NIGK17012U Remote Sensing in Land Science Studies

Volume 2026/2027
Education

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

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, being at the core of Land System Science. 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 and hybrid classifications. We will explore techniques to enhance classifications and boost classification accuracies, state-of-the-art accuracy assessment methods, multisource image modality 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, including examples from contemporary research e.g., mapping of woody resources, characterization of wetland types and dynamics, land-cover changes and socio-economic footprints.

 

This course complements the course “Remote Sensing of the Biogeosphere” and serves as a stepping stone into the more advanced course “Satellite Image Processing and Analysis in the Big Data Era”. The course is particularly tailored for students interested in the human dimensions of land system changes.

 

Learning Outcome

Knowledge:

  • Sources of data; various satellite remote sensing data and their applications in Land System Science,
  • Theoretical background of advanced classification and change detection algorithms,
  • Strategies for collection of training data for classification methods,
  • State-of-the-art accuracy assessment reports

 

Skills:

  • Able to download, pre-process and create large multi-modal satellites data sets 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 Google Earth Engine,
  • Able to select and enhance classifications with ancillary data 
  • Able to apply method to detect and quantify gradual and abrupt changes 

 

Competencies:

Advanced skills in application of satellite remote sensing in LULCC studies and Land System Science

Please see Absalon course page.

BSc in Geography and Geoinformatics or equivalent. Experience in basic remote sensing (including image classification and change detection) is a prerequisite.
The form of teaching consists of theory exercises combined with ad hoc lectures. For the teaching plan, please see Absalon.
  • Category
  • Hours
  • Preparation
  • 171
  • Theory exercises
  • 35
  • Total
  • 206
Oral
Individual
Continuous feedback during the course of the semester
Credit
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.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.
Re-exam

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 assesment

Please see learning outcomes.