Urban & Town Planning (CE-504 (A)) - Unit 5 Short Notes

UNIT 5: GEOGRAPHIC INFORMATION SYSTEMS, REMOTE SENSING, AND SUSTAINABLE ENERGY FOR URBAN PLANNING

I. FOUNDATIONS OF REMOTE SENSING

A. Basic Concepts and Principles

• Spectral Reflectance: The fraction of incident electromagnetic radiation reflected by a surface. Different earth features have unique spectral signatures.

  • Soil: Reflectance increases with wavelength (low in visible, higher in NIR). Moisture decreases reflectance.

  • Vegetation: Low reflectance in visible (chlorophyll absorption), high in Near-Infrared (NIR) due to cell structure. The difference between Red and NIR is key for vegetation indices (e.g., NDVI).

  • Water: Low reflectance overall; absorbs NIR and SWIR strongly. Suspended sediments or shallow water increase reflectance.

  [!TIP] Exam Focus: Be ready to sketch a graph comparing spectral reflectance curves for soil, vegetation, and water across the electromagnetic spectrum (Visible to SWIR).

• Ideal Remote Sensing System: A conceptual system with:

  1. High Spatial, Spectral, Temporal, and Radiometric Resolution.

  2. Synoptic Coverage: Ability to observe large areas at once.

  3. Multi-temporal Capability: Frequent revisit times.

  4. Objectivity & Repeatability: Consistent, unbiased data collection.

  5. Cost-Effectiveness & Accessibility.

  Key Limitation: No real system is ideal; trade-offs always exist (e.g., high spatial resolution often means lower temporal resolution).

B. Satellite Platforms and Orbits

• Geostationary Satellite: Orbits at ~36,000 km above equator; orbital period = Earth's rotation (24 hrs). Stays fixed over one point. Used for weather monitoring and telecommunications. Example: INSAT series (India).

• Sun-synchronous Satellite: Orbits at lower altitude (700-800 km); passes over any given point at same local solar time. Provides consistent lighting conditions for change detection. Used for earth resource observation. Example: Landsat, Sentinel, IRS series.

• Indian Satellite Program (Chandrayaan-3):

  • Objectives: Safe and soft landing on lunar south pole, rover operations, in-situ scientific experiments.

  • Salient Features: Indigenous landing module (Vikram), rover (Pragyan), propulsion module. Demonstrated advanced landing technologies. Not an earth observation satellite but showcases India's remote sensing/space capability.

• Earth Resource Satellites vs. Weather Satellites:

  | Feature | Earth Resource Satellites | Weather Satellites | | :--- | :--- | :--- | | Primary Goal | Land, ocean, resource mapping | Atmospheric & meteorological monitoring | | Orbit | Usually Sun-synchronous | Often Geostationary (continuous view) | | Sensors | Multispectral, high spatial res. | Broadband (visible, IR, water vapor channels) | | Revisit Time | Days to weeks | Minutes to hours (Geo) |

C. Sensor Characteristics and Resolution

• Spatial Resolution: Size of the smallest object that can be detected (e.g., 10m pixel). Ground Sample Distance (GSD).

• Spectral Resolution: Number and width of specific wavelength bands (e.g., multispectral: 4-10 bands; hyperspectral: hundreds of narrow bands).

• Temporal Resolution: Time interval between successive observations of the same area (revisit period). Depends on orbit and sensor swath.

• Radiometric Resolution: Ability to detect differences in brightness; number of digital levels (e.g., 8-bit = 256 levels).

D. Key Terminology

• Synoptivity: Ability to capture a wide-area, "snapshot" view of the Earth's surface in a single image. Key for regional planning and disaster assessment.

• Repetivity (Revisit Capability): Frequency with which a satellite can image the same location. Critical for monitoring dynamic urban processes, crop cycles, and disaster progression.

II. FUNDAMENTALS OF GEOGRAPHIC INFORMATION SYSTEMS (GIS)

A. Core Definition and Structure

• Definition: A computer-based system for capturing, storing, managing, analyzing, and displaying spatially referenced (geographic) data.

• Key Components (The 5 P's):

  | Component | Description | | :--- | :--- | | Hardware | Computer, GPS, scanners, plotters, storage. | | Software | GIS package (e.g., ArcGIS, QGIS), DBMS, tools. | | Data | Spatial data (maps, imagery) & Attribute data (tables). Most critical & expensive component. | | People | Users, specialists, managers. | | Procedures | Workflow, data standards, analysis models. |

B. Spatial Data Framework

• Map Projections: Mathematical methods to represent the 3D Earth on a 2D map. Essential because the Earth is spherical and maps are flat. All projections distort shape, area, distance, or direction.

