3 EC
Semester 1, period 2
5132RESE3Y
Remote Sensing - sensing the environment from a distance - includes a powerful set of computational techniques and methods for storing, analyzing and visualizing information retrieved from satellite imagery, aerial photographs or other means of remote sensing, such as geophysical prospecting, like Light Detection and Ranging (LiDAR). Here, techniques will be introduced to guide students through the basics of remote sensing using the software environment of ArcGIS Pro.
In the self-tuition assignments we offer GIS-based tools and techniques, amongst others preprocessing techniques, suitability analyses, raster-based analysis, model building, and path-distance analysis. The remote sensing tools and techniques include, for example 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 10 mandatory assignments, you will continue with a project. This 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
10 Quizzes, available on Canvas, datasets, instructive tutorials
ArcGISPro - to be installed on own laptops before the course starts
Lecture presentation, intro to the project
The lecture is an introduction into the course set-up and the theory of remote sensing and helps you to understand the theory, relevance , but also the complexity of remote sensing tools and techniques. The laptop practicals offers you a wide range of skills and examples remote sensing applications and its products using GIS.
|
Activity |
Hours |
remark |
|
Lectures |
4 |
The first lecture is necessary to understand the course structure, expectations, course requirements and remote sensing background; the second part will Introduce you to the Project |
|
Online Laptop Practical |
52 |
Mandatory: quizzes will guide you through the first 10 modules. Additionally 4 sessions spread over 2.5 weeks are projected for a Project (two students) on remote sensing satellite classification |
|
Self-study |
30 |
Time to read, prepare, write and consume information during online laptop practical and the project period |
|
Total |
84 |
Note: the remote sensing course is in parallel with the World Food and ecosystem course: carefully plan your time! |
Programme's requirements concerning attendance (OER-B):
Additional requirements for this course:
The course is on campus, the presence and absence rules are as follows:
| Item and weight | Details |
|
Final grade | |
|
0.05 (5%) Upload your ESRI certificate and grade as a zipped file | Must be ≥ 5 |
|
0.05 (5%) Quiz Module 1 | Must be ≥ 5 |
|
0.05 (5%) Quiz Module 2 | Must be ≥ 5 |
|
0.05 (5%) Quiz Module 3 | Must be ≥ 5 |
|
0.05 (5%) Quiz Module 4 | Must be ≥ 5 |
|
0.075 (8%) Quiz Module 5 | Must be ≥ 5 |
|
0.075 (8%) Quiz Module 6 | Must be ≥ 5 |
|
0.075 (8%) Quiz Module 7 | Must be ≥ 5 |
|
0.075 (8%) Quiz Module 8 | Must be ≥ 5 |
|
0.1 (10%) Quiz Project Module 9 Land Use and Land Cover Classification (LULC) in China using Landsat | Must be ≥ 5 |
|
0.15 (15%) Upload your Map package | Must be ≥ 5 |
|
0.2 (20%) Upload your Technical Report | Must be ≥ 5 |
For each quiz there is a deadline, which is the day after the practical session at 23.59. After the deadline, submission will be closed. There is a resit possibility for individual quizzes during the last week of the course. Submission on Canvas will be opened for one week, for those who did not meet the first deadline. The maximum grade for each newly submitted quiz is 7.0. In case this resit in week 50 is not met, there will be a resit in February, 2022. Two weeks before that deadline submission will be reopened for those who did not meet the resit during the last week of the project. The maximum grade for an individual module in February is 6.0.
Deliverables for the project assignment are: 1. a short technical report and 2. a digital map package which will both be evaluated. Not meeting the deadline in December means a resit on February 1, 2021. Re-submission will be possible between January 18 and February 1. Maximum grades for the short technical report and the digital map package will be 7.0.
