Urban & Town Planning (CE-504 (A)) - Unit 4 Short Notes

UNIT 4: SPATIAL TECHNOLOGIES & SUSTAINABLE RESOURCE ASSESSMENT FOR URBAN PLANNING

4.1 FOUNDATIONS OF REMOTE SENSING (RS)

4.1.1 Core Concepts & System Components

• Definition: Remote Sensing is the science and art of obtaining information about an object, area, or phenomenon without being in direct contact with it, using sensors mounted on platforms.

• Ideal RS System Components:

  1. Energy Source (Sun or own source)

  2. Atmosphere (interaction medium)

  3. Target (object of interest)

  4. Sensor (detects & records energy)

  5. Platform (carries sensor, e.g., satellite, aircraft)

  6. Data Processing & Interpretation (output information)

  Diagram: Energy flow from source → atmosphere → target → reflected/emitted energy → sensor → platform → ground station → processed data → interpreter.

• Spectral Reflectance: The ratio of reflected radiation to incident radiation as a function of wavelength.

  • Vegetation: High reflectance in NIR, low in red (used for NDVI). Characteristic "red edge."

  • Soil: Generally low, increasing smoothly with wavelength; curve shape depends on moisture, texture, organic matter.

  • Water: Very low reflectance in NIR & SWIR; high in visible blue-green; absorbs in red & NIR. Curve is overall low.

  Exam Tip: Be prepared to sketch and label these three curves on the same graph for comparison.

• Electromagnetic Spectrum (EMS): RS uses specific portions:

  • Visible (0.4-0.7 µm): Color, basic land cover.

  • Near-Infrared (NIR, 0.7-1.3 µm): Vegetation health, biomass.

  • Shortwave Infrared (SWIR, 1.3-3 µm): Soil moisture, vegetation water content.

  • Thermal Infrared (TIR, 3-14 µm): Surface temperature, heat islands.

  • Microwave (1 mm - 1 m): All-weather, day/night; surface roughness, soil moisture.

4.1.2 Platforms & Sensors

• Satellite Orbits:

  | Feature | Geostationary Orbit (GEO) | Sun-Synchronous Orbit (SSO) | | :--- | :--- | :--- | | Altitude | ~36,000 km | ~700-800 km | | Orbital Period | 24 hrs (matches Earth's rotation) | ~90-100 mins | | Inclination | 0° (equatorial) | ~98° (polar) | | Coverage | Fixed view of ~1/3 Earth | Whole Earth sweeps | | Resolution | Low (km scale) | High (m scale) | | Key Use | Weather monitoring, communications | Earth observation, mapping |

• Indian Satellite Program (Key Missions):

  • IRS Series: Primary Earth observation (Resourcesat, Cartosat, LISS series).

  • INSAT Series: Meteorological & communication.

  • Chandrayaan-3: Lunar exploration (demonstrated safe landing, rover operations).

  • Objectives: Resource monitoring, disaster management, urban planning, agriculture, cartography.

• Earth Observation Satellites:

  • Earth Resource Satellites (e.g., Landsat, Sentinel-2): Multi-spectral, moderate resolution, focus on land/coastal zones.

  • Weather Satellites (e.g., INSAT, GOES): Broad spectral bands (visible, IR, water vapor), high temporal resolution.

4.1.3 Sensor Resolution & Characteristics

• Four Types of Resolution:

  1. Spatial: Minimum separable distance on ground (e.g., 30m for Landsat). Defines "sharpness."

  2. Spectral: Number & width of wavelength bands (e.g., multispectral vs. hyperspectral). Defines "color" detail.

  3. Temporal: Revisit time for same area. Defines "how often."

  4. Radiometric: Number of brightness levels (e.g., 8-bit = 256 levels). Defines "sensitivity to brightness."

• Synoptivity: Ability to capture a large, continuous area in a single view.

• Repetitivity: Ability to repeatedly observe the same area at regular intervals.

4.1.4 Data Acquisition & Interpretation

• Visual Image Interpretation Elements: Size, Shape, Tone/Color, Texture, Pattern, Shadow, Association/Context.

• Digital vs. Visual Analysis:

  | Visual Image Analysis | Digital Image Analysis | | :--- | :--- | | Human interpreter, analog | Computer algorithms, digital | | Uses interpretation elements | Uses spectral statistics | | Subjective, qualitative | Objective, quantitative | | Low cost, flexible | High throughput, reproducible |

• Image Pre-processing & Filtering:

  • Purpose: Correct systematic errors (radiometric, geometric), enhance features, reduce noise.

  • Common Filters:

    • Low-pass (Smoothing): Reduces noise, blurs edges.

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

    • Band-ratioing: Reduces illumination effects, highlights spectral differences.

