Research article

Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest

Aline Bernarda Debastiani , Carlos Roberto Sanquetta, Ana Paula Dalla Corte, Naiara Sardinha Pinto, Franciel Eduardo Rex

Aline Bernarda Debastiani
University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil. Email: aline.debastiani@gmail.com
Carlos Roberto Sanquetta
University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
Ana Paula Dalla Corte
University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
Naiara Sardinha Pinto
University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil
Franciel Eduardo Rex
University of State of Parana, Avenida Rio Grande do Norte, 1525 87701-020 - Centro - Paranavaí - PR, Brazil

Online First: July 30, 2019
Debastiani, A., Sanquetta, C., Corte, A., Pinto, N., Rex, F. 2019. Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest. Annals of Forest Research DOI:10.15287/afr.2018.1267


The aim of the present study is to evaluate the potential of C-band SAR data from the Sentinel-1/2 instruments and machine learning algorithms for the estimation of forest above ground forest biomass (AGB) in a high-biomass tropical ecosystem. This study was carried out in Jamari National Forest, located in the Brazilian Amazon. The response variable was AGB (Mg/ha) estimated from airborne laser surveys. The following treatments were considered as model predictors: 1) Sentinel-1 Sigma 0 at VV and VH polarizations; 2) (1) plus Sentinel-1 textural metrics; 3) (2) plus Sentinel-2 bands and derived vegetation indices (LAI, RVI, SAVI, NDVI).Our modeling design estimated the relative importance of SAR vs. optical variables in explaining AGB. The modeling was performed with twelve machine-learning algorithms including, neural network and regression tree. The addition of texture and optical data provided a noticeable improvement (3%) over models with SAR backscatter only. The best model performance was achieved with the Random Tree algorithm. Our results demonstrate the potential of freely-available SAR data and machine learning for mapping AGB in tropical ecosystems.


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