MODULE HANDBOOK
Module name Remote Sensing
Module level, if applicable 2nd year Code, if applicable SST-407 Semester(s) in which the
module is taught 4th (fourth) Person responsible for the
module Achmad Fauzan, S.Pd., M.Si.
Lecturer Tuti Purwaningsih, S.Stat., M.Si.
Language Bahasa Indonesia
Relation to curriculum Compulsory courses in the second year (4th semester) Bachelor Degree Types of
teaching and learning
Class size Attendance time (hours per week per semester)
Form of active participation
Workload
(hours per semester)
Lecture 50-60 2.5 Problem
solving
Face to face teaching 35 Structured activities 48 Independent study 48
Exam 5
Total Workload 136 hours
Credit points 3 CUs / 5.1 ECTS Requirements according to
the examination regulations
Minimum attendance at lectures is 75%. Final score is evaluated based on assignment, mid-term exam, and final exam.
Recommended prerequisites Students have taken Disaster Management (SST-202).
Related course Geo Statistics I (SST-509) Geo Statistics II (SST-609)
Module objectives/intended learning outcomes
After completing this course, the students have ability to:
CO 1. understand principles of remote sensing, google earth engine, band combination, visualization and types of spektrum
CO 2. Can practice make a spectral curv response, validation, classification and accuration, analyzing time series and vegetation monitoring
Content
1. Introduction to Remote Sensing 2. Introduction to Google Earth Engine
3. Understand the band combination and image visualization 4. From Spectrum to Index, and Finding the Right Image 5. Image Classification - part 1
6. Image Classification - part 2 7. Plotting Spectral Response Curves
8. Validation of Classification and Accuracy Assessment 9. Working with SAR Data on Google Earth Engine 10. Work with Terrestrial Laser Scanning (TLS) data on
CloudCompare
11. Time Series Analysis in Google Earth Engine
12. Monitoring the vegetation condition in Google Earth Engine
Study and examination requirements and forms of examination
The final mark will be weighted as follows:
No Assessment components
Assessment types Weight (percentage)
1 CO 1 Assignment,
Midterm Exam
50%
2 CO 2 Assignment, Final
Exam
50%
Media employed Google Classroom, relevant websites, slides (power points), video, interactive media, white-board, laptop, LCD projector
Reading list
1. Alfred Stein, Freek van der meer, Ben Gorte.Spatial Statistics for Remote Sensing
2. Sabin. 1978. Remote Sensing and Interpretation. Mc Graw Hill.
New York
3. Shaun R Levick. 2019. Geospatial Ecology and Remote Sensing.
Charles Darwin University, Darwin, Australia.
Mapping CO, PLO, and ASIIN’s SSC
ASIIN PLO
E N T H U S I A S T I C
Knowledge
a b c d Ability e f
Competency g
h CO1
CO2 i
j k l
