Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data
Gonçalves, Anny Francielly Ataide Fernandes, Márcia Rodrigues de Moura Silva, Jeferson Pereira Martins Silva, Gilson Fernandes da Almeida, André Quintão de Cordeiro, Natielle Gomes Silva, Lucas Duarte Caldas da Scolforo, José Roberto Soares
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data
Publicação on-line; Bibliography p. 8-11; (50 ref.); 2 tables; 2 illus.; Summary (En)
ABSTRACT
The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to
estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted
based on the remote sensing data, taking into consideration the individual bands and vegetation
index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the
combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues
were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508
and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the
combined MSI and SRTM data as predictors. The volume estimation using spectral data showed
satisfactory results, highlighting the importance of topography in the prediction of the volume
of wood for the area under investigation.
Keywords: atlantic forest, remote sensing, forest inventory, measurement.
MATA ATLÂNTICA
SENSORIAMENTO REMOTO
INVENTÁRIO FLORESTAL
MEDIÇÃO
Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data
Publicação on-line; Bibliography p. 8-11; (50 ref.); 2 tables; 2 illus.; Summary (En)
ABSTRACT
The objective of this study was to evaluate the use of the MSI Sentinel-2 and SRTM data to
estimate the volume of wood in a Semidecidual Seasonal Forest. Regression equations were fitted
based on the remote sensing data, taking into consideration the individual bands and vegetation
index of the MSI, elevation values and their derivatives obtained from the SRTM mission and the
combination of the data drawn from the MSI and SRTM. RMSE and graphic analysis of residues
were used to assess the accuracy of the fitted equations. The best model revealed values of 0.6508
and RMSE of 20.41% in the fit, and of 0.5680 and RMSE of 26.61% in the validation, using the
combined MSI and SRTM data as predictors. The volume estimation using spectral data showed
satisfactory results, highlighting the importance of topography in the prediction of the volume
of wood for the area under investigation.
Keywords: atlantic forest, remote sensing, forest inventory, measurement.
MATA ATLÂNTICA
SENSORIAMENTO REMOTO
INVENTÁRIO FLORESTAL
MEDIÇÃO
BINAGRI