MODULE HANDBOOK
Module name Remote Sensing
Module level, if applicable Bachelor Code, if applicable SST-407 Subtitle, if applicable -
Courses, if applicable Remote Sensing Semester(s) in which the
module is taught 4th (fourth) Person responsible for the
module Chair of lab. Statistical Disaster Management Lecturer Tuti Purwaningsih, S.Stat., M.Si.
Language Bahasa Indonesia
Relation to curriculum Compulsory courses of scientific interest in the fourth year (4th semester) Bachelor Degree
Type of teaching, contact
hours 150 minutes lectures and 180 minutes structured activities per week.
Workload
Total workload is 130 hours per semester, which consists of 150 minutes lectures per week for 14 weeks, 180 minutes structured activities per week, 180 minutes individual study per week, in total is 16 weeks per semester, including mid exam and final exam.
Credit points 3
Requirements according to the examination regulations
Students have taken Remote Sensing course (SST-407) and have an examination card where the course is stated on.
Recommended prerequisites Students have taken Disaster Management (SST-202).
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 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
