PhD position in Remote Sensing of Forest Disturbances

The Department of Forest Resource Management conducts education and research in the areas of forest planning, forest remote sensing, forest inventory and sampling, forest mathematical statistics and landscape studies. The department is also responsible for the implementation of the ongoing environmental monitoring programs the National Forest Inventory, National Inventory of the Landscape in Sweden, Terrestrial Habitat Monitoring and the Butterfly and Bumblebee Inventory. In total, we are about 100 employees. More information can be found at www.slu.se/srh .

Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/

The project is “Developing remote sensing techniques for monitoring forest damage and disturbance in a changing climate”. Global warming has triggered forest abiotic and biotic disturbances to an unprecedented intensity in Europe in recent years. Windstorms, drought, insect outbreaks, and forest fires are exacerbated by extreme weather, weakening the forest ecosystem and devastating the sustainable management of forest resources. Such abiotic and biotic threats to the forest under global warming highlight the urgent need for monitoring forest health and disturbance. This project aims at developing improved remote sensing techniques and methods for monitoring forest disturbance and damages at different scales, caused by, e.g., drought, insect outbreaks, wind storms, and forest fires. New methods and frameworks will be proposed adapting to the new generation of remote sensing data and new needs under climate issues.

The project is co-funded by SLU Forest Damage Centre research school and the Horizon project RESDINET. Therefore, the doctoral student will work in the SLU Department of Forest Resource Management and closely collaborate with researchers from other SLU departments and from Finland and Slovakia through the project RESDINET.

The PhD project will develop remote sensing techniques and methods for mapping and monitoring forest disturbance and damages at different scales. The potential disturbances include forest insects such as European bark beetles, drought, windstorms, and forest fires. The potential remote sensing data include multispectral and hyperspectral drone images, optical satellite images, laser scanning data, and radar data. The work will include developing automatic methods to extract information from remote sensing data and statistical methods to estimate damages. The analysis will be applied from single trees to landscape scales.

The PhD student is expected to be involved in collecting field data and drone data in southern and potentially northern Sweden, including inventory of tree health status and physiological monitoring. Research visits to the project partners are possible based on interest.

Required:

• Master degree in Engineering, Computing Science, Forestry, or other relevant subject.
• Fluent in English for communication and scientific writing

Meriting:

Knowledge and experience in

• scientific programming
• remote sensing data processing and analysis
• image analysis, signal processing etc.
• machine learning methods and statistics
• forest damages
• field data collection
• driver licence level B (car)

We put great importance to personal qualities, such as the ability to collaborate, solve problems, and work independently.

Desirable:

• technical skills in electronics

SLU, Umea

Employment as a doctoral student 4 years of education

According to agreement, preferably the summer of 2023.

We welcome your application no later than 2023-05-01, use the button below.

To qualify for third-cycle (Doctoral) courses and study programmes, you must have a second-cycle (Master’s) qualification. Alternatively, you must have conducted a minimum of four years of full-time study, of which a minimum of one year at second-cycle level.

Applicants will be selected based on their written application and CV, degree project, copies of their degree certificate and transcript of records from previous first and second-cycle studies at a university or higher education institution, two personal references, and knowledge of English. More information about the English language requirements can be found here: www.slu.se/en/education/programmes-courses/doctoral-studies/new-doctoral-students/english-language-requirements/

Please note that applicants invited to interview must submit attested copies of their degree certificate, a transcript of records from previous first and second-cycle studies at a university or higher education institution. Applicants who are not Swedish citizens need to submit an attested copy of their passport’s information page containing their photograph and personal details.

Read about the PhD education at SLU at www.slu.se/en/education/programmes-courses/doctoral-studies/

https://internt.slu.se/en/my-employment/employee-associations/kontaktpersoner-vid-rekrytering/

The Swedish University of Agricultural Sciences (SLU) has a key role in the development for sustainable life, based on science and education. Through our focus on the interaction between humans, animals and ecosystems and the responsible use of natural resources, we contribute to sustainable societal development and good living conditions on our planet. Our main campuses are located in Alnarp, Umeå and Uppsala, however, the university also operates at research stations, experimental forests and teaching sites throughout Sweden.

SLU has around 3,000 employees, 5,000 students and doctoral students and a turnover of over SEK 3 billion. We are investing in attractive environments on all of our campuses. We strive to provide a work environment characterised by inclusivity and gender equality, where different experiences generate conversations between people and pave the way for science, creativity and development. Therefore, we welcome applications from people with diverse backgrounds and perspectives.

Contact person

URL to this pagehttps://www.slu.se/en/about-slu/work-at-slu/jobs-vacancies/?rmpage=job&rmjob=8188&rmlang=UK