- Teacher: Professor Zubairul Islam
Geospatial
-
-
Select the course you wish to join and submit your enrolment request. Your request will be reviewed by the administrator. Once approved, the course will appear under My Courses.
-
Available courses
General Course Objective
The course will enable students to examine two neighbouring Nigerian States/FCTs and use geospatial evidence to understand the human and physical geography surrounding their shared boundary.
Students will learn to identify:
- the shared interstate boundary;
- LGAs directly adjoining that boundary;
- settlements located within 5 km on either side;
- settlement concentration close to the border;
- roads and bridges connecting communities across or along the border;
- rivers, streams, canals and drainage that may influence boundary interpretation and movement;
- health-service locations and accessibility;
- places and communities potentially affected by an interstate dispute;
- evidence that should be verified in the field before drawing conclusions.
The final goal is not to decide which State is right or wrong. It is to teach students how spatial data can support conflict understanding, field investigation, negotiation, service coordination and peaceful border management.
- Teacher: Professor Zubairul Islam
General Course Objective
The course will help students understand how population, demographic characteristics, settlement structure, accessibility, health-service location, roads, drainage, and spatial patterns can support sustainable-development planning at the Local Government Area level in Nigeria.
The objective is not to learn Python programming.
Students will use Google Colab as a guided analytical environment to:
- choose any State/FCT and LGA/Area Council;
- understand the selected LGA in its geographic and administrative context;
- examine where settlements are located;
- estimate total population;
- understand male and female population composition;
- examine children under 1 and under 5;
- study settlement-level population distribution;
- examine population density;
- understand health-facility accessibility;
- interpret roads, bridges, drainage and settlement patterns;
- work with the exported CSV in Excel;
- use the results for practical planning questions such as vaccination outreach, zero-dose risk screening, health-service access, infrastructure planning and spatial inequality.
- Teacher: Professor Zubairul Islam
- Teacher: Professor Zubairul Islam
Discovering Earth from Space
Remote Sensing with Google Colab and Google Earth Engine
Course Summary
This beginner-friendly practical course introduces you to the principles and applications of satellite remote sensing through an interactive Google Colab notebook and Google Earth Engine.
You do not need previous experience in remote sensing, GIS or Python. The main goal is to understand what satellites measure, interpret satellite observations, compare different types of evidence and communicate your findings responsibly.
Think about this:
What can satellites tell us about a place you know?
What You Will Do
Throughout the course, you will use one complete Google Colab notebook containing 27 practical activities. You will investigate real satellite observations rather than simply reading about remote sensing.
- Draw and confirm your own study-area polygon.
- Explore satellite images of your selected area.
- Understand electromagnetic energy and how Earth surfaces interact with it.
- Compare satellites, sensors and their data characteristics.
- Interpret true-colour, false-colour and shortwave-infrared images.
- Calculate and interpret NDVI, NDWI and MNDWI.
- Examine Sentinel-1 radar backscatter.
- Identify candidate open-water areas.
- Compare optical and radar evidence.
- Export maps, charts, tables and supporting information.
Your Five-Chapter Learning Journey
| Chapter | Colab Cells | What You Will Explore |
|---|---|---|
| 1. Discover Remote Sensing | 1–5 | Set up the notebook, select a study area, view your first satellite image, understand the observation chain and identify sample locations. |
| 2. Electromagnetic Energy, Atmosphere and Surfaces | 6–10 | Explore the electromagnetic spectrum, energy interactions, clouds, atmospheric effects and spectral measurements. |
| 3. Satellites and Their Data Characteristics | 11–15 | Compare sensors, examine metadata, investigate spatial detail, observation availability and dataset selection. |
| 4. Multispectral Images, Colour Composites and Indices | 16–22 | Create clear-pixel composites, examine different colour combinations, and calculate NDVI, NDWI and MNDWI. |
| 5. Sentinel-1 Water Mapping and Final Export | 23–27 | Examine radar observations, backscatter and speckle, identify candidate open water, compare optical and radar evidence, and export your results. |
Three Tools Work Together
Moodle
Provides explanations, learning activities, questions, instructions and guidance.
Google Colab
Provides the practical notebook where you run the numbered activities and examine results.
Google Earth Engine
Provides access to satellite datasets and cloud-based geospatial processing.
How You Will Learn
Most practical activities follow a simple investigation cycle:
Predict → Run → Observe → Change → Explain
- Predict: Think about what you expect before running the activity.
- Run: Run the numbered Colab cell.
- Observe: Examine the map, chart, table or information produced.
- Change: Where instructed, change one setting and compare the result.
- Explain: Describe what you observed and what it may mean.
Think Like a Remote-Sensing Investigator
Satellite observations often provide evidence, not a complete explanation. You will therefore be encouraged to use careful language such as:
- “I observed…”
- “This may indicate…”
- “Another explanation could be…”
- “I would need … to check.”
Important Ideas to Remember
- A satellite carries a sensor; the sensor measures electromagnetic energy.
- The Esri satellite map used for orientation is a reference background. It is not necessarily the same image or acquisition date as the Sentinel data you analyse.
- Changing image colours or display contrast does not change the measured reflectance values.
- A false-colour image uses different bands for display. False colour does not mean false data.
- A multi-date composite combines observations from more than one date and should not be interpreted as a single photograph.
