Masters Courses (University of Tokyo)

 
5. Remote Sensing and GIS

Previous course now divided into two courses

711-67 Remote Sensing

Course Objective: This lecture provides the basic principle, methodologies and applications in remote sensing. In particular applications to environment and disaster monitoring are highlighted.

Grading Policy: Grading will be determined as follows: 

Report = 50 percent; 

Presentation = 50 percent; 

Total: 100 percent

Prerequisite: Theory and Method of Transport and Urban Infrastructure (711-59)

Course Content: 

I. Principles of Remote Sensing
   1. Spectral Signature
   2. Remote Sensor
   3. Remote Sensing Processes and Models
II. Optical Remote Sensing - Forward Process
   4. Remote Sensors and Sensor Models
   5. Interaction Models in Remote Sensing
III. Optical Remote Sensing - Inversion Process
   6. Classification
   7. Physical Quality Measurement　- Water quality mapping
   8. Physical Quality Measurement　- Land surface parameters mapping
   9. Vegetation Index
   10. Height Measurement (DEM)
IV. Microwave Remote Sensing
   11. Synthetic Aperture Radar
V. Image Processing and Analysis
   12. Preprocessing of Data
   13. Image Processing
   14. Image Analysis
VI. Frontiers in Remote Sensing
VII. Applications
   15. Environmental Monitoring
   16. Disaster Monitoring
   17. Others

Material: Remote Sensing Note, Japan Association on Remote Sensing, Image processing software TNT and WinAsean

711-57 Spatial Statistical Analysis

Course Objective: This course is an introduction to advanced linear regression models and their underlying theories.

Grading Policy: Grading system will be based on the midterm examination (30%) and the final examination (70%).

Prerequisite: It will be assumed that you are familiar with introductory topics on statistics and matrix algebra. For instance, can you answer the following questions? What is a probability density function? What is a normal distribution? What is ordinary least squares method? What is maximum likelihood estimation? Are you familiar with matrix/vector operations? Do you have knowledge about basics of differential calculus at the matrix/vector level? If you wish to take this course, these will be kind of prerequisites.

Course Content: 

After reviewing the ordinary linear regression model, it will focus mainly on two subjects; how to deal with spatial dependence of random error terms in a linear regression model and how to rationally predict the future state of a region by using the linear regression model. The backgrounds and motivations of these two subjects will be presented in the first session of class.

Chap. 1. Introduction
Chap. 2. Ordinary Linear Regression Model
         2.1 Model and Assumptions
         2.2 Ordinary Least Squares Estimation
         2.3 Maximum Likelihood Estimation
         2.4 Unbiased Estimation of Error Variance
         2.5 Best Linear Unbiased Estimator (BLUE)
Chap. 3. Generalized Linear Regression Model
         3.1 Model and Assumptions
         3.2 Generalized Least Squares Estimation
         3.3 Maximum Likelihood Estimation
Chap. 4. Spatial Regression Model
         4.1 Spatial Auto regression Model (SAR)
         4.2 Maximum Likelihood Estimation
Chap. 5. Best Linear Unbiased Prediction (BLUP)
         5.1 Beast Linear Unbiased Prediction
         5.2 Application of BLUP to SAR

Material: There is no required textbook. Below is a list of good econometric textbooks. You are recommended to consult them for your more understanding. Johnston, J, et.al. (1997) Econometric Methods, Fourth Edition, McGraw-Hill. Kmenta, J. (1997) Elements of Econometrics, Second Edition, The University of Michigan Press. Judge, G. et.al. (1985) The Theory and Practice of Econometrics, Second Edition, Wiley. The other references about spatial econometrics and spatial statistics will be presented in class as necessary. Also, reference materials will be given as occasion.


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