Module Assessment Guideline: IRDR0004 (Part-B)

As part of the IRDR0004 module assessment (Part-B or Component 002), you are required to submit an individual technical report, which constitutes 50% of the overall module mark.

This  coursework  will  reflect  the  skills  you  have  developed  through  the  lectures (teaching weeks 6-10, Term I), computer lab tutorials, and independent learning. You are strongly encouraged to dedicate sufficient time to practicing in the computer labs, attending office hours, and seeking guidance from the module team, which includes the module lead and postgraduate teaching assistants (PGTAs).

The  lectures will  primarily  cover theoretical  concepts, while the  computer cluster sessions will provide practical experience in an interactive and engaging environment. To  enhance  your  understanding, you  should  complement  classroom  learning  by reading relevant literature, reviewing practice and module materials, and watching supplementary videos as part of your independent study.

The assessment will test your core competencies in Geographic Information Systems (GIS) and remote sensing (RS) tools and techniques, requiring you to apply these skills effectively in your analysis and interpretation. For your project, adopt a clear, focused, and well-articulated approach. Define your project aim and objectives in a scientific and structured manner to ensure clarity and purpose in your work.

Coursework-2 (GIS and RS)

1. Technical Report

a)  Submission Format:

i.     A 1,500-word individual technical report submitted as a single PDF file.

ii.     The submission must include all raw files used for data analysis in a single  zipped  folder  (e.g.,  shapefiles,  masked  satellite  images, processed raster layers, scripts, and any other demographic or statistical data).

b)  Weighting: 50% of the module mark

c)  Submission Deadline: Wednesday, 05 February 2025 at 1:00 pm (UK time)

You  are  encouraged  to  allocate  sufficient  time  for  downloading  data,  cleaning, preparing layers, and conducting analyses. Marks will be awarded based on the quality of work, strength of scientific arguments, demonstration of critical thinking, quantitative data analysis skills, technical writing proficiency, and clarity of presentation.

2. Topic

The report will focus on a  comparative study, analysing the ‘before’ and ‘after’ scenarios  or  conducting  a  change  detection  analysis  over  a  reasonable  period. You are encouraged to select a  medium-sized area where significant land cover changes have occurred. Examples of suitable topics include , but are not limited to:

a)  Deforestation  and  Land  Cover  Change  in  the  Amazon  Rainforest,  Brazil: Analyse the spatial and temporal  patterns of deforestation  in  the Amazon rainforest over the past decade and its impact on vegetation and biodiversity.

b)  Wildfire-Induced  Land  Cover  Changes  in  California,  USA:  Investigate  the effects of recurring wildfires on vegetation, urban areas, and ecosystems in California, focusing on land cover transformations between two time periods.

c)  Urban Expansion and Land Use Change in Nairobi, Kenya: Analyse the impact of rapid urbanisation on green spaces and agricultural land over a decade.

d)  Glacier  Retreat in the Himalayas: Investigate changes in glacier extent and surrounding land cover in the Himalayas over a decade, focusing on the effects of climate change.

e)  Desertification   in   the   Sahel   Region,   Africa:   Study   the   progression   of desertification and its impact on vegetation and agricultural productivity over a 10-year period.

f)   Urban Expansion and Refugee Settlement in Juba, South Sudan: Examine how conflict-induced migration has affected urban expansion and natural resource depletion in Juba.

2.1. Example Used in Practical Labs:

In our computer practical labs, we examined Land Cover Change Detection in the Kutupalong  Refugee  Camps  in  Cox’s  Bazar,  Bangladesh,  focusing  on  the transformation  of  land  cover  following  the   influx  of   nearly  a   million  displaced populations between 2017 and 2022. However, this specific example cannot be used for your assessment as it was covered during classroom instruction.

2.2. Steps to Follow

i.     Select  a  Study  Area  and  Topic:  Choose  a  topic  related  to  disaster  risk reduction or humanitarian crises. Ensure the study area is manageable and relevant to the module’s themes. Also, ensure the chosen area aligns with a recognised administrative boundary, facilitating reproducibility and integration with official data sources.

ii.     Download  Relevant  Data: Acquire  the  study area  boundary shapefile and appropriate Landsat 8 satellite images.

iii.     Produce  Land Cover Maps: Create land cover maps for two distinct time periods, ensuring a gap of at least 5 to 10 years between them.

iv.     Calculate  NDVI:  Perform a  Normalised Difference Vegetation  Index (NDVI) analysis for both time periods.

v.     Calculate Land Surface Temperature (LST): Derive land surface temperature for the two time periods to observe changes.

vi.     Incorporate Additional  Data:  Incorporate additional geospatial or statistical datasets from credible secondary sources for advanced analysis.

vii.     Write the Technical Report: Prepare your report following this guideline.

