FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2019121546> ?p ?o ?g. }

• W2019121546 endingPage "351" @default.
• W2019121546 startingPage "351" @default.
• W2019121546 abstract "A gestão dos recursos naturais requer a estimativa de uma série de parâmetros para o apoio da identificação de alternativas mais adequadas para a gestão e manejo das áreas florestais. Em particular, os ecossistemas florestais exigem um complexo e crescente conjunto de informações, e os inventários florestais fornecem informações preciosas, entretanto, espacialmente, de forma não contínua. Muitos trabalhos científicos vêm direcionando esforços para o desenvolvimento de metodologias que relacionam os dados da terra com informações de imagens multiespectrais. A modelagem dessas relações pode estender as estimativas dos dados de inventário florestal em áreas não amostradas. Neste trabalho, foi avaliado o desempenho de uma análise não paramétrica, com a utilização do algoritmo K-Nearest Neighbor em imagens SPOT5. Foram avaliados os resultados obtidos na espacialização de atributos florestais em uma área em Molise, na Itália. Entre as diversas metodologias para os cálculos das distâncias espaciais, o uso do método baseado nas distâncias multirregressivas não paramétricas apresentaram os melhores resultados. A densidade e número de espécies levantados em campo apresentaram um coeficiente de correlação de Pearson ρ = 0,58, comparativamente às informações obtidas nas imagens multispectrais, ligeiramente inferior aos obtidos para a área basal e volume, que obtiveram, respectivamente, ρ = 0,62 e 0,71.AbstractApplication of k-nearest neighbor on multispectral images to estimate forest parameters. Natural resources management requires several parameters estimate in order to support the identification of the best alternatives to forest areas management. In particular, forest ecosystems require a complex and increasing set of descriptive information, where forest inventories put up important information, however not in a continuous spatial way. Lately, several scientific researches have been focusing on establishing methodologies to relate data from field to those obtained from multispectral images. Modeling these relations can extend the estimates of forest inventory data to not sampled areas. This research evaluated performance of non-parametric analysis using the K-Nearest Neighbor (k-NN) on SPOT 5 images. It evaluated the results obtained from the spatialization of some forest attributes in a forest area located at Molise, Italy. Among several methodologies for spatial distance calculations, the use of multiregressive non-parametric distances revealed the best results. Density and number of species on the ground revealed a Pearson correlation coefficient of r = 0.58 as compared to data obtained from multispectral images, lightly lower than the obtained for basal area and volume, which were r = 0.62 and 0.71, respectively.Keywords: Forest inventory; remote sensing; basal area; volume." @default.
• W2019121546 created "2016-06-24" @default.
• W2019121546 creator A5012903475 @default.
• W2019121546 creator A5029992341 @default.
• W2019121546 creator A5058175574 @default.
• W2019121546 creator A5064106744 @default.
• W2019121546 creator A5074278727 @default.
• W2019121546 date "2013-09-13" @default.
• W2019121546 modified "2023-09-27" @default.
• W2019121546 title "APLICAÇÃO DE K-NEAREST NEIGHBOR EM IMAGENS MULTISPECTRAIS PARA A ESTIMATIVA DE PARÂMETROS FLORESTAIS" @default.
• W2019121546 cites W1522370673 @default.
• W2019121546 cites W1974815625 @default.
• W2019121546 cites W1997731494 @default.
• W2019121546 cites W2002981337 @default.
• W2019121546 cites W2004122708 @default.
• W2019121546 cites W2020829640 @default.
• W2019121546 cites W2039960261 @default.
• W2019121546 cites W2043142216 @default.
• W2019121546 cites W2051195850 @default.
• W2019121546 cites W2052368806 @default.
• W2019121546 cites W2054296973 @default.
• W2019121546 cites W2055242945 @default.
• W2019121546 cites W2056252913 @default.
• W2019121546 cites W2061804983 @default.
• W2019121546 cites W2063144659 @default.
• W2019121546 cites W2070676234 @default.
• W2019121546 cites W2083466838 @default.
