MEng Aerospace Engineering

LIDAR and SAR images provide day-and-night and weather-independent images they are used for several applications in the fields of geosciences and climate change research, environmental and Earth system monitoring, change detection, security-related applications and planetary exploration (Moreira et al, 2013).

The course will be focusing on practical classes mainly using the R studio and SNAP software freely available from ESA (European Space Agency) and also the freely available images from Sentinel 1 (SAR in C band). Some guest speakers from other institutions will be invited to present their work using SAR and LIDAR image processing and other kind of remote sensing images to monitor and estimate forest and land use and land cover characteristics.

Examples of topics that will be covered include:

Week 1: What Synthetic Aperture RADAR (SAR) is 
Week 2: SAR parameters to consider for a study.
Week 3: Geometric Properties of SAR images and statistic properties of SAR measurements
Week 4: Physical content of SAR image
Week 5: What LIDAR is/ and LIDAR image processing
Week 6:  Reading week
Week 7: Practical 1: SAR image pre-processing (Backscattering)
Week 8: Practical 2: SAR image manipulation (Polarimetry)
Week 9: Practical 3: SAR image manipulation (Interferometry)
Week 10: Practical 4: SAR application: example-classification
Week 11: mini-project surgery
Week 12: mini-project surgery

Reference:
Moreira et al, 2013. http://www2.geog.ucl.ac.uk/~mdisney/teaching/teachingNEW/3051/PPRS_7/esa_sar_tutorial.pdf 
SNAP: http://step.esa.int/main/download/snap-download/

The unit aims to:

Give opportunity to the students to learn the principles, techniques, methods of LIDAR (Light Detection and Ranging) and SAR (Synthetic Aperture Radar) remote sensing. Different applications of LIDAR and SAR images will be explored with a focus on forest structure and land use / land cover monitoring. At the moment with the increasable growth of the Earth Observation (EO) science and industry it is important to offer opportunities to the students to be in contact with advanced technologies in the remote sensing field.

The intended learning outcomes of providing opportunities for students to engage with advanced technologies in the remote sensing field align closely with the objectives of enhancing student outcomes, particularly in terms of employability skills required for the CUIP.

• Enhanced Technical Proficiency: Through exposure to advanced technologies in remote sensing, students will develop a deeper understanding of Earth Observation (EO) science. They will gain hands-on experience with cutting-edge tools and methodologies, thereby enhancing their technical proficiency in remote sensing techniques. This technical expertise is highly valued in the industry and will prepare students for careers in fields such as geospatial analysis, environmental monitoring, and resource management.
• Critical Thinking and Problem-Solving Skills: Engaging with advanced technologies in remote sensing encourages students to think critically and analytically about complex datasets and real-world applications. They will learn to interpret and analyse EO data, identify patterns, and derive meaningful insights. These problem-solving skills are essential for addressing challenges in various industries, including environmental science, urban planning, and agriculture, thus making students more competitive in the job market.
• Effective Communication and Collaboration: Working with advanced technologies in remote sensing often involves interdisciplinary collaboration and communication. Students will have opportunities to collaborate with peers, researchers, and industry professionals, fostering teamwork and communication skills. They will learn to articulate their findings effectively through presentations, reports, and project deliverables, preparing them for collaborative work environments and client interactions in their future careers.
• Adaptability and Innovation: The rapid growth of the Earth Observation science and industry requires individuals who are adaptable and innovative. By engaging with advanced technologies, students will develop the agility to navigate evolving technologies and methodologies in remote sensing. They will also be encouraged to explore innovative approaches to address complex challenges, fostering a mindset of creativity and innovation that is highly sought after in today's job market.
• Industry-Relevant Experience: Exposure to advanced technologies in remote sensing provides students with practical, industry-relevant experience that is directly applicable to the demands of the job market. Through hands-on projects and internships, students will gain valuable experience working with state-of-the-art equipment and software, preparing them for entry-level positions and internships within the EO science and industry sector. This experiential learning opportunity enhances their employability and positions them as valuable assets to potential employers.

