Samacheer Kalvi 12th Geography Geoinformatics Exercise Solutions

Geoinformatics is the science and technology of gathering, storing, processing, analysing, and visualising geographic (spatial) information. It integrates three key technologies — GIS (Geographic Information System), GPS (Global Positioning System), and Remote Sensing — to manage and interpret data about the Earth's surface.

GIS (Geographic Information System) is a computer-based system for capturing, storing, manipulating, analysing, and displaying geographic data. It links spatial data (where things are) with attribute data (what things are). Five Components of GIS: 1. Hardware — Computer, scanner, printer, GPS receiver, server. 2. Software — GIS software (ArcGIS, QGIS, GRASS) for data processing and analysis. 3. Data — Spatial data (maps, satellite images) and attribute data (tables, statistics). 4. People — GIS professionals, analysts, and users who operate the system. 5. Methods/Procedures — Standardised workflows and protocols for data management and analysis.

| Feature | Vector Data | Raster Data | |---|---|---| | Representation | Points, lines, polygons | Grid of cells (pixels) | | Precision | High precision, smooth boundaries | Pixelated, lower precision | | Storage | Compact, efficient | Large file sizes | | Best for | Discrete features (roads, boundaries) | Continuous data (temperature, elevation) | | Analysis | Topology, network analysis | Overlay, surface analysis | | Example | Road network, district boundaries | DEM, satellite imagery |

Remote sensing is the science of acquiring information about an object or area from a distance, typically from satellites or aircraft, without physical contact. Types based on energy source: 1. Passive remote sensing: Uses natural energy (sunlight) reflected or emitted by the Earth's surface. Examples: Optical sensors, thermal sensors. 2. Active remote sensing: Emits its own energy and measures the reflected signal. Examples: Radar (SAR), LiDAR. Types based on platform: 1. Ground-based: Sensors on towers or tripods. 2. Aerial: Sensors on aircraft or drones. 3. Satellite: Sensors on orbiting satellites (most common).

GPS (Global Positioning System) is a satellite-based navigation system that provides location (latitude, longitude, altitude) and time information anywhere on Earth. How it works: 1. A GPS constellation of 24+ satellites orbits the Earth at ~20,200 km altitude. 2. Each satellite continuously broadcasts its position and time. 3. A GPS receiver on the ground picks up signals from multiple satellites. 4. The receiver calculates the distance to each satellite using the time delay of the signal. 5. By trilateration (intersection of distances from at least 4 satellites), the receiver determines its exact position. Applications: Navigation, surveying, disaster management, military, agriculture, tracking vehicles.

1. Spatial resolution: The smallest object or area that can be detected. Expressed as the ground area represented by one pixel (e.g., 30 m for LANDSAT, 0.65 m for Cartosat-2). 2. Spectral resolution: The number and width of wavelength bands a sensor can detect. Higher spectral resolution = more bands = better discrimination of features (e.g., multispectral = 4-7 bands; hyperspectral = 200+ bands). 3. Radiometric resolution: The sensitivity of a sensor to differences in energy intensity. Measured in bits — an 8-bit sensor can detect 256 levels of intensity; a 12-bit sensor can detect 4,096 levels. 4. Temporal resolution: The frequency with which a satellite revisits the same area. For example, LANDSAT revisits every 16 days; MODIS revisits daily.

1. IRS-1A (1988) — India's first operational remote sensing satellite 2. IRS-1B (1991) — Continued IRS-1A mission 3. IRS-1C (1995) — Improved spatial resolution (5.8 m PAN) 4. IRS-1D (1997) — Similar to IRS-1C 5. Oceansat-1 (1999) — Ocean colour monitoring 6. Oceansat-2 (2009) — Ocean surface wind and colour 7. Resourcesat-1 (2003) — Resource mapping with LISS-III, AWiFS 8. Resourcesat-2 (2011) — Enhanced resource monitoring 9. Cartosat-1 (2005) — High-resolution cartographic mapping (2.5 m) 10. Cartosat-2 (2007) — Very high resolution (0.8 m) 11. Cartosat-3 (2019) — Highest resolution (0.25 m) 12. RISAT-1 (2012) — Radar imaging satellite (all-weather capability) 13. NISAR (2024) — NASA-ISRO Synthetic Aperture Radar satellite