  • Common Type - UTM (Universal Transverse Mercator): Conformal projection (preserves shape). Divides Earth into 60 zones (6° wide). Uses meters. Widely used for large-scale engineering and urban planning maps.

• Coordinate Systems:

  • Geographic Coordinate System (GCS): Uses latitude/longitude (angular units, degrees). Based on a spheroid/ellipsoid model (e.g., WGS84).

  • Projected Coordinate System (PCS): Uses linear units (meters, feet). Based on a map projection (e.g., UTM Zone 44N). Used for accurate distance/area calculations in GIS analysis.

C. Data Acquisition and Management

• Sources of Data for GIS:

  1. Primary Data Collection: GPS, ground surveys, digitizing from maps.

  2. Secondary Data: Published maps, existing databases, Remote Sensing imagery, census data, government records.

  3. Commercial Data Vendors.

• Objectives of Data Collection in GIS:

  • To create a comprehensive spatial database for a study area.

  • To ensure data accuracy, precision, and relevance to the planning problem.

  • To establish topology (spatial relationships) and metadata (data about data).

  • To enable efficient storage, retrieval, and integration of diverse datasets.

III. SPATIAL DATA MODELS AND STRUCTURES

A. Fundamental Data Models

• Vector Data Model: Represents features as discrete points, lines, and polygons.

  • Points: Wells, landmarks.

  • Lines: Roads, rivers, power lines.

  • Polygons: Land parcels, lakes, building footprints.

  • Topology: Explicitly stores connectivity (e.g., which lines form a polygon boundary). Advantage: Compact, high accuracy, excellent for discrete features. Limitation: Complex for continuous surfaces (e.g., elevation).

• Raster Data Model: Represents space as a grid of cells (pixels). Each cell has a value representing an attribute (e.g., land cover class, elevation).

  • Advantage: Simple structure, ideal for continuous phenomena (e.g., satellite imagery, elevation models), easy spatial analysis (map algebra).

  • Limitation: Large data volume, blocky appearance at low resolution, less precise for boundaries.

• Comparison Table:

  | Aspect | Vector | Raster | | :--- | :--- | :--- | | Data Structure | Points, Lines, Polygons | Grid of Pixels/Cells | | Best For | Discrete features, boundaries | Continuous surfaces, imagery | | Accuracy | High (precise coordinates) | Depends on cell size | | Storage | Compact for complex features | Large for high-res. | | Analysis | Network, overlay, proximity | Map algebra, filtering | | Output | Smooth, scalable graphics | Pixelated, fixed scale |

B. Data Operations

• Data Conversion:

  • Vector to Raster (Rasterization): Polygon/line features are converted to a grid based on a cell size. Used for integrating vector maps with satellite imagery.

  • Raster to Vector (Vectorization): Raster cells with same value are grouped to form polygons/lines. Used for extracting boundaries from classified images.

• Procedure for Inputting a Map and Creating Shapefiles:

  1. Georeferencing: If starting from a paper map/print, scan it and assign real-world coordinates using known control points (GCPs).

  2. On-Screen Digitizing: Load georeferenced image in GIS. Use editing tools to trace features (points, lines, polygons) directly on screen.

  3. Attribute Assignment: After digitizing a feature (e.g., a road polygon), open its attribute table and enter descriptive data (e.g., road name, type, width).

  4. Save as Shapefile: The digitized features with their attributes are saved as a shapefile (.shp, .shx, .dbf, .prj). The .shp stores geometry, .dbf stores attributes.

IV. IMAGE PROCESSING AND INTERPRETATION

A. Analysis Approaches

• Visual Image Analysis: Human interpreter views image (hardcopy or on screen) using elements of interpretation (see below). Subjective, relies on experience.

• Digital Image Analysis: Computer processes pixel values using algorithms. Objective, repeatable, suitable for large areas. Includes preprocessing and classification.

B. Visual Interpretation Techniques

• Elements of Interpretation (Use in combination):

  • Location: Where is the feature? (Relation to other features).

  • Size: Absolute or relative dimensions.

  • Shape: Geometric form (e.g., circular tank, rectangular building).

  • Tone/Color: Brightness or hue on the image.

