12 PhD positions within the project "TRuStEE": Training on Remote Sensing for Ecosystem modElling - Deadline December 12, 2016
Twelve PhD positions are offered in the frame of the project TRuStEE - Training on Remote Sensing for Ecosystem modelling, a Marie Sklodowska-Curie Innovative Training Network Action.
TRuStEE will train a new generation of scientists with complementary and interdisciplinary skills in ecosystem modelling, plant physiology, remote sensing technologies and big data analysis, addressing the specific objectives:
- to identify essential biodiversity variables (EBVs) and the link with plant traits (PTs) and ecosystem functional properties (EFPs), inferable from remote sensing;
- to investigate a completely new avenue for assessing vegetation photosynthetic efficiency from remote sensing measurements of canopy fluorescence;
- to assimilate diverse remote sensing data streams with varying spatial and temporal resolution in dynamic ecosystem models;
- to exploit new satellite missions (e.g. ESA-FLEX, ESA-Sentinels, NASA-GEDI) and Earth Observation products for the upscaling of PTs, EBVs and EFPs.
Each of these positions is for a period of 36 months starting in January – March, 2017.
Friday 11 November 2016
The Fondazione Edmund Mach is involve in the PhD project ESR position 9 - Remote sensing tools for monitoring grassland plant leaf traits and ecosystem functioning along an altitudinal gradient.
Details of the project
Ecosystem functional properties strongly influence ecosystem processes, and their spatial and temporal characterization is rather challenging. Plant leaf traits are able to provide crucial information towards the understanding of the ecosystem functional properties and the mechanisms underlying the provision of ecosystem services.
Within the same functional type, extreme differences can be observed in plant traits (such as Leaf Area Index, leaf pigment content, leaf water and nitrogen content, green ratio).
Plant leaf traits in different grassland associations vary in response to environmental, anthropogenic and climatic factors. In mountain environments, the compression of the climatic life zones mostly due to altitudinal variations leads to an extreme spatial variability of plant traits and ecosystem functional properties.
Hyperspectral remote sensing can provide useful data for plant leaf traits characterisation and mapping, providing valuable inputs to the ecosystem modelling community.
The variability of plant leaf traits within the grassland functional type and their ability to provide information on both the ecosystem functional properties and the ecosystem services will be assessed along an altitudinal gradient, ranging from the low altitude temperate meadows up to the subalpine grasslands and alpine tundra ecosystems.
Radiative Transfer Model inversion will allow grassland plant leaf traits estimation (LAI, canopy height, leaf nitrogen and water content) within different grassland canopies. The advantages of integrating LiDAR and hyperspectral data will be investigated, and the impact of canopy structure (spatial distribution of photosynthetic and non photosynthetic canopy elements) on the reflectance response and on the models’ ability to estimate plant leaf traits and functional properties.
General information and conditions
PhD tuition fees for the ESR are covered and the research project is aimed at defending a thesis and obtaining a PhD degree. In addition to their individual scientific projects, all positions will benefit from further continuing training, which includes summer schools and secondments (All ESRs will be seconded at least once at another partner premises), a variety of training courses as well as transferable skill courses, active participation in workshops and conferences, and exposure to SMEs and Universities from the different European countries involved in TRuStEE.
Details on the 12 PhD positions and How to apply are provided at the website http://ltda-disat.it/?page_id=944
Application deadline: 12 December 2016. Applications received shortly after the deadline are likely to be considered. Priority will be given to applications received within the deadline.