|
|
| ORMI7420 | Using Sattellite Images in Forest Ecosystems | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of FOREST ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Doç. Dr. Uzay KARAHALİL | | Co-Lecturer | Prof. Mehmet MISIR | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | Understanding the satellite images, an important source of data, increasingly used in forest resources management in recent years. Furthermore, teaching usage and performing case studies in other forestry activities generally. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Students will be able to learn different types and characteristics of satellite images. | 2,3,10 | 1 | | PO - 2 : | Students will be able to be well grounded in different types of image processing programs | 2,3,10 | 1 | | PO - 3 : | Students will be able to open, cut, coordinate and enrich of different satellite images. | 2,3,10 | 3,4 | | PO - 4 : | Students will be able to compose and use programs used in forestry. | 2,3,10 | 3,4 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| This course aims to provide the use of satellite images in forestry, to introduce different types and characteristics of satellite images especially Sentinel and LANDSAT. Processing and analysis with SNAP software. Downloading, cutting, resampling the images. Supervised and unsupervised classification. Analysis with LiDAR data. Active radar image processing. |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction, content of the course, sources be followed | | | Week 2 | Sattelites, history of the satellites | | | Week 3 | Natural resource satellites | | | Week 4 | IKONOS and Landsat Satellites | | | Week 5 | Resolution and bands in sattelites | | | Week 6 | Opening and combining the bands of images | | | Week 7 | Cutting and mosaic the images. | | | Week 8 | Rectification of the images. | | | Week 9 | Mid term | | | Week 10 | İmage enhancement methods | | | Week 11 | Supervised classification in images | | | Week 12 | Unsupervised classification | | | Week 13 | Assignement | | | Week 14 | Processing lidar data | | | Week 15 | An overview of the course and explanation of understood the issues. | | | Week 16 | Final exam | | | |
| 1 | Lillesand, T. M., Kiefer, R. W.,1987. Remote Sensing And Image Interpretation. Second edition. John Wiley-Sons Ltd. Canada. | | | |
| 1 | Mather, P. M., 1999. Computer Processing of Remotely- Sensed Images. Second edition. John Wiley-Sons Ltd. England. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Homework/Assignment/Term-paper | 9
13 | 16/11/2021
13/12/2021 | 4
4 | 50 | | End-of-term exam | 16 | 10/01/2022 | 4 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 13 | 39 | | Sınıf dışı çalışma | 3 | 5 | 15 | | Uygulama | 3 | 5 | 15 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 10 | 2 | 20 | | Proje | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 5 | 2 | 10 | | Dönem sonu sınavı | 5 | 1 | 5 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 104 |
|