Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
REMOTE SENSİNG UCOG431 7. Semester 3 + 0 3.0 6.0

Prerequisites None

Language of Instruction Turkish
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery Understand the principles of remote sensing and digital image processing; APPLİCATİONS OF REMOTE SENSİNG (ENVI)
Course Coordinator
Instructors Merve ALTUNDAL ÖNCÜ
Assistants
Goals This course provides students with an introduction to the principles of remote sensing, and the application of these techniques to the environmental and life sciences.
Course Content Understand the principles of remote sensing and digital image processing; Understand the principles of geographic information systems (GIS); Gain experience in the applications of remote sensing and GIS to solving problems in the environmental and life sciences;
Learning Outcomes 1) Learn the basic concepts of Remote Sensing.
2) Learn how to organize the data gathered with RS.
3) Classify Satellite Image

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week What is Remote Sensing? Lecture; Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
2. Week The History of Remote Sensing Lecture; Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
3. Week Remote Sensing Basic Concepts Lecture; Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
4. Week Remote Sensing Basic Concepts Lecture; Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
5. Week Remote Sensing Basic Concepts Lecture; Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
6. Week Data of RS Lecture; Question Answer
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
7. Week Remote Sensing Software and ENVI Lecture; Question Answer; Problem Solving
Opinion Pool
Problem Based Learning 		Presentation (Including Preparation Time)
8. Week ENVI Software General Functions Lecture; Question Answer; Problem Solving
Opinion Pool
Scenario Based Learning 		Presentation (Including Preparation Time)
9. Week ENVI Software General Functions Lecture; Question Answer; Problem Solving
Opinion Pool
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
10. Week ENVI Software Basic Functions Lecture; Question Answer; Problem Solving; Demonstration
Opinion Pool
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
11. Week Image Preprocessing Lecture; Question Answer; Problem Solving; Demonstration
Opinion Pool
Scenario Based Learning 		Presentation (Including Preparation Time) Practice (Teaching Practice, Music/Musical Instrument Practice, Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice)
12. Week Mosaic, Image Consolidation Lecture; Question Answer; Problem Solving; Demonstration
Opinion Pool
Scenario Based Learning 		Homework Presentation (Including Preparation Time)
13. Week Unsupervised Classification Lecture; Problem Solving; Demonstration
Opinion Pool
Scenario Based Learning 		Homework Presentation (Including Preparation Time)
14. Week Supervised Classification Lecture; Question Answer; Problem Solving; Demonstration
Opinion Pool
Scenario Based Learning 		Homework Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
Lilesand,T.Kiefer,R.F.,Chipman,J. 2008.Remote Sensing and Image Interpretation,USA
Schowengert,R.A., 1997.Remote Sensing, Models and Methods for Image Processing, Academic Press,USA
Sesören,A.1999. Uzaktan Algılamada Temel Kavramlar,Mart Matbaacılık,İstanbul
Tatar,Y.,Tatar,O. 2006. Jeolojide Uzaktan Algılama,Cumhuriyat Üniv.Yay.No:102, Sivas

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 2
Work Hour outside Classroom (Preparation, strengthening) 14 6
Homework 2 2
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 4
Final Exam 1 1
Time to prepare for Final Exam 1 4
1 1
1 1
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • ECTS credits and course workload