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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| LANDSCAPE ARCHITECTURE (MASTER) (WITH THESIS) | |
| Code | PM587 |
| Name | Remote Sensing Environmental Change Detection and Time Series Analysis |
| Term | 2024-2025 Academic Year |
| Term | Fall |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Prof. Dr. HAKAN ALPHAN |
| Course Instructor |
Prof. Dr. HAKAN ALPHAN
(Güz)
(A Group)
(Ins. in Charge)
|
Course Goal / Objective
At the end of this course, the student is expected to learn GIS-based analysis of natural and technological risks such as drought, flood and inundation, extreme natural events, transportation and storage of hazardous wastes, landslides, forest fires, surface and groundwater pollution.
Course Content
Digital image processing and evaluation for change detection. Image pre-processing for different change detection methods, their importance. Change detection methods: (1) Image algebra methods: image extraction, image proportioning, image regression and change vector analysis. Detection of binary change and naming the change information obtained by this method. Two-time image algebra operations using plant index such as NDVI, SAVI, MSAVI and other index data such as NDBI. (2) Conversion methods: Principal Component Analysis (PCA), Kauth-Thomas (Tasseled Cap) and Gramm-Schmidt transformations. Application of the transformation to bi- and multi-time datasets. (3) Classification-based methods: post-classification comparison, spectral and temporal mixture analysis, expectation maximization (EM), uncontrolled classification, and hybrid methods. (4) Advanced models: Li-Strahler Reflection and Reflection Mixing models, Biophysical Parameter Method. (5) GIS and other visual analysis methods. Important points in choosing algorithms, methods and approaches to be used for change detection in urban areas, forest areas and coastal areas. Advantages and disadvantages of different change detection methods. Determinants/constraints in constructing an ideal change detection.
Course Precondition
None
Resources
Eastman, J. R., 2016. TERRSET Tutorial. Clark University Press. 391P.
Notes
Eastman, J. R., 2016. TERRSET Tutorial. Clark University Press. 468P.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Applies the principles of digital image interpretation. |
| LO02 | Applies image pre-processing methods and decides on the right methods when necessary. |
| LO03 | Knows the application areas of pre-classification change detection methods and applies these methods. |
| LO04 | Knows post-classification change detection methods and their application. |
| LO05 | Applies change information in the form of maps and statistics. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Analyzes the information obtained during the collection, interpretation and application of data related to the field, taking into account social, scientific, cultural and ethical values. | 2 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Describes current knowledge in the same or a different field, based on undergraduate level qualifications. | |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | Gains and applies the ability to identify, define, formulate and solve engineering problems. | 3 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Collects data related to the field, analyzes and interprets the results. | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Uses the knowledge of the principles, processes and tools of Landscape Architecture together with solutions in the professional field. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Works effectively individually or by taking responsibility in multi-disciplinary teams. | 3 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | He/she follows the developments in science and technology and renews himself/herself on issues related to his/her field. | 3 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | He/she independently carries out a study that requires expertise in his/her field. | 3 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | It uses the knowledge and competence to reflect the philosophy, elements, principles and tools of landscape design into the detailed landscape design process. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Develops positive attitudes and behaviors regarding lifelong learning in the field of Landscape Architecture and adopts the universal conditions required by the profession. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Uses advanced computer software, information and communication technologies at the level required by the field. | 5 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Landscape Architecture uses contemporary communication methods to develop and explain ideas and presents them visually, verbally or in writing. | |
| PLO13 | Yetkinlikler - Alana Özgü Yetkinlik | It adopts the principle of complying with scientific and ethical values in all its works. | |
| PLO14 | Yetkinlikler - Alana Özgü Yetkinlik | To be able to develop strategy, policy and implementation plans on issues related to his/her field and evaluate the results obtained within the framework of quality processes. | |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | Evaluates the knowledge and skills acquired in the field with a critical approach. | |
| PLO16 | Yetkinlikler - Alana Özgü Yetkinlik | It presents plans and design proposals that are sensitive to society, area and nature for different landscape types. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to digital image processing for change detection. Announging scopes of micro-projects and formation of project groups | Review of course content, flow and learning outcomes. | Öğretim Yöntemleri: Anlatım, Tartışma, Beyin Fırtınası |
| 2 | Pre-processing requirements for change detection, their significance level, overview and classification of change detection methods | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 3 | Image algebra methods: Image differencing, image ratioing,image regression, and change vector analysis | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 4 | Image algebra methods: Binary change detection, and, labeling change detection | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Beyin Fırtınası |
| 5 | Image algebra methods: Change detection using vegetation indices such as NDBI made by using NDVI, SAVI, MSAVI | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Tartışma, Beyin Fırtınası, Gösteri |
| 6 | Image transformation methods, Principal components analysis (PCA), Kauth-Thomas (Tasseled Cap) and Gramm-Schmidt transformations | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 7 | Transforming bi-temporal and multitemporal data | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Alıştırma ve Uygulama |
| 8 | Mid-Term Exam | Sözlü Sınav, performans değerlendirmesi | Ölçme Yöntemleri: Sözlü Sınav, Performans Değerlendirmesi |
| 9 | Classification method: post-classification comparison, spectral and temporal mixture analysis, expectation maximization, unsupervised classification, etc. | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 10 | Advanced methods of change detection | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Beyin Fırtınası |
| 11 | GIS and other analysis methods | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 12 | Change detection for forest, urban, agriculture and wetland areas | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Beyin Fırtınası |
| 13 | Advantages and disadvantages of selecting appropriate change detection procedure , determiners and constraints in fictionalization of ideal change detection | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 14 | Project presentations | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Tartışma, Beyin Fırtınası |
| 15 | Project presentations (continued) | Lecture, Brainstorming, Question and Answer, Discussion | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Tartışma, Beyin Fırtınası |
| 16 | Term Exams | Oral exam, performance evaluation | Ölçme Yöntemleri: Sözlü Sınav, Performans Değerlendirmesi |
| 17 | Term Exams | Oral exam, performance evaluation | Ölçme Yöntemleri: Performans Değerlendirmesi, Sözlü Sınav |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 100 | 40 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||