamp; Unicamp. |
Título: |
Comparison of classification algorithms of images for the mapping of the land covering in Tasso Fragoso municipality, Brazil. |
Ano de publicação: |
2021 |
Fonte/Imprenta: |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V. V-3-2021, p. 167-173, 2021. |
DOI: |
https://doi.org/10.5194/isprs-annals-V-3-2021-167-2021 |
Idioma: |
Inglês |
Notas: |
This research is funded by the São Paulo Research Foundation (FAPESP), grant number 2019/26222-6. |
Conteúdo: |
Abstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformations of the terrestrial surface, this process being directly associated with the potential of classifiers to differentiate the most diverse data present in the images, a fundamental aspect for the use of remote sensing data. This article evaluates the performance of different classification algorithms in the mapping classes of land use and land cover in medium resolution images from the Landsat 8 program, the test area of this test corresponds to the Municipality of Tasso Fragoso (State Maranhão - Brazil), stands out for a typical vegetation cover of the Cerrado Biome, presents similar spectral patterns that induce high difficulty of class differentiation automatically. In this paper, were analyzed the machine learning algorithms C5.0 and Random Forest in comparison to traditional classification algorithms being the Minimum Distance and the Spectral Angle Mapper. The best results were generated by Random Forest with 90% accuracy and Kappa of 0.861, followed by the C5.0 algorithm. Traditional algorithms, on the other hand, presented a lower precision rate with global accuracy, not exceeding 75% of accuracy and Kappa varying between 0.507 and 0.627. The accuracy of the producer showed that all the algorithms, in major or minor tendency presented difficulties in to differentiate the areas, with rates of mistakes varying between 25 and 75%, being the main, the confusion with pastoral areas. MenosAbstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformations of the terrestrial surface, this process being directly associated with the potential of classifiers to differentiate the most diverse data present in the images, a fundamental aspect for the use of remote sensing data. This article evaluates the performance of different classification algorithms in the mapping classes of land use and land cover in medium resolution images from the Landsat 8 program, the test area of this test corresponds to the Municipality of Tasso Fragoso (State Maranhão - Brazil), stands out for a typical vegetation cover of the Cerrado Biome, presents similar spectral patterns that induce high difficulty of class differentiation automatically. In this paper, were analyzed the machine learning algorithms C5.0 and Random Forest in comparison to traditional classification algorithms being the Minimum Distance and the Spectral Angle Mapper. The best results were generated by Random Forest with 90% accuracy and Kappa of 0.861, followed by the C5.0 algorithm. Traditional algorithms, on the other hand, presented a lower precision rate with global accuracy, not exceeding 75% of accuracy and Kappa varying between 0.507 and 0.627. The accuracy of the producer showed that all the algorithms, in major or minor tendency presented difficulties in to differentiate th... Mostrar Tudo |
Palavras-Chave: |
Algoritmos de aprendizado de máquina; Bioma cerrado; Cerrado Biome; Classificação digital; Classification algorithms; Cobertura da terra; Digital Classification; Landsat 8; Machine learning algorithms; Maranhão State; Performance Indexes; Random Forest. |
Thesagro: |
Uso da Terra. |
Thesaurus NAL: |
Land cover; Land use. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/223984/1/PL-Comparison-classification-algorithm-ISPRS-2021.pdf
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Marc: |
LEADER 02934naa a2200373 a 4500 001 2132498 005 2023-03-03 008 2021 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.5194/isprs-annals-V-3-2021-167-2021$2DOI 100 1 $aPEREIRA, P. R. M. 245 $aComparison of classification algorithms of images for the mapping of the land covering in Tasso Fragoso municipality, Brazil.$h[electronic resource] 260 $c2021 500 $aThis research is funded by the São Paulo Research Foundation (FAPESP), grant number 2019/26222-6. 520 $aAbstract. One of the main applications of satellite images is the characterization of terrestrial coverage, that from the use of classification techniques, allows the monitoring of spatial transformations of the terrestrial surface, this process being directly associated with the potential of classifiers to differentiate the most diverse data present in the images, a fundamental aspect for the use of remote sensing data. This article evaluates the performance of different classification algorithms in the mapping classes of land use and land cover in medium resolution images from the Landsat 8 program, the test area of this test corresponds to the Municipality of Tasso Fragoso (State Maranhão - Brazil), stands out for a typical vegetation cover of the Cerrado Biome, presents similar spectral patterns that induce high difficulty of class differentiation automatically. In this paper, were analyzed the machine learning algorithms C5.0 and Random Forest in comparison to traditional classification algorithms being the Minimum Distance and the Spectral Angle Mapper. The best results were generated by Random Forest with 90% accuracy and Kappa of 0.861, followed by the C5.0 algorithm. Traditional algorithms, on the other hand, presented a lower precision rate with global accuracy, not exceeding 75% of accuracy and Kappa varying between 0.507 and 0.627. The accuracy of the producer showed that all the algorithms, in major or minor tendency presented difficulties in to differentiate the areas, with rates of mistakes varying between 25 and 75%, being the main, the confusion with pastoral areas. 650 $aLand cover 650 $aLand use 650 $aUso da Terra 653 $aAlgoritmos de aprendizado de máquina 653 $aBioma cerrado 653 $aCerrado Biome 653 $aClassificação digital 653 $aClassification algorithms 653 $aCobertura da terra 653 $aDigital Classification 653 $aLandsat 8 653 $aMachine learning algorithms 653 $aMaranhão State 653 $aPerformance Indexes 653 $aRandom Forest 700 1 $aCOSTA, F. W. D. 700 1 $aBOLFE, E. L. 700 1 $aMACARRINGE, L. 700 1 $aBOTELHO, A. C. 773 $tISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V. V-3-2021, p. 167-173, 2021.
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Embrapa Agricultura Digital (CNPTIA) |
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