Silvan Leinss: Katalogdaten im Frühjahrssemester 2019 |
| Name | Herr Dr. Silvan Leinss |
| Departement | Bau, Umwelt und Geomatik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 102-0528-01L | Experimental and Computer Laboratory (Year Course)   | 10 KP | 2P | D. Braun, U. Gfeller, M. Giuliani, M. Haupt, M. Holzner, J. Jimenez-Martinez, S. Leinss, M. Magdali, M. Maurer, J. Wang, Z. Wang | |
| Kurzbeschreibung | In the Experimental and Computer Laboratory students are introduced to research and good scientific practice. Experiments are conducted in different disciplines of environmental engineering. Data collected during experiments are compared to the corresponding numeric simulations. The results are documented in reports or presentations. | ||||
| Lernziel | The student will learn the following skills: basic scientific work, planning and conducting scientific experiments, uncertainty estimations of measurements, applied numerical simulations, modern sensor technology, writing reports. | ||||
| Inhalt | The Experimental and Computer Laboratory is building on courses in the corresponding modules. Material from these courses is a prerequisite or co-requisite (as specified below) for participating in the Experimental and Computer Laboratory (MODULE: Project in the Experimental and Computer Laboratory): - AIR: Air Quality Measurements - WASTE: Anaerobic Digestion - ESD: Environmental Assessment - GROUND: Groundwater Field Course Kappelen - WRM: Modelling Optimal Water Allocation - FLOW: 1D/2D Open Chanel Flow Modelling - LAND: Landscape Planning and Environmental Systems - RIVER: Discharge Measurements - HydEngr: Hydraulic Experiments - RemSens: Microwave Measurements - SOIL: Soil and Environmental Measurements Lab | ||||
| Skript | Written material will be available. | ||||
| 102-0617-01L | Methodologies for Image Processing of Remote Sensing Data | 3 KP | 2G | I. Hajnsek, O. Frey, S. Leinss | |
| Kurzbeschreibung | The aim of this course is to get an overview of several methodologies/algorithms for analysis of different sensor specific information products. It is focused at students that like to deepen their knowledge and understanding of remote sensing for environmental applications. | ||||
| Lernziel | The course is divided into two main parts, starting with a brief introduction to remote sensing imaging (4 lectures), and is followed by an introduction to different methodologies (8 lectures) for the quantitative estimation of bio-/geo-physical parameters. The main idea is to deepen the knowledge in remote sensing tools in order to be able to understand the information products, with respect to quality and accuracy. | ||||
| Inhalt | Each lecture will be composed of two parts: Theory: During the first hour, we go trough the main concepts needed to understand the specific algorithm. Practice: During the second hour, the student will test/develop the actual algorithm over some real datasets using Matlab. The student will not be asked to write all the code from scratch (especially during the first lectures), but we will provide some script with missing parts or pseudo-code. However, in the later lectures the student is supposed to build up some working libraries. | ||||
| Skript | Handouts for each topic will be provided. | ||||
| Literatur | Suggested readings: T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008 J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall Series in Geograpic Information Science, 2000 | ||||