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

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...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology: - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants. - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis: - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis. - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping: - Photozincography development allows maps to be split into layers for vegetation, water, etc. - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography: - Waldo Tobler's 1959 publication details using computers for cartography. - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

Vineesh V, Geography

Search This Blog