Remote Sensing & GIS (CE-504 (B)) - Unit 5 Short Notes

UNIT 5: REMOTE SENSING & GIS - EXAM-FOCUSED SHORT NOTES

I. Fundamentals of Remote Sensing

1. Spectral Reflectance Characteristics

• Definition: The proportion of incident electromagnetic radiation reflected by a surface as a function of wavelength.

• Key Concept: Different Earth surface features have unique spectral signatures (reflectance curves) across the electromagnetic spectrum.

• Typical Curves:

  • Vegetation: Low reflectance in blue & red (chlorophyll absorption), high in near-infrared (NIR) (cell structure scattering). Peak in red edge.

  • Soil: Generally smooth, increasing curve with wavelength. Influenced by moisture, organic matter, texture.

  • Water: Low overall reflectance. High absorption in NIR & SWIR. Reflectance increases with sediment/turbidity.

  DiagramSEARCH: "spectral reflectance curves soil vegetation water comparison"

2. Ideal Remote Sensing System

• A conceptual system with perfect characteristics: high spatial, spectral, and temporal resolution; perfect radiometric accuracy; global coverage; real-time data delivery; and low cost. Real systems involve trade-offs between these parameters.

3. Synoptivity & Repetitivity

• Synoptivity: Ability to observe a large area (regional/global) simultaneously at a single instant. Example: Weather satellites imaging entire continents.

• Repetitivity: Ability to revisit and image the same area at regular intervals. Example: Landsat's 16-day revisit cycle for monitoring change.

  ![TIP] These are key advantages of satellite RS over aerial photography. Synoptivity enables large-area studies; repetitivity enables time-series analysis.

II. Remote Sensing Platforms and Sensors

1. Satellite Orbits

2. Sensor Resolutions

3. Indian Satellite Missions (Focus: Chandrayaan-3)

• Chandrayaan-3 Objectives:

  1. Demonstrate safe & soft landing on lunar surface.

  2. Demonstrate rover operations on Moon.

  3. Conduct in-situ scientific experiments on lunar surface.

• Salient Features:

  • Lander (Vikram) & Rover (Pragyan) configuration.

  • Landing site: 69.367621°S, 32.348126°E (near south pole).

  • Propulsion Module acted as communication relay & performed spectral studies of Earth from lunar orbit.

  • Scientific Payloads: For lunar surface plasma, thermal, seismicity, and mineralogy studies.

4. Earth Resource vs. Weather Satellites

III. Image Processing and Interpretation

1. Classification Techniques

2. Digital vs. Visual Image Analysis

3. Image Filtering

• Purpose: Enhance image features or suppress noise using a kernel/mask.

• Common Types:

  • Low-Pass (Smoothing): Reduces noise, blurs edges. (e.g., Mean filter).

  • High-Pass (Sharpening): Enhances edges, details. (e.g., Laplacian, Sobel).

  • Directional: Enhances linear features in a specific orientation.

4. Stereoscopy & Stereoscope

• Stereoscopy: Technique of viewing two overlapping images (stereopair) from slightly different perspectives to perceive 3D depth.

• Stereoscope: Optical device used to view stereopairs. Enables extraction of elevation data (photogrammetry) and better interpretation of terrain.

5. Errors in Remote Sensing

IV. Geographic Information Systems (GIS) Fundamentals

1. Definition & Key Components

• Definition: A computer-based system for capturing, storing, analyzing, managing, and presenting spatial (geographic) data and its attribute (non-spatial) data.

• Key Components (5-Piece Model):

  1. Hardware: Computer, storage, GPS, plotters.

  2. Software: GIS package (ArcGIS, QGIS), DBMS.

  3. Data: Spatial data (maps, imagery) + Attribute data (tables). Most critical & costly component.

  4. People: Skilled users, managers, analysts.

  5. Methods: Procedures, workflows, analysis models.

2. Data Sources & Objectives of Collection

• Sources:

  • Primary: Field surveys (GPS, total station), digitizing existing maps, remote sensing imagery.

  • Secondary: Government agencies (Survey of India, Census), published maps, existing databases.

• Objectives of Data Collection:

  • To create a base map.

  • To update existing information.

  • For specific project analysis (e.g., site suitability, network routing).

  • To monitor changes over time.

3. Integration of Spatial & Attribute Data

• Achieved through a unique identifier (Key Field).

• Process: Each spatial feature (e.g., a polygon for a district) has a Feature ID in its geometry file. The attribute table has a corresponding record with the same ID and descriptive fields (e.g., District_Name, Population). The GIS software links them internally.

  Example: Polygon_123 (in shapefile) ↔ Record_123 with {Name: "Bhopal", Pop: 2.5M} (in dBase table).

4. Data Models: Vector vs. Raster

5. Coordinate Systems & Map Projections

• Importance: Earth is 3D, maps are 2D. Projections are mathematical transformations to minimize distortion in shape, area, distance, or direction.

