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

doi:10.1590/2179-8087.037918

Acessibilidade / Reportar erro

Brasil

Español English

sumário « anterior atual seguinte »

Sumário

Original Article, Forest Management • Floresta Ambient. 26 (spe 1) • 2019 • https://doi.org/10.1590/2179-8087.037918 copy

Wood Volume Estimation in a Semidecidual Seasonal Forest Using MSI and SRTM Data

Authorship SCIMAGO INSTITUTIONS RANKINGS

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

Equation	Equations with selected variables
1	$\hat{V} = {25679.735}^{} - 70340.987 B_{2}^{} + 3932.279 E V I^{} - 22066.116 T V I^{}$
2	$\hat{V} = {742.5123223}^{} + 3.7160581 R o u g h n e s s^{} - 4.0559078 A l t i t u d e^{} - 0.2103762 O r i e n t a t i o n^{}$
3	$\hat{V} = 27890^{} - 351400 B_{2}^{} + 103900 E V I^{} - 36710 N D V I^{} - 88140 M S A V I^{} + 22.73 D e c l i v i t y^{} - 3.91 A l t i t u d e^{*}$

Equation	Adjustment		Cross validation
Equation	${\bar{R}}^{2}$	RMSE (m³ha^–1)	${\bar{R}}^{2}_{c v}$	RMSE_CV (m³ha^–1)
1	0.2490	90.3904 (32.33%)	0.1340	106.7766 (38.19%)
2	0.2572	89.8910 (32.15%)	0.0842	115.1961 (41.20%)
3	0.6508	57.0594 (20.41%)	0.5680	74.3793 (26.61%)

Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro Rodovia BR 465 Km 7, CEP 23897-000, Tel.: (21) 2682 0558 | (21) 3787-4033 - Seropédica - RJ - Brazil
E-mail: floram@ufrrj.br

Acompanhe os números deste periódico no seu leitor de RSS

[1] Anny Francielly Ataide GonçalvesRua Aquenta Sol, Campus Universitário, CP 3037, CEP 37200-000, Lavras, MG, Brasil e-mail: annyfrancielly@gmail.com

Equation	Equations with selected variables
1	$\hat{V} = {25679.735}^{} - 70340.987 B_{2}^{} + 3932.279 E V I^{} - 22066.116 T V I^{}$
2	$\hat{V} = {742.5123223}^{} + 3.7160581 R o u g h n e s s^{} - 4.0559078 A l t i t u d e^{} - 0.2103762 O r i e n t a t i o n^{}$
3	$\hat{V} = 27890^{} - 351400 B_{2}^{} + 103900 E V I^{} - 36710 N D V I^{} - 88140 M S A V I^{} + 22.73 D e c l i v i t y^{} - 3.91 A l t i t u d e^{*}$