Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods

Background In fast-growing forests such as Eucalyptus plantations, the correct determination of stand productivity is essential to aid decision making processes and ensure the efficiency of the wood supply chain. In the past decade, advances in remote sensing and computational methods have yielded n...

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Bibliographic Details
Published in:	New Zealand journal of forestry science Vol. 48; no. 1; pp. 1 - 17
Main Authors:	dos Reis, Aliny Aparecida, Carvalho, Mônica Canaan, de Mello, José Marcio, Gomide, Lucas Rezende, Ferraz Filho, Antônio Carlos, Acerbi Junior, Fausto Weimar
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 16-01-2018 SCION
Subjects:	Artificial neural networks Biomedical and Life Sciences Computer applications Data processing Decision making Decision trees Dimensional analysis Eucalyptus Forest management Forest mapping Forest productivity Forest surveys Forestry Kriging interpolation Landsat Landsat satellites Learning algorithms Learning theory Life Sciences Machine learning Neural networks Plantations Regression analysis Remote sensing Research Article Satellite imagery Satellites Software Spatial analysis (Statistics) Spectral bands Supply chains Support vector machines Wood Brazil Machine learning algorithms Random forest Multiple linear regression Support vector machine Forest inventory Artificial neural networks
Online Access:	Get full text
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