Empiric relations between dendrometric characteristics of the Brazilian dry forest and Landsat 5 TM data

Authors

  • André Quintão de Almeida Universidade Federal de Sergipe
  • Anabel Aparecida de Mello UFS
  • Antônio Luiz Dória Neto UFS
  • Raphael Cavalcanti Ferraz UFS

DOI:

https://doi.org/10.1590/S1678-3921.pab2014.v49.18976

Keywords:

vegetation index, NDVI, REDD, reducing emissions, Savi, remote sensing

Abstract

The objective of this work was to adjust models to estimate dendrometric characteristics of the Brazilian dry tropical forest (Caatinga) from Landsat 5 TM sensor data. Measures for tree diameter and height were taken in 60 inventory plots (400 m2), in two municipalities of the state of Sergipe, Brazil. Basal area and wood volume were estimated using an allometric equation and form factor (f = 0.9). Explanatory variables were taken from the TM sensor, after radiometric and geometric correction, having considered, in the analysis, six spectral bands, with 30 m spatial resolution, besides the indexes of simple ratio (SR), of normalized difference vegetation (NDVI), and of soil‑adjusted vegetation (Savi). To choose the best explanatory variables, the coefficient of determination (R2), the root mean square error (RMSE), and the Bayesian information criterion (BIC) were considered. The basal area per hectare did not show a significant correlation with any of the explanatory variables used. The best models were adjusted to tree mean height per plot (R2 = 0.4; RMSE = 13%) and to wood volume per hectare (R2 = 0.6; RMSE = 42%). The metrics derived from the Landsat 5 TM sensor have great potential to explain variation in the mean height of trees and in the wood volume per hectare, in remaining areas of the tropical dry forest in the Brazilian Northeast.

Author Biography

Anabel Aparecida de Mello, UFS

DEF/INVENTARIO

Published

2014-06-11

How to Cite

Almeida, A. Q. de, Mello, A. A. de, Dória Neto, A. L., & Ferraz, R. C. (2014). Empiric relations between dendrometric characteristics of the Brazilian dry forest and Landsat 5 TM data. Pesquisa Agropecuaria Brasileira, 49(4), 306–315. https://doi.org/10.1590/S1678-3921.pab2014.v49.18976

Issue

Section

REMOTE SENSING