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| ORML7371 | Veget. Analy. by Multi-Spectral Satel.Images | 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 | Prof. Dr. Mehmet MISIR | | Co-Lecturer | - | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Vegetation analysis in forest ecosystems, relation to stand characteristics vegetation, vegetation modelling |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Determine the vegatiton indices | 2,6 | 1,3, | | PO - 2 : | Determine vegetation types wit remote sensing | 2,3,6 | 1,3, | | PO - 3 : | Develop and model the relation between vegetation and stand characteristics | 2,6 | 2,5, | | PO - 4 : | Develop and present a case study results to the class using real time exercise of ERDAS Imagine software | 2,6 | 3,5, | | 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 | | |
| Characteristics of Digital Image Data, Image Preprocessing, Image Interpretation, Image Enhancement, Mothods of Image Enjancement, Band Ratios, Vegetation Indices (NDVI, TNDVI, RVI, IPVI, LAI, vb.), P |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Remote Sensing, Data Sources, Remote Sensing Systems | | | Week 2 | Resolution in Remote Sensing | | | Week 3 | Satellites, natural resources satellites | | | Week 4 | Landsat, Spot, Ikonos,Quickbird, Rasat and Göktürk satellites general features of the application areas | | | Week 5 | Image Pre-processing and Processing Methods | | | Week 6 | Vegetation indices | | | Week 7 | Vegetation indices | | | Week 8 | Image classification | | | Week 9 | Mid-term exam | | | Week 10 | classification on satellite imagery and practices | | | Week 11 | Vegetation indices practices wit ERDAS Imagine | | | Week 12 | Vegetation indices practices wit ERDAS Imagine | | | Week 13 | Vegetation indices practices wit ERDAS Imagine | | | Week 14 | Student presentations | | | Week 15 | Student presentations | | | Week 16 | Final exam | | | |
| 1 | BOZKURT, N.E., ZONTUL, M., ASLAN, Z. 2018, AURUM MÜHENDİSLİK SİSTEMLERİ VE MİMARLIK DERGİSİ,Cilt 2, Sayı 1
| | | 2 | Xue, J. and Su, B. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications, Volume 2017 |Article ID 1353691 | https://doi.org/10.1155/2017/1353691 | | | 3 | https://www.sciencedirect.com/topics/earth-and-planetary-sciences/vegetation-index | | | 4 | https://cdn-acikogretim.istanbul.edu.tr/auzefcontent/19_20_Guz/uzaktan_algilama/1/index.html | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 07/04/2022 | 1 | 30 | | Presentation | 12 | 29/04/2022 | 1 | 20 | | End-of-term exam | 16 | 25/05/2022 | 1 | 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 | 14 | 42 | | Laboratuar çalışması | 2 | 3 | 6 | | Arasınav için hazırlık | 3 | 10 | 30 | | Arasınav | 2 | 1 | 2 | | Uygulama | 2 | 4 | 8 | | Ödev | 3 | 2 | 6 | | Dönem sonu sınavı için hazırlık | 3 | 5 | 15 | | Dönem sonu sınavı | 1 | 1 | 1 | | Diğer 1 | 2 | 5 | 10 | | Diğer 2 | 2 | 3 | 6 | | Total work load | | | 126 |
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