• Coordinate Systems:

  • Geographic: Uses latitude & longitude (angular units) on a spheroid/ellipsoid (e.g., WGS84).

  • Projected (Planar): Uses X, Y (linear units like meters) on a flat surface. UTM is a prime example.

• Map Projection Types:

  • Cylindrical (e.g., Mercator - conformal, preserves shape).

  • Conic (e.g., Albers - equal-area, good for mid-latitudes).

  • Azimuthal (e.g., Stereographic - preserves shape from point).

• UTM Projection:

  • Universal Transverse Mercator.

  • World divided into 60 longitudinal zones (6° wide).

  • Transverse Mercator projection (conformal).

  • Uses False Easting/Northing (in meters) to avoid negative coordinates.

  • Formula (Simplified): Zone Number = ⌊(Longitude + 180)/6⌋ + 1.

  DiagramSEARCH: "UTM zone world map grid"

V. GIS Data Management and Analysis Operations

1. Inputting Maps & Creating Shapefiles

• Procedure:

  1. Georeferencing: Assign real-world coordinates to a scanned map/image using Ground Control Points (GCPs).

  2. Digitization: Manually (on-screen) or automatically trace features (points, lines, polygons) from the georeferenced image.

  3. Attribute Table Creation: For each feature digitized, a record is created in an associated table (.dbf). Define fields (columns) like ID, Name, Type.

  4. Save as Shapefile: The geometry (.shp), attribute (.dbf), index (.shx), and projection (.prj) files are saved together as a shapefile.

2. Buffer Analysis

• Definition: Creation of a zone around a map feature at a specified distance.

• Process: GIS generates a new polygon (buffer zone) at a set distance from input features (point, line, polygon).

• Formula (for constant buffer): Buffer_Zone = Input_Feature ± Buffer_Distance.

• Applications: Noise pollution zones around highways, service areas for facilities (hospitals, schools), riparian buffer zones.

3. Overlay Analysis

• Definition: Spatial operation that overlays multiple thematic layers to create a new layer, combining their attributes.

• Common Types:

  • Intersection: Output contains areas where all input layers overlap. Preserves attributes of all inputs.

  • Union: Output contains all areas from all input layers. Most comprehensive.

  • Identity: Like intersection, but retains all features from one layer (the "identity" layer) and their attributes, even if they don't overlap.

  • Clip: Extracts features of one layer that fall within the boundary of another.

• Application: Land Use/Land Cover (LULC) Change Detection: Overlay LULC maps from two different years to identify areas of conversion (e.g., forest → agriculture).

VI. Integration of Remote Sensing and GIS

1. Challenges in Using RS Data within GIS

• Data Compatibility: Different formats, projections, resolutions.

• Geometric Accuracy: RS images require precise georeferencing to match GIS vector layers.

• Raster-Vector Integration: Converting between models leads to loss of information (generalization in vectorization, jagged edges in rasterization).

• Temporal Mismatch: RS acquisition time vs. attribute data collection time.

• Scale & Resolution Mismatch: High-res RS data may be too detailed for regional GIS analysis; low-res may be too coarse.

• Data Volume & Processing: Large hyperspectral or high-spatial-res images are computationally intensive.

2. LULC Change Assessment using RS & GIS

1. Acquire: Multi-temporal satellite images (pre- and post-change period).

2. Pre-process: Atmospheric correction, geometric correction, co-registration.

3. Classify: Perform supervised/unsupervised classification on each image to generate LULC thematic maps.

4. GIS Overlay: Use Overlay Analysis (Intersection) in GIS on the two classified raster/vector layers.

5. Analyze: Generate change matrix (crosstabulation) to quantify gains/losses in each class. Map "from-to" categories.

6. Validate: Use ground truth data or higher-resolution imagery.

3. Applications in Water Resources

• Watershed Delineation & Management: Using DEMs (from RS) in GIS to define catchments.

• Surface Water Mapping & Monitoring: Identify lakes, rivers, reservoirs; monitor seasonal changes.

• Flood Inundation Mapping & Risk Assessment: Overlay flood extent (from RS) with infrastructure (GIS layers).

• Groundwater Potential Zoning: Integrating RS-derived parameters (lineaments, drainage, lithology) with GIS overlay and weighting (AHP).

• Pollution Tracking: Mapping point sources (GIS) and plume extent (RS thermal/chlorophyll).

4. Applications in Traffic Management

• Network Analysis: GIS modeling of road networks for shortest path, service area, and location-allocation problems.

• Traffic Flow Monitoring: Using high-temporal-res RS (e.g., from satellites or drones) to estimate traffic density.

• Route Optimization: For public transport, emergency services (ambulance, fire).

• Impact Assessment: Overlay planned infrastructure with environmental/land use layers (from RS) for EIA.

• Parking Management: Spatial inventory and analysis of parking zones.