Inhalt des Dokuments
Dr. Alby Duarte Rocha
- Dr. Alby Duarte Rocha
- © Dr. Alby Duarte Rocha
Research Assistant
Phone: +49 (0)30 / 314 – 24 95 3
Email: a.duarterocha(at)tu-berlin.de
Room: EB 203b
| Date and place of birth: | 1976 (São Paulo, São Paulo, Brazil) |
| Since 2020 | Post-doctoral Research Assistant Technische Universität Berlin Institute of Landscape Architecture and Environmental Planning, Department of Geoinformation Processing for Landscape and Environmental Planning |
| 2014-2019 | PhD - Remote Sensing University of Twente, Netherlands International Institute for Geo-Information Science and Earth Observation (ITC). Department of Natural Resources |
| 2005-2019 | Project Manager Observatory of Indicators of Sustainability (ORBIS) Curitiba | Paraná | Brazil |
| 2005-2007 | Master in Geodesy - Remote Sensing Federal University of Paraná (UFPR) Department of Geodesy | Brazil |
| 2003-2005 | Lecturer Federal University of Paraná - UFPR Department of Statistics | Curitiba | Brazil |
| 2000-2003 | Consultant Werkema Associate Consultants Belo Horizonte | Brazil |
| 1996-2000 | Statistician Curitiba Research and Urban Planning Institute (IPPUC) Curitiba | Brazil |
| 1994-1998 | Bachelor in Statistics Federal University of Paraná (UFPR) Department of Statistics | Brazil |
| 1998 | Bachelor, Federal University of Paraná (UFPR), Brazil |
| 2007 | Master, Federal University of Paraná (UFPR), Brazil |
| 2019 | Doctorate, University of Twente, The Netherlands |
Research Topics
- Biophysical and biochemical plant trait predictive models using hyperspectral data.
- Assessment of Spatiotemporal Models, Machine Learning Algorithms and Radiative Transfer Models to predict/retrieval (quantitative) vegetation parameters with Remote Sensing.
- Modelling Evapotranspiration in urban landscapes with the support of remote sensing.
Articles
2019
Rocha, A.D.; Groen, T.A.; Skidmore, A.K., 2019. Spatially-explicit modelling with support of hyperspectral data can improve prediction of plant traits. Remote Sensing of Environment, Doi: 10.1016/j.rse.2019.05.019, (111200).
2018
Rocha, A., Groen, T., Skidmore, A., Darvishzadeh, R. and Willemen, L., 2018. Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency. Remote sensing, 10(8), p.1263. Doi:10.1016/j.isprsjprs.2017.09.012
2017
Rocha, A.D., Groen, T.A., Skidmore, A.K., Darvishzadeh, R. and Willemen, L., 2017. The Naïve Overfitting Index Selection (NOIS): A new method to optimize model complexity for hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing, 133, pp.61-74. Doi:10.3390/rs10081263
2012
AFB Antunes, A Duarte, 2012. Characterization of the growth of urban areas by means of QUICKBIRD images through object-oriented segmentation. Proceedings of the 4th GEOBIA, Rio de Janeiro, Brazil, 191.
2011
Rocha, A.D. and Antunes, A.F.F.B., 2011. O desafio de caracterizar objetos relevantes ao planejamento urbano a partir de imagens de satélite de alta resolução. Revista Brasileira de Cartografia, (64 ESP. 1).
2008
Rocha, Alby Duarte, and Alzir Felippe Buffara Antunes, 2008. Caracterização de áreas de expansão urbana como subsídio ao planejamento urbano por meio de técnicas de segmentação orientada a objetos de imagens Quickbird. Boletim de Ciências Geodésicas 14.3.
2000
Rocha, A. D., Okabe, I., Martins, M. E. A., Machado, P. H. B., & Mello, T. C. D. (2000). Qualidade de vida, ponto de partida ou resultado final?. Ciência & saúde coletiva, 5, 63-81.
2019
Duarte Rocha, A., 2019. Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). Enschede, The Netherlands, Doi:10.3990/1.9789036548625.
2007
Duarte Rocha, A., 2007. Caracterização de áreas de expansão urbana como subsídio ao planejamento urbano por meio de técnicas de segmentação orientada a objetos de imagens Quickbird. Universidade Federal do Paraná, Setor de Ciencias da Terra, Programa de Pós-Graduação em Ciencias Geodésicas. Curitiba.