University of Wisconsin–Madison
Spatial Analysis For Conservation and Sustainability

Eduarda Silveira

Postdoctoral Research Associate

esilveira@wisc.edu

(608) 890-3160

126A Russell Labs
1630 Linden Drive
University of Wisconsin-Madison
Madison, WI 53706

duda

Education

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.

Curriculum Vitae (PDF)

Research interests

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.

Personal interests

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

Selected Publications

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.