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ORM3017 | Remote Sensing in Forestry | 2+0+0 | ECTS:3 | Year / Semester | Fall Semester | Level of Course | First Cycle | Status | Compulsory | Department | DEPARTMENT of FOREST ENGINEERING | Prerequisites and co-requisites | None | Mode of Delivery | | Contact Hours | 14 weeks - 2 hours of lectures per week | Lecturer | Doç. Dr. Uzay KARAHALİL | Co-Lecturer | Prof. Mehmet MISIR | Language of instruction | Turkish | Professional practise ( internship ) | None | | The aim of the course: | All students of forestry are introduced to the basic ideas and practical operation of processes such as the interpretation, mapping and measurement of remote sensing data in inventory and surveying tasks, all of which are common in forestry.
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Learning Outcomes | CTPO | TOA | Upon successful completion of the course, the students will be able to : | | | LO - 1 : | define and explain the importance of Remote Sensing and photo interpretation as applied to forest resource management, history of remote sensing, basic principles of remote sensing, the electromagnetic spectrum.
| 3,4 | | LO - 2 : | appoint and characterize stand types on areal photos
| 3,4 | | LO - 3 : | explain satellites, sensors, image processing, interpretation, correction, True color view, false color view, classification, filtering.
| 3,4 | | LO - 4 : | classify a satellite image with ERDAS Imagine.
| 3,4 | | LO - 5 : | develop and present a case study results to the class using real time exercise of ERDAS Imagine software
| 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), LO : Learning Outcome | |
Remote sensing in forestry; 3-D view; scale and resolution, satellite images used in natural resources; forestry practices using areal photography.
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Course Syllabus | Week | Subject | Related Notes / Files | Week 1 | Planning in Forestry, The role and importance of remote sensing in planning, | | Week 2 | Description of Remote Sensing, History, Data Sources, Remote Sensing Systems | | Week 3 | Electromagnetic Spectrum, Film, Photograph, Aerial Photo, Film types used in airal photo | | Week 4 | Shift in aerial photographs, Preparing of the Flight Plan, Overlap rate. Scale, flight altitude and the various calculations on aerial photo | | Week 5 | Measurement of tree height, Measurement of crown size and determination of crown closure | | Week 6 | 3-D vision and the effective area of aerial photo | | Week 7 | Definitİon of tree species and stand types on aerial photo | | Week 8 | Satellites, natural resources satellites | | Week 9 | Mid-term exam | | Week 10 | Resolution of satellite imagery | | Week 11 | Landsat, Spot and Quickbird satellites general features of the application areas | | Week 12 | Bilsat, Ikonos, Rasat and IRS/ERS satellites general features of the application areas | | Week 13 | Image Preprocessing, image processing, classification on satellite imagery | | Week 14 | Image Preprocessing and image processing on satellite imagery with ERDAS Imagine | | Week 15 | Image clasification with SNAP | | Week 16 | End-of-term exam | | |
1 | Köse, S., Cömert, Ç. 1999, Ormancılıkta Foto Yorumlama, Orman Fakültesi Yayınları, No:1, Artvin. | | 2 | Soykan, B. 1986, Ormancılıkta Foto Yorumlama, KTÜ Orman Fakültesi, Yayın No: 9, Trabzon. | | |
1 | Sesören, A., 1999. Uzaktan Algılamada Temel Kavramlar. Mart Matbaacılık Sanatları,İstanbul. | | 2 | Musaoğlu, N., 1999. Elektro-Optik ve Aktif Mikrodalga Algılayıcılardan Elde EdilenUydu Verilerinden Orman Alanlarında Meşcere Tiplerinin ve Yetişme OrtamıBirimlerinin Belirlenme Olanakları, Doktora Tezi, İ.T.Ü., Fen Bilimleri Enstitüsü,İstanbul | | |
Method of Assessment | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | Mid-term exam | 0 | | | 0 | Project | 9 | 13.12.2020 | 336 | 50 | Oral exam | 0 | | | 0 | End-of-term exam | 16 | 02.02.2021 | 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 | 2 | 14 | 28 | Sınıf dışı çalışma | 2 | 5 | 10 | Laboratuar çalışması | 0 | 0 | 0 | Arasınav için hazırlık | 7 | 4 | 28 | Arasınav | 0 | 0 | 0 | Uygulama | 0 | 0 | 0 | Klinik Uygulama | 0 | 0 | 0 | Ödev | 13 | 2 | 26 | Proje | 0 | 2 | 0 | Kısa sınav | 0 | 0 | 0 | Dönem sonu sınavı için hazırlık | 5 | 2 | 10 | Dönem sonu sınavı | 1 | 1 | 1 | Diğer 1 | 0 | 0 | 0 | Diğer 2 | 0 | 0 | 0 | Total work load | | | 103 |
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