CANKIRI KARATEKIN UNIVERSITY Bologna Information System


  • Course Information
  • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    The Methods for Prediction of Biomass in Forestry ORM571 FALL 3+0 Faculty E 6
    Learning Outcomes
    1-Identify the concept of biomass and its importance
    2-Points the usage areas of biomass in forestry.
    3-Comments the measurement techniques of biomass in the field
    4-The use of regression models in the estimation of biomass comments
    5-Evaluates the development process of regression models for biomass estimation
  • ECTS / WORKLOAD
  • ActivityPercentage

    (100)

    NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14456
    Assignments5023060
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)0000
    Project0000
    Laboratory 0000
    Final exam (exam + preparation) 5012020
    0000
    Total Workload (hours)   178
    Total Workload (hours) / 30 (s)     5,93 ---- (6)
    ECTS Credit   6
  • Course Content
  • Week Topics Study Metarials
    1 The concept of Biomass S1 section 1 should be read
    2 The application areas of biomass in forestry S1 section 1 should be read
    3 Historical development process for biomass studies S2 section 1 should be read
    4 The components of biomass S2 section 1 should be read
    5 Measuring biomass in stands S3 Biomass section should be read
    6 The classification of methods for prediction of biomass S3 Biomass section should be read
    7 The usages of regression models for predicting biomass K2 Section 2 should be read
    8 The prediction of over-ground biomass S3 Biomass section should be read
    9 The prediction of under-ground biomass S3 Biomass section should be read
    10 The single-entry biomass equations S2 section 4 should be read
    11 The double-entry biomass equations S2 section 4 should be read
    12 The prediction of total biomass in forest stand S1 section 5 should be read
    13 The usages of mixed effect regression models for prediction of biomass SR section 1 should be read
    14 The use of remote sensing techniques for prediction of biomass SR 2 section 1 should be read
    Prerequisites -
    Language of Instruction Turkish
    Responsible Ass. Prof. Dr. Muammer ŞENYURT
    Instructors

    1-)Doçent Dr. Muammer Şenyurt

    Assistants -
    Resources R1. Röser, D., Asikainen, A., Raulund-Rasmussen & K., Stupak, I. (2008). Sustainable Use of Forest Biomass for Energy: A Synthesis with Focus on the Baltic and Nordic Region (Managing Forest Ecosystems), Springer Series, 250 p. R2. Saraçoğlu, N. (1998). SakallıKızılagaç (Alnus glutinosa (L.) Gaertn subsp. barbata (C.A. Mey.) Yalt.) Biyokütle Tabloları, K.T.Ü Fen Bilimleri Enstitüsü, Doktora tezi, Trabzon. R3. Günel, A. (1982). Orman Hasılat Bilgisi Ders Notları, İ.Ü. Orman Fakültesi, 89 s.
    Supplementary Book SR1. Pretzcsh, H. (2009). Forest Dynamics: from Measurement To Model, Springer International, Berlin, Germany, 664 s. SR2. Steininger, M.K. (2000). Satellite Estimation of Tropical Secondary Forest Above-ground Biomass: Data from Brazil and Bolivia, Int. J. Remote Sensing, Vol. 21, No. 6 & 7, pp. 1139?1157.
    Goals To reach the concept of biomass in forest, its importance, the application areas, the measurement techniques for biomass in fieldworks, the development process for regression models used in biomass predictions.
    Content -
  • Program Learning Outcomes
  • Program Learning Outcomes Level of Contribution
    1 Must learn the methods of both improving the basic sciences and engineering knowledge and obtaining new knowledges at a level of expertise 3
    2 Must be able to design, develop, and apply methods and experiments at advanced level to solve forestry problems, and analyses and interpret their results -
    3 Must be able to provide solutions for the country?s forestry and environmental problems by considering global, public and ecosystem conditions 4
    4 Must be able to setup interdisciplinary approach to reach an advanced solution for forestry problems -
    5 Must be able to act in an advanced level of professional ethics and responsibility during the identification and resolution of problems encountered in forestry -
    6 Must be able to do the task in a single or multi-disciplinary working groups, and be able to show effective communication 3
    7 Must have the ability to effective use of both information technologies and a foreign language at an advanced level -
    8 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 3
    9 Must be able to use the tools and techniques required for forestry applications at an advanced level -
    10 Must be able to think, interpret, analyse and synthesize forestry practices at an advanced level by using a three dimensional perspective 2
    11 Must be able to research and survey any kinds of natural resources and event, and write advanced reliable reports by using the achieved findings -
    12 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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