Post-Doc position in hyperspectral remote sensing for forest trait retrievals and biodiversity estimation
Location:
University of Milano Bicocca, Department of Earth and Environmental Sciences, Remote Sensing of Environmental Dynamics Laboratory, Milan, Italy
Posted:
April 22, 2022. Position Open until filled.
Overview:
The objective of this position is to capitalise on newly available Earth Observation data sources offering complementary spatial resolutions, wide spectral ranges and revisit capabilities to advance the capability to monitor biodiversity changes from space.
In particular, the main aim of the project is to develop a PRISMA data processing chain for the generation of Earth Observation products related to plant functional traits from PRISMA reflectance data and the exploitation of plant trait maps to generate products related to functional diversity.
Important tasks are to:
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develop algorithms for the retrieval of plant traits (e.g. leaf and canopy pigment content, leaf and canopy water content, leaf and canopy nitrogen content, leaf area index) from PRISMA reflectance data through machine learning algorithms and radiative transfer models;
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create plant trait maps and validate them in forest ecosystems;
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apply multivariate and machine learning methods to estimate functional diversity based on a combination of leaf and canopy traits;
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investigate possible integration with other earth observation (EO) products;
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evaluate the value of the products generated by PRISMA data in the context of forest biodiversity monitoring
Requirements Include:
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PhD with specialization in optical remote sensing of vegetation
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Very good oral and written proficiency in English (C1 level- Common European Framework of Reference for Languages – CEFRL)
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Good experience in computer programming
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Experience in data analysis (time series analysis, statistics, numerical analysis, etc.)
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Experience within applications towards plant science / ecology / biodiversity
