Remote Sensing & GIS
Remote Sensing & GIS
Remote Sensing (RS) is the science of obtaining information about objects or areas from a distance, typically from satellites or aircraft. Geographic Information System (GIS) is a computer-based system for capturing, storing, analyzing, and displaying geographically referenced data. Together, RS and GIS are powerful tools for monitoring the environment, managing resources, urban planning, disaster management, and agricultural assessment. India's space programme, led by ISRO, is a global leader in remote sensing applications.
Key Dates
Gaspard-Felix Tournachon took the first known aerial photograph from a balloon over Paris — precursor to modern remote sensing
TIROS-1 (USA) — first weather satellite launched; ushered in the era of satellite-based Earth observation
ISRO (Indian Space Research Organisation) established by Vikram Sarabhai — headquartered in Bengaluru
Landsat-1 (USA) launched — the first satellite specifically designed for land observation and remote sensing
Aryabhata — India's first satellite (built by ISRO, launched by Soviet Union); marked India's entry into the space age
Bhaskara-I — India's first experimental remote sensing satellite; carried TV and microwave sensors for ocean and land observation
IRS-1A launched — India's first indigenous remote sensing satellite; India became the 5th country with RS capability
GPS (US) declared fully operational with 24 satellites — transformed global positioning, surveying, and navigation
Resourcesat-1 launched — India's advanced RS satellite with LISS-III, LISS-IV, and AWiFS sensors for agriculture and forestry
Cartosat-1 launched — India's first stereoscopic satellite; 2.5 m resolution for cartographic applications
Chandrayaan-1 carried remote sensing instruments to the Moon — discovered water molecules on the lunar surface
Bhuvan geoportal launched by ISRO — India's own alternative to Google Earth using Indian satellite data
RISAT-1 launched — India's first indigenous SAR satellite; all-weather, day-night imaging capability
NavIC (IRNSS) constellation of 7 satellites deployed — India's sovereign satellite navigation system
Geospatial data guidelines liberalized — private companies and individuals allowed to create and disseminate maps without prior approval
Remote Sensing — Principles and Types
Remote sensing is based on the principle that every object reflects, absorbs, and emits electromagnetic radiation (EMR) in a characteristic way — its spectral signature. By measuring the EMR reflected or emitted by objects from a distance (using sensors on satellites, aircraft, or drones), we can identify and analyze those objects without physical contact. Key principles: (1) Electromagnetic Spectrum — RS uses different portions of the EM spectrum: visible light (0.4-0.7 µm) for natural color images; near-infrared (NIR, 0.7-1.3 µm) for vegetation monitoring (healthy vegetation strongly reflects NIR); shortwave infrared (SWIR, 1.3-3 µm) for moisture content and mineral identification; thermal infrared (TIR, 3-14 µm) for surface temperature measurement; microwave (1 mm-1 m) for all-weather, day-and-night sensing (penetrates clouds). (2) Types of Remote Sensing: Passive RS — the sensor detects naturally reflected sunlight or emitted thermal radiation; examples: optical cameras, multispectral/hyperspectral scanners, thermal sensors; limited by daylight and cloud cover; most RS satellites are passive. Active RS — the sensor emits its own energy and measures the reflected signal; examples: RADAR (Radio Detection and Ranging) — uses microwave pulses; can operate through clouds, haze, and at night; SAR (Synthetic Aperture Radar) provides high-resolution all-weather imagery; LiDAR (Light Detection and Ranging) — uses laser pulses to measure precise distances; used for terrain mapping, forest canopy measurement, and urban 3D modeling. (3) Spatial Resolution — the size of the smallest feature that can be detected; high-resolution: <1 m (Cartosat-2: 0.65 m); medium: 1-30 m (IRS LISS-III: 23.5 m, Landsat: 30 m); low: >100 m (MODIS: 250-1000 m, used for large-area monitoring). Higher resolution means greater detail but smaller area coverage. (4) Temporal Resolution — how frequently a satellite revisits the same location; daily (MODIS), 5 days (Sentinel-2), 16 days (Landsat), 22-24 days (IRS).
