Postdoc in remote sensing and ecosystem modelling Swiss Federal Institute for Forest, Snow and Landscape Research

-





Postdoc in remote sensing and ecosystem modelling Swiss Federal Institute for Forest, Snow and Landscape Research

Swiss Federal Institute for Forest, Snow and Landscape Research WSL
The Swiss Federal Institute for Forest, Snow and Landscape Research WSL is part of the ETH Domain. Approximately 600 people work on the sustainable use and protection of the environment and on the handling of natural hazards.
The Research Unit Forest Dynamics assesses the effects of changing environmental conditions on forest ecosystem functioning. We are offering for 7 months a position as
Postdoc in remote sensing and ecosystem modelling
You will investigate ecosystem photosynthesis and impacts of extreme cold temperature events in the early season. The challenge is to identify causes, understand mechanisms, and to quantify effects at the continental scale. To achieve this, you will work with ecosystem eddy covariance flux measurements from a large number of sites, develop model parametrisations to resolve apparent model bias, and investigate links between flux measurements and multispectral remote sensing data. You will be part of a diverse group with complimentary expertise, relevant for this project and with a strong collaborative philosophy. You will benefit from a world-leading academic environment at WSL and ETH Zürich (through co-supervision) and the excellent quality of life in Switzerland. The funding for this project is seven months and opens the door for upcoming opportunities within the groups of project collaborators at WSL and ETH Zürich. Your working place will be at WSL in Birmensdorf (approx. 20 min outside of Zurich). For further information about the project please visit the website https://stineb.github.io/project/photocold/
You hold a PhD in ecology or environmental sciences with a particular focus in remote sensing or environmental modelling. This position requires independent and creative thinking to formulate hypotheses; familiarity with plant ecophysiology, remote sensing and eddy covariance data; a robust skill set for methods in environmental data science (analysis of large datasets and mechanistic ecosystem modelling modelling); and the curiosity and intrinsic motivation to address an important research challenge for a better understanding of global environmental change and climate impacts.
Please send your complete application to Stefania Pe, Human Resources WSL, by uploading the requested documents through our webpage. Applications via email will not be considered. Questions regarding the position can be directed by email or phone to project collaborators Prof. A. Gessler (arthur.gessler@wsl.ch, +41 44 739 28 18), Prof. B. Stocker (bestocke@ethz.ch, +41 44 632 48 90), Dr. Petra D'Odorico (petra.dodorico@wsl.ch, +41 44 739 20 46), or Dr. Christian Ginzler (christian.ginzler@wsl.ch, +41 44 739 25 51). The WSL strives to increase the proportion of women in its employment, which is why qualified women are particularly called upon to apply for this position.
Apply now
Apply with xeebo
Zürcherstrasse 111, CH-8903 Birmensdorf
Company-Video






Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Grey level thresholding. Level slicing. Contrast stretching lo p

-
Image
Grey level thresholding. Level slicing. Contrast stretching. Image enhancement Lillesand and Kiefer (1994) explained the goal of image enhancement procedures is to improve the visual interpretability of any image by increasing the apparent distinction between the features in the scene. This objective is to create "new" image from the original image in order to increase the amount of information that can be visually interpreted from the data. Enhancement operations are normally applied to image data after the appropriate restoration procedures have been performed. Noise removal, in particular, is an important precursor to most enhancements. In this study, typical image enhancement techniques are as follows: Grey level thresholding Grey level thresholding is a simple lookup table, which partitions the gray levels in an image into one or two categories - those below a user-selected threshold and those above. Thresholding is one of many methods for creating a binary mask for an i
Read more

Spatial filtering, Remote Sensing. Low-pass filter. High-pass filter

-
Spatial filtering encompasses another set of digital processing functions which are used to enhance the appearance of an image. Spatial filters are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture. It refers to the frequency of the variations in tone that appear in an image. "Rough" textured areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while "smooth" areas with little variation in tone over several pixels, have low spatial frequencies. A common filtering procedure involves moving a 'window' of a few pixels in dimension (e.g. 3x3, 5x5, etc.) over each pixel in the image, applying a mathematical calculation using the pixel values under that window, and replacing the central pixel with the new value. The window is moved along in both the row and column dimensions one pixel at a time and the ca
Read more