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Shunan Feng

Ph.d.-studerende

Shunan Feng

PhD project: Micro, meso- and macro-scale variability of the albedo of rotting ice surfaces in the Dark Zone of the Greenland Ice Sheet

University: Aarhus University

Department: Department of Environmental Science

Section: Environmental Microbiology and Circular Resource flow – EMCIF

Research group: Cryo microbiology

 

Supervisor(s):  Martyn Tranter

(Co-supervisor(s):) Joseph Mitchell Cook, Alexandre Magno Barbosa Anesio      

Project term: 01.12.2020 – 30.11.2023

Master’s degree: MSc in Earth Sciences, specialized in remote sensing and glaciology, Uppsala University, Sweden

Profil

BACKGROUND

Microbe communities play an important role in regulating the glacier surface albedo and consequently the glacier melt. The relationship between glacier algae pigment, biomass and glacier surface “bioalbedo” is of vital importance for us to understand the mechanism and process of glacier algae’s impact on Greenland Ice Sheet. I am interested in understanding the process and climate response of earth surface, particularly time series analysis by combining the in-situ measurement, remote sensing or other geospatial data and model results.

AIM

The project aims to understand the dynamics of ice surfaces in the Dark Zone of the Greenland Ice Sheet at different scales. The ultimate goal is to utilize the data to develop a conceptual model of the evolution of surface albedo in the Greenland Dark Zone.

This is done by answering the following questions (hopefully):

  1. How to acquire, process and analyze hyperspectral imagery captured by UAV?
  2. What is the relationship between glacier algae pigment, biomass and glacier surface “bioalbedo”?
  3. What are the limitations of current detection methods (e.g. band ratio, machine learning etc.)?
  4. How to extract the maximum information from very noisy images captured by different sensor?
  5. How to upscale the analysis from micro to macro scale?

RESEARCH OUTLINE

The project is to understand the relationship between glacier algae pigment, biomass and glacier surface “bioalbedo” at various scales. We will utilize remote sensing data from hyperspectral UAV, satellite and other sources to derive the glacier surface albedo. The glacier algae pigment and biomass will be estimated from combinations of band ratio and machine learning technique. Finally the optimal algorithm will be translated to a cloud computing platform and upscale to larger area.

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