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Week
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Topics
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Study Metarials
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1
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The concept and history of remote sensing
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R1-Introduction Section
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2
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Remote sensing systems
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R1-Introduction Section
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3
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Data acquisition in remote sensing
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R1-Introduction Section
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4
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General information about radar images
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R1-Introduction Section
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5
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Processing of radar images
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R1-Method Section and R2-Method Section
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6
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Introducing radar images with different bands
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R1-Introduction Section
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7
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Where radar images are used in forestry applications
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R1-Introduction Section
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8
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Use of radar images in the site inventory
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R1- Introduction and Method Section
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9
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Use of radar images in predicting stand parameters
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R2-Introduction Section
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10
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Topraküstü biyomasın tahmin edilmesinde radar görüntülerinin kullanımı
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R1- Introduction, Material and Method Section
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11
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Use of radar images in estimating aboveground biomass
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R1-Method Section
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12
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Combining Radarsat data and other satellite data together with the Erdas Imagine software program
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R1-Method Section
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13
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Obtaining data on some radar images (palsar, radarsat, asar etc.) by using Erdas Imagine software program-I
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R1-Method Section
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14
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Obtaining data on some radar images (palsar, radarsat, asar etc.) by using Erdas Imagine software program-II
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R1-Method Section
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Prerequisites
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None
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Language of Instruction
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Turkish
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Responsible
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Professor Dr. Alkan GÜNLÜ
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Instructors
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1-)Profesör Dr. Alkan Günlü
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Assistants
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-
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Resources
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R1. Günlü, A. (2009). Yetişme ortamı envanterinin doğrudan, dolaylı ve uzaktan algılama yöntemleriyle belirlenmesi ve karşılaştrılması. Doktora Tezi, Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü.
R2. Günlü, A., & Ercanlı, İ. (2020). Artificial neural network models by alos palsar data for aboveground stand carbon predictions of pure beech stands: a case study from Northern
of Turkey. Geocarto International, 35, 17-28.
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Supplementary Book
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-
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Goals
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Due to the increasing developments in remote sensing technology in recent years, various features of remote sensing data have been presented to users. Especially the possibilities of using remote sensing data in forest inventory and the main active and passive sensing systems that should be considered in the selection of these data should be explained. In this context, it is aimed to give necessary information about where and how radar images obtained with active detection systems are used in forestry studies.
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Content
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First of all, information about radar images will be given and explanatory information will be given about the use of these images in forestry.
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Program Learning Outcomes |
Level of Contribution |
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1
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Must learn the methods of both improving the basic sciences and engineering knowledge and obtaining new knowledges at a level of expertise
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3
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2
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Must be able to design, develop, and apply methods and experiments at advanced level to solve forestry problems, and analyses and interpret their results
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3
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3
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Must be able to provide solutions for the country?s forestry and environmental problems by considering global, public and ecosystem conditions
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-
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4
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Must be able to setup interdisciplinary approach to reach an advanced solution for forestry problems
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3
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5
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Must be able to act in an advanced level of professional ethics and responsibility during the identification and resolution of problems encountered in forestry
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-
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6
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Must be able to do the task in a single or multi-disciplinary working groups, and be able to show effective communication
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-
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7
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Must have the ability to effective use of both information technologies and a foreign language at an advanced level
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3
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8
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Must be able to describe, foresee and solve the current problems in the fields of forestry and other related problems at advanced level brought by current global developments
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-
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9
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Must be able to use the tools and techniques required for forestry applications at an advanced level
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4
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10
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Must be able to think, interpret, analyse and synthesize forestry practices at an advanced level by using a three dimensional perspective
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-
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11
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Must be able to research and survey any kinds of natural resources and event, and write advanced reliable reports by using the achieved findings
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-
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12
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Must be able to understand the necessity of life-long learning at an advanced level, and to be able to use the methods that keeps obtained knowledge up date
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-
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