Stem volume of tropical forests from polarimetric radar

Abstract

In this study, we investigated the potential of polarimetric synthetic aperture radar (PolSAR) data for the estimation of stem volume in tropical forests. We used calibrated L-band, high incidence angle data from the airborne system SAR-R99B, acquired over an experimental area in the Tapajós National Forest, Pará, Brazil. To evaluate the potential of PolSAR data for this application we used regression analysis, in which first-order models were fit to predict stem volume per hectare, as determined from field measurements. Unlike previous studies in tropical forests, the set of potential explanatory variables included a series of PolSAR attributes based on phase information, in addition to power measurements. Model selection techniques based on coefficient of determination (R ²) and mean square error (MSE) identified several useful subsets of explanatory variables for stem volume estimation, including backscattering coefficient in HH polarization, cross-polarized ratio, HH-VV phase difference, polarimetric coherence, and the volume scatter component of the Freeman decomposition. Evaluation of the selected models indicated that PolSAR data can be used to quantify stem volume in the study site with a root mean square error (RMSE) of about 20–29 m³ ha⁻¹, corresponding to 8–12% of the mean stem volume. External validation using independent data showed average prediction errors of less than 14%. Saturation effects in measured versus modelled volume were not observed up to volumes of 308 m³ ha⁻¹ (biomasses of ∼357 Mg ha⁻¹). However, no formal assessment of saturation was possible due to limitations of the volume range of the dataset.

Acknowledgments

This study was supported by the Council for Advanced Professional Training (CAPES), and the National Council for Scientific and Technological Development (CNPq). We acknowledge Brazil's National Institute for Space Research (INPE), through the MAPSAR Project, for institutional support, and LBA/Santarém for logistic support during the field work. We thank Corina Freitas and Camilo Rennó for assisting us with statistical analysis, and Antônio Henrique Correia and José Cláudio Mura for assistance with the SAR-R99B processing at INPE. We also thank Roberto Ventura (CENSIPAM) and Nilo Andrade (FAB-COMGAR) for acquisition/distribution of the SAR-R99B data, Fernando Miranda (PETROBRAS) for providing the corner reflectors, and Dan Donato for suggestions that improved the clarity of this manuscript. Acknowledgements of field data collection to: Erly Pedroso, Joni Oliveira, Raimundo dos Santos, Paulo César Albuquerque, Cláudia Cristina dos Santos, and Marcos Elmiro. The research described in this paper was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

Notes

^† The non-flooded forests are classified into four distinct environments, defined primarily by soil type and relief: plateau forest, lowland forest, slope forest, and ‘campinarana’ (lower forests on white-sand soils). Lowland forests are those occurring in alluvial plains along ‘igarapés’ (small streams; Hopkins 2005).