|
|
| ORMI7420 | Using Sattellite Images in Forest Ecosystems | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring 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 such as Sentinel and Landsat. | 2,10 | 1, | | PO - 2 : | Students will be able to be well grounded in different types of image processing programs especially SNAP software | 2,10 | 1,4, | | PO - 3 : | Students will be able to download, open, cut different satellite images and calculate different vegetation indexes | 2,10 | 1,4, | | PO - 4 : | Students will be able to perform unsupervised and supervised classification and do change analysis with self-defined classes | 2,10 | 1,4, | | 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 | Characteristics of Landsat, Sentinel, Göktürk and İmece | | | Week 4 | Resolution and bands in sattelites | | | Week 5 | SNAP (Sentinel Application Platform) software, its installation and capabilities | | | Week 6 | Downloading, opening and combining the bands of images | | | Week 7 | Cutting and mosaicing the images | | | Week 8 | Resampling the images, computing NDVI and vegetation indexes | | | Week 9 | 1st Mid term | | | Week 10 | Unsupervised classificaiton (EM Cluster analysis) with sattelite images | | | Week 11 | Supervised classification (Random forest etc.) with images (With digitizing training areas on the images) | | | Week 12 | 2nd Mid term | | | Week 13 | Supervised classification with images (With the help of digitized stand type maps) | | | Week 14 | Analyses and processing of LiDAR data | | | Week 15 | Passive sattelite images (radar) and its analyses | | | 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 (%) | | Mid-term exam | 9
12 | 15/04/2024
15/05/2024 | 4
4 | 50 | | End-of-term exam | 16 | 12/06/2024 | 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 | 13 | 39 | | Laboratuar çalışması | 3 | 13 | 39 | | Arasınav için hazırlık | 8 | 4 | 32 | | Arasınav | 4 | 2 | 8 | | Dönem sonu sınavı için hazırlık | 8 | 3 | 24 | | Dönem sonu sınavı | 5 | 1 | 5 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 186 |
|