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 multisource image fusion techniques, and analysis of large data composites, including cloud computation environment, such as GoogleEarth Engine. We will primarily concentrate on the utilization of freely available datasets: optical, such as Landsat, Sentinel-2 and MODIS imagery, and radar Sentinel-1. We will also revisit the utility of microsatellites, such as Planetscope. Last but not least, we will cover socio-economic and environmental applications, such as mapping wildfires, floodings, land-cover change, but also socio-economic footprint, i.e., with night-time lights (VIIRS and DMSP OLS).

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 the application of remote sensing to study land-cover transformation, such as urban sprawl and decline, 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 interests. 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 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.

 

Skills:

  • 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

 

Competencies:

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 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 in the class.

Oral
Individual
Continuous feedback during the course
ECTS
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
Criteria for exam assessment

Please see learning outcomes.

Single subject courses (day)

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

Kursusinformation

Language
English
Course number
NIGK17012U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 3
Schedulegroup
C
Capacity
22
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Geosciences and Management
Contracting department
  • Department of Geoscience and Natural Resource Management
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Alexander Prishchepov   (4-6873777947706e7535727c356b72)
Saved on the 28-02-2022

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