Overlay analysis is a GIS operation that combines two or more data layers to create new spatial information. It analyses the relationship between different geographic features. Example: Overlaying a soil map, slope map, and rainfall map to identify suitable areas for a particular crop. The intersection of all favourable conditions shows the optimal zone. Types of overlay: 1. Union — Combines all features from both layers. 2. Intersect — Keeps only the overlapping area. 3. Identity — Keeps all features of one layer plus the overlapping features of the other.

Components of GIS: 1. Hardware: The physical equipment — computers, servers, scanners, digitisers, plotters, GPS receivers. Modern GIS also uses cloud computing and mobile devices. 2. Software: GIS software provides tools for data entry, management, analysis, and display. Examples: ArcGIS (ESRI), QGIS (open source), GRASS GIS, MapInfo. 3. Data: The most important and expensive component. Two types: - Spatial data: Where things are — coordinates, topology, maps (vector and raster). - Attribute data: What things are — tables, statistics linked to spatial features. Data sources include satellite imagery, GPS surveys, digitised maps, field surveys, and census data. 4. People: GIS professionals who design, implement, and operate the system — cartographers, analysts, managers, and end-users. 5. Methods/Procedures: Standardised protocols for data collection, input, analysis, and output. These ensure consistency and quality. Applications of GIS: 1. Disaster Management: Flood zoning, earthquake risk assessment, cyclone track mapping, wildfire spread prediction. GIS helps in early warning, evacuation planning, and relief operations. 2. Agriculture: Crop suitability analysis, soil mapping, precision farming, pest monitoring, yield estimation. ISRO's FASAL (Forecasting Agricultural Output Using SPACE) uses GIS and remote sensing. 3. Urban Planning: Land use mapping, infrastructure planning, transport network design, smart city planning, slum mapping, and zoning. 4. Environmental Monitoring: Deforestation tracking, wetland mapping, biodiversity assessment, pollution monitoring, and climate change impact analysis. 5. Water Resources: Watershed management, groundwater mapping, irrigation planning, flood forecasting, and drought monitoring. 6. Transportation: Route optimisation, traffic management, logistics planning, and road network analysis. 7. Military and Defence: Terrain analysis, strategic mapping, border surveillance, and target identification. 8. Health: Disease mapping (e.g., COVID-19 hotspots), health facility mapping, epidemiological analysis, and health resource allocation. 9. Forestry: Forest cover monitoring, wildlife habitat mapping, fire detection, and illegal logging detection. 10. Land Records: Digital land mapping, cadastral surveys, property taxation, and land use certification.