The Fundamentals of SAR and LIDAR for Environment Monitoring module places a strong emphasis on supporting the development of students' digital skills throughout the course. This is reflected in the structure of the unit and the intended learning outcomes, as outlined below:

• Integrated Digital Learning Activities: Practical classes throughout the module, such as SAR image processing, polarimetry, interferometry, LIDAR image processing, and land use/land cover change detection, are designed to enhance students' digital literacy. These activities involve hands-on experience with software tools commonly used in remote sensing analysis, such as SAR and LIDAR data processing software packages. Students engage in data manipulation, analysis, and interpretation, thereby developing proficiency in digital data handling and manipulation techniques.
• Digital Resource Utilization: The module leverages digital resources, including online databases, journals, and interactive tutorials, to supplement traditional teaching materials.

  Syllabus

  Syllabus (indicative curriculum content):

  Week 1: Introduction to Synthetic Aperture RADAR (SAR)

  • Overview of SAR technology
  • Principles of SAR imaging
  • Applications of SAR in remote sensing and environmental monitoring

  Week 2: SAR Parameters for Study Design

  • Understanding SAR parameters such as resolution, polarization, and frequency
  • Considerations for selecting SAR parameters for specific study objectives
  • Case studies illustrating the importance of parameter selection in SAR studies

  Week 3: Geometric Properties and Statistical Characteristics of SAR Data

  • Geometric properties of SAR images: resolution, spatial coverage, and distortion correction
  • Statistical properties of SAR measurements: speckle noise, coherence, and backscattering coefficient
  • Methods for analysing and interpreting SAR data for environmental studies

  Week 4: Physical Content of SAR Images

  • Interpretation of SAR imagery: features, patterns, and anomalies
  • Understanding the physical properties of objects reflected in SAR images
  • Case studies demonstrating the extraction of meaningful information from SAR data

  Week 5: Introduction to Light Detection and Ranging (LIDAR)

  • Principles of LIDAR technology
  • Applications of LIDAR in terrain mapping, forestry, and urban planning
  • Basics of LIDAR data processing and interpretation

  Week 6: Reading Week

  Week 7: Practical Session 1: SAR Image Pre-processing (Backscattering)

  • Hands-on exercises in SAR image pre-processing techniques
  • Understanding backscattering and its implications for SAR data analysis
  • Data manipulation using SAR software tools

  Week 8: Practical Session 2: SAR Image Manipulation (Polarimetry)

  • Introduction to SAR polarimetry and its applications
  • Techniques for polarimetric SAR image processing
  • Analysis of polarimetric SAR data for vegetation mapping and classification

  Week 9: Practical Session 3: SAR Image Manipulation (Interferometry)

  • Introduction to SAR interferometry and its principles
  • Processing and interpretation of interferometric SAR (InSAR) data
  • Applications of InSAR

  Week 10: Practical Session 4: SAR Application: Example-Classification

  • Hands-on exercises in SAR data classification techniques
  • Application of SAR data for land cover classification and change detection
  • Evaluation of classification results and interpretation of thematic maps

  Week 11-12: Mini-Project Surgery

  • Guidance and support for students to develop and execute mini-projects
  • Topics may include SAR or LIDAR data analysis, image interpretation, and report writing
  • Discussion of mini-project preliminary findings and informal feedback

  Teaching and learning methods

  • 4 Lectures – 2 hours each
  • 1 Lecture (LIDAR-theory and practical) - 3 hours
  • 6 Practical classes in the computer cluster – 3 hours each
  • 1 Reading week

  Knowledge and understanding

  • Have an understanding of principles and applications of remote sensing using SAR and LIDAR.
  • Develop an awareness of the wide range of remote sensing SAR and LIDAR systems and understand how and why they suit different environmental applications.
  • Understand the importance of using LIDAR and SAR datasets for Earth Observation, Ecological and Climate modeling scientific communities, and the possibilities of use and discoveries emerging in science and industry from their use.

  Intellectual skills

  • Develop research skills such as selective reading, critical thinking, and evaluating scientific evidence.
  • Develop logical reasoning and numerical (multivariate data-handling and statistical analyses) skills.
  • Develop research plans for LIDAR and SAR image applications and analyses: questions, hypotheses, planning, execution, analyses, and conclusions.

