Information
zum Modul
Dr. Lisa-Maria Mandl
Dr. Lisa Mandl is a postdoctoral researcher at the Chair of Earth Observation for Ecosystem Management at the Technical University of Munich, where she also completed her PhD. Previously, she studied Geography (Bachelor, LMU Munich, 2018) and Applied Geoinformatics (Master, University of Salzburg, 2021). Her research focuses on optical and active remote sensing as well as spatio-temporal analyses of forest ecosystems. She utilizes Sentinel-2 data and spaceborne LiDAR in particular to characterize forest structure and detect forest stress at an early stage. A central focus is on predicting forest recovery success after disturbances by combining knowledge- and data-driven models.
- Explain the relationship between electromagnetic radiation, geo objects and the generation of geoinformation
- Give insight into different kinds of sensors, systems and satellite platforms
- Explain where and how to obtain remote sensing data
- Explain how to handle different kinds and formats of data
- Demonstrate and enable student to apply methods and the typical workflow of processing remote sensing data for information extraction
- Enable students to assess data and evaluate analysis results for typical application purposes
- Demonstrate typical examples for applied remote sensing
Core UNIGIS modules 1-3 or equivalent understanding of GIS, data modelling, data structures and data acquisition. Basic knowledge in working with GIS Software and data.
Nice-to-have: Basic knowledge in physics, especially in optics. Prior knowledge on programming is recommended.
We would like to inform you that this is an exclusively English language module, hence any kind of communication with the module lecturer should be in English. A discussion forum is maintained in moodle in order to support efficient module instruction. You are requested to submit all your questions related to this module to this forum only. The lecturer will check all incoming comments on a regular basis. She will answer your questions or provide you with pointers for solving your problems. The module is delivered in form of an instructed self-study that is based on explorative learning process. Theoretical concepts are complemented with practice oriented examples demonstrated with help of multimedia elements. Upon completion of the module students are requested to evaluate the module, which is a part of our quality assurance policy and practice.
ERDAS IMAGINE 2023 – for this module we provide you with a license for the latest version
ArcGIS Pro, QGIS
Literature: Lillesand, T., Kiefer, R. W. & Chipman, J. 2008. Remote Sensing and Image Interpretation. 6th Edition, John Wiley & Sons, New York.
ArcGIS Pro
Please contact UNIGIS.office@plus.ac.at if you have further questions on system requirements.
The assessment is based on your completed assignments. They must be submitted in written format to the Dropbox within the required time period. If assignments are submitted late, the lecturer is not obligated to grade them.
Dauer
Sprache
Wertigkeit
Kosten
The module is free of charge for UNIGIS students working to meet their elective subject requirements. Included are:
- all related study materials
- supervision and assessment
- module accreditation according to the curriculum
ClubUNIGIS members can register at a price of € 420,-.
Included are:
- all related study materials
- supervision and assessment
- course certificate upon completion of the module
Lektionen
des Moduls
Lektion 1 – Electromagnetic spectrum and interaction of radiation with matter and atmosphere
This lesson introduces the main concepts of remote sensing.
Lektion 2 – Data acquisition process
How is remote sensing data acquired? What is important to know about the workflow of data acquisition and related processes? How does it influence the information we want to extract from the data? This lesson should help you to understand the processes and constraints along a common data acquisition workflow.
Lektion 3 – Sensors and platforms
There is a variety of available remote sensing data representing earth´s surface, acquired during different times of the year. The sensors which are the eyes of the sensor platform have different properties in resolution, channels, etc. Similarly, platforms have different properties such as the time period they rescan the same area on the surface. In everyday life the different cost models force one to perform a cost-benefit calculation. These differences are the key parameters to decide which sensor is optimal to a specific question.
Lektion 4 – Visual image interpretation
This lesson gives an insight into the process of interpreting images visually. How do our eyes “scan” and what do they “see”? How does our brain “interpret” what we “see”? It is important to comprehend the human visual system to understand the limitations and potential sources of error that could be included in digital image interpretation process.
Lektion 5 – Basic methods of image processing
Colour or contrast enhancement, channel mixing and basic data compression methods are explained in this lesson.
Lektion 6 – Radiometric and geometric correction
The focus of this lesson is the process of assigning a reference system to an image, i.e. to relate each pixel of this image to a point/area on earth’s surface.
Lektion 7 – Advanced methods of image processing
The lesson deals with filtering operations and pan-sharpening, as well as other advanced methods like principal component transformation, channel ratios and texture transformations.
Lektion 8 – Digital image analysis – classification methods part 1
The step from pixel-based image data to objects and classes using unsupervised and supervised classification methods is a key element in remote sensing and the main benefit from this discipline.
Lektion 9 – Digital image analysis – classification methods part 2
Methods of accuracy assessment are used after the classification/analysis stage to evaluate the quality of a classification.
Lektion 10 – Planning a remote sensing image application
The planning process is a key element in remote sensing. Designing carefully a workflow of retrieving information from remote sensing data prevents common mistakes and ensures the cost efficiency of your daily work.
Lektion 11 – Active systems
Radar and Laser scanning are active systems that have some advantages over classical remote sensing approaches and data sources.
Lektion 12 – Applications examples
In this lesson you will get an idea of the various applications of remote sensing. The examples discussed refer to the production of useful (and reliable) maps.
The goal of turning geo-spatial data into information is a question of cartography and/or illustration as well as of classification and filtering of the extracted information to fulfil the map purpose;
The development of Land Use/Land Cover (LULC) classification for mapping purposes;
The application of remote sensing imagery in regional planning, water resources management and geology;
The detection and monitoring of changes (change detection) using remote sensing data..