Remote Sensing in Land Science Studies

Course content

Mapping Land-Use Land-Cover Change (LULCC) with satellite remote sensing provides a reference for understanding the trajectories, patterns, drivers, and consequences of land-cover change. During the course, we will take a look at the advancement of classification and change detection techniques to map LULCC and land-use intensity. The course will cover state-of the-art non-parametric machine learning classification methods (e.g., SVM, Random Forests, hybrid classifications deep learning), enhancement of classifications, accuracy assessment, and image fusion techniques, and analysis of large data composites. We will primarily concentrate on utilization of freely available datasets: optical, SAR and LiDAR, such as Landsat and MODIS imagery, new products available via the Copernicus program, such as from Sentinel-1 and Sentinel-2 satellites, but also commercial datasets.

The course builds on prior knowledge of working with satellite imagery, such as passing via Bachelor level class-Introduction to Remote Sensing, Classification of Spatial Data. However, we will try to accommodate the students without prior experience in remote sensing. The course also complements the class on Remote Sensing of the Biogeosphere. The course will be particularly useful for students who envision interdisciplinary use of satellite remote sensing in the Biogeosphere and Anthroposphere studies (human dimensions of land-cover change).

We will concentrate on application of remote sensing to study land-cover transformation, such as urban sprawl, agricultural and forestry dynamics. However, students are more than welcome to bring their own research projects, as well as to suggest alternate topics in line with their own interest. Students are also highly encouraged to incorporate knowledge gained via other classes and to perform interdisciplinary projects.

Education

MSc Programme in Geography and Geoinformatics
MSc Programme in Geography and Geoinformatics with a Minor Subject

Learning outcome

Knowledge:

  • Sources of data, currently operating non-commercial and commercial platforms and their applications in Land System Science and LULCC studies.
  • Theoretical background behind advanced classification and change detection algorithms associated with relevant up-to-date scientific literature
  • Strategies for collection of training data for classification methods
  • Approaches in data fusion and construction of large satellite imagery composites
  • State-of-the art accuracy assessment reports

 

Skills:

  • Able to download, pre-process, fuse satellite imagery
  • Able to select, parameterize and evaluate classification methods with the use of commercial (ENVI) and non-commercial software (R, QGIS, GRASS, Enmap-Box)
  • Able to select and enhance classifications with ancillary data (e.g, texture, phenology metrics, topography, etc).
  • Able to map subtle changes with time-series analysis of fine resolution data (e.g., Landsat-based Landtrendr, BFAST)
  • Perform interdisciplinary research with the aid of satellite data via lectures, readings of up-to-date publications, via labs and by performing a course project

 

Competencies:

Advanced skills in application of satellite remote sensing in Land System Science.

The form of teaching is theory exercises combined with ad hoc lectures. For the teaching plan, please see Absalon.

Please see Absalon course page.

BSc in Geography and Geoinformatics or equivalent. Prior experience in remote sensing is encouraged.

The course builds on prior knowledge of working with satellite imagery, such as passing via Bachelor level classes such as Introduction to Remote Sensing, Classification of Spatial Data. However, we will try to accommodate the students without prior experience in remote sensing. If you do not take earlier remote sensing class (es) please contact first the instructor prior the enrolling into the class.

ECTS
7,5 ECTS
Type of assessment
Written assignment, Ongoing preparation throughout the course
Oral examination, 20 minutes
The written assignment is prepared during the course and must be handed in prior to the exam week. The oral exam uses the written assignment as its point of departure. There is no preparation for the oral exam. It includes the titles listed in the officially approved reading list. A combined grade is given after the oral exam.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners
Criteria for exam assessment

Please see learning outcomes.

Single subject courses (day)

  • Category
  • Hours
  • Theory exercises
  • 35
  • Project work
  • 35
  • Preparation
  • 136
  • English
  • 206