Search result: Catalogue data in Autumn Semester 2022
Environmental Sciences Master  | |||||||||||||||||||||
 Major in Forest and Landscape Management | |||||||||||||||||||||
| Electives | |||||||||||||||||||||
| Methods and Tools | |||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | ||||||||||||||||
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| 701-1316-00L | Physical Transport Processes in the Natural Environment | W | 3 credits | 2G | J. W. Kirchner | ||||||||||||||||
| Abstract | Fluid 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. | ||||||||||||||||||||
| Learning objective | Students 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. | ||||||||||||||||||||
| Content | dimensional 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 notes | The course is under development. Lecture materials will be distributed as they become available. | ||||||||||||||||||||
| 701-1677-00L | Quantitative Vegetation Dynamics: Models from Tree to Globe | W | 3 credits | 3G | H. Lischke, U. Hiltner, B. Rohner | ||||||||||||||||
| Abstract | The course introduces basic concepts and applications of dynamic vegetation models at various temporal and spatial scales. Different modeling approaches and underlying principles are presented and critically discussed during the lectures. In the integrated exercise parts, students work in a number of small projects with some of the introduced models to gain practical experience. | ||||||||||||||||||||
| Learning objective | Students will - be enabled to understand, assess and evaluate the fundamental properties of dynamic systems using vegetation models as case studies - obtain an overview of dynamic modelling techniques and their applications from the individual plant to the global level - understand the basic assumptions of the various model types, which dictate the applicability and limitations of the respective model - be enabled 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. | ||||||||||||||||||||
| Content | Models 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 - Theoretical models 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 notes | Handouts will be available in the course and for download | ||||||||||||||||||||
| Literature | Will be indicated at the beginning of the course | ||||||||||||||||||||
| Prerequisites / Notice | - Ideally basic experiences in modelling and systems analysis - Basic knowledge of programming, ideally in R - Good knowledge of general ecology, ideally of vegetation dynamics and forest systems | ||||||||||||||||||||
| 701-1682-00L | Dendroecology | W | 3 credits | 3G | C. Bigler, K. Treydte, G. von Arx | ||||||||||||||||
| Abstract | The 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. | ||||||||||||||||||||
| Learning objective | The 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 - Formation and structure of wood and tree rings - Wood anatomy and intra-seasonal tree-ring growth - Continuous and discontinuous tree-ring characteristics - Sampling and measuring of tree rings - Crossdating methods (visual, skeleton plots, quantitative) - Detrending and standardization of tree-ring series - Development of tree-ring chronologies - Water transport in trees - Stable isotopes in tree rings - Climate influences, climate-growth relationships, climate reconstructions - Reconstruction of 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 notes | Lecture notes (in English) will be handed out in the class. The lecture notes and further documents (papers, software) can be downloaded from Moodle (https://moodle-app2.let.ethz.ch) following registration for the course. | ||||||||||||||||||||
| Literature | Literature lists will be handed out in the class. | ||||||||||||||||||||
| Prerequisites / Notice | Time 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 | ||||||||||||||||||||
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| 701-1776-00L | Geographic Data Processing with Python and ArcGIS  Number of participants limited to 30. Waiting list will be deleted 13.09.2022. | W | 1 credit | 2U | A. Baltensweiler | ||||||||||||||||
| Abstract | The 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. | ||||||||||||||||||||
| Learning objective | The 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. | ||||||||||||||||||||
| Content | The 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 notes | Lecture notes, exercises and worked out solutions to them will be provided. | ||||||||||||||||||||
| Literature | Lutz 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. (2020): Advanced Python Scripting for ArcGIS Pro. Esri Press. Allen, D. A. (2014): GIS Tutorial for Python Scripting. ESRI Press. | ||||||||||||||||||||
| Prerequisites / Notice | Basic knowledge of ArcGIS is assumed. | ||||||||||||||||||||
| 701-3001-00L | Environmental Systems Data Science: Data Processing **Students who have taken 701-3001-00L Environmental Systems Data Science in autumn semester 2020 or 21 are not allowed to take this lecture. The content is similar.** Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 | W | 2 credits | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | ||||||||||||||||
| Abstract | Students 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. | ||||||||||||||||||||
| Learning objective | The 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 / Notice | 252-0840-02L 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 | ||||||||||||||||||||
| 701-3003-00L | Environmental Systems Data Science: Machine Learning Number of participants is limited to 80. Course registration starts on 31.08.2022. Priority is given to the target groups until 23.09.2022, Target groups Agricultural Sciences MSc Environmental Sciences MSc Atmospheric and Climate Science MSc Environmental Sciences PhD Agricultural Sciences PhD Waiting list will be deleted on 30.09.2022 | W | 3 credits | 2G | L. Pellissier, E. J. Harris, J. Payne, M. Volpi | ||||||||||||||||
| Abstract | Students are introduced to advanced data science where environmental data are analyzed using state of the art machine learning methods. Starting from known statistical approaches, they learn the principle of more advanced machine learning methods with practical application. The course enables students to plan their own data science project in their specialization and to apply machine learning mode | ||||||||||||||||||||
| Learning objective | The students are able to • select an appropriate model related to a research question and dataset • describe the steps from data preparation to running and evaluating models • prepare data for running machine learning with dependent and independent variable • build and validate regressions and neural network models • understand convolution and deep learning models • access online resources to keep up with the latest data science methodology and deepen their understanding | ||||||||||||||||||||
| Content | • The data science workflow • Data preparation for running and validating machine learning models • Get to know machine learning approaches including regression, random forest and neural network • Model complexity and hyperparameters • Model parameterization and loss • Model evaluations and uncertainty • Deep learning with convolutions | ||||||||||||||||||||
| Literature | Building on existing data science resources | ||||||||||||||||||||
| Prerequisites / Notice | Math IV, VI (Statistics); R, Python; ESDS I | ||||||||||||||||||||
| 401-0627-00L | Smoothing and Nonparametric Regression with Examples | W | 4 credits | 2G | S. Beran-Ghosh | ||||||||||||||||
| Abstract | Starting 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. Selected numerical examples will be used for motivation. The presented methods will also be applicable elsewhere. | ||||||||||||||||||||
| Learning objective | The 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. | ||||||||||||||||||||
| Content | Rough Outline: - Parametric estimation methods: selection of important results o Method of Least squares: regression & diagnostics - Nonparametric curve estimation o Density estimation, Kernel regression, Local polynomials, Bandwidth selection, various theoretical results related to consistency o Selection of special topics (as time permits, we will discuss some of the following): rapid change points, mode estimation, partial linear models, probability and quantile curve estimation, etc. - Applications: potential areas of applications will be discussed such as, change assessment, trend and surface estimation and others. | ||||||||||||||||||||
| Lecture notes | Brief summaries or outlines of some of the lecture material will be communicated to registered students by Email. Additional comments may appear at https://www.wsl.ch/en/employees/ghosh.html. NOTE: These notes will tend to be just sketches whereas only the in-class lessons will contain complete information. | ||||||||||||||||||||
| Literature | References: - Kernel Smoothing: Principles, Methods and Applications, by Sucharita Ghosh, Wiley. - 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. Additional references will be given out in the lectures. | ||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: A background in Linear Algebra, Calculus, Probability & Statistical Inference including Estimation and Testing. | ||||||||||||||||||||
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