3. Report Content

The technical report should be creative, involve critical thinking, and reflect innovative ideas. A recommended structure is provided below:

a)  Title Page:

    Project title

   Candidate number (but do not include your name anywhere, including in maps, diagrams, or illustrations)

    Module details

   Word count

   Signed statement:

“I declare the following work is my own and, where the work of others has been used, it has been clearly identified. ”

b)  Abstract (150 words max):

    Provide a concise summary of the report, including the key findings.

c)  Introduction:

   Clearly state the aim and objectives.

    Include a brief, focused background and literature review if necessary.

d)  Methods:

    Detail the methodology, including data sources, datasets used, study area description and justification for selection, and analytical methods.

    Ensure sufficient detail for reproducibility.

e)  Results:

    Present  key  findings  using  well-labelled  tables,  diagrams,  charts, illustrations and figures.

    Use visuals to enhance understanding and highlight important patterns.

f)   Discussion and Conclusion (Combined):

    Interpret the results, linking them to your research aim and objectives.

    Provide a critical analysis of the findings and discuss their implications.

g)  References:

    Ensure all sources cited in the report are included in the references section and formatted consistently according to the required style (e.g., Harvard or APA).

    Keep the reference list concise (5-10 entries recommended) but ensure it is complete and includes all key sources used in the analysis.

h)  Appendices:

    Use a few appendices for additional material (if needed), ensuring the report can stand alone without them.

4. Report Format

The report must be a maximum of 1,500 words (+10% allowance), excluding the following:

    Title page

    Declaration

    Abstract

    Captions, equations, tables, and figures

    AI Statement

    References

    Appendices

4.1. Word Count Compliance:

    Reports with fewer than  1,500 words may result in automatic failure of the coursework.

    Reports exceeding 1,650 words will incur a penalty of up to 10% of the total marks.

    Note: If the coursework is both over-/under-length and late, the greater of the penalties will be applied.

4.2. Formatting Requirements:

    Page Size: A4

    Margins: Normal

    Orientation: Portrait

    Font: Arial

    Font Size: 12

    Font Colour: Automatic

    Line Spacing: 1.5

    Paragraph Alignment: Align Left or Justify

    Page Numbers: Bottom of the page, aligned to the right

4.3. Referencing and Citations:

    Use APA or Harvard (latest edition) for referencing and citations.

    Ensure consistency in citations and references throughout the report.

4.4. Plagiarism Policy:

    Any submission with a Turnitin similarity score exceeding 10% will be flagged for investigation.

    Cases of suspected plagiarism will follow the university’s official procedure.

https://www.ucl.ac.uk/academic-manual/sites/academic-manual/files/student_academic_misconduct_adjudication_and_penalties.pdf

5. Report Requirements

The report should demonstrate your ability to:

    Construct a well-structured, organised, and clear report that adheres to academic standards.

    Present valid arguments using scientific evidence, supporting your findings with credible sources.

    Provide clear visual aids such as maps, figures, diagrams, and tables to enhance understanding and communicate effectively.

    Understand the material you are presenting, showing mastery of the topic and the analytical techniques applied.

    Demonstrate  technical  skills  in  handling  geospatial  and  statistical  data effectively, using appropriate tools and methods.

    Apply innovative data analytical techniques to generate meaningful and well-articulated visuals that aid in interpreting the results.

    Show originality in your ideas, crafting a scientifically valid and meaningful technical report that reflects critical thinking and analytical depth.

6. Generative AI (GenAI) Policy

For this assessment,UCL Category 1applies, meaning that you are permitted to use Generative AI (GenAI) tools to assist with revising and preparing your work. However, the final submission must be entirely your own original work. It is your responsibility to ensure that any use of GenAI aligns with UCL’s academic integrity standards and that the content you submit reflects your understanding, effort, and critical thinking.

You  are  not  allowed  to  use  GenAI  tools  to  create  figures,  diagrams,  maps,  or illustrations for this coursework. All visual content must be created using appropriate geospatial, statistical, or graphic software relevant to the module, such as QGIS, ArcGIS Pro, Python, or R. Visual outputs must be the product of your own analytical work and software skills.

If you use GenAI tools during any stage of your work, you must include a statement under the heading “AI Usage Declaration” in your report. This statement should clearly describe how GenAI was used and demonstrate how your usage complies with UCL’s academic integrity guidelines. Failure to include this declaration may lead to your work being flagged for investigation.

It is acceptable to use GenAI tools for tasks such as checking spelling, grammar, or adjusting the tone of your writing. However, it is essential that this usage does not alter the content or meaning of your work. You must fully understand and be able to explain all  aspects  of  your  submission,   including  your  analysis,   interpretations,  and conclusions. Misrepresentation of AI usage or over-reliance on AI-generated content that  compromises  originality   may   result   in   penalties   under   UCL’s   academic regulations.

