Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
ADVANCED REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS AND PRACTICES 805000805240 3 + 0 3.0 10.0

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 The concepts and uses of Geographical Information System (GIS), basic concepts and procedures used in GIS and RS, concepts and foundation of remote sensing, sensors and instruments, remote-sensed image processing, image analyses and classification, application of remote sensing in natural sciences, history of GIS, GIS data structures and sources, GIS softwares, applications, and resources, fundamentals and methodologies, components of GIS, data sources,, vector and raster data, projection systems, modeling, spatial analyses, surface analyses, spatial interpolation, satellite image data sources and specifications, appropriate uses and limitations, integration with GIS.
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) Develop data analysis strategies.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week The concepts and uses of Geographical Information System (GIS) Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
2. Week Basic concepts and procedures used in GIS and RS Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
3. Week Concepts and foundation of remote sensing sensors and instruments Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
4. Week Remote-sensed image processing Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
5. Week Image analyses and classification Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
6. Week Application of remote sensing in natural sciences Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
7. Week History of GIS, GIS data structures and sources Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
8. Week GIS softwares, applications, and sources Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
9. Week Fundamentals and methodologies, components of GIS, data sources Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
10. Week Attribute data in GIS, vector and raster data Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
11. Week Projection systems, modeling, spatial analyses Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
12. Week Surface analyses, spatial interpolation, satellite image data sources and specifications Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
13. Week Basic principles and methods, satellite image data sources and properties Appropriate uses and limitations, integration with GIS Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)
14. Week Basic principles and methods, satellite image data sources and properties Appropriate uses and limitations, integration with GIS Lecture; Question Answer; Problem Solving; Discussion
Brainstorming
Problem Based Learning 		Presentation (Including Preparation Time)

Sources Used in This Course
Recommended Sources
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.
Erdas Imagine User Guide, 1999. The Fifth Edition, ERDAS, Inc. Atlanta, 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 LevelDK1DK2DK3DK4DK5DK6DK7
PY155555555
PY255555555
PY355555555
PY455555555
PY555555555
PY855555555
PY955555555
PY1055555555
PY1655555555

*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) 10 5
Homework 5 15
Practice (Teaching Practice, Music/Musical Instrument Practice , Statistics, Laboratory, Field Work, Clinic and Polyclinic Practice) 6 3
Midterm Exam 1 2
Time to prepare for Midterm Exam 1 50
Final Exam 1 61
Time to prepare for Final Exam 1 2
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
Quick Access Hızlı Erişim Genişlet
Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload