Multitemporal analysis on remote sensing data 1900-3-MAR-GKT-WA

The aim of this course is to prepare students for planning, data selecting, processing and analysing time series data from satellites. This topic is related to Module 2 of the E-TRAINEE course (E-learning course on Time Series Analysis in Remote Sensing for Understanding Human-Environment Interactions, https://web.natur.cuni.cz/gis/etrainee/), developed by four partner universities within ERASMUS+ Strategic partnership programme. Some parts of the Theme 1 from Module 1 will also be used, which is oriented towards the basis of time series theory and methods of its analysis in remote sensing.

The course will cover such Themes as fundamentals of time series, satellite multispectral data principles, temporal information in satellite data, pre-processing of time series data, multitemporal classification, vegetation changes/disturbances mThe aim of this course is to prepare students for planning, data selecting, processing and analysing time series data from satellites. This topic is related to Module 2 of the E-TRAINEE course (E-learning course on Time Series Analysis in Remote Sensing for Understanding Human-Environment Interactions, https://web.natur.cuni.cz/gis/etrainee/), developed by four partner universities within ERASMUS+ Strategic partnership programme. Some parts of the Theme 1 from Module 1 will also be used, which is oriented towards the basis of time series theory and methods of its analysis in remote sensing.

The course will be realized as a series of lectures and exercises which the students will learn on their own based on material from the e-learning platform, and then presentations of their own projects are planned. Students can choose the form of final presentation (oral presentation or simple scientific article preparation).

The course accentuates the development of students' English language skills and familiarizes them with English remote sensing terminology. This will allow them to present their results in the form of an oral presentation or scientific article, allowing them to gain new skills in this area.onitoring and validation of obtained results.
Students will use QGIS, RStudio and Google Earth Engine platform to perform their analysis.

The course accentuates the development of students' English language skills and familiarizes them with English remote sensing terminology.

Student workload: 6 ECTS = 6 × 25h = 150h (in direct contact 4 ECTS)
(N) – work in direct contact with the teacher,
(S) – student's own (independent) work.
Classes (lecture) = 15h (N)
Classes (exercises) = 45h (N)
• Consultations = 15h (N)
• Project consultations = 20h (N)
• Passing the exercises, lecture test, exam = 5h (N)
• Preparation (independently) for the exam = 15 hours (S)
• Independent preparation for exercises – 1 hour/week. = 15h (S)
• Design work = 20h (S)
TOTAL = approx. 150 hours

Type of course

elective courses

Mode

Blended learning

Prerequisites (description)

- The B2 level English (spoken and written) - knowledge of remote sensing basis - knowledge of basic programming (R and JavaScript in Google Earth Engine) - Google account with free space on Google Drive, Earth Engine account

Course coordinators

Adriana Marcinkowska-Ochtyra

Learning outcomes

After completing the course students:
KNOWLEDGE (K_W01; K_W04; K_W07; W_14):
- know the basic issues of the multitemporal analysis on satellite remote sensing data,
SKILLS (K_U01; K_U07):
- use terminology oriented on multitemporal analysis on remote sensing data in English in the presentation of the results,
ATTITUDES (K_K01):
- improve their professional skills;
- understand the need to search for new technologies;
- care about the reliability of their research work.

Assessment criteria

The final grade for the course consists of half of the theoretical part (lectures) and half of the practical part (exercises).

The grade on exercises part (50% of all) depends on:
finished exercises related to the Themes (50% of the exercises part),
own project development (can be done in pairs) and presentation of the results (50% of the exercises part, possibility of choosing the presentation method: oral presentation or in the form of a simple scientific article - example scientific writing guideline: https://writingajournalarticle.wordpress.com/).
Attendance is counted as completion of the exercise. All exercises must be done.

The grade on the lectures part (50% of all) is based on the final test. However, after each completed Theme, students will receive a few questions related to this and the answers sent to the lecturers will be scored. Obtaining a certain number of points can replace the final test.

The student has the right to improve their negative grade.

Bibliography

The literature for each Theme is included in the appropriate Module of the course.
Example publications:
Chuvieco, E. (2020). Fundamentals of satellite remote sensing: An environmental approach. CRC press. https://doi.org/10.1201/9780429506482
Gómez, C., White, J. C., & Wulder, M. A. (2016). Optical remotely sensed time series data for land cover classification: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 116, 55-72. https://doi.org/10.1016/j.isprsjprs.2016.03.008
Kuenzer, C., Dech, S., & Wagner, W. (2015). Remote sensing time series. Remote Sensing and Digital Image Processing, 22, 225-245. https://link.springer.com/book/10.1007/978-3-319-15967-6
Mayr, S., Kuenzer, C., Gessner, U., Klein, I., & Rutzinger, M. (2019). Validation of earth observation time-series: A review for large-area and temporally dense land surface products. Remote Sensing, 11(22), 2616. https://doi.org/10.3390/rs11222616

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 1900-3-MAR-GKT-WA in USOSweb