Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data
Material type:
ArticleSubject(s): Online resources:
In:
Floresta e Ambiente (Brazil) v. 26(special number n.1) p. 1-11; (2019)Summary:
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.
| Item type | Current library | Collection | Call number | Vol info | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Periódicos
|
Biblioteca Nacional de Agricultura - Binagri Agrobase - Periódicos | Periódicos agrícolas | 2019 26( n. especial 1) | Online | 2025-0453 |
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.
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