Search result: Catalogue data in Autumn Semester 2020

Environmental Sciences Master Information
Major in Forest and Landscape Management
Electives
Methods and Tools
NumberTitleTypeECTSHoursLecturers
701-1316-00LPhysical Transport Processes in the Natural EnvironmentW3 credits2GJ. W. Kirchner
AbstractFluid flows transport all manner of biologically important gases, nutrients, toxins, contaminants, spores and seeds, as well as a wide range of organisms themselves. This course explores the physics of fluids in the natural environment, with emphasis on the transport, dispersion, and mixing of solutes and entrained particles, and their implications for biological and biogeochemical processes.
ObjectiveStudents will learn key concepts of fluid mechanics and how to apply them to environmental problems. Weekly exercises based on real-world data will develop core skills in analysis, interpretation, and problem-solving.
Contentdimensional analysis, similarity, and scaling
solute transport in laminar and turbulent flows
transport and dispersion in porous media
transport of sediment (and adsorbed contaminants) by air and water
anomalous dispersion
Lecture notesThe course is under development. Lecture materials will be distributed as they become available.
701-1677-00LQuantitative Vegetation Dynamics: Models from Tree to Globe Information W3 credits3GH. Bugmann, H. Lischke, B. Rohner
AbstractThis course provides hands-on experience with models of vegetation dynamics across temporal and spatial scales. The underlying principles, assets and trade-offs of the different approaches are introduced, and students work in a number of small projects with these models to gain first-hand experience.
ObjectiveStudents will
- be able to understand, assess and evaluate the fundamental properties of dynamic systems using vegetation models as case studies
- obtain an overview of dynamic modelling techniques from the individual plant to the global level
- understand the basic assumptions of the various model types, which dictate the skill and limitations of the respective model
- be able to work with such model types on their own
- appreciate the methodological basis for impact assessments of future climate change and other environmental changes on ecosystems.
ContentModels of individuals
- Deriving single-plant models from inventory measurements
- Plant models based on 'first principles'

Models at the stand scale
- Simple approaches: matrix models
- Competition for light and other resources as central mechanisms
- Individual-based stand models: distance-dependent and distance-independent

Models at the landscape scale
- Simple approaches: cellular automata
- Dispersal and disturbances (windthrow, fire, bark beetles) as key mechanisms
- Landscape models

Global models
- Sacrificing local detail to attain global coverage: processes and entities
- Dynamic Global Vegetation Models (DGVMs)
- DGVMs as components of Earth System Models
Lecture notesHandouts will be available in the course and for download
LiteratureWill be indicated at the beginning of the course
Prerequisites / Notice- Basic training in modelling and systems analysis
- Good knowledge of general ecology, vegetation dynamics, and forest systems
701-1682-00LDendroecologyW3 credits3GC. Bigler, K. Treydte, G. von Arx
AbstractThe course dendroecology offers theoretical and practical aspects of dendrochronology. The impact of different environmental influences on tree-ring characteristics will be shown. The students learn various methods to date tree rings and they understand how ecological and environmental processes and patterns can be reconstructed using tree rings.
ObjectiveThe students...
- understand, how wood is configured and how tree-ring structures are formed.
- are able to identify and describe different tree-ring structures.
- understand the theoretical and practical aspects of the dating of tree rings.
- know the effects of different abiotic and biotic environmental influences (climate, site, competition, insects, fire, physical-mechanical influences) on trees and tree rings.
- discover a tool for understanding and reconstructing global change processes.
- learn software to date, standardize and analyze tree rings.
- get hands-on experience based on the demonstration of wood (increment cores, stem discs, wedges), sampling in the field, and measuring and dating of tree rings in the tree-ring lab.
- solve R-based exercises (R tutorial will be provided) and answer questions in Moodle.
- work out an independent research question related to a dendroecological topic and write a short literature review based on scientific papers.
Content- Overview and history of dendrochronology
- Principles of dendrochronology
- Evolution of tree rings
- Formation and structure of wood and tree rings
- Intra-seasonal tree-ring growth
- Continuous and discontinuous tree-ring characteristics
- Sampling and measuring
- Crossdating methods (visual, skeleton plots, quantitative)
- Standardization of tree-ring series
- Development of tree-ring chronologies
- Dendrogeomorphology, dendrohydrology, dendroglaciology
- Stable isotopes
- Climate, climate-growth relationships, climate reconstructions
- Age and size structures, forest dynamics (regeneration, growth, competition, mortality)
- Disturbance ecology (fire, insects, blowdown)
- Application of tree-ring research in practice and in interdisciplinary research projects
- Field and lab day (date for one entire day or two half days will be searched together with the students in the beginning of the semester): discussion of different dendroecological questions in the forest; sampling of trees; insight into different tree-ring projects in the lab (Swiss Federal Institute for Forest, Snow and Landscape Research WSL)
Lecture notesLecture notes (in English) will be handed out in the class.

The lecture notes and further documents (papers, software) can also be downloaded from Moodle (Link) following registration for the course.
LiteratureLiterature lists will be handed out in the class.
Prerequisites / NoticeTime schedule (total of 90 hours): There will be 12 lectures with each two hours (total of 24 hours presence) as well as a field and lab day (8 hours presence). In addition, the students are expected to put 18 hours into the preparation of the lectures as well as 18 hours for the exercises. 4 hours are reserved for the lab work and 18 hours for the project.

