Module GEOG3261: REMOTE SENSING

Department: Geography

GEOG3261: REMOTE SENSING

Prerequisites

• GEOG2591 Handling Geographic Information

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• To develop advanced knowledge and skills in the remote sensing applications which are currently at the forefront of ecological monitoring, hazard assessment and environmental management.

Content

• Revision of key Level 2 concepts and methods including image referencing and classification
• Knowledge of the legislative framework associated to the UAV industry.
• Topography production from imagery (using photogrammetry and Structure from Motion)
• Topography analysis: point-cloud elevation data versus raster format Digital Elevation Models.
• Remote sensing of the cryosphere: tracking ice loss and glacier retreat from satellite and UAV data.
• Fluvial Remote Sensing: data collection and platforms suitable to riverine environments, from UAVs to satellites. Role of remote sensing in river management.
• Biosphere Remote Sensing: quantifying river habitats from the air and space using panchromatic and multispectral imaging. Role of remote sensing in ecological monitoring for the purpose of conservation and management.
• Geohazard Assessment and Mitigation: quantifying events such as landslides, floods and vegetation loss (deforestation) in a range of settings using appropriate datasets.
• Usage and potential of Artificial Intelligence methods in environmental remote sensing.

Learning Outcomes

• On successful completion of the course students are expected to be able to:
• Understand the role and input of earth observation into current environmental debates
• Show a basic theoretical knowledge of the most important methods for computer processing and the interpretation of environmental remote sensing data
• Discuss and evaluate relevant peer review papers on the subject
• Evaluate the use of remote sensing for some important environmental problems in a critical way
• Discuss and evaluate the potential environmental management applications of UAVs in the UK and abroad.
• Evaluate the potential of machine and deep learning in environmental remote sensing

• Access web-archived remote sensing data
• Apply the theoretical material covered in the lectures to real-world environmental remote sensing data sets
• Use advanced remote sensing software in a student led project
• Develop a quantitative appreciation for the errors in remotely sensed data
• Introduction to Python coding and GIS scripting in the context of maching learning

• Students are expected to:
• Present logical written arguments supported with quantitative evidence
• Be able to critically analyse remote sensing data in a given application
• Be able to work both independently and in a group on a remote sensing project
• Understand remote sensing methods and critically select the most appropriate in a given application

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures introduce students to the theory and practice of remote sensing and indicate how to develop knowledge through wider reading
• Practicals will enable the students to gain 'hands on' experience with some of the tools and techniques in remote sensing. They will also have the chance to apply the concepts introduced in lectures to solve real-world problems. Practical exercises introduce students to analytical techniques that will be required by the group and individual based projects
• Seminars allow students to develop skills in presenting scientific data
• The student project will develop research skills
• Students will think about research design, hypothesis testing, data processing, data analysis and presentation
• Individually, students will produce a written report and try to place their results in the context of the peer review scientific literature
• The project assessment explicitly addresses Learning Outcomes 2, 3 and 4 (see above)
• The unseen examination will test students' ability to marshal and focus evidence gained from reading and practical experience of using remote sensing data
• The examination questions will cover both theory and practical elements of the module practice and case studies
• The examination assessment explicitly addresses Learning Outcomes 1, 2, 3 and 4 (see above)

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Formative feedback will be provided through verbal feedback on individual presentations during the seminars. Additionally, at the end of each class, a short formative quiz, which is not handed in, will help students revise key points of the lecture.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University