  Exam Tip: "Systematic errors" are predictable (sensor calibration, Earth curvature). "Non-systematic" are random (atmospheric noise).

4.2 FOUNDATIONS OF GEOGRAPHIC INFORMATION SYSTEMS (GIS)

4.2.1 Definition & Components

• Definition: A computer-based system for capturing, storing, managing, analyzing, and displaying geographically referenced data.

• Key Components (6-P Framework):

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

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

  3. Data: Spatial (maps, imagery) + Attribute (tables, text).

  4. People: Users, managers, technicians.

  5. Procedures: Workflows, methods, models.

  6. Network: Connectivity for data sharing (LAN/WAN/Internet).

4.2.2 Spatial & Attribute Data Integration

• Integration Mechanism: Each spatial feature (point, line, polygon) has a unique Primary Key (ID). The attribute table stores non-spatial characteristics linked via this ID.

  • Example: A polygon representing "Park" has ID P101. Its attribute row contains Name="City Park", Area=5.2ha, Type="Recreational".

• Data Sources:

  • Primary: Field survey (GPS), digitization, photogrammetry.

  • Secondary: Existing maps, census data, satellite imagery, government databases.

• Objectives of Data Collection: To build a comprehensive, accurate, and current spatial database for specific planning tasks (e.g., infrastructure inventory, environmental monitoring).

4.2.3 Data Models & Structures

• Vector Data Model:

  • Features: Points (0D, e.g., well), Lines (1D, e.g., road), Polygons (2D, e.g., lake, plot).

  • Topology: Rules defining spatial relationships (connectivity, adjacency, containment). Ensures data integrity.

• Raster Data Model:

  • Structure: Grid of cells (pixels). Each cell has a value representing a theme (e.g., elevation, land cover).

  • Resolution: Cell size (e.g., 30m x 30m). Finer resolution = larger file size.

• Vector vs. Raster Comparison:

  | Aspect | Vector | Raster | | :--- | :--- | :--- | | Data Structure | Coordinates (x,y) | Grid of cells | | Storage | Compact (for discrete features) | Large (for fine resolution) | | Analysis | Topology-based, network, precise | Cell-based, map algebra, suitable for continuous surfaces | | Output | Scalable, high-quality maps | Pixelated when zoomed | | Best For | Boundaries, networks, cadastre | Elevation (DEM), imagery, suitability surfaces |

4.2.4 Coordinate Systems & Map Projections

• Coordinate Systems:

  • Geographic (Angular): Latitude/Longitude (degrees). Based on a spheroid/ellipsoid (e.g., WGS84). Units are degrees.

  • Projected (Planar): Converts spherical surface to flat plane using a map projection. Units are linear (meters, feet). Example: UTM (Universal Transverse Mercator). Divides Earth into 60 zones (6° wide). Preserves shape & area locally.

• Map Projection: Mathematical transformation of 3D Earth surface to 2D map. Purpose: To enable measurement (distance, area, angle) on a flat map. Importance in GIS: All spatial analysis and accurate mapping require a consistent, known projected coordinate system. Lat/Long is not suitable for distance/area calculations.

  Common Projections in Planning: UTM (most common), State Plane (USA), Lambert Conformal Conic (mid-latitude regions).

4.2.5 Data Input, Conversion & Management

• Procedure for Inputting a Map & Creating Shapefiles:

  1. Georeferencing: Assign real-world coordinates to a scanned map/image.

  2. Digitization: Manually trace features (points, lines, polygons) on screen to create vector data.

  3. Attribute Table Creation: Populate the table with relevant fields (e.g., Road_Name, Road_Type).

  4. Saving: Export as a shapefile (simple vector format, .shp, .shx, .dbf files).

• Data Conversion:

  • Raster to Vector: Vectorization (tracing edges, often automated with thresholding). Used to create boundaries from satellite imagery.

  • Vector to Raster: Rasterization (assigning cell values based on underlying vector feature). Used to create grids for spatial modeling.

• Problems of Using RS Data in GIS:

  1. Geometric Distortions: Sensor, platform, Earth rotation effects require rigorous correction.

  2. Radiometric Issues: Atmospheric scattering/absorption (haze), varying sun angle.

  3. Scale Mismatch: RS pixel size vs. GIS vector detail.

  4. Data Volume: Large imagery files require significant storage/processing.

  5. Classification Accuracy: Misclassification errors propagate into GIS analysis.

  6. Temporal Compatibility: Aligning RS data from different dates/seasons with GIS vector layers.

4.3 GIS SPATIAL ANALYSIS TECHNIQUES

4.3.1 Basic Query & Measurement

• Attribute Query: Select features based on non-spatial criteria (e.g., SELECT * FROM Parcels WHERE Land_Use = 'Residential').

• Spatial Query: Select features based on spatial relationships (e.g., "Find all wells within 500m of the river").

