Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
REMOTE SENSING, GEOGRAPHIC INFORMATION SYSTEMS AND PRACTICES 805000715190 3 + 0 3.0 8.0

Prerequisites None

Language of Instruction Turkish
Course Level Graduate Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors Sibel CANAZ SEVGEN
Assistants
Goals Within the scope of the course it is aimed that is to bring students proficiency of defining terms related to spatial data, and creating and analyzing data bases in geographical information systems part; and proficiency of defining basic terms and principles, designing remote sensors and describing the usage areas of remote sensing in remoted sensing part.
Course Content Basic concepts and procedures used in GIS and Remote Sensing (RS), remote sensing, sensors and tools concepts and basis, processing of remote sensed image, image analysis and classification, GIS components, data sources, vector and cellular data, projection systems, spatial analysis, surface analysis, spatial interpolation analysis, applications and resources, basic principles and methods, satellite image, satellite image classification application, value maps
Learning Outcomes 1) Comments on the data base analyses easily.
2) Learns the data design in his/her area of specialization.
3) Specializes on land development and management.
4) Develops his/her research ability.
5) Gains scientific analysis ability.
6) Develops his/her project preparation ability.
7) Contribute his/her publishing ability.
8) Gains independent study ability.
9) Makes interdisciplinary studies.
10) Improve leadership skills
11) Improve leadership skills

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Basic concepts and procedures used in GIS and Remote Sensing (RS) Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
2. Week Basic concepts and procedures used in GIS and Remote Sensing (RS) Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
3. Week Remote sensing, sensors and tools concepts and basis Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
4. Week Remote sensing, sensors and tools concepts and basis Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
5. Week Processing of remote sensed image, image analysis and classification Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
6. Week Processing of remote sensed image, image analysis and classification Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
7. Week GIS components, data sources, vector and cellular data Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
8. Week Midterm exam Question Answer
9. Week Projection systems, spatial analysis Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
10. Week Surface analysis, spatial interpolation analysis Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
11. Week Surface analysis, spatial interpolation analysis Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
12. Week Spatial analyses, surface analyses, spatial interpolation Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
13. Week RS basic principles and methods, satellite image Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
14. Week Satellite image classification application, value maps Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Homework
15. Week Satellite image classification application, value maps Question Answer
Brainstorming
Brain Based Learning 		Presentation (Including Preparation Time) Activity (Web Search, Library Work, Trip, Observation, Interview etc.)
16. Week Final exam Question Answer
Brainstorming
Problem Based Learning 		Homework

Sources Used in This Course
Recommended Sources
• 1999. Erdas Imagine User Guide, The Fifth Edition, ERDAS, Inc. Atlanta, USA.
• Booth, B. and Mitchell, A., 1999-2001. Getting Started with Arcgis, USA.
• Campbell, J.B., 2008. Introduction to Remote Sensing, Fourth Ed., The Guilford Press, New York, USA.
• Cracknell, A.P. and Hayes, L., 2007. Introduction to Remote Sensing, Second Ed., CRC Press, Boca Raton, USA.
• Laurin, R. and Thompson, D., 1999.Fundamental of Spatial Information Systems, The APIC Series, Academic Press, San Diego, USA.
• Lillesand, T. and Kiefer, R., 1987. Remote Sensing and Image Interpretation, John Wiley &Sons, USA.
• Richards, J.A., 1993. Remote Sensing and Digital Image Analysis, Springer and Verlag, the Netherlands.
• Ustin, S.L., 2004. Remote Sensing for Natural Resource Management and Environmental Monitoring, Manual of Remote Sensing, John Wiley and Sons, 3rd Edition, Volume 4. USA.
• Yomralıoğlu, T., 2002. Coğrafi Bilgi Sistemleri Temel Kavramlar ve Uygulamalar. Akademi Kitapevi, Ankara.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5DK6DK7DK8DK9DK10
PY150000000000
PY250000000000
PY350000000000
PY450000000000
PY550000000000
PY644444444444
PY755555555555
PY844444444444

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 15 6
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 10 3
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 7 3
Midterm Exam 1 3
Time to prepare for Midterm Exam 6 3
Final Exam 1 3
Time to prepare for Final Exam 8 3
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
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload