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Course Description Course Name : Remote Sensing and Geographic Information System In Environment Study Course Code : UA-515 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. SÜHA BERBEROĞLU Learning Outcomes of the Course : At the end of the course, the students gain an understanding of interaction of electromagnetic energy with soil, water and vegetation. gain an understanding of environmental models and their potentials in resource management Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To gain an understanding of the principles of remote sensing, radiation, sensors and platforms, data acquisition, image processing, interactions of electromagnetic energy with plants, soil and water, remote sensing applications for land and water resources. The place and potentials of remote sensing and GIS in integrated environmental management, as well as case studies in local and global scales for resource management and biodiversity conservation. Course Contents : Environmental models, applications of remote sensing and GIS in land and water management Language of Instruction : Turkish Work Place : Lecture hall and Laboratory   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Introduction to remote sensing and electromagnetic energy, Remote sensing platforms and resolutions Reading the related literature and lecture notes Lecture, discussion and demonstration 2 Sensors and their applications including, land, water and meteorology. Sensors and platforms for landscape planning Reading the related literature and lecture notes Lecture, discussion and demonstration 3 Image processing and analysis, manual processing Reading the related literature and lecture notes Lecture, discussion and demonstration 4 Image pre-processing and analysis, image enhancement, Image classification Reading the related literature and lecture notes Lecture, discussion and demonstration 5 Change detection Reading the related literature and lecture notes Lecture, discussion and demonstration 6 Data integration, Indices and data convertion Reading the related literature and lecture notes Lecture, discussion and demonstration 7 Soil salinity detection using remote sensing; Hyper-spectral rmote sensing Reading the related literature and lecture notes Lecture, discussion and demonstration 8 Remote sensing in change detection; practical work Reading the related literature and lecture notes Lecture, discussion and demonstration, Laboratory exercise 9 Crop yield estimation using remote sensing Reading the related literature and lecture notes Lecture, discussion and demonstration 10 Remote sensing and hydrology Reading the related literature and lecture notes Lecture, discussion and demonstration 11 Mid-term exam 12 Remote sensing and carbon cycle studies. Reading the related literature and lecture notes Lecture, discussion and demonstration 13 Remote sensing and water quality monitoring studies. Reading the related literature and lecture notes Lecture, discussion and demonstration 14 Remote sensing and forest biomass monitoring studies. Reading the related literature and lecture notes Lecture, discussion and demonstration 15 Project presentations homework, presentation Lecture, discussion and demonstration 16/17 Final exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Lecture notes and handouts Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 50     Homeworks/Projects/Others 1 50 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 The students acquire knowledge on potential practical fields of use of remotely sensed data, and use their theoretical and practical knowledge for problem solution in the related professional disciplines. 4 2 The students identify, describe, formulate and solve problems in engineering, and for this purpose, they are able to select appropriate techniques and apply analytical methods and models. 0 3 The students generate information using remotely sensed data and GIS together with database management skills. 0 4 The students gain knowledge to use current data and methods for multi-disciplinary research. 5 5 The students gain technical competence and skills in using recent GIS and remote sensing software. 0 6 The students have basic information about data collection, management, and analysis through integrating GIS and remote sensing, and are able to solve engineering problems using modern tools and technologies. 0 7 The students develop an understanding of sustainable resource management and planning to meet human needs by taking ecological factors into consideration in light of the current research data. 5 8 The students acquire the necessary knowledge and skills to understand a system, a system component or process for planning purposes, using modern techniques and methods. 4 9 At the end of the programme, the students acquire advanced knowledge on remote sensing and GIS theory. 3 10 The students gain knowledge on remote sensing technologies, sensors and platforms and remotely sensed data. 2 11 The students gain knowledge on temporal, radiometrici, spatial and spectral characteristics of remotely sensed data, as well as optic and active remote sensing systems and their interpretation. 1 12 The students develop the necessary skills for selecting and using appropriate techniques and tools for engineering practices, using information technologies effectively, and collecting, analysing and interpreting data. 3 13 The students gain the necessary skills to access information, review the literature, use databases and other sources of information, as well as lifelong learning awareness and the skills to follow scientific and technological developments for personal improvement. 2 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total 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 1 14 14     Mid-term Exams (Written, Oral, etc.) 1 6 6     Final Exam 1 6 6 Total Workload: 138 Total Workload / 25 (h): 5.52 ECTS Credit: 6