Semester-wise syllabus outline for an M.Tech in Remote Sensing (RS) and GIS

 

Semester 1:

 Core Fundamentals 

1. Principles of Remote Sensing 

   - Electromagnetic spectrum, sensor systems (optical, thermal, LiDAR), satellite platforms (Landsat, Sentinel, MODIS), image acquisition. 

2. Geographic Information Systems (GIS) Fundamentals

   - Spatial data models (vector/raster), coordinate systems, geodatabases, topology, basic geoprocessing. 

3. Digital Image Processing 

   - Image enhancement, classification (supervised/unsupervised), spectral indices (NDVI, NDWI), accuracy assessment. 

4. Remote Sensing Physics & Radiometry 

   - Atmospheric correction, reflectance/emissivity, scattering mechanisms, radiometric calibration. 

5. Lab 1: Basic RS & GIS Tools 

   - Hands-on with ERDAS Imagine, ArcGIS/QGIS, ENVI; digitization, layer stacking, simple classification. 

 

Semester 2:

Advanced Techniques & Applications 

1. Spatial Databases & Web GIS 

   - PostgreSQL/PostGIS, spatial SQL, cloud-based GIS (Google Earth Engine), REST APIs, GeoServer. 

2. Microwave Remote Sensing & SAR 

   - Synthetic Aperture Radar (SAR) principles, interferometry (InSAR), polarimetry, applications in disaster monitoring. 

3. Geospatial Data Analysis 

   - Spatial statistics (autocorrelation, kriging), machine learning for RS (Random Forest, CNNs), time-series analysis. 

4. Elective 1 (e.g., Hyperspectral Remote Sensing or Urban Planning & Smart Cities) 

5. Lab 2: Advanced Applications 

   - SAR processing (Sentinel-1 in SNAP), LiDAR point cloud analysis, Python scripting (GDAL, Rasterio). 

 

Semester 3:

Specialization & Project Work 

1. Elective 2 (e.g., Climate Change Studies or Disaster Management) 

2. Elective 3 (e.g., Machine Learning in RS or Agricultural Remote Sensing) 

3. Geospatial Project Management 

   - SDLC for GIS projects, cost-benefit analysis, ethical considerations (data privacy, open-source vs. proprietary). 

4. Project Work Part 1 

   - Thematic projects (e.g., land-use change detection, flood modeling, urban heat island analysis). 

5. Fieldwork & Data Collection 

   - Ground truthing, GPS surveys, drone/UAV-based data acquisition, sensor integration. 

 

Semester 4:

Thesis/Dissertation 

- Independent Research Thesis 

  - Focus areas: 

    - AI-driven crop yield prediction, 

    - Glacier retreat monitoring using SAR/optical data, 

    - 3D city modeling with LiDAR, 

    - Real-time GIS for disaster response. 

  - Thesis submission and defense. 

 

Elective Options (Semesters 2–3):

- Oceanography & Coastal Zone Management 

- Biodiversity Conservation & Habitat Mapping 

- Geospatial Big Data Analytics 

- IoT & Sensor Networks for Environmental Monitoring 

- Hydrological Modeling (SWAT, HEC-RAS) 

- GeoAI (Integration of AI/ML with GIS) 

 

Key Tools & Software: 

- RS Software: ERDAS Imagine, ENVI, SNAP, Google Earth Engine. 

- GIS Platforms: ArcGIS Pro, QGIS, GRASS GIS, GeoDa. 

- Programming: Python (Geopandas, Scikit-learn, TensorFlow), R (terra, raster packages). 

- Data Sources: USGS Earth Explorer, Copernicus Open Access Hub, NASA Earthdata. 

 

Lab/Practical Focus: 

1. Satellite Data Processing: 

   - Preprocessing (atmospheric correction, pansharpening), time-series analysis for deforestation. 

2. Drone/UAV Applications: 

   - Orthomosaic generation (Pix4D, Agisoft), multispectral data analysis for precision agriculture. 

3. Web GIS Development: 

   - Building interactive dashboards (Dash, Leaflet), deploying spatial APIs. 

4. Field Surveys: 

   - Using GPS devices (Trimble), integrating field data with satellite imagery.