  • Texture: Roughness/smoothness (e.g., forest vs. grassland).

  • Pattern: Spatial arrangement (e.g., orchards, urban grid).

  • Shadow: Can reveal height/shape but also hide details.

  • Association: Features that commonly occur together (e.g., airport with runways, terminals).

• Use of Tools: Stereoscope for viewing stereo-pair images (overlapping photos from different angles) to perceive 3D depth and elevation.

C. Digital Image Processing

• Image Filtering: Applying mathematical operations to enhance or suppress features.

  • Low-pass (Smoothing): Reduces noise, blurs edges (e.g., for generalizing).

  • High-pass (Edge Enhancement): Sharpens edges, highlights boundaries (e.g., for feature detection).

• Classification Methods:

  • Supervised Classification: User trains the computer by selecting "training sites" (areas of known land cover). Algorithm learns spectral signature of each class and classifies entire image. Steps: Select training samples → Compute statistics → Classify → Evaluate accuracy.

  • Unsupervised Classification: Algorithm automatically groups pixels into clusters (spectrally similar) without prior knowledge. User then interprets and assigns land cover names to clusters. Useful for unknown areas or initial exploration.

• Spectral Reflectance Curves: Graph plotting reflectance (%) vs. wavelength for a feature. Unique signature used for both visual and digital classification. The area between Red and NIR bands is crucial for vegetation.

V. GIS ANALYTICAL AND SPATIAL OPERATIONS

A. Core Spatial Analysis Functions

• Overlay Analysis: Combining two or more thematic layers to create a new layer showing spatial relationships.

  • Types: Point-in-polygon, line-in-polygon, polygon-on-polygon (union, intersection, identity, erase).

  • Application (Urban Planning): Identify suitable areas by overlaying constraints (flood zones, slope >15%, protected areas) with opportunity layers (proximity to roads, existing infrastructure).

• Buffer Analysis: Creating a zone of specified distance around a feature (point, line, polygon).

  • Concept: "Zone of influence" or "area of impact."

  • Application: Generate noise pollution zones around highways, identify buildings within 500m of a new metro station, create riparian buffers along rivers.

B. Data Integration

• Integration of Spatial Data and Attribute Data: Achieved through a common key or identifier.

  • Spatial Data (Geometry): Stored in the shapefile's .shp (e.g., polygon coordinates for a land parcel).

  • Attribute Data (Descriptive): Stored in the .dbf table (e.g., Parcel_ID, Owner, LandUse, Area).

  • The Parcel_ID in the spatial layer's attribute table links to the same ID in a separate, external database table (e.g., property tax records), enabling relational joins. This is the core of GIS—linking where to what.

VI. ERRORS, CHALLENGES, AND INTEGRATION

A. Errors in Remote Sensing

• Systematic Errors: Predictable, correctable errors inherent in the sensor or platform.

  • Examples: Sensor calibration drift, scan skew, geometric distortion from Earth's curvature, atmospheric scattering (if not modeled).

  • Correction: Pre-processing (radiometric, geometric correction).

• Non-systematic (Random) Errors: Unpredictable, cannot be fully modeled.

  • Examples: Atmospheric haze/variability at time of pass, mixed pixels (pixel contains multiple land covers), registration errors between layers.

  • Mitigation: Ground truthing, using high-resolution data, careful image selection.

B. Integration Challenges (RS data in GIS)

1. Geometric Mismatch: Different RS images or between RS and vector maps may have different projections, resolutions, or registration errors. Requires precise georeferencing and resampling.

2. Attribute Incompatibility: RS classifications (pixel-based) may not match vector-based planning zones (parcel-based). Requires zonal statistics or vectorization.

3. Scale and Resolution Issues: Coarse RS data may not show fine urban details needed for parcel-level planning.

4. Temporal Mismatch: RS image date may not align with the timeframe of other GIS layers (e.g., census data).

5. Data Volume & Processing: High-resolution satellite imagery is large and computationally intensive for GIS.

VII. APPLICATIONS IN URBAN & TOWN PLANNING

A. Land Use/Land Cover (LULC) Studies

• Using RS & GIS for LULC Change Assessment:

  1. Classify satellite images from different dates (e.g., 2000, 2010, 2020) using supervised/unsupervised methods.

  2. Perform post-classification comparison (pixel-by-pixel) or spectral change detection.

  3. Generate change matrices and transition maps showing what LULC class changed to what (e.g., agriculture → built-up).

  4. Analyze drivers (population growth, infrastructure) and plan for sustainable future growth.

B. Infrastructure and Resource Management

• Water Resources: Mapping watersheds, surface water bodies, groundwater potential zones (using DEM, slope, geology, drainage), monitoring reservoir siltation, assessing irrigation command area.

