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MA Julian Cabezas

Gruppe: Vegetation

Karlsruher Institut für Technologie (KIT)
Institut für Geographie und Geoökologie
Kaiserstr. 12
76131 Karlsruhe

Julian Cabezas

Remote sensing, ecology, statistics


  • Detection of disturbances in forest ecosystems
  • Aboveground carbon and richness modelling in peatland ecosystems
  • Application of machine learning methods for remote sensing data

Curriculum vitae

2016 Research assistant at Karlsruhe Institute of Technology (KIT). Project: “Satellite-based Monitoring of invasive species in central-Chile” (SaMovar)
2015 esearch assistant at Universidad de Chile. Project: “Study for the definition of protection areas in the east piedmont of Santiago”
2013-2014 Freelance advisor on GIS for environmental impact evaluation reports
2010-2015 Engineering in Renewable Natural Resources, University of Chile. Thesis title: “Effects of management on carbon stocks and vegetation of an anthropogenic peatland in the island of Chiloe, Chile”


Cabezas, J., Galleguillos, M. and J. Perez-Quezada. (2015). Predicción de la riqueza de plantas vasculares en un humedal de la Isla de Chiloé utilizando variables texturales derivadas de teledetección. Poster presented at the IV Native Flora Congress, Concepción, Chile. Perez-Quezada, J., Brito, C, Cabezas, J., Salvo, P., Lemunao, P. Flores, E., Valdés, A., Fuentes, JP, Galleguillos, M. and C. Pérez. (2015). Carbon stocks of an old-growth forest and peatland an anthropogenic in southern Chile. Poster presented at EGU 2015 Vienna, Austria.


Contributions in peer-reviewed journals
2016Cabezas, J., Galleguillos, M., Perez-Quezada, J. (2016): Predicting Vascular Plant Richness in a Heterogeneous Wetland Using Spectral and Textural Features and a Random Forest Algorithm. IEEE Geoscience and Remote Sensing Letters 13(5), pp. 646–650. 10.1109/LGRS.2016.2532743

2015Cabezas, J., Galleguillos, M., Valdés, A., Fuentes, J. P., Pérez, C., Perez-Quezada, J. (2015): Evaluation of impacts of management in an anthropogenic peatland using field and remote sensing data. Ecosphere 6(12), pp. 1–24. Unter den Bedingungen von Open Access verfügbar 10.1890/ES15-00232.1