- NDVI, NDWI and MNDWI are indicators. They do not automatically identify every land-cover condition correctly.
- A dark radar pixel is not automatically water, and candidate open water is not automatically flood extent.
By the End of the Course
You should be able to:
- Define and examine a study area using satellite imagery.
- Explain the basic principles of remote sensing.
- Recognise important satellite and sensor characteristics.
- Interpret common multispectral image displays.
- Calculate and interpret NDVI, NDWI and MNDWI.
- Interpret basic Sentinel-1 radar backscatter patterns.
- Identify candidate open-water areas.
- Compare optical and radar evidence critically.
- Export and communicate your remote-sensing results.
Your Final Learning Portfolio
As you progress through the notebook, you will create maps, charts, tables and interpretation notes. In the final activity, you will export the main course results so that they can be reviewed, discussed or used in your final submission.
You Are Not Expected to Write Python
The notebook already contains the required code. Your task is to run the activities, observe the outputs, change selected settings when instructed, and explain what the results mean. The most important skill in this course is interpretation, not programming.
- Teacher: Professor Zubairul Islam
This practical introductory course teaches agriculture undergraduate students how to estimate crop water requirements and prepare a historical irrigation schedule using Google Colab and FAO-56 methods. No previous programming or GIS experience is required.
Students select a study location by clicking an interactive map, examine soil properties, calculate soil-water storage, choose a crop and planting date, and retrieve historical weather data. They then calculate reference evapotranspiration (ET₀), crop coefficients (Kc), crop evapotranspiration (ETc), and a daily root-zone water balance.
The course is organised into ten chapters:
- Introduction and Selection of the Study Location
- Understanding Soil Properties
- Calculating Soil-Water Storage
- Crop Selection, Growth Stages and Root Development
- Climate and Growing-Season Weather
- Calculating Crop Water Requirements
- Soil-Water Balance and Irrigation Requirements
- Preparing an Irrigation Schedule and Calendar
- Assessing Environmental Suitability and Model Limitations
- Exporting Results and Completing the Field Practical
Learning outcomes
By completing this course, students will be able to:
- Select a study location and explain the role of coordinates.
- Interpret physical, chemical and water-retention properties of soil.
- Calculate available soil water and root-zone storage.
- Select and interpret FAO crop parameters and growth-stage references.
- Retrieve and interpret growing-season weather data.
- Calculate and graph ET₀, Kc and ETc.
- Estimate irrigation depths and volumes using a daily water balance.
- Prepare an irrigation calendar and explain the assumptions and limitations of the results.
How the practical works
One complete Google Colab notebook is used throughout the course. Students save their own copy and run the cells sequentially as they progress through the chapters. Later calculations depend on variables created earlier. If the Colab runtime disconnects or resets, students must rerun the required earlier cells and reselect their study point.
Students need a Google account, an internet connection and a computer with a modern web browser. The exercises use public soil and climate datasets; no Earth Engine account is required.
The irrigation calendar reconstructs a completed historical growing season for learning purposes. It is not a weather forecast or a field-validated irrigation recommendation.
- Teacher: Professor Zubairul Islam
Geospatial Evidence and Google Earth for Legal Applications introduces undergraduate Law students to the use of geographic information as supporting evidence in legal investigation, documentation and decision-making. Using Google Earth, students learn coordinates and location referencing, historical imagery interpretation, measurement of straight-line and route distances, identification of changes in buildings and land features, and the creation of organised case folders containing points, lines, polygons, measurements and supporting observations. The course emphasises clear documentation, reproducibility and responsible interpretation of geospatial evidence.
- Teacher: Professor Zubairul Islam
Can an average be misleading? Does a larger sample always give a better answer? Does a relationship between two things mean that one causes the other?
Discover the answers through everyday examples, colourful graphs and practical activities in Google Colab.
This beginner-friendly course introduces descriptive and inferential statistics to students from all disciplines. No previous programming experience is required. You will use ready-to-run code to explore data, change settings, observe patterns and explain your findings.
WHAT YOU WILL LEARN
• Recognise different types of data and identify common data problems.
• Create and interpret bar charts, histograms, boxplots and scatterplots.
• Understand averages, variation and unusual observations.
• Explore probability, sampling and confidence intervals.
• Compare groups and interpret statistical evidence.
• Investigate relationships using correlation and simple regression.
• Complete a small statistical investigation and export your results.
HOW YOU WILL LEARN
Follow a simple routine: Guess → Run → See → Change → Explain.
The course contains seven units supported by one complete Google Colab notebook with 35 guided activities. You will work with clearly labelled fictional datasets, interactive controls and examples from everyday life.
YOUR SEVEN UNITS
1. Meet Your Data: Can We Trust It?
2. Make Data Speak: Which Graph Tells the Story?
3. The Average Can Fool You!
4. Could It Just Be Chance?
5. Did It Make a Difference?
6. Do These Things Go Together?
7. You Are the Data Detective.
WHAT YOU NEED
A computer with internet access, a Google account for using Colab, and basic familiarity with numbers and percentages.
FINAL PROJECT
Choose a fictional dataset from agriculture, business, health, engineering, communication, education or environmental science. Produce two useful graphs, apply an appropriate statistical method and prepare a short report explaining your findings and limitations.
Start with curiosity. Build confidence through practice.
- Teacher: Professor Zubairul Islam