India's Remote Sensing Programme — ISRO Satellites
India's remote sensing programme, managed by ISRO (Indian Space Research Organisation, established 1969), is one of the most comprehensive in the world. India's fleet of Earth observation satellites: (1) IRS (Indian Remote Sensing) Series — India's backbone RS satellite constellation; IRS-1A (1988) was the first; subsequent satellites include IRS-1B, 1C, 1D, P6 (Resourcesat), P5 (Cartosat); used for agriculture, water resources, forestry, geology, environment, and disaster management. (2) Resourcesat Series — Resourcesat-1 (2003), Resourcesat-2 (2011), Resourcesat-2A (2016); carry LISS-III (23.5 m resolution, 4 bands) and LISS-IV (5.8 m, 3 bands) sensors; AWiFS sensor (56 m) for wide-area crop monitoring; primary satellites for agricultural crop monitoring, forest mapping, and water resource management. (3) Cartosat Series — high-resolution cartographic satellites; Cartosat-1 (2005, 2.5 m stereo), Cartosat-2 (2007, sub-metre: 0.65 m), Cartosat-2A/B/C/D; used for large-scale mapping, urban planning, infrastructure planning, and defense applications; sub-metre resolution rivals commercial US satellites. (4) RISAT (Radar Imaging Satellite) — RISAT-1 (2012), RISAT-2 series; SAR satellites providing all-weather, day-night imaging; critical for agriculture (crop monitoring during cloudy monsoon season), flood monitoring, and strategic surveillance. (5) Oceansat Series — for ocean applications; measures sea surface temperature, wind speed, chlorophyll concentration, ocean colour; critical for fisheries, cyclone monitoring, and climate studies. (6) INSAT/Kalpana — geostationary satellites for meteorological observation; INSAT-3D/3DR provide continuous weather imagery over the Indian Ocean region; critical for IMD's cyclone tracking and weather forecasting. (7) HySIS (Hyperspectral Imaging Satellite, 2018) — India's first operational hyperspectral satellite; 55 spectral bands in VNIR and SWIR; used for mineral mapping, environmental monitoring, and agriculture.
GIS — Geographic Information System
A Geographic Information System (GIS) is a computer-based system for collecting, storing, managing, analyzing, and displaying geographically referenced data. GIS integrates data from multiple sources (remote sensing, surveys, GPS, census, administrative records) and links spatial data (where things are) with attribute data (what things are). Components of GIS: (1) Hardware — computers, servers, GPS devices, digitizers, plotters, scanners. (2) Software — GIS applications (ArcGIS, QGIS, MapInfo, ISRO's Bhuvan); enables data input, storage, query, analysis, and visualization. (3) Data — the most critical component; two types: Raster data (grid of cells/pixels — satellite images, DEMs), Vector data (points, lines, polygons — GPS locations, roads, boundaries); data sources include satellite imagery, aerial photographs, ground surveys, GPS, census data, and administrative records. (4) People — trained GIS analysts and decision-makers. (5) Methods/Procedures — analysis workflows and standards. GIS Operations: (a) Overlay Analysis — combining multiple map layers (e.g., slope + soil + rainfall) to identify suitable locations for specific activities; (b) Buffer Analysis — creating zones around features (e.g., 500 m buffer around rivers for CRZ); (c) Spatial Query — finding features that meet specific spatial criteria ("find all schools within 2 km of a flood-prone area"); (d) Network Analysis — finding optimal routes, service areas; (e) Terrain Analysis — slope, aspect, watershed delineation from Digital Elevation Models (DEMs); (f) Spatial Statistics — identifying patterns, clusters, and trends in spatial data. GIS differs from simple mapping: it enables analysis and decision-making, not just visualization. In India, GIS is used extensively by the National Informatics Centre (NIC), Survey of India, ISRO/NRSC, state planning departments, and district administrations.
GPS and India's NavIC System
Global Positioning System (GPS) is a satellite-based navigation system that provides location (latitude, longitude, altitude) and time information anywhere on Earth. Originally developed by the US Department of Defense (operational since 1995), GPS uses a constellation of 24+ satellites in medium Earth orbit (~20,200 km altitude). GPS accuracy: civilian — about 3-5 m; military — about 1 m; with differential corrections (DGPS) — sub-metre accuracy. Other Global Navigation Satellite Systems (GNSS): GLONASS (Russia), Galileo (European Union), BeiDou (China). India's NavIC (Navigation with Indian Constellation), formerly IRNSS (Indian Regional Navigation Satellite System), is India's indigenous satellite navigation system: launched 2013-2018 with 7 satellites (3 in geostationary orbit, 4 in inclined geosynchronous orbit); provides two services: Standard Positioning Service (SPS) for civilian use (~5 m accuracy) and Restricted Service (RS) for authorized users (~0.1 m accuracy); covers India and a region extending 1,500 km around it; independent of GPS — ensures that India has sovereign navigation capability not dependent on foreign systems; NavIC receivers are being integrated into mobile phones (Qualcomm Snapdragon chipsets support NavIC since 2020), fishing boats (INCOIS Fishermen Safety programme), and vehicle tracking systems; applications include precision agriculture, disaster management, fleet management, surveying, and timing synchronization. GAGAN (GPS Aided Geo Augmented Navigation) — India's satellite-based augmentation system (SBAS) developed by ISRO and AAI (Airports Authority of India); enhances GPS accuracy for aviation use; provides corrections that improve GPS accuracy to about 3 m for civil aviation precision approaches; India is the third country/region (after USA and Europe) to have an operational SBAS.
