Image processing and machine learning applied to Earth Observation of the cryosphere
Image processing and machine learning applied to Earth Observation of the cryosphere
批准号:
2438667
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2020
资助国家:
英国
项目状态:
未结题
起止时间:
2020 至 --
中文摘要
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英文摘要
A number of recent studies have begun to highlight the potential to analyse remote sensing imagery to monitor changes that have occurred to the Earth's ice sheets, ice caps and glaciers over the last few decades. A common technique has been to map changes to glacier frontal positions using optical imagery, which has shown significant and widespread retreat in locations such as the Antarctic Peninsula (Cook et al., 2016), parts of East Antarctica (Miles et al., 2013) and in parts of Greenland and glaciated Arctic islands (Carr et al., 2013). In West Antarctica, Christie et al. (2016; 2018) have mapped pervasive retreat of the grounding line over the last 40 years using a combination of Landsat and InSAR. These studies are just opening the door to the vastly wider potential for deploying remote sensing data and auxiliary information, where the ambition is to upscale activities from manual mapping of features such as ice fronts and grounding lines to more automated techniques inherited from machine learning. The ultimate goal of this project is to develop a framework for making best use of the existing remote sensing and geophysical data to map changes around the polar ice masses for the last 50 years, and to use these data to inform us on the processes responsible.The aim of the proposal is to formalise a framework for making best use of available remote sensing (e.g. Landsat, MODIS, ASTER, SPOT) and geophysical data (radar sounding) to capture past change to polar ice masses. It is anticipated that the initial focus will be on Antarctica, but the ambition is to develop a workflow applicable to all polar ice masses. There is considerable flexibility in the direction that the project may ultimately take.Key research questions are as follows:1. How can mapping ice front and grounding-line change be optimised taking advantage of techniques developed in image processing/analysis and/or machine learning?2. Over decadal timescales, are ice-front changes around Antarctica suitable proxies for grounding-line changes?3. Have changes at the ice front been transmitted inland in terms of optically-detectable signals of ice-flow change?4. What are the underlying causes of the glaciological observations recovered by these techniques?
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