  Practical skills

  • Learn practically how to process, analyze, and interpret LIDAR and SAR images in environmental studies.
  • Be able to handle and apply technical concepts of LIDAR and SAR remote sensing and critically evaluate the results.
  • Source appropriate LIDAR and SAR images from online image archives; Apply key algorithms to interpret SAR remotely sensed imagery; Manage raster data, other spatial data files, and field datasets or field surveys; Process and analyze SAR and LIDAR images.

  Transferable skills and personal qualities

  • Develop communication, writing, and presentation skills.
  • Develop the ability to abstract, synthesize, rethink ideas, and share them.
  • Develop logical reasoning, numerical (multivariate data-handling and statistical analyses), and presentation skills.

  Assessment methods

  Method	Weight
  Written assignment (inc essay)	40%
  Project output (not diss/n)	60%

  Feedback methods

  Formative Assessment Task 1
  Discussion during select lectures on theoretical content.
  10 to 15 minutes.
  Feedback during classes.
  Expected outcome: Improvement of theoretical understanding and critical thinking skills.

  Formative Assessment Task 2
  Practical classes and discussions related to the proposed practical exercises.
  3 hours.
  Feedback during classes.
  Expected outcome: Enhancement of practical skills and problem-solving abilities.

  Assessment task 1
  An essay to be developed based in the theoretical content. The students will choose one essay question out of five.
  900 words.
  Feedback provided in 15 days.
  40% weighting.

  Assessment task 2
  A mini-project based on the practical classes. This coursework will be delivered in the format of a conference poster.
  700 words including figures, graphics and/or maps as results of the mini-project.
  Feedback provided in 15 days.
  60% weighting.

  Recommended reading

  Books available in the library:

  • Jin, Ya-Qiu, and Feng Xu. Polarimetric scattering and SAR information retrieval. John Wiley & Sons, 2013.

  SAR Handbook (NASA) - https://ntrs.nasa.gov/api/citations/20190002563/downloads/20190002563.pdf
  SAR Tutorials (ESA)

  • Woodhouse, I.H., 2017. Introduction to microwave remote sensing. CRC press.
  • Lee, J.S. and Pottier, E., 2009. Polarimetric radar imaging: from basics to applications. CRC press.
  • Hajnsek I. Polarimetric Synthetic Aperture Radar: Principles and Application. Springer Nature; 2021 - (OPEN ACESS).
  • Dong, P. and Chen, Q., 2017. LiDAR remote sensing and applications. CRC Press.
  • Maltamo, M., Næsset, E. and Vauhkonen, J., 2014. Forestry applications of airborne laser scanning. Concepts and case studies. Manag For Ecosys, 27, p.2014.

  Study hours

  Scheduled activity hours
  Lectures	11
  Practical classes & workshops	18

  Independent study hours
  Independent study	121

  Teaching staff

  Staff member	Role
  Polyanna Da Conceicao Bispo	Unit coordinator

  Additional notes

  The Fundamentals of SAR and LIDAR for Environment Monitoring module is committed to promoting equality, diversity, and inclusion throughout its teaching, learning, and assessment practices. We recognize the importance of providing a diverse range of perspectives and examples to create an inclusive learning environment that reflects the global community and addresses underrepresentation in the field of Earth Observation.

  Inclusive Curriculum Design: The module content incorporates examples and case studies from diverse geographical regions, with a particular focus on forests in the Global South, including Brazil, Colombia, India, Indonesia, and other countries. By highlighting examples from minority backgrounds and underrepresented regions, we aim to broaden students' perspectives and challenge stereotypes about who contributes to scientific knowledge and innovation.

  Representation of Minority Authors: Texts and papers authored by women, Black, Asian, and Minority Ethnic (BAME) authors, for instance, are included in the module's recommended reading list. By showcasing the work of scholars from diverse backgrounds, we aim to amplify underrepresented voices and highlight the valuable contributions of individuals from diverse communities to the field of remote sensing and environmental science.

  Inclusive Teaching Practices: The module is delivered by a black female scientist from South America, whose background and experiences bring a unique perspective to the course content. Through her expertise and leadership, students are exposed to diverse viewpoints and role models, promoting inclusivity and representation within the academic environment.

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