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metadata.artigo.dc.title: Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State, Brazil
metadata.artigo.dc.creator: Reis, Aliny Aparecida dos
Franklin, Steven E.
Mello, José Marcio de
Acerbi Junior, Fausto Weimar
metadata.artigo.dc.subject: Remote sensing
Operational land imager
Synthetic aperture radar
Digital elevation model
Principal component analysis
Sensoriamento remoto
Imagens de terra operacional
Radar de abertura sintética
Modelo de elevação digital
Análise do componente principal
metadata.artigo.dc.publisher: Taylor & Francis 2019
metadata.artigo.dc.identifier.citation: REIS, A. A. dos et al. Volume estimation in a Eucalyptus plantation using multi-source remote sensing and digital terrain data: a case study in Minas Gerais State, Brazil. International Journal of Remote Sensing. Basingstoke, v. 40, n. 7, p. 2683-2702, 2019.
metadata.artigo.dc.description.abstract: In this study, we tested the effectiveness of stand age, multispectral optical imagery obtained from the Landsat 8 Operational Land Imager (OLI), synthetic aperture radar (SAR) data acquired by the Sentinel-1B satellite, and digital terrain attributes extracted from a digital elevation model (DEM), in estimating forest volume in 351 plots in a 1,498 ha Eucalyptus plantation in northern Minas Gerais state, Brazil. A Random Forest (RF) machine learning algorithm was used following the Principal Component Analysis (PCA) of various data combinations, including multispectr al and SAR texture variables and DEM-based geomorphometric derivatives. Using multispectral, SAR or DEM variables alone (i.e. Experiments (ii)–(iv)) did not provide accurate estimates of volume (RMSE (Root Mean Square Error) > 32.00 m3 ha−1) compared to predictions based on age since planting of Eucalyptus stands (Experiment (i)). However, when these datasets were individually combined with stand age (i.e. Experiments (v)–(vii)), the RF models resulted in better volume estimates than those obtained when using the individual multispectral, SAR and DEM datasets (RMSE < 28.00 m3 ha−1). Furthermore, a model that integrated the selected variables of these data with stand age (Experiment (viii)) improved volume estimation significantly (RMSE = 22.33 m3 ha−1). The large and increasing area of Eucalyptus forest plantations in Brazil and elsewhere suggests that this new approach to volume estimation has the potential to support Eucalyptus plantation monitoring and forest management practices.
metadata.artigo.dc.language: en_US
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