• Measurement: Calculate length, perimeter, area, distance between features.

4.3.2 Overlay Analysis

• Concept: Integrating two or more thematic layers to create a new layer with combined properties.

• Types:

  • Intersection: Output retains only the area where all input layers overlap. (e.g., Slope < 15% AND Soil Type = Loam AND Ownership = Public).

  • Union: Output retains all areas from all input layers. (Combines everything).

  • Identity: Similar to intersection, but retains all attributes from the "identity" layer and intersecting features from the "input" layer.

• Application in Suitability Analysis: Overlay multiple criteria layers (slope, soil, land cover, access) to identify optimal sites for development.

4.3.3 Proximity Analysis

• Buffer Analysis: Creating zones of specified distance around a feature.

  • Procedure: Specify buffer distance(s), generate polygon(s) around point/line/polygon features.

  • Applications: Noise buffers around highways, protection zones around water bodies, service areas for facilities.

4.3.4 Network & Terrain Analysis

• Network Analysis: Modeling connectivity (roads, pipes, power lines). Used for:

  • Service Area: Area reachable within a time/distance.

  • Routing: Finding shortest/fastest path.

  • Location-Allocation: Optimal placement of facilities.

• Terrain Analysis (from DEM - Digital Elevation Model):

  • Slope: % or degree of incline. Slope = arctan(Δz / Δx)

  • Aspect: Compass direction of slope face.

  • Viewshed: Visible areas from an observer point.

  • Watershed/Drainage: Flow direction, accumulation, delineation.

4.4 INTEGRATED APPLICATIONS OF RS & GIS IN URBAN & REGIONAL PLANNING

4.4.1 Land Use/Land Cover (LULC) Change Assessment

• Procedure:

  1. Acquire multi-temporal satellite images (e.g., 2000, 2010, 2020).

  2. Pre-process: Atmospheric correction, geometric registration (coregistration).

  3. Classify: Use supervised/unsupervised classification to create LULC maps for each date.

  4. Post-classification Comparison: Overlay classified maps in GIS. Use cross-tabulation to quantify changes (e.g., Agricultural -> Urban).

  5. Analyze: Calculate change matrices, transition probabilities, visualize sprawl.

• Change Detection Techniques: Post-classification comparison (most common), image differencing (NDVI), vegetation index trajectories.

4.4.2 Water Resources Management

• Watershed Delineation: Using DEM in GIS to define drainage boundaries.

• Surface Water Mapping: Identify water bodies from multispectral imagery (using NIR absorption).

• Groundwater Potential Zoning: Overlay factors (lithology, lineaments/drainage from RS, slope, soil, land use) in GIS.

• Flood Risk Assessment: Combine flood hazard maps (from hydraulic modeling/RS inundation mapping) with exposure data (settlements, infrastructure) in GIS.

• Drought Monitoring: Use vegetation indices (NDVI, VCI) from time-series RS data to assess vegetation stress.

4.4.3 Other Urban & Infrastructure Applications

• Urban Growth Modeling: Use cellular automata or SLEUTH models with RS-derived urban extent as base.

• Infrastructure Planning: Route optimization for roads/utilities (GIS network analysis), asset management.

• Traffic Management: RS (high-res imagery, UAV) for traffic flow monitoring, parking inventory, accident analysis.

• Environmental Monitoring:

  • Urban Heat Island: Map surface temperature from TIR bands.

  • Air Quality: Correlate satellite aerosol optical depth (AOD) with ground monitors.

  • Water Quality: Assess turbidity, chlorophyll in coastal/estuarine waters.

4.5 RENEWABLE ENERGY RESOURCES: ASSESSMENT & PLANNING PERSPECTIVE

4.5.1 Global & Indian Context

• India's Potential & Role: High potential in solar (~500 GW/km²), wind (~300 GW at 80m hub height), biomass (agricultural residues).

• Government Targets: 500 GW non-fossil capacity by 2030 (NDC target).

• Strategies for Efficient Management:

  • Energy Audit: Systematic study to identify energy use, waste, and conservation opportunities.

  • Conservation: Efficient appliances, building codes (ECBC), industrial process optimization.

  • Grid Integration: Smart grids, forecasting, storage solutions.

4.5.2 Solar Energy

• Solar Thermal: Conversion of sunlight to heat (e.g., solar water heaters, parabolic troughs for steam).

• Solar Photovoltaic (PV) Systems:

  • Solar Cell Materials: Crystalline Silicon (mono, poly), Thin Films (CdTe, CIGS, a-Si), Perovskites (emerging).

  • Maximum Power Point Tracking (MPPT): Algorithm to operate PV system at its Maximum Power Point (MPP) which varies with irradiation & temperature.

