Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures

Silva, Miriam Rodrigues da; Carvalho Júnior, Osmar Abílio de; Guimarães, Renato Fontes; Gomes, Roberto Arnaldo Trancoso; Silva, Cristiano Rosa

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Élément Dublin Core	Valeur	Langue
dc.contributor.author	Silva, Miriam Rodrigues da	-
dc.contributor.author	Carvalho Júnior, Osmar Abílio de	-
dc.contributor.author	Guimarães, Renato Fontes	-
dc.contributor.author	Gomes, Roberto Arnaldo Trancoso	-
dc.contributor.author	Silva, Cristiano Rosa	-
dc.date.accessioned	2022-04-26T12:36:40Z	-
dc.date.available	2022-04-26T12:36:40Z	-
dc.date.issued	2020	-
dc.identifier.citation	SILVA, Miriam Rodrigues da et al. Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures, Geocarto International, v. 35, n. 13, 1400-1414, 2020. DOI: https://doi.org/10.1080/10106049.2019.1581266.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/43537	-
dc.language.iso	Inglês	pt_BR
dc.publisher	Taylor & Francis	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Séries temporais	pt_BR
dc.subject.keyword	Agricultura	pt_BR
dc.subject.keyword	Fenologia vegetal	pt_BR
dc.identifier.doi	https://doi.org/10.1080/10106049.2019.1581266	pt_BR
dc.relation.publisherversion	https://www.tandfonline.com/doi/full/10.1080/10106049.2019.1581266	pt_BR
dc.description.abstract1	his research aims to detect the wheat crop in the Northwest region of Rio Grande do Sul (Brazil) using MODIS NDVI time series. Detection of wheat crops presents two difficulties: (a) high variation of wheat phenological curves due to climatic fluctuations during the crop cycle and (b) the plantations are in an environment with different types of rural and urban targets. In solving these problems, we propose a classification based on the nearest neighbour (a specific case of the k-NN method) from the similarity and distance metrics combined with the determination of the best threshold value to individualize the wheat mask. The nearest neighbour classification using minimum distance (Kappa of 0.75) obtained a result equivalent to that of cosine similarity (Kappa of 0.74) as attested by the McNemar test. The planted area result was comparable to official statistics from the Brazilian Institute of Geography and Statistics obtained through direct interviews.	pt_BR
Collection(s) :	Artigos publicados em periódicos e afins