Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/42585
metadata.artigo.dc.title: Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil
metadata.artigo.dc.creator: Reis, Aliny Aparecida dos
Franklin, Steven E.
Acerbi Junior, Fausto Weimar
Ferraz Filho, Antonio Carlos
Mello, José Marcio de
metadata.artigo.dc.subject: Digital elevation model
Geomorphometrics
Site index
Random forest
Eucalyptus plantation
Modelo digital de elevação
Geomorfometria
Floresta aleatória
Plantação de eucalipto
metadata.artigo.dc.publisher: Taylor & Francis
metadata.artigo.dc.date.issued: 2020
metadata.artigo.dc.identifier.citation: REIS, A. A. dos et al. Classification of Eucalyptus plantation Site Index (SI) and Mean Annual Increment (MAI) prediction using DEM-based geomorphometric and climatic variables in Brazil. Geocarto International, Hong Kong, 2020. DOI: https://doi.org/10.1080/10106049.2020.1778103.
metadata.artigo.dc.description.abstract: Digital elevation model (DEM) data were used with climate data to estimate productivity in 19 Eucalyptus plantations in Minas Gerais state, Brazil. Typically, plantation and individual stand growth and productivity estimates, such as Site Index (SI) and Mean Annual Increment (MAI), are based on field measures of height, tree diameter and age. Using a Random Forest modelling approach, SI and MAI were related to: (i) DEM-based geomorphometric variables and (ii) WorldClim historical macro-climatic measures. Three operational SI classes (high, medium and low productivity) in 180 stands were mapped with an overall accuracy of 91.6%. Medium and high productivity sites were the most accurately classified. Low productivity sites had 76.5% producer’s accuracy and 92.9% user’s accuracy, and were the most extensive in the study area. Such sites are considered of high importance from a plantation management perspective since additional forestry operations are likely required to address low productivity and growth.
metadata.artigo.dc.identifier.uri: https://www.tandfonline.com/doi/abs/10.1080/10106049.2020.1778103?journalCode=tgei20
http://repositorio.ufla.br/jspui/handle/1/42585
metadata.artigo.dc.language: en_US
Appears in Collections:DCF - Artigos publicados em periódicos

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