Tectona grandis canopy cover predicted by remote sensing

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorSantos, Isabel Carolina de Lima-
Autor(es): dc.creatorSantos, Alexandre dos-
Autor(es): dc.creatorCosta, Jerffersoney Garcia-
Autor(es): dc.creatorRosa, Anderson Melo-
Autor(es): dc.creatorZanuncio, Antonio José Vinha-
Autor(es): dc.creatorZanetti, Ronald-
Autor(es): dc.creatorOumar, Zakariyyaa-
Autor(es): dc.creatorZanuncio, José Cola-
Data de aceite: dc.date.accessioned2026-02-09T12:28:08Z-
Data de disponibilização: dc.date.available2026-02-09T12:28:08Z-
Data de envio: dc.date.issued2021-09-24-
Data de envio: dc.date.issued2021-09-24-
Data de envio: dc.date.issued2020-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/48241-
Fonte completa do material: dc.identifierhttps://doi.org/10.1007/s11119-020-09748-w-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1161895-
Descrição: dc.descriptionThe phytosanitary status of Tectona grandis plantations are monitored conventionally with periodic data collection in the field, which is often costly and has low efficiency. The objective of this research was to develop a methodology to predict the canopy cover of T. grandis plantations using multispectral images of the Sentinel-2 (S2) satellite and photographic imagery. The study was carried out in a T. grandis plantation of seminal origin, in Cáceres, Mato Grosso state, Brazil. Hemispherical photographic (HP) images of the plant canopy were obtained with a digital camera coupled to a “fisheye” lens fixed at 1.3 m high at two dates in the rainy and the dry season. Cloudless and no shadow images of the S2 satellite bands were concurrently obtained with the field images. Multivariate permutative analysis of variance (PERMANOVA) and partial least squares regression (PLSR) were used to predict canopy cover percentage. The accuracy of the predicted T. grandis canopy cover (%) by the PLSR model approach was 77.8 ± 0.09%. The results indicate that a PLS model calibrated with 28 HP sample images can accurately estimate the percentage canopy cover for a continuous area of T. grandis plantations and facilitate mapping of canopy heterogeneity to monitor threats of diseases, mortality, fires, pests and other disturbances.-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourcePrecision Agriculture-
Palavras-chave: dc.subjectForest cover-
Palavras-chave: dc.subjectMultivariate analysis-
Palavras-chave: dc.subjectPartial least squares regression-
Palavras-chave: dc.subjectSentinel-2-
Palavras-chave: dc.subjectCobertura florestal-
Palavras-chave: dc.subjectAnálise multivariada-
Palavras-chave: dc.subjectRegressão de mínimos quadrados parciais-
Título: dc.titleTectona grandis canopy cover predicted by remote sensing-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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