Skip to main content

PhD fellow in Earth Observation of terrestrial ecosystem stability to climate extremes Københavns Universitet - University of Copenhagen



PhD fellow in Earth Observation of terrestrial ecosystem stability to climate extremes Københavns Universitet - University of Copenhagen


The Department of geosciences and natural resource management is offering a PhD scholarship in Earth Observation and ecosystem science, with a focus on studying large-scale vegetation die-offs using high to very high-resolution satellite imagery.

'Living well, within the limits of our planet' is the overarching goal of the United Nations Sustainable Developments Goals. Underneath this initiative lie vital concerns related to ongoing non-sustainable changes in the global Earth System and their implications for our society. Climate extremes have been responsible for large-scale shifts in ecosystem functioning, biodiversity loss and loss in ecosystem services, and projections show that extreme events will increase in the near future. Despite the significance of such environmental pressures, major gaps remains regarding our understanding of the resilience of global terrestrial ecosystems to climate change and extremes events.

The PhD position is part of the newly funded 'DRYTIP' project (5 years), financed by the VILLUM FONDEN and lead by Associate Professor Stéphanie Horion. The project aims at advancing fundamental and technical knowledge related to drought-induced vegetation die-offs by coupling insights from Earth observation (EO), Dynamic Vegetation Model and Field ecology.

The role of the PhD student will be (1) to investigate a global set of drought-induced vegetation die-offs combing field data with climate and EO proxies; and (2) to develop advanced EO based methods to monitor vegetation die-offs and quantify ecosystem stability to climate extremes (i.e. ecosystem response to disturbance and recovery potential). The project includes fieldwork to key case studies and international research stay.

Principal supervisor is associate professor Stephanie Horion, Department of Geoscience and Natural Resource Management, smh@ign.ku.dk. Co-supervisor is professor Claus Beier, Head of Department of Geoscience and Natural Resource Management, cb@ign.ku.dk.

The position is open from 1 August 2021 or as soon as possible thereafter.

Job description
The position is available for a 3-year period and your key tasks as a PhD student at SCIENCE are:

To manage and carry out your research project
Attend PhD courses
Write scientific articles and your PhD thesis
Teach and disseminate your research
To stay at an external research institution for a few months, preferably abroad
Work for the department and take part in IGNs PhD-community
Formal requirements
Applicants should hold an MSc 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 (if any), relevant experience in remote sensing and ecology, as well as on programming skills (e.g. r, python). Fieldwork experience and experience with dense time series of remote sensing and climate data are an advantage.

Work environment
Your work place 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 can be found at https://ign.ku.dk/english/.

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.

The starting salary is currently at a minimum DKK 330,817 (approx. €44,108) including annual supplement (+ pension at a minimum DKK 53,811). Negotiation for salary supplement is possible.

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

Please include

A cover letter (1 page) describing your background, personal qualities, research interest and motivation for applying for this position
CV (max 2 pages)
Diploma and transcripts of records (BSc and MSc)
Other information for consideration, e.g. list of publications (if any)
A short abstract of the MSc Thesis (max. 300 words)
1-3 reference letters (if any)
The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

The deadline for applications is 15 April 2021, 23:59 GMT +2.

Procedure and Shortlisting
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee. Afterwards an assessment committee will be appointed to evaluate the selected applications. The applicants will be notified of the composition of the committee and the final selection of a successful candidate will be made by the Head of Department, based on the recommendations of the assessment committee and the interview committee.

The main criterion for selection will be the research potential of the applicant and the above mentioned skills. The successful candidate will then be requested to formally apply for enrolment as a PhD student at the PhD school of Science. You can read more about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.

Inquiries about the position can be made to Associate Professor Stephanie Horion (smh@ign.ku.dk).

General information about PhD programmes at SCIENCE is available at https://www.science.ku.dk/phd.




Vineesh V
Assistant Professor of Geography,
Government College Chittur, Palakkad
Government of Kerala.
https://vineesh-geography.business.site

Comments

Post a Comment

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...
Post a Comment
Read more

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process :    - The algorithm starts by divid...
Post a Comment
Read more

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature  A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...
Post a Comment
Read more

Applications of Remote Sensing

Remote sensing is the science of obtaining information about the Earth's surface without direct contact, using sensors mounted on satellites, aircraft, or drones. It is widely applied in environmental monitoring, agriculture, urban studies, and disaster management due to its synoptic coverage, temporal continuity, and objective data acquisition.  Vegetation, Soil, and Water Studies 1.1 Vegetation Studies Vegetation analysis using remote sensing is based on the spectral reflectance behavior of plants . Healthy vegetation absorbs most of the red light for photosynthesis and reflects strongly in the near-infrared (NIR) region due to internal leaf structure. Key Concepts and Terminologies Spectral Signature : Unique reflectance pattern of vegetation across wavelengths. Chlorophyll Absorption : Strong absorption in blue and red wavelengths. Canopy Reflectance : Combined reflectance of leaves, branches, and background soil. Important Vegetation Indices NDVI (Normalized Difference Vegetat...
Post a Comment
Read more

Classification of Mixed Pixels

In remote sensing, a mixed pixel (also called a mixed cell or mixel ) is a pixel that contains more than one land-cover type inside its area. This is very common when: spatial resolution is coarse (e.g., 30 m, 250 m) land cover is patchy or complex boundaries between features exist (forest–agriculture, land–water edges) Traditional hard classification assigns each pixel to one single class only → either forest or soil or water. But mixed pixels contain fractions of several classes simultaneously, so hard classification produces errors. Two major solutions are: Spectral Mixture Analysis (SMA) Fuzzy Classification 1. Spectral Mixture Analysis (SMA) (Also called Linear Spectral Unmixing ) ✔ Concept SMA assumes that the reflectance recorded by a pixel is a linear combination of the reflectance of pure materials within that pixel. These pure materials are called endmembers . ✔ Endmembers Endmembers are spectrally pure classes found...
Post a Comment
Read more