Principles of Remote Sensing: Remote sensing is based on the interaction of electromagnetic radiation (EMR) with the Earth's surface. The sensor detects the radiation that is reflected, emitted, or scattered by objects on the ground. 1. Energy source — Sun (passive) or satellite sensor (active) provides EMR. 2. Interaction with atmosphere — EMR passes through the atmosphere (some is absorbed or scattered). 3. Interaction with target — The Earth's surface reflects, absorbs, or transmits the radiation depending on the material. 4. Sensor detects energy — The sensor records the reflected/emitted radiation. 5. Transmission to ground station — Data is transmitted to a receiving station on Earth. 6. Processing and interpretation — Raw data is processed into images and interpreted. Types of Remote Sensing: A. By Energy Source: 1. Passive Remote Sensing: Uses natural sunlight reflected or emitted by the Earth. Examples: Optical sensors (LANDSAT, IRS LISS), thermal sensors. 2. Active Remote Sensing: Emits its own energy and measures the return. Examples: Radar (RISAT), LiDAR, SONAR. Works day and night and through clouds. B. By Platform: 1. Ground-based — Towers, tripods, handheld sensors. 2. Aerial — Aircraft, drones (UAVs), balloons. 3. Satellite — Orbiting platforms (most common for large-area coverage). C. By Spectral Range: 1. Optical — Visible and near-infrared (0.4–1.0 μm). 2. Thermal infrared — Detects heat emission (3–14 μm). 3. Microwave — Radar, all-weather capability (1 mm–1 m). Indian Remote Sensing Satellite Programme: India has one of the world's largest civilian remote sensing satellite programmes, operated by ISRO: | Satellite | Year | Key Feature | |---|---|---| | IRS-1A | 1988 | First Indian RS satellite, LISS-I (72.5 m) | | IRS-1C | 1995 | PAN (5.8 m), LISS-III (23.5 m) | | Oceansat-1 | 1999 | Ocean Colour Monitor (OCM) | | Resourcesat-1 | 2003 | LISS-III, AWiFS, LISS-IV | | Cartosat-1 | 2005 | 2.5 m stereo imaging | | Cartosat-2 | 2007 | 0.8 m resolution | | Oceansat-2 | 2009 | OCM, scatterometer | | RISAT-1 | 2012 | SAR (all-weather, day-night) | | Resourcesat-2A | 2016 | Enhanced resource monitoring | | Cartosat-3 | 2019 | 0.25 m resolution | | NISAR | 2024 | NASA-ISRO joint SAR mission | Applications of Indian RS Satellites: - Agriculture: Crop forecasting, drought assessment - Water resources: Irrigation monitoring, flood mapping - Forestry: Forest cover change detection - Oceanography: Fishery zone identification, ocean surface winds - Urban planning: City growth monitoring, slum mapping - Disaster management: Flood, cyclone, and earthquake assessment

GPS (Global Positioning System): GPS is a satellite-based radio navigation system owned by the US government and operated by the US Space Force. It provides location, navigation, and timing services worldwide. Three Segments of GPS: 1. Space Segment: A constellation of 24+ operational satellites in 6 orbital planes at ~20,200 km altitude. Each satellite orbits the Earth every 12 hours. At least 4 satellites are visible from any point on Earth at any time. 2. Control Segment: Ground-based tracking stations that monitor satellite health, orbits, and clocks. Includes a master control station, monitoring stations, and ground antennas. Primary station at Schriever Air Force Base, Colorado. 3. User Segment: GPS receivers that pick up satellite signals and calculate position. Ranges from handheld devices to sophisticated military receivers. Working Principle — Trilateration: 1. Each satellite broadcasts its position and exact time. 2. The GPS receiver calculates its distance from each satellite: Distance = Speed of light x Time delay. 3. With 3 satellites, the receiver can determine a 2D position (latitude, longitude). 4. With 4+ satellites, the receiver determines 3D position (latitude, longitude, altitude) plus corrects the receiver's clock error. 5. The intersection of these distance spheres gives the receiver's position. Accuracy: - Standard GPS: ~10–15 m accuracy. - Differential GPS (DGPS): ~1–3 m accuracy (uses a base station with known position). - RTK GPS: ~1–2 cm accuracy (real-time kinematic, for surveying). Applications of GPS: 1. Navigation: Car, ship, and aircraft navigation; route planning. 2. Surveying: Land surveying, boundary marking, construction. 3. Agriculture: Precision farming, tractor guidance, variable rate application. 4. Disaster Management: Tracking cyclones, flood monitoring, coordinating relief. 5. Military: Target identification, troop movement, missile guidance. 6. Transportation: Fleet tracking, fleet management, logistics optimisation. 7. Science: Tectonic plate movement, atmospheric studies, wildlife tracking. 8. Everyday: Mobile phone location services, fitness trackers, ride-sharing apps. Other Global Navigation Satellite Systems (GNSS): - GLONASS (Russia) - Galileo (European Union) - BeiDou (China) - NavIC/IRNSS (India — regional system covering India and surrounding region)

Geoinformatics (GIS + GPS + Remote Sensing) plays a crucial role in all phases of disaster management: mitigation, preparedness, response, and recovery. 1. Disaster Mitigation (Risk Reduction): - Flood hazard zoning: GIS overlay of rainfall, terrain, river networks, and land use to identify flood-prone areas. Example: Flood zoning maps for the Cauvery delta in Tamil Nadu. - Cyclone vulnerability mapping: ISRO satellite data used to identify coastal areas vulnerable to cyclones. Example: Vulnerability mapping of Tamil Nadu's coastline for cyclone risk. - Earthquake microzonation: GIS mapping of seismic zones, soil types, and building vulnerability. Example: Seismic microzonation of Chennai and Coimbatore. 2. Disaster Preparedness (Early Warning): - Cyclone tracking: INSAT and Oceansat satellites track cyclone paths. Example: Accurate tracking of Cyclone Nivar (2020) and Cyclone Michaung (2023) enabled timely evacuation in Tamil Nadu. - Flood forecasting: Satellite-derived rainfall data combined with GIS-based hydrological models. Example: Central Water Commission uses satellite data for flood forecasting in the Cauvery basin. - Drought monitoring: NDVI (Normalised Difference Vegetation Index) from satellite data detects vegetation stress. Example: Tamil Nadu's drought-prone districts monitored using IRS data. 3. Disaster Response (Emergency Management): - Rapid damage assessment: High-resolution satellite imagery (Cartosat) captured before and after a disaster reveals the extent of damage. Example: Assessment of flood damage in Chennai (2015) using Cartosat imagery. - GPS-based rescue coordination: GPS enables real-time tracking of rescue teams and resource deployment. - Communication: INSAT satellites provide communication links when ground networks fail. 4. Disaster Recovery (Rehabilitation): - Reconstruction planning: GIS helps identify safe sites for rehabilitation. Example: Identifying elevated ground for rebuilding after the 2004 tsunami in Tamil Nadu's coastal villages. - Monitoring recovery: Time-series satellite data tracks the pace of reconstruction. - Long-term planning: Updated hazard maps and GIS databases inform future urban planning to reduce vulnerability. Indian Institutional Framework: - ISRO: Provides satellite data and analysis through NRSC (National Remote Sensing Centre). - NDMA: National Disaster Management Authority uses GIS for policy and planning. - TN SDMA: Tamil Nadu State Disaster Management Authority uses geoinformatics for cyclone, flood, and drought management.

Digital Image Processing (DIP) in remote sensing involves using computer algorithms to enhance, interpret, and extract information from satellite imagery. Key Steps in Digital Image Processing: 1. Image Pre-processing: - Geometric correction: Removing distortions caused by sensor angle, Earth rotation, and topography. Transforms the image to a standard map projection. - Radiometric correction: Removing atmospheric effects and sensor errors to obtain true surface reflectance values. - Image registration: Aligning images from different dates or sensors to the same coordinate system. 2. Image Enhancement: - Contrast stretching: Improves the visual quality of an image by expanding the range of brightness values. - Spatial filtering: Enhances or suppresses specific features — low-pass filters smooth the image; high-pass filters enhance edges. - Colour compositing: Combining different spectral bands as red, green, and blue to create false colour or true colour images. Example: Standard False Colour Composite (SFCC) uses NIR-Red-Green bands, making vegetation appear red. 3. Image Classification: - Supervised classification: The user defines training sites (known land cover types), and the computer classifies all pixels based on spectral similarity to these sites. Methods: Maximum Likelihood, Minimum Distance, Support Vector Machine. - Unsupervised classification: The computer groups pixels into clusters based on spectral similarity without user-defined training sites. Methods: K-means, ISODATA. 4. Vegetation Indices: - NDVI (Normalised Difference Vegetation Index): NDVI = (NIR - Red) / (NIR + Red). Values range from -1 to +1. Higher values indicate healthier vegetation. Used for crop monitoring, deforestation detection, and drought assessment. 5. Change Detection: - Comparing satellite images of the same area from different dates to identify changes in land use, forest cover, urban sprawl, etc. Example: Monitoring urban expansion of Chennai from 2000 to 2025 using multi-temporal Landsat data. 6. Accuracy Assessment: - Comparing classified results with ground truth data to calculate accuracy. Expressed as an error matrix (confusion matrix) showing producer's accuracy, user's accuracy, and overall accuracy. Software Used: ERDAS Imagine, ENVI, QGIS, GRASS GIS, Google Earth Engine.