The class language is German and English, on request English only.

Requirements:
Basics of biology, ecology and forest ecology
701-1776-00LGeographic Data Processing with Python and ArcGIS Restricted registration - show details
Number of participants limited to 15.
W1 credit2UA. Baltensweiler
AbstractThe course communicates the basics of the programming language Python and gives a general introduction into the geoprocessing framework of ArcGIS. In addition various Python libraries (numyp, Scipy, GDAL, statsmodels, pandas, Jupyter Notebook) will be introduced which increase the functional range of the geoprocessing framework substantially.
ObjectiveThe students learn the basics of geographic data processing based on the programming language Python and ArcGIS (arcpy). They get the ability to implement their own processing sequences and models for geoprocessing. The students are able to integrate open source libraries in their Python scripts and know how the libraries are applied to spatial datasets.
ContentThe course communicates a deepened understanding of the geoprocessing frameworks arcpy and covers basic language concepts of Python such as datatypes, control structures and functions. In addition the application of popular Python libraries in combination with spatial datasets will be shown.
Lecture notesLecture notes, exercises and worked out solutions to them will be provided.
LiteratureLutz M. (2013): Learning Python, 5th Edition, O'Reilly Media
De Smith M., Goodchild, M.F., Longley, P. A. (2018): Geospatial Analysis, 6th Edition, Troubador Publishing Ltd.
Zandbergen P. A. (2014): Python Scripting for ArcGIS. Esri Press.
Allen, D. A. (2014): GIS Tutorial for Python Scripting. ESRI Press.
Prerequisites / NoticeBasic knowledge of ArcGIS is assumed.
701-3001-00LEnvironmental Systems Data Science Restricted registration - show details W3 credits2GL. Pellissier, J. Payne, B. Stocker
AbstractStudents are introduced to a typical data science workflow using various examples from environmental systems. They learn common methods and key aspects for each step through practical application. The course enables students to plan their own data science project in their specialization and to acquire more domain-specific methods independently or in further courses.
ObjectiveThe students are able to
● frame a data science problem and build a hypothesis
● describe the steps of a typical data science project workflow
● conduct selected steps of a workflow on specifically prepared datasets, with a focus on choosing, fitting and evaluating appropriate algorithms and models
● critically think about the limits and implications of a method
● visualise data and results throughout the workflow
● access online resources to keep up with the latest data science methodology and deepen their understanding
Content● The data science workflow
● Access and handle (large) datasets
● Prepare and clean data
● Analysis: data exploratory steps
● Analysis: machine learning and computational methods
● Evaluate results and analyse uncertainty
● Visualisation and communication
Prerequisites / Notice252-0840-02L Anwendungsnahes Programmieren mit Python
401-0624-00L Mathematik IV: Statistik
401-6215-00L Using R for Data Analysis and Graphics (Part I)
401-6217-00L Using R for Data Analysis and Graphics (Part II)
701-0105-00L Mathematik VI: Angewandte Statistik für Umweltnaturwissenschaften
401-0627-00LSmoothing and Nonparametric Regression with Examples Information W4 credits2GS. Beran-Ghosh
AbstractStarting with an overview of selected results from parametric inference, kernel smoothing will be introduced along with some asymptotic theory, optimal bandwidth selection, data driven algorithms and some special topics. Examples from environmental research will be used for motivation, but the methods will also be applicable elsewhere.
ObjectiveThe students will learn about methods of kernel smoothing and application of concepts to data. The aim will be to build sufficient interest in the topic and intuition as well as the ability to implement the methods to various different datasets.
ContentRough Outline:

- Parametric estimation methods: selection of important results
o Maximum likelihood, Method of Least squares: regression & diagnostics

- Nonparametric curve estimation
o Density estimation, Kernel regression, Local polynomials, Bandwidth selection
o Selection of special topics (as time permits, we will cover as many topics as possible) such as rapid change points, mode estimation, robust smoothing, partial linear models, etc.

- Applications: potential areas of applications will be discussed such as, change assessment, trend and surface estimation, probability and quantile curve estimation, and others.
Lecture notesBrief summaries or outlines of some of the lecture material will be posted at Link.

NOTE: The posted notes will tend to be just sketches whereas only the in-class lessons will contain complete information.

LOG IN: In order to have access to the posted notes, you will need the course user id & the password. These will be given out on the first day of the lectures.
LiteratureReferences:
- Statistical Inference, by S.D. Silvey, Chapman & Hall.
- Regression Analysis: Theory, Methods and Applications, by A. Sen and M. Srivastava, Springer.
- Density Estimation, by B.W. Silverman, Chapman and Hall.
- Nonparametric Simple Regression, by J. Fox, Sage Publications.
- Applied Smoothing Techniques for Data Analysis: the Kernel Approach With S-Plus Illustrations, by A.W. Bowman, A. Azzalini, Oxford University Press.
- Kernel Smoothing: Principles, Methods and Applications, by S. Ghosh, Wiley.

Additional references will be given out in the lectures.
Prerequisites / NoticePrerequisites: A background in Linear Algebra, Calculus, Probability & Statistical Inference including Estimation and Testing.
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