Remote Sensing and GIS(Skill Based Elective)

Paper Code: 
SENV 601
Credits: 
4
Contact Hours: 
60.00
Max. Marks: 
100.00
Objective: 
This course will enable the students to –
1. Understand the basic principles of remote sensing
2. Understand applications of remote sensing and GIS in Environmental Conservation
11.00
Unit I: 
Introduction to Remote Sensing
• Concept and Scope of Remote Sensing: Definition, history, process and characteristics of remote sensing system, Advantages and Limitations, Remote Sensing scenario in Indian context
• Concept of Electromagnetic Radiation (EMR): EMR properties, EMR wavelength regions and their applications
• EMR-Atmospheric Interactions: Scattering, absorption, transmission, atmospheric windows
• EMR-Earth Interactions: Concept of spectral signature and Spectral Reflectance Curves
• Types of Remote Sensing: Passive, Active, Thermal and Microwave
• Sensors and Orbits: Types and characteristics of Sensors, types of sensor resolutions, types of satellite orbits, Specifications of some popular satellites- Earth Resources Satellites- Landsat, SPOT, IRS, Cartosat, IKONOS; and Meteorological Satellites- NOAA, INSAT, GOES
12.00
Unit II: 
Thermal and Microwave remote sensing
• Thermal Remote Sensing: concept of Blackbody and Emissivity, Physical Laws, Thermal Infrared Radiation properties, Thermal Infrared Atmospheric windows, Interaction of Thermal
radiations with Earth surface, Thermal sensors and its application
• Microwave Remote Sensing: Basic principles, Polarization, Spatial resolution, Radar Image Geometry, Radar Environmental considerations, Side Looking Radar (SLAR) and Synthetic Aperture Radar (SAR) system operation, Relief Displacement, shadows and Speckle effect, types of Microwave sensors and its application
19.00
Unit III: 
Image Interpretation and Digital Image Processing
• Analog versus Digital image
• Image Interpretation: Elements of Visual Image Interpretation, Ground truth and ground truthing equipments (Use of radiometers, computer printouts, Thematic maps)
• Fundaments of Digital Image: Basic concept of Digital image, Digital Image data format, Colour concept and colour combinations
• Image pre-processing: Atmospheric, Radiometric and Geometric Errors and correction, mosaicking
• Image Classification: Unsupervised (Isodata, K-mean); and Supervised (Minimum Distance, Parallelepiped, Maximum Likelihood, Mahalanobis Distance) Classification systems
10.00
Unit IV: 
GIS, GPS and Remote sensing
• Geographic Information System (GIS): Definition, components of GIS, Variables (points, lines, polygon), GIS data (Spatial and Attribute data; Raster and Vector data), GIS softwares, file organization and formats, Overview on concept of DBMS, RDBMS, and SDBMS for geo-data handling, Advantages and limitations of GIS
• Global Positioning System (GPS): Introduction, Satellite constellation, GPS segments, Errors, Factors affecting GPS Accuracy, NAVSTAR, GAGAN, IRNSS
Unit V: 
Applications of Remote sensing and GIS
Application of GIS, GPS and Remote sensing in:
o Forest Resource Management
o Air Quality Monitoring and Management
o Water resource management and hydrogeology
o Soil Quality Management
o Wildlife studies
o Geology and Geomorphology
o Urban/rural area planning and landuse
o Disaster Management
ISRO Bhuvan Geo portal applications
 
 
Note: Practical exercises (related to the theory) will be conducted using GIS softwares (QGIS/ArcGIS Pro).
Essential Readings: 
• Joseph, G. (2018). Fundamentals of Remote Sensing. Universities Press
• Campbell, J.B., and Wynne, R. (2011). Introduction to Remote Sensing. Fifth Edition. The Guilford Press
• Gatrell, A. and Markku, L. (1998). GIS and Health. Philadelphia. Taylor and Francis, Inc.
• Griffth, D. A. and Layne,L.J.(1999). A Casebook For Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets. New York. Oxford Press.
• Gupta, R. P. (2003). Remote sensing geology. New York .Springer.
• Kaplan, Elliot D., and Christopher Hegarty. 2005. Understanding GPS: Principles and Applications, Second Edition. Artech.
Academic Year: