Entrepreneurship Development & Management (CE-504 (D)) - Unit 5 Short Notes

UNIT 5: TECHNOLOGY APPLICATIONS FOR ENTREPRENEURSHIP (RS, GIS & RENEWABLE ENERGY)

A. REMOTE SENSING FUNDAMENTALS

Principles & Systems

• Spectral Reflectance: The ratio of energy reflected by a surface to the energy incident upon it, varying across wavelengths. Different Earth surface features (soil, vegetation, water) have distinct spectral reflectance curves.

  • Soil: Curve is relatively smooth and featureless; reflectance increases with wavelength. Influenced by moisture, organic matter, texture.

  • Vegetation: Characteristic "cloverleaf" pattern. Low reflectance in blue & red (chlorophyll absorption), high in green (reflectance peak), very high in Near-Infrared (NIR) due to cell structure.

    DiagramSEARCH: "spectral reflectance curves soil vegetation water"

  • Water: Low, flat reflectance across visible spectrum; absorbs strongly in NIR and SWIR. Suspended sediments/moisture increase reflectance.

• Ideal Remote Sensing System: A system that accurately measures and records the electromagnetic energy from a target without altering it. Key components:

  1. Energy Source (Sun or active sensor).

  2. Atmosphere-Object Interaction.

  3. Sensor (detects & records energy).

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

  5. Data Reception, Processing & Interpretation.

  [!TIP] Exam often asks for a block diagram. Draw: Source → Atmosphere → Target → Atmosphere → Sensor → Platform → Ground Station → User.

• EM Spectrum & Interaction: Remote sensing uses specific wavelengths (visible, IR, microwave). Interaction with atmosphere causes scattering/absorption (e.g., ozone absorbs UV). Interaction with Earth's surface involves reflection, absorption, transmission.

Platforms & Sensors

• Satellite Orbits:

  • Geostationary (GEO): ~36,000 km altitude, 0° inclination, orbital period = 24 hrs. Stays fixed over one longitude. Continuous view of ~1/3 Earth. Used for weather monitoring (meteorological satellites). Low spatial, high temporal resolution.

  • Sun-synchronous (Polar): ~700-800 km altitude, near-polar (~98° inclination). Orbital plane precesses to maintain constant sun angle on ground. Passes over same location at same local solar time. High spatial, fixed temporal resolution (repetitivity). Used for Earth resource mapping (e.g., Landsat).

  [!TIP] Key differentiators: Synoptivity (wide-area coverage) is high for GEO; Repetitivity (regular revisit) is fixed for Sun-synchronous.

• Types of Satellites:

  • Earth Resource Satellites (ERS): Sun-synchronous, multi-spectral sensors for land/ocean resource assessment (e.g., IRS, Landsat, Sentinel).

  • Weather Satellites: Geostationary (GOES) or polar-orbiting (NOAA), with sensors for cloud cover, temperature, humidity.

• Indian Satellite Program & Chandrayaan-3:

  • Objectives: Demonstrate safe & soft landing on lunar surface; rover operations; in-situ scientific experiments.

  • Salient Features: Lander (Vikram) & Rover (Pragyan); propulsion module; landing site near south pole (~69.37°S); instruments for lunar surface plasma, thermal, seismicity, and mineral composition studies.

Image Characteristics & Interpretation

• Sensor Resolution:

  1. Spatial: Minimum separable distance on ground (pixel size). E.g., 30m (Landsat), <1m (commercial).

  2. Spectral: Number & width of wavelength bands (multispectral, hyperspectral).

  3. Temporal: Revisit time for same area.

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

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

• Digital vs. Visual Analysis:

  • Digital: Computer-based, quantitative, uses spectral statistics, classification algorithms. Faster for large areas.

  • Visual: Human interpreter, qualitative, uses knowledge & experience, better for complex/heterogeneous areas.

• Stereoscope & Stereoscopic Interpretation: Uses overlapping stereo-pair images to perceive 3D depth. Allows measurement of heights (e.g., forest canopy, buildings) and interpretation of terrain.

B. GIS FUNDAMENTALS & DATA MANAGEMENT

Core Concepts & Definition

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

• Key Components (5 P's):

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

  2. Software: ArcGIS, QGIS, GRASS.

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

  4. People: Skilled users, managers.

  5. Procedures: Workflows, standards.

• Objectives of GIS Data Collection: To create an accurate, current, and comprehensive spatial database for specific applications (planning, management, analysis).

Data Models & Structures

• Vector Data Model: Represents features as discrete objects.

  • Points: Zero-dimension (wells, trees).

  • Lines: 1D (roads, rivers).

  • Polygons: 2D (lakes, parcels).

  • Attribute data linked via unique ID.

• Raster Data Model: Represents space as a grid of cells (pixels). Each cell has a value representing a feature (e.g., elevation, land cover). Continuous data (e.g., DEMs, imagery).

• Vector vs. Raster Comparison:

• Attribute Data Integration: Spatial data (geometry) is linked to attribute data (descriptive tables) via a primary key (e.g., FID). This linkage is fundamental to GIS queries and analysis.

Coordinate Systems & Map Projections

• Geographic Coordinate System (GCS): Uses latitude/longitude on a spheroid/ellipsoid (e.g., WGS84). Units are degrees. Distorts distance/area/shape away from equator.

• Projected Coordinate System (PCS): Projects GCS onto a 2D plane using a map projection. Uses linear units (meters, feet). Example: UTM (Universal Transverse Mercator). Divides Earth into 60 zones; minimizes distortion within each zone.

• Map Projections:

  • Why Important? To flatten 3D Earth to 2D map; essential for accurate measurement (distance, area, angle) on a map.

  • Types by Property:

    • Conformal: Preserves shape (angles). Distorts area. e.g., Mercator, UTM.

    • Equal Area: Preserves area. Distorts shape. e.g., Albers, Lambert.

    • Equidistant: Preserves distance from one or two points.

  [!TIP] No projection preserves all properties—distortion is inevitable. Choose based on analysis need.

Data Input, Conversion & Quality

• Inputting Maps & Creating Shapefiles:

  1. Digitizing: Manual (tablet) or on-screen (heads-up) tracing of features from scanned maps/aerial photos.

  2. Georeferencing: Assigning real-world coordinates to a raster image using Ground Control Points (GCPs).

  3. Creating Shapefile: In GIS software, choose geometry type (point/line/polygon), define attribute table fields, then digitize or import.

• Data Conversion:

  • Vector to Raster: Polygon/line to grid cells (e.g., for spatial modeling).

  • Raster to Vector: Cell clusters to polygons/lines (e.g., contour lines from DEM).

• Sources of GIS Data:

  • Primary: Collected via GPS, ground survey, digitization.

  • Secondary: From existing maps, satellite imagery, government agencies (Survey of India, NRSC), open data portals.

• Errors in Data:

  • Systematic: Biased, consistent errors (e.g., incorrect projection, instrument calibration). Can be modeled and removed.

  • Non-Systematic (Random): Unpredictable errors (e.g., human digitzing error, positional noise). Reduced by better procedures/statistics.

C. SPATIAL ANALYSIS TECHNIQUES

Image Processing (Pre-classification)

• Image Filtering: Applying a kernel (matrix) to an image to enhance or suppress features.

  • Smoothing (Low-pass): Reduces noise (e.g., mean filter). Blurs edges.

  • Edge Enhancement (High-pass): Sharpens edges, highlights boundaries (e.g., Laplacian, Sobel).

Classification Techniques

• Supervised Classification:

  1. Training: User selects representative training sites for each class (e.g., "forest", "urban").

  2. Algorithm: Computes spectral signature (mean, variance) for each training class.

  3. Classification: Assigns each pixel to the class with highest statistical probability (e.g., Maximum Likelihood).

  • Requires user knowledge; accurate if training sites are good.

• Unsupervised Classification:

  1. Clustering: Algorithm (e.g., ISODATA, K-means) groups pixels into clusters based on spectral similarity without prior knowledge.

  2. Interpretation: User then interprets and labels the clusters into meaningful land cover classes.

  • Good for exploratory analysis; may produce spectrally pure but geographically meaningless classes.

GIS Analytical Operations

• Overlay Analysis: Combines spatial layers to identify relationships.

  • Union: Outputs all features from both layers.

  • Intersection: Outputs only areas where both layers overlap.

  • Identity: Similar to intersection but retains all attributes from one layer.

• Buffer Analysis: Creates a zone of specified distance around a feature (point, line, polygon). Applications: Impact zone near a road (noise/pollution), service area for a facility, proximity analysis.

• Query & Measurement: Attribute Query (SQL: SELECT * FROM roads WHERE type='Highway'). Spatial Query (select features based on location: SELECT wells WITHIN 500m OF river). Measurement: Calculate length, perimeter, area.

D. INTEGRATION OF RS & GIS & APPLICATIONS

Integration & Workflow

• Role of RS as Data Source: RS provides primary, up-to-date, synoptic spatial data (imagery, DEMs) which serves as the spatial backbone for GIS. GIS provides the environment to manage, analyze, and integrate RS data with other thematic layers (soil, administrative boundaries).

• Problems/Challenges:

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

  • Data Volume: Large imagery datasets require high storage/processing.

  • Geometric Accuracy: Need precise georeferencing for GIS overlay.

  • Thematic Accuracy: Classification errors propagate into GIS analysis.

Key Application Domains

• Land Use/Land Cover (LULC) Change Assessment:

  1. Pre-process: Georeference, atmospheric correct multi-temporal satellite images.

  2. Classify: Use supervised/unsupervised classification to generate LULC maps for different years.

  3. Post-classification Comparison: Compare classified maps to detect changes.

  4. Change Detection Matrix: Cross-tabulation table showing transitions (e.g., Forest → Agriculture).

     DiagramCANVAS: "Flowchart showing RS image preprocessing → classification → GIS overlay → change matrix"

• Water Resources Management:

  • Watershed Delineation: Using DEMs in GIS to define drainage patterns & watershed boundaries.

  • Surface Water Mapping: Classify satellite imagery to identify/extent of water bodies.

  • Drought Assessment: Use vegetation indices (NDVI) from RS to monitor vegetation stress.

  • Flood Inundation Mapping: Compare pre/post-flood imagery to map affected areas.

  • Groundwater Potential Zone Identification: GIS overlay of factors (lithology, slope, drainage, lineaments) derived from RS & other sources.

• Other Applications (Brief):

  • Traffic & Urban Planning: RS for urban sprawl mapping; GIS for network analysis, facility location.

  • Agriculture: Crop inventory, health monitoring (NDVI), drought impact.

  • Forestry: Forest cover mapping, degradation assessment.

  • Disaster Management: Rapid damage assessment (earthquake, cyclone) using RS; GIS for resource deployment & evacuation planning.

E. RENEWABLE ENERGY TECHNOLOGIES (FOR SUSTAINABLE ENTERPRISES)

Solar Energy

• Solar Thermal Conversion: Collectors (flat plate, concentrating) absorb solar radiation to heat a fluid (water/air/oil). Systems: Active (pumps), Passive (natural circulation). Used for water heating, space heating, power generation (CSP).

• Photovoltaic (PV) Systems:

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

  • Maximum Power Point Tracking (MPPT): Algorithm/controller that continuously adjusts the electrical operating point of the PV array to extract maximum power under varying irradiance & temperature. Typically uses DC-DC converter with feedback control.

• Factors Affecting Solar Radiation at Surface:

  1. Atmospheric Attenuation: Absorption (O₃, H₂O, CO₂), scattering (Rayleigh, Mie).

  2. Earth-Sun Geometry: Latitude, season (solar declination), time of day (solar angle), slope & aspect of ground.

Wind Energy

• Wind Energy Conversion System (WECS) Components:

  • Rotor (blades), Nacelle (gearbox, generator), Tower, Yaw system, Controller, Transformer.

• Specifications of Windmills:

  • Rotor Diameter: Determines swept area & thus power capacity.

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

  • Capacity: Rated power output (kW/MW) at standard wind speed (e.g., 12 m/s).

• Electronic Load Controller (ELC): Regulates generator output to match load/demand. Prevents overspeeding by diverting excess power to a dummy load (heater) when battery is full/load is low. Uses power electronics (thyristors).

Biomass Energy

• Biomass Conversion Processes:

  • Thermal:

    • Combustion: Direct burning for heat/power.

    • Gasification: Partial oxidation at high T → producer gas (CO, H₂, CH₄).

    • Pyrolysis: Thermal decomposition in absence of air → bio-oil, char, gas.

  • Biochemical:

    • Anaerobic Digestion: Microbial breakdown → biogas (CH₄, CO₂).

    • Fermentation: Sugars → ethanol (biofuel).

Ocean Energy

• Wave Energy Conversion:

  • Principle: Convert kinetic/potential energy of waves to electricity (oscillating water column, point absorber, attenuator).

  • Advantages: High energy density, predictable.

  • Limitations: Harsh marine environment, corrosion, maintenance, grid connection, environmental impact.

• Ocean Thermal Energy Conversion (OTEC):

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

  • Open Cycle: Warm seawater → flash evaporator → low-pressure steam → turbine → condenser (cold water). Produces freshwater.

  • Closed Cycle: Working fluid (e.g., ammonia) evaporates in warm water heat exchanger → turbine → condenses in cold water heat exchanger.

  • Closed Cycle Advantages: No saltwater in turbine, no freshwater production needed, higher efficiency.

Geothermal Energy

• Classification of Geothermal Sources:

  1. Hydrothermal: Hot water/steam in permeable rock (most common: dry steam, flash steam, binary).

  2. Geopressurized: Hot water 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 T, extreme depth; not yet commercially viable).

Small Hydro Power

• Small Head Hydro Development: Typically < 25 m head, capacity < 10 MW (definition varies).

  • Concept: Use natural flow & head of small streams/rivers.

  • Components: Weir/Intake → Penstock → Turbine (Kaplan, Francis, Turgo) → Generator → Tailrace.

  • Potential: High in hilly regions (Himalayas, Western Ghats). Low environmental impact, run-of-river schemes common.

F. ENERGY MANAGEMENT & EFFICIENCY

Energy Audit

• Concept: A systematic procedure to obtain knowledge of energy consumption profile, identify areas of energy wastage, and recommend improvement measures.

• Types of Energy Audit:

  1. Preliminary Audit (Walk-through): Quick, low-cost; identifies obvious areas for saving.

  2. Detailed Audit: In-depth measurement, data logging, economic analysis of all opportunities.

  3. Investment-Grade Audit: Highly detailed, includes engineering design, life-cycle costing, for major investment decisions.

Energy Efficiency

• Factors Affecting Energy Efficient Motors:

  • Design: High-grade core materials (silicon steel), optimized winding, reduced friction (bearings).

  • Size & Loading: Motors operate most efficiently at 75-100% rated load.

  • Power Quality: Voltage imbalance, harmonics increase losses.

  • Maintenance: Cleanliness, bearing lubrication.

• Strategies for Efficient Energy Management:

  • Industrial: Process optimization, waste heat recovery, efficient motors/drives, compressed air leak management.

  • Commercial: HVAC optimization, lighting retrofits (LED), building envelope sealing.

  • Domestic: Appliance star-rating, behavioral changes, solar water heaters.

Role & Potential of Renewable Energy

• Global & Indian Context:

  • Global: Rapid growth (solar, wind leading). Driven by climate goals, cost reduction.

  • India: Ambitious targets (500 GW non-fossil by 2030). High solar potential (Thar Desert), strong wind zones (coastal, Tamil Nadu). Large programs (National Solar Mission). Current status: ~40% installed capacity from renewables (as of 2023).

• Advantages over Conventional:

  • Environmental: Low/zero GHG & air pollutant emissions.

  • Energy Security: Reduces import dependence.

  • Sustainability: Inexhaustible sources.

  • Decentralization: Can be deployed off-grid, rural electrification.

  • Economic: Creating new jobs & industries; long-term price stability.