3 EC
Semester 1, period 2
5132RESE3Y
Remote Sensing includes a powerful set of computational techniques and methods for storing, analyzing and visualization of information retrieved from satellite imagery, aerial photograps or other means of remote sensing, such as geophysical prospecting. Here, techniques will be introduced to guide students through the basics of remote sensing using the software environment of ArcGISPro.
In the self-tuition assignments we offer GIS-based tools and techniques, for example preprocessing techniques, suitability analyses, raster-based analysis, model building, and path-distance analysis. The remote sensing tools and techniques comprise amongst others supervised classification, change analysis, band-ratio analysis, image enhancement and computing of vegetation and other indices which can be used in food production, land use and land cover change applications.
Images and datasets used are highly diverse and range from Digital Elevation Models (DEMs), Landsat imagery, SPOT imagery, Sentinel imagery, orthophotos to thematic layers such as digital soil, geomorphological, Land Use Land Cover and other data.
The techniques and skills are applied in the assignments to a wide variety of landscapes and environmental and to diverse situations and/or topics such as flooding in Bangladesh, desertification in the Sahel zone, Land Use and Land Cover change in China, urban areas, and more.
After finishing a selection of assignments, the students continues with a special topic. The research project is documented in a technical remote sensing report with accompanying digital products .
various scientific papers which will be available via links on Canvas
https://www.nrcan.gc.ca/sites/www.nrcan.gc.ca/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf
9 Quizzes, available on Canvas, datasets, instructive tutorials
ArcGISPro - to be installed on own laptops before the course starts
Lecture presentation
An introduction lecture into the course set-up and the theory of remote sensing helps the student understand the value, relevance and complexity of remote sensing. The lecture and laptop practicals raises interest and awareness of fundamental theories behind necessary and the wide application field of remote sensing products.
|
Activity |
Hours |
remark |
|
Lecture |
2 |
Necessary to understand course structure, course requirements and remote sensing background |
|
Laptop Practical |
52 |
Mandatory: quizzes will guide you through the first 9 modules. Additional 4 weeks are for a short Project on remote sensing classification |
|
Self study |
30 |
Time to read, prepare and consume information during laptop practical |
|
Total |
84 |
|
Programme's requirements concerning attendance (OER-B):
Additional requirements for this course:
| Item and weight | Details |
|
Final grade |
The assessment includes:
9 individual quizzes on Canvas and a group assignment on a remote sensing topic. Deliverables for the group assignment are 1. a technical report and 2. a digital geodatabase which will be evaluated. For deadlines, weights and requireents, see course structure, further in this document.
| Leerdoel: | Toetsonderdeel 1: |
|---|---|
| #1.The student can explain the basic concepts of remote sensing: wavelengths, spatial/temporal/spectral resolution, absorption & reflection, band ratios, NDVI, supervised classification | Quiz 1, 6,7,9 |
| #2.The student can select, download and pre-process satellite images for use in spatial models | Quiz 1, 5 |
| #3.The student can apply various remote sensing tools and techniques abd soatial analysis techniques using the GIS software ArcGISPro for the identification, mapping and quantification of Land Use and Land Cover, especially in relation to agricultural areas | Quiz 2, 3,4,7 |
| #4.The student can quantify geodiversity by computing an index based on derivatives from digital elevation models and thematic datasets (Soil GRIDs , Lithology) for selected biomes | Quiz 8 |
| #5.The student can evaluate Land use and land cover variation, based on satellite processing in important food production areas | Quiz 9 |
| #6.The student can write a short technical remote sensing report and manage digital remote sensing data | Quiz 1-9, Project |
Not applicable
Up to 20 days after the announcement of the result students have the right of inspection of their individual work (all forms
of assessment) on Canvas. Feedback is given by means of Canvas (automated tests and standard grading of the these tests). Feedback for each module is placed on Canvas as soon as all students of all groups have done and saved their tests for that module. In the week after the last Module is due, feedback to all of the modules will be disclosed on Canvas so the students can inspect their whole work. During an individual appointment you can discuss the the assessment. For this course we will NOT schedule a Collective Assessment Evaluation. Please note: you loose the right of feedback when you don’t make an appointment within 20 days after the announcement of the results without good reasons.
The assignments during the first 9 weeks are individual assignments. these will be reviewed automatically and electronic feedback will be given.
The project is an activity for three students. details will be posted on Canvas. Inspection of the work can be requested in contact with the coordinator or group assistant.
Individual assignments: 9 quizzes, see for title under Course structure. detailed descriptions are provided on the Canvas site for each assignement. Feedback mostly digital within the quizzes.
Team assignment: 1 technical report and a digital geodatase. Feedback during 4 laptop practicals.
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
| Week number | Topics | Deadline | Remarks | Weight |
| 44 |
Lecture: introduction to Remote Sensing Laptop: Introduction to Remote Sensing & GIS |
November 4 | Must be ≥ 5.0, retake allowed | 5% |
| 45 | Laptop: Earthquake analysis | November 11 | Must be ≥ 5.0, retake allowed | 5% |
| 45 | Laptop: Hawaii coffee been suitability analysis | November 11 | Must be ≥ 5.0, retake allowed | 5% |
| 46 | Laptop: Wildlife corridor design in a cultural landscape | November 18 | Must be ≥ 5.0, retake allowed | 5% |
| 46 | Laptop: Including webservices in remote sensing analysis | November 18 | Must be ≥ 5.0, retake allowed | 5% |
| 47 | Laptop: Flooding in agricultural systems: Bangladesh | November 25 | Must be ≥ 5.0, retake allowed | 10% |
| 47 | Laptop: Using indices in earth observation | November 25 | Must be ≥ 5.0, retake allowed | 10% |
| 48 | Laptop: Quantification of geodiversity | December 2 | Must be ≥ 5.0, retake allowed | 10% |
| 48 | Laptop: Land Use and Land Cover Classification | December 2 | Must be ≥ 5.0, retake allowed | 10% |
| 49 | Laptop: Project | December 15 15 |
Must be ≥ 5.5, retake allowed
|
report 20% digital geodatabase: 15% |
| 49 | Laptop: Project | |||
| 50 | Laptop: Project | |||
| 50 | Laptop: Project | |||
|
The maximum score for a second attempt of a quiz is 6.0 The final mark of the total course must be ≥ 5.5 which will be rounded to a 6.0, a retake is allowed |
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The schedule for this course is published on DataNose.
In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, we decided to include the table below in all course guides.