Applications of RS and GIS in India
Remote sensing and GIS are applied across virtually every sector of governance and resource management in India: (1) Agriculture — crop area estimation, crop health monitoring (using vegetation indices like NDVI), drought assessment, precision agriculture, crop insurance (PMFBY uses satellite data for crop loss assessment), soil mapping; the FASAL (Forecasting Agricultural output using Space, Agro-meteorology and Land-based observations) programme provides pre-harvest crop production estimates to the Ministry of Agriculture. (2) Forest and Environment — forest cover mapping (Forest Survey of India's ISFR uses IRS data), fire detection (real-time using MODIS/VIIRS sensors), wasteland mapping, wildlife habitat monitoring, air and water quality monitoring; ISRO's National Carbon Project uses RS for carbon stock estimation. (3) Water Resources — groundwater mapping, watershed delineation, reservoir monitoring, flood mapping, irrigation command area planning; National Aquifer Mapping Programme (NAQUIM) by CGWB uses RS and GIS. (4) Disaster Management — flood mapping using SAR (RISAT data during monsoon), cyclone tracking (INSAT imagery), landslide susceptibility mapping, earthquake damage assessment, drought monitoring (using soil moisture from SMAP/SMOS); ISRO's Decision Support Centre (DSC) provides near-real-time disaster support to NDMA and state agencies. (5) Urban Planning — city master plans, unauthorized construction detection, land use change monitoring, smart city planning, traffic management; AMRUT cities use satellite-based land use maps. (6) Mining and Geology — mineral exploration, illegal mining detection, environmental impact assessment, geological mapping; the Geological Survey of India uses RS extensively. (7) Defense and Security — border monitoring, terrain analysis, target identification; Cartosat-2 series imagery is crucial for strategic applications.
ISRO's Geospatial Platforms — Bhuvan and VEDAS
ISRO has developed several web-based platforms for disseminating geospatial data and services to citizens and government agencies: (1) Bhuvan (bhuvan.nrsc.gov.in) — launched in 2009; India's own geoportal, an alternative to Google Earth; provides multi-sensor, multi-temporal satellite imagery of India; offers thematic layers (land use, soil, watershed, weather, disaster); supports 2D and 3D visualization; open APIs for developers; used by over 100 government projects; available as a mobile app; provides free satellite imagery at 2.5 m resolution for the entire country. (2) VEDAS (Visualisation of Earth observation Data and Archival System) — a cloud-based platform for accessing, processing, and analyzing ISRO's archived satellite data; supports time-series analysis, change detection, and multi-source data integration. (3) MOSDAC (Meteorological and Oceanographic Satellite Data Archival Centre) — provides weather satellite data, cyclone tracking, and ocean observations. (4) India-WRIS (Water Resources Information System) — a web GIS-based portal for water resources data; integrates data from multiple agencies; provides information on rivers, basins, reservoirs, and groundwater. (5) Bhoonidhi — ISRO's satellite data dissemination portal; provides access to data from ISRO and foreign satellites. National Spatial Data Infrastructure (NSDI) — established by the Department of Science and Technology (DST) in 2006; a framework for sharing geospatial data across government agencies; aims to create a metadata catalogue of all geospatial datasets, establish data standards, and facilitate interoperability. The Geospatial Information Regulation Bill — India previously had strict regulations on geospatial data (maps >1:1 million scale required government approval); the new guidelines (2021) significantly liberalized access to geospatial data and mapping, allowing private companies and individuals to collect, create, and disseminate maps and geospatial data without prior approval (with restrictions on sensitive areas).
International Earth Observation Programmes
Remote sensing is a global endeavour with numerous international satellite programmes: (1) