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

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?" Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Development and scope of Environmental Geography and Recent concepts in environmental Geography

Environmental Geography studies the relationship between humans and nature in a spatial (place-based) way. It combines Physical Geography (natural processes) and Human Geography (human activities). A. Early Stage 🔹 Environmental Determinism Concept: Nature controls human life. Meaning: Climate, landforms, and soil decide how people live. Example: People in deserts (like Sahara Desert) live differently from people in fertile river valleys. 🔹 Possibilism Concept: Humans can modify nature. Meaning: Environment gives options, but humans make choices. Example: In dry areas like Rajasthan, people use irrigation to grow crops. 👉 In this stage, geography was mostly descriptive (explaining what exists). B. Evolution Stage (Mid-20th Century) Environmental problems increased due to: Industrialization Urbanization Deforestation Pollution Geographers started studying: Environmental degradation Resource management Human impact on ecosystems The field became analytical and problem-solving...

Change Detection

Change detection is the process of finding differences on the Earth's surface over time by comparing satellite images of the same area taken on different dates . After supervised classification , two classified maps (e.g., Year-1 and Year-2) are compared to identify land use / land cover changes . Goal To detect where , what , and how much change has occurred To monitor urban growth, deforestation, floods, agriculture, etc. Basic Concept Forest → Forest = No change Forest → Urban = Change detected Key Terminologies Multi-temporal images : Images of the same area at different times Post-classification comparison : Comparing two classified maps Change matrix : Table showing class-to-class change Change / No-change : Whether land cover remains same or different Main Methods Post-classification comparison – Most common and easy Image differencing – Subtract pixel values Image ratioing – Divide pixel values Deep learning methods – Advanced AI-based detection Examples Agricult...

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...

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...

Vineesh V, Geography

Search This Blog