• W2019121546 cites W2083696454 @default.
• W2019121546 cites W2086653474 @default.
• W2019121546 cites W2090220078 @default.
• W2019121546 cites W2095357153 @default.
• W2019121546 cites W2103847815 @default.
• W2019121546 cites W2110588562 @default.
• W2019121546 cites W2110840436 @default.
• W2019121546 cites W2124837560 @default.
• W2019121546 cites W2126762572 @default.
• W2019121546 cites W2163241395 @default.
• W2019121546 cites W2169765847 @default.
• W2019121546 cites W2989983865 @default.
• W2019121546 doi "https://doi.org/10.5380/rf.v43i3.18083" @default.
• W2019121546 hasPublicationYear "2013" @default.
• W2019121546 type Work @default.
• W2019121546 sameAs 2019121546 @default.
• W2019121546 citedByCount "2" @default.
• W2019121546 countsByYear W20191215462015 @default.
• W2019121546 countsByYear W20191215462017 @default.
• W2019121546 crossrefType "journal-article" @default.
• W2019121546 hasAuthorship W2019121546A5012903475 @default.
• W2019121546 hasAuthorship W2019121546A5029992341 @default.
• W2019121546 hasAuthorship W2019121546A5058175574 @default.
• W2019121546 hasAuthorship W2019121546A5064106744 @default.
• W2019121546 hasAuthorship W2019121546A5074278727 @default.
• W2019121546 hasBestOaLocation W20191215461 @default.
• W2019121546 hasConcept C113238511 @default.
• W2019121546 hasConcept C121332964 @default.
• W2019121546 hasConcept C130217890 @default.
• W2019121546 hasConcept C154945302 @default.
• W2019121546 hasConcept C18903297 @default.
• W2019121546 hasConcept C198979508 @default.
• W2019121546 hasConcept C205649164 @default.
• W2019121546 hasConcept C2983946233 @default.
• W2019121546 hasConcept C41008148 @default.
• W2019121546 hasConcept C58640448 @default.
• W2019121546 hasConcept C86803240 @default.
• W2019121546 hasConcept C97137747 @default.
• W2019121546 hasConceptScore W2019121546C113238511 @default.
• W2019121546 hasConceptScore W2019121546C121332964 @default.
• W2019121546 hasConceptScore W2019121546C130217890 @default.
• W2019121546 hasConceptScore W2019121546C154945302 @default.
• W2019121546 hasConceptScore W2019121546C18903297 @default.
• W2019121546 hasConceptScore W2019121546C198979508 @default.
• W2019121546 hasConceptScore W2019121546C205649164 @default.
• W2019121546 hasConceptScore W2019121546C2983946233 @default.
• W2019121546 hasConceptScore W2019121546C41008148 @default.
• W2019121546 hasConceptScore W2019121546C58640448 @default.
• W2019121546 hasConceptScore W2019121546C86803240 @default.
• W2019121546 hasConceptScore W2019121546C97137747 @default.
• W2019121546 hasIssue "3" @default.
• W2019121546 hasLocation W20191215461 @default.
• W2019121546 hasOpenAccess W2019121546 @default.
• W2019121546 hasPrimaryLocation W20191215461 @default.
• W2019121546 hasRelatedWork W1909378829 @default.
• W2019121546 hasRelatedWork W1996532971 @default.
• W2019121546 hasRelatedWork W2001720621 @default.
• W2019121546 hasRelatedWork W2015373329 @default.
• W2019121546 hasRelatedWork W2160562149 @default.
• W2019121546 hasRelatedWork W2195601633 @default.
• W2019121546 hasRelatedWork W2297080984 @default.
• W2019121546 hasRelatedWork W239633216 @default.
• W2019121546 hasRelatedWork W4225596744 @default.
• W2019121546 hasRelatedWork W4255878288 @default.
• W2019121546 hasVolume "43" @default.
• W2019121546 isParatext "false" @default.
• W2019121546 isRetracted "false" @default.
• W2019121546 magId "2019121546" @default.
• W2019121546 workType "article" @default.