Minimize PhD - Multi-scale spectral remote sensing of vegetation stress

Date added: 02 May 2018

Organisation: FindAPhD on behalf of University of Tasmania

Location: Tasmania, Australia

Project description

The College of Science and Engineering, School of Technology, Environments and Design, is offering a 3-year fully funded PhD scholarship for an Honours or equivalent graduate in Geography and Spatial Sciences.

The study will be part of an Australian Research Council project "Bridging scales in remote sensing of vegetation stres" aiming at development of new remote sensing methods for mapping pre-visual stress and vegetation health at regional scales from optical Earth observations of the latest space-borne missions. The new approaches under development will use modern computer radiative transfer models in combination with measurements from unmanned aircraft systems (UAS/drones).

The PhD study will pave the way towards regular satellite monitoring of plant health across extensive and inaccessible Australian landscapes.

The successful candidate will learn how to retrieve health-indicating traits of vegetation, for instance content of photosynthetic pigment or plant water content, from spectral information of airborne and satellite images. S/he will be trained in modelling and inversions of the virtual optical remote sensing data simulated in the Discrete Anisotropic Radiative Transfer (DART) model.

As a virtual computer simulator, DART requires input parameters that could be acquired with small size unmanned aircraft systems (drones) carrying on-board various optical spectral instruments. Coupling of drone-based measurements and radiative transfer modelling will enable creation of quantitative space-borne maps derived from satellite platforms of the European Space Agency (ESA) known as Copernicus Sentinels and the future ESA Earth Explorer mission FLEX.

Knowledge and skills that will be ranked highly include:

  • Practical skills in optical remote sensing image analysis and data processing software (ENVI, Erdas, Idrisi, etc.);
  • Advanced computer skills in both Windows and Linux OS;
  • Expertise in statistical data analyses, including advanced machine learning approaches (random forest, support vector regression, neural networks, etc.);
  • Active ability of computer scripting/programming (in Matlab and/or Python, R, IDL, BASH, etc.);
  • Previous experience in vegetation radiative transfer modelling at both leaf (PROSPECT/FLUSPECT) as well as canopy level (4SAIL, DART, FLIGHT, etc.).

Learn more about the role and how to apply.


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