For deadlines, weights and requirements, see course structure, further in the course manual.
| 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 0,1,6,7,9 |
| #2.The student can select, download and pre-process satellite images for use in spatial models | Quiz 1,2,5 |
| #3.The student can apply various remote sensing tools and techniques and spatial analysis techniques using the GIS software ArcGIS Pro for the identification, mapping and quantification of Land Use and Land Cover, especially in relation to agricultural areas | Quiz 1,3,4,7 |
| #4.The student can quantify geodiversity by computing an index based on metrics from digital elevation models and thematic datasets (geology, soils ) for the island of Hawaii | 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 0-9, 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 finished 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 that 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 quizzes numbered 0-9 are individual assignments. These will mostly be reviewed automatically, and partly by staff.
The project is an activity for two students. Details will be posted on Canvas. Inspection of the work can be requested in contact with the coordinator or the group assistant.
Individual assignments: 10 quizzes (0-9), see for title under Course structure. Detailed descriptions are provided on the Canvas site for each assignment. Feedback is mostly digital within the quizzes.
Team assignment: 1 technical report and a map package. Feedback during the laptop practicals.
All quizzes, the project technical report and the map package are graded.
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 |
Activity |
Deadline activity |
Requirements | Weight |
| 44 |
Lecture November 3: course structure and introduction to Remote Sensing - Seijmonsbergen 0 - Laptop: Introduction to ArcGIS Pro |
November 5, 23.59 |
Must be ≥ 5.0, retake allowed in week 50 of the course | 5% |
| 45 |
1 - Laptop: Getting started to Remote Sensing & GIS |
November 9, 23.59 | Must be ≥ 5.0, retake allowed | 5% |
| 45 | 2 - Laptop: Earthquake analysis | November 12, 23.59 | Must be ≥ 5.0, retake allowed | 5% |
| 46 | 3 - Laptop: Ecoduct location in a cultural landscape | November, 16, 23.59 | Must be ≥ 5.0, retake allowed | 5% |
| 46 | 4 - Laptop: Locating coffee been using the suitability modeler | November 19, 23.59 | Must be ≥ 5.0, retake allowed | 5% |
| 47 |
5 - Laptop: Living atlas and web services data in earth observation |
November 23, 23.59 | Must be ≥ 5.0, retake allowed | 7.5% |
| 47 |
Lecture November 25: Introduction to Project - Seijmonsbergen 6 - Laptop: Flooding in agricultural systems: Bangladesh |
November 26, 23.59 | Must be ≥ 5.0, retake allowed | 7.5% |
| 48 | 7 - Laptop: Using indices in earth observation | November 30, 23.59 | Must be ≥ 5.0, retake allowed | 7.5% |
| 48 | 8 - Laptop: Quantification of geodiversity of Hawaii | December 3, 23.59 | Must be ≥ 5.0, retake allowed | 7.5% |
| 48 | 9 - Laptop: Project Land Use and Land Cover Classification China | December 7, 23.59 | Must be ≥ 5.0, retake allowed | 10% |
| 49 and 50 - Laptop: Project |
Project on land use and land cover classification Week 50: resit of modules |
December 17, 23.59 | Project must be ≥ 5.5, retake allowed, see resit date |
Report: 20% datasets: 15% |
The schedule for this course is published on DataNose.
Not applicable
Not applicable
In order to provide students some insight how we use the feedback of student evaluations to enhance the quality of education, a summary of the evaluation of last year in available on Canvas. The coordinators response of last year:
"Home learning and teaching remote sensing required a total change in the way skills, theory and practice can be offered. Covid
did influence self discipline, flexibility and lack of time did not help either to rebuild the course". Introducing a project information lecture halfway proved succesfull and helped students prepare for the last two deliverables of the course. The size of the imagery used in the project has been reduced to cope with computational restrictions. We, as the remote sensing team, kept running notes about issues that we and students ran into, during the practical. We have solved those, but point of attention still are: using well-equipped hardware from student side: windows-based computers on which ArcGIS Pro runs smoothly; there are many issues with Apple-based computers, which is not recommended. Pre-installation of the software before the course starts (students are informed on the procedure).