    • Procedure: DC-DC converter (e.g., buck/boost) adjusts duty cycle. Controller (Perturb & Observe, Incremental Conductance) continuously senses PV voltage/current and perturbs load to find point where dP/dV ≈ 0.

    Key Formula: MPP condition: dP/dV = I + V*(dI/dV) = 0 → dI/dV = -I/V.

• Factors Affecting Solar Radiation at Surface:

  1. Atmospheric Effects: Scattering (Rayleigh, Mie), absorption (ozone, water vapor, CO₂).

  2. Latitude & Season: Solar declination angle.

  3. Weather: Cloud cover, humidity.

  4. Surface Slope & Aspect (Topography).

  5. Air Mass: Path length through atmosphere.

• GIS Application: Solar potential mapping using RS-derived surface albedo, slope, aspect, and global radiation models (e.g., r.sun in GRASS GIS).

4.5.3 Wind Energy

• Principle: Kinetic energy of wind → mechanical rotation (blades) → electrical energy (generator).

• Windmill Specifications:

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

  • Rated Speed: Wind speed at which generator reaches nominal power (~12-15 m/s).

  • Cut-out Speed: Maximum safe speed for shutdown (~25 m/s).

  • Capacity Factor (CF): \boxed{CF = \frac{\text{Actual Annual Energy Output}}{\text{Rated Power} \times 8760 \text{ hours}}} (Typical: 25-45%).

  • Hub Height: Higher = better wind resource (less surface friction).

4.5.4 Biomass & Bioenergy

• Conversion Processes:

  | Process | Output | Example | | :--- | :--- | :--- | | Direct Combustion | Heat, Steam, Power | Bagasse cogeneration | | Gasification | Syngas (CO, H₂) | For engines/turbines | | Anaerobic Digestion | Biogas (CH₄, CO₂) | From dung, waste | | Biofuels | Biodiesel, Bioethanol | Jatropha, sugarcane |

4.5.5 Hydropower

• Small Hydro Power (SHP): Typically < 25 MW (India). Classified by Head:

  • High Head: > 30m (Pelton wheel).

  • Medium Head: 10-30m (Francis turbine).

  • Low Head: < 10m (Kaplan, bulb turbine).

• Environmental Considerations: Fish ladders, sediment management, flow regime alteration, submergence.

4.5.6 Ocean Energy

• Tidal Energy: Harnesses kinetic energy of tides (barrage, tidal stream turbines). Predictable but site-specific.

• Wave Energy: Converts surface wave motion (oscillating water column, point absorbers, attenuators). High potential, technology challenging.

• Ocean Thermal Energy Conversion (OTEC): Exploits temperature difference (ΔT ~20°C) between warm surface & cold deep water.

  • Open Cycle: Warm seawater flash-evaporated in vacuum chamber → drives turbine → fresh water vapor condensate.

  • Closed Cycle: Working fluid (e.g., ammonia) vaporized by warm seawater → drives turbine → condensed by cold seawater.

  Advantage of Closed Cycle: No direct contact with seawater, less scaling/corrosion, can use any working fluid.

4.5.7 Geothermal Energy

• Classifications:

  1. Hydrothermal: Hot water/steam reservoirs (most common, e.g., Geysers, Puga Valley).

  2. Geopressured: Hot brine under high pressure (contains methane).

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

  4. Magma: Molten rock (very high temp, extreme tech challenge).

4.5.8 Other & Emerging Technologies

• Magneto-Hydrodynamic (MHD) Generation: Ionized hot gas (seeded with alkali metal) passed through magnetic field → direct electricity (no moving parts). High efficiency potential.

• Hydrogen Energy: "Energy carrier." Produced via electrolysis (using RE) or reforming. Used in fuel cells.

4.5.9 Energy Management Technologies

• Energy Audit:

  • Concept: Verification, monitoring, analysis of energy flows for conservation.

  • Types:

    1. Preliminary Audit: Quick walk-through, major areas identified.

    2. Detailed Audit: In-depth measurement, data logging, detailed report with calculations.

    3. Investment-Grade Audit: Comprehensive, includes financial analysis (ROI, NPV) for projects.

• Energy Efficient Motors: Factors: High efficiency design (better materials, larger core), Premium Efficiency (IE3, IE4), proper sizing, power factor correction, variable speed drives (VSDs).

• Electronic Load Controller (ELC): Used in wind/biomass systems to maintain constant power/voltage output despite fluctuating input. Uses power electronics (thyristors, IGBTs) to dump excess energy in dump loads (heaters).

• Role of Power Electronics (Thyristor):

  • Thyristor (SCR): 4-layer PNPN semiconductor. Acts as a controlled switch.

  • Applications in RE: AC/DC conversion (rectifiers), DC/AC inversion (inverters for PV/wind), speed control of motors, MPPT in PV systems, ELC circuits. Enables efficient, controllable power flow.