• Traffic Management: Mapping road networks (vector), analyzing traffic flow patterns (using temporal RS data or GPS), identifying congestion hotspots, planning new corridors using overlay with land use and population density.

C. Urban Monitoring

• Role of Synoptivity and Repetivity:

  • Synoptivity: Provides complete, integrated view of the entire urban area (built-up, green spaces, water bodies) in a single framework. Essential for comprehensive city planning.

  • Repetivity: Enables monitoring of urban sprawl, slum growth, construction activity, and vegetation health over time. Frequent revisit (e.g., from Sentinel-2, 5-day) allows near-real-time monitoring for enforcement and rapid response.

VIII. RENEWABLE ENERGY FOR SUSTAINABLE URBAN DEVELOPMENT

A. Macro Perspective

• Role & Potential: Decarbonization, energy security, job creation, rural electrification. Global potential is massive but underutilized.

• Prospects in India: Vast solar potential (Thar Desert), strong wind corridors (Tamil Nadu, Gujarat), large biomass base (agricultural residue). Government targets: 500 GW non-fossil capacity by 2030. Challenges: Intermittency, grid integration, initial cost, land availability.

• Strategies for Efficient Energy Management:

  1. Energy Conservation: Using less energy for same service (e.g., BEE star ratings).

  2. Energy Efficiency: Using technology that requires less energy (e.g., LED bulbs, efficient motors).

  3. Load Management: Shifting demand to off-peak hours.

  4. Integration of Renewables: Rooftop solar, solar-wind hybrids.

  5. Smart Grids & Demand Response.

B. Solar Energy Systems

• Principle of Conversion (Solar Thermal): Solar radiation (shortwave) is absorbed by a collector (flat plate, parabolic trough) and converted to heat in a working fluid (water, oil, air). This thermal energy is used for water heating, space heating, or to generate steam for a turbine (CSP).

• Photovoltaic (PV) Systems & MPPT:

  • PV cells convert photons directly to DC electricity.

  • Maximum Power Point Tracking (MPPT): Algorithm/controller that continuously adjusts the duty cycle of a DC-DC converter to operate the PV array at its Maximum Power Point (MPP) on the I-V curve, which varies with irradiance and temperature.

  • Procedure: 1. Sense PV voltage (V) and current (I). 2. Calculate power (P=V×I). 3. Use algorithm (e.g., Perturb & Observe) to slightly change load. 4. If power increases, continue in same direction; else, reverse. 5. Achieve MPP where dP/dV=0.

• Factors Causing Variation in Solar Radiation at Earth's Surface:

  1. Earth-Sun Distance (seasonal).

  2. Solar Declination (latitude, season).

  3. Atmospheric Effects: Absorption (O₃, H₂O, CO₂), scattering (Rayleigh, Mie), clouds.

  4. Surface Albedo: Reflectivity of ground.

  5. Slope & Aspect of terrain.

  6. Air Mass: Path length through atmosphere.

• Solar Cell Materials:

  • Crystalline Silicon (c-Si): Monocrystalline (high efficiency, expensive), Polycrystalline (lower cost, slightly lower efficiency). Dominant (>90% market).

  • Thin-Film: Amorphous Silicon (a-Si), Cadmium Telluride (CdTe), Copper Indium Gallium Selenide (CIGS). Flexible, lower efficiency, lower cost per W.

  • Emerging: Perovskites (high efficiency potential, stability issues), Organic PV (OPV).

C. Wind Energy

• Specifications of Windmills for Power Generation:

  • Rotor Diameter: Determines swept area (∝ D²) and thus power capacity.

  • Hub Height: Higher = stronger, less turbulent wind.

  • Rated Power: Maximum output at rated wind speed (typically 12-15 m/s).

  • Cut-in Wind Speed: Minimum speed to start generation (~3-4 m/s).

  • Cut-out Wind Speed: Speed at which turbine shuts down for safety (~25 m/s).

  • Capacity Factor: Actual output over time / Rated output. Typically 25-45%.

  • Power Coefficient (Cp): Efficiency of converting wind kinetic energy to mechanical power. Betz Limit = 16/27 ≈ 59.3% (theoretical max).

