Postdoc of Forest surveillance with remote sensing and artificial intelligence

Remote sensing provides spatially continuous, and periodic data on vegetation conditions and have long been identified as a promising tool for large-scale forest resource assessment and detection and monitoring of forest damage from both biotic and abiotic stressors. At the same time recent advances in deep learning-based analysis have leveraged possibilities for monitoring woody resources across large-scales, yet at the level of individual trees.

The postdoc’s duties will include research within assessment of forest resources and structures as well as improving identification of the impacts of pests and droughts and their severity at different spatial scales. Specifically, the work will focus on tailoring U-Net type of deep learning architectures to be able to characterize tree structures in high and very high-resolution imagery. A European-scale forest damage and mortality monitoring workflow will be developed from the use of the rich spectral information provided by the constellations of European Copernicus Sentinel 1 and 2 satellite systems, providing high-temporal information in a 10m spatial resolution. This will be complemented by forest and woody resource monitoring based on recent advances in the use of PlanetScope nano-satellites and the availability of nation-wide orthophotos for selected countries. Both represent exciting new data sources for large-scale forest resource and damage monitoring at the level of single tree crowns, given the complementary availability of near-daily multispectral information in a 3m spatial resolution and temporal snapshots of sub-meter resolution data.

Formal requirements

Applicants should hold a PhD degree in Geography, Geoinformatics, Environmental Sciences, or related. We are seeking a highly motivated and ambitious individual with good interpersonal and communication skills. Fluency in spoken and written English is a requirement. As criteria for the assessment, emphasis will also be laid on previous publications, relevant experience in remote sensing and forest monitoring, as well as on programming skills (e.g. r, python). Proven experience with high-resolution imagery and machine/deep learning techniques are expected as well as proven experience with handling and processing large image datasets.

Work environment

Your workplace will be the Department of Geosciences and Natural Resource Management (IGN), which conducts research and education on the past, present and future physical, chemical and biological environments of the Earth and their interactions with societal and human systems to provide graduates and research in support of sustainable future solutions for society. The department has strong experience in interdisciplinary collaboration within and beyond the department.

Further information on the Department is linked at http://www.science.ku.dk/english/about-the-faculty/organisation/. Inquiries about the position can be made to Vivian Kvist Johansen.

The position is open from March 1st 2025 or as soon as possible thereafter and will be available for 3.5 years.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

Terms of employment

The position is covered by the Memorandum on Job Structure for Academic Staff.

Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.

Negotiation for salary supplement is possible.

The application, in English, must be submitted electronically by clicking APPLY NOW below.

Please include

• Curriculum vitae
• Diplomas (Master and PhD degree or equivalent)
• Research plan – description of current and future research plans
• Complete publication list
• Separate reprints of 3 particularly relevant papers

The deadline for applications is 5th January 2025, 23:59 GMT +1.

After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.

You can read about the recruitment process at http://employment.ku.dk/faculty/recruitment-process/.