7. Mark Scheme

Report Structure and Writing Style. (5 Marks)

•     Clear and coherent structure.

•     Logical flow of paragraphs and sections.

•     Diagrams and tables appropriately referenced in the text.

•     Complete and well-organised reference section.

•    Accurate and consistent citation of references throughout the report.

•     Proper spelling, grammar, and punctuation.

•     Fluidity and clarity of sentences.

Figures and Tables (20 Marks)

•     Use of original and innovative figures, tables or other types of illustrations.

•     Relevance and effectiveness in supporting the report.

•     High quality, clarity, and appropriateness of visual elements, including captions and legends.

•     Cartographic elements meet professional standards.

Content (25 Marks)

•    Application of suitable geospatial and statistical techniques.

•     Demonstration of scientific and technical competence.

•    Ability to maintain a clear argument and effectively fulfil research objectives.

•     Emphasis  on   methodology,  data  analysis,  generating   meaningful   results, unloading all sorts of raw data, and interpreting findings.

•     Originality and selection of an attention-grabbing and suitable topic.

•     Reliability of data sources, raw data and layers, and accuracy of results.

Total = 50 Marks

8. Additional Instructions

a)  Compliance with Instructions:

    Marks will be deducted for failing to follow the instructions, including those regarding deadlines, report structure, font, word limits, format, and other specified requirements.

    Missing  critical  instructions,  such  as  failing  to  meet  the  submission deadline, may result in automatic failure of the coursework.

b)  Marking Process:

   The coursework will be assessed by the module tutor and postgraduate teaching  assistants  (PGTAs)  through  a  first   and  second  marking process.

c)  Updates to Instructions:

    Instructions may be revised or updated as necessary. Always ensure you download the latest version of the document and read it thoroughly before submission.

d)  Research Aim and Objectives:

    Ensure you have access to all necessary datasets before formulating your research aim and objectives.

e)  Data Usage:

   You may use multiple datasets from single or multiple sources for your analysis.

   Avoid relying on data from individuals or organisations that may not provide  it on time.  Use  publicly and freely available  secondary data sources to design your project efficiently.

f)   Blind Marking:

    Do not include your name in any part of your submission. Use only your candidate number to ensure blind marking.

8.1. Importance of Selecting an Appropriate Study Area for Landsat 8 Analysis

Selecting an appropriate study area is critical for ensuring the effectiveness and accuracy of the analysis when using Landsat 8 images. Several factors must be considered to optimise the results and minimise potential challenges:

a)  Resolution of Landsat 8 Images:

    Landsat 8 provides moderate spatial resolution, with most bands at 30 metres per pixel. This resolution is well-suited for analysing medium- sized cities or urban areas, as it captures significant patterns without overwhelming computational resources.

   Too  Small  Areas:   If  the  study  area   is  too  small   (e.g.,  a  single neighbourhood), the resolution maybe insufficient to capture meaningful variations or details, leading to poor results.

   Too Large Areas: Very large areas may require multiple scenes to cover the extent, increasing the complexity of data handling and analysis.

b)  Extent of Change:

    Medium-sized cities or urban areas experiencing noticeable land cover changes over time (e.g., urban expansion, deforestation, or disaster impacts) are ideal. Areas with minimal changes may not provide enough variability to analyse effectively.

c)  Availability of Imagery:

    Landsat 8 provides consistent coverage since 11 February 2013, but availability of cloud-free scenes can vary, especially  in  regions with frequent cloud cover. Choosing a location with accessible, clear imagery reduces the risk of gaps in analysis.

d)  Dealing with Multiple Scenes:

    If the study area spans multiple Landsat scenes, you must ensure proper mosaicking (using the ‘Merge’ tool) and alignment. This can introduce complexity and potential errors in analysis, especially for beginners.

e)  Seasonal Variations:

    Land  cover  can  vary  significantly with  seasons,  especially  in  areas affected by agriculture, vegetation cycles, or snow. Selecting imagery from comparable seasons (e.g., summer-to-summer comparisons) is crucial to avoid seasonal bias in the results.

f)   Data Processing Complexity:

    Large or complex areas might require advanced techniques, such as atmospheric  correction  or  cloud  masking,  which  can  be  resource- intensive.  A  balanced  approach  ensures  that  you  can  focus  on meaningful analysis without being overwhelmed by data preparation.

By considering these factors, you can ensure that your chosen study area aligns well with the capabilities of Landsat 8 and the objectives of their analysis, leading to more accurate and meaningful results.