D. Biomass Energy

• Biomass Conversion Processes:

  | Process Type | Method | Products | Examples | | :--- | :--- | :--- | :--- | | Thermal | Combustion, Gasification, Pyrolysis | Heat, Producer Gas, Bio-oil, Char | Direct burning of agricultural waste for heat; gasifier for engine fuel. | | Chemical | Transesterification | Biodiesel | Jatropha, used cooking oil → Biodiesel. | | Biochemical | Anaerobic Digestion, Fermentation | Biogas (CH₄+CO₂), Bioethanol | Cattle dung → Biogas; sugarcane juice → Ethanol. |

E. Other Renewable Sources

• Wave Energy:

  • Advantages: High energy density, predictable (based on wind), abundant.

  • Limitations: Harsh marine environment, corrosion, maintenance difficulty, intermittent, visual/noise impact, technology not yet mature/commercial.

• Ocean Thermal Energy Conversion (OTEC):

  • Principle: Uses temperature difference (ΔT ~20°C) between warm surface water and cold deep water to run a heat engine.

  • Closed Cycle: Uses a low-boiling-point working fluid (e.g., ammonia). Warm surface water vaporizes fluid → drives turbine → cold deep water condenses vapor. More common, less environmental impact.

  • Open Cycle: Warm seawater itself is flash-evaporated in vacuum chamber → steam drives turbine → steam condensed by cold seawater → produces desalinated water. Produces fresh water but more complex.

• Geothermal Energy:

  • Classifications of Sources:

    1. Hydrothermal: Hot water/steam reservoirs (most exploited). Examples: Geysers, hot springs.

    2. Geo-pressurized: Hot water/brine under high pressure in deep sedimentary basins.

    3. Hot Dry Rock (HDR): Hot impermeable rock. Requires artificial fracturing (EGS).

    4. Magma: Molten rock (very high temp, extremely challenging).

• Small Head Hydro Power (SHP): Hydro plants with head < 20m (sometimes defined as <30m). Uses run-of-river or small dams. Advantages: Lower environmental/social impact than large dams, suitable for remote areas, quick to build.

F. Energy Efficiency and Management in Urban Context

• Energy Efficient Motors:

  • Affecting Factors: Motor design (e.g., premium efficiency IE3/IE4), power factor (use capacitors for correction), load factor (avoid under-loading), voltage stability, proper sizing, maintenance (bearing lubrication, alignment).

• Energy Audit:

  • Concept: A systematic procedure to obtain baseline energy consumption data, identify areas of energy wastage, and recommend savings measures with cost-benefit analysis.

  • Types:

    1. Preliminary Audit (Walk-through): Quick, low-cost, identifies obvious savings.

    2. Detailed Audit: Comprehensive, uses measurements, detailed analysis, and investment-grade recommendations.

    3. Targeted Audit: Focuses on a specific system (e.g., HVAC, lighting).

• Electronic Load Controllers (ELC): Used in isolated renewable energy systems (e.g., biomass gasifier, micro-hydro) to maintain constant load on the prime mover (engine/turbine) despite variable consumer demand.

  • Function: Diverts excess power to a dump load (heater) when generation > demand, preventing overspeed and damage. Ensures stable frequency/voltage.

G. Emerging Technologies

• Magneto-hydrodynamic (MHD) Generation: Converts thermal energy of a hot, ionized gas (plasma) directly into electricity by passing it through a magnetic field (Faraday's law). No moving parts. Potential for higher efficiency than conventional thermal plants. Challenges: Material science (high temp, corrosive plasma), seed material (for ionization).

• Thyristor (SCR - Silicon Controlled Rectifier): A 4-layer (PNPN), 3-terminal semiconductor switch.

  • Basic Role in Power Control: Acts as a controllable diode. Once triggered (gate pulse), it latches ON until current falls below holding level. Used for phase-angle control in AC circuits (e.g., light dimmers, motor speed control) and as a switch in DC/AC converters (e.g., in inverters for solar PV systems, HVDC transmission). Key for power electronics in renewable energy systems.

Final Exam Strategy: For 7-mark questions, structure answers with: 1) Clear Definition (if applicable), 2) Key Points (4-5 bullet points with brief explanation), 3) Relevant Example/Application (1-2 lines). For 14-mark questions, provide detailed explanation with diagrams where specified (e.g., spectral curve, ideal RS system, OTEC cycles). Always link concepts back to urban planning applications where possible.