Postdoctoral Research Associate
126A Russell Labs
1630 Linden Drive
University of Wisconsin-Madison
Madison, WI 53706
2018: Ph.D., Remote Sensing of Forests, Department of Forest Science, Federal University of Lavras, Brazil.
2007: M.Sc., Remote Sensing of Forests, Department of Forest Science, Federal University of Lavras, Brazil.
2005: Forest Engineer, Department of Forest Science, Federal University of Lavras, Brazil.
My research is focused on using remotely sensed and spatial analysis to support forest mapping, monitoring and modelling. I am particularly interested in optical remote sensing data in service of natural environment and climate change related studies.
I love to spend my free time with friends and family having a good beer or wine. I also love to play soccer, tennis or any other sport.
Where I'm From
Silveira, E.M.O., Terra, M.C.N.S., ter Steege, H.; Maeda, E.E.; Acerbi-Júnior, F.W.; Scolforo, J.R.S. Tree species diversity and aboveground carbon in Brazilian Southeast domains of Savanna, Atlantic Forest and Semi-Arid Woodland. Forest Ecology and Management, 2019. doi: 10.1016/j.foreco.2019.117575.
Silveira, E.M.O., Espírito-Santo, F.D., Wulder, M.A., Acerbi-Júnior, F.W., Carvalho, M.C., Mello, C.R., Mello, J.M., Shimabukuro, Y.E., Terra, M.C.N.S., Carvalho, L.M.T., Scolforo, J.R.S., 2019. Pre-stratified modelling plus residuals kriging reduces the uncertainty of aboveground biomass estimation and spatial distribution in heterogeneous savannas and forest environments. Forest Ecology and Management 445, 96–109. doi:10.1016/j.foreco.2019.05.016.
Silveira, E.M.O., Silva, S.H.G., Acerbi-Júnior, F.W., Carvalho, M.C., Carvalho, L.M.T., Scolforo, J.R.S., Wulder, M.A., 2019. Object-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment. International Journal of Applied Earth Observation and Geoinformation 78, 175–188. doi:10.1016/j.jag.2019.02.004.
Silveira, E.MO., Bueno, I.T., Acerbi-Júnior, F.W., Mello, J.M., Scolforo, J.R.S., Wulder, M.A., 2018. Using spatial features to reduce the impact of seasonality for detecting tropical forest changes from Landsat time series. Remote Sensing 10 (808), 1-21. doi:10.3390/rs10060808.