Remote Sensing in Land Science Studies

Course content

Mapping Land-Use Land-Cover Change (LULCC) through satellite remote sensing offers valuable insights into understanding the trajectories, patterns, drivers, and consequences of land-cover alterations. Throughout this course, we will delve into the evolution of classification and change detection techniques designed to map LULCC and gauge land-use intensity.

The course curriculum encompasses non-parametric machine learning classification methods, including SVM, Random Forests, hybrid classifications. We will explore techniques to enhance classifications and boost classification accuracies, state-of-the-art accuracy assessment methods, multisource image fusion techniques, and the analysis of large data composites, including cloud computation environments like Google Earth Engine. Our primary focus will be on the utilization of freely accessible datasets, including optical imagery such as Landsat, Sentinel-2, and MODIS, as well as radar data from Sentinel-1 and microsatellites like PlanetScope. Additionally, we will cover applications in socio-economic and environmental domains, such as mapping wildfires, floods, land-cover changes, and socio-economic footprints, as seen through night-time lights (VIIRS and DMSP OLS).

This course assumes a prior knowledge and experience of working with satellite imagery, equivalent to completing undergraduate-level classes like Introduction to Remote Sensing and Classification of Spatial Data. However, we are committed to accommodating students without prior remote sensing experience. Moreover, this course complements the Remote Sensing of the Biogeosphere class and serves as a stepping stone into more advanced course Satellite Image Processing and Analysis in the Big Data Era. The course is particularly tailored for students who anticipate utilizing satellite remote sensing in interdisciplinary studies of the Biogeosphere and Anthroposphere (examining the human dimensions of land-cover change).

Our primary focus will be on applying remote sensing to investigate land-cover transformations, including urban sprawl and decline, as well as agricultural and forestry dynamics. Nonetheless, students are encouraged to bring their own research projects or propose alternative topics that align with their personal interests. We also highly encourage students to integrate knowledge gained from other courses and embark on interdisciplinary projects. Here are some examples of interdisciplinary projects that have evolved into published research papers:


MSc Programme in Geography and Geoinformatics
MSc Programme in Geography and Geoinformatics with a minor subject

Learning outcome


  • 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 from multisource satellite constellations, and construction of large satellite imagery composites,
  • State-of-the-art accuracy assessment reports,
  • Aspects of spatially explicit modeling with produced land change maps.



  • Able to download, pre-process, fuse satellite imagery from different sources (e.g., optical, radar).
  • Able to select, parameterize and evaluate classification methods -focus on the use of non-commercial software R and GoogleEarth Engine,
  • 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 Sentinel-2 and Landsat-like data (e.g., with Landtrendr, BFAST methods)
  • 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



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

The form of teaching is exercises combined with lectures. We will also run the discussion on reading and new satellite platforms. 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 highly 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 case by case. If you do not take earlier remote sensing class (es) please contact the instructor first prior to enrolling in the class for extra consultation.

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Written assignment, Ongoing preparation throughout the course
Oral examination, 20 minutes
Type of assessment details
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.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners

Identical to ordinary exam.

The student has the following options:

Is the quality of the written assignment not acceptable, the student can choose to either hand in a new or revised report.

Is the quality of the written assignment acceptable, the student can choose to either hand in a revised report or resubmit the original report from the ordinary exam.

The written assignment must be handed in prior to the re-examination week. The oral exam uses the written assignment as its point of departure. It includes the titles listed in the officially approved reading list.

Criteria for exam assessment

Please see learning outcomes.

Single subject courses (day)

  • Category
  • Hours
  • Preparation
  • 171
  • Theory exercises
  • 35
  • English
  • 206


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 3
The number of places might be reduced if you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study Board of Geosciences and Management
Contracting department
  • Department of Geoscience and Natural Resource Management
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Alexander Prishchepov   (4-717c80825079777e3e7b853e747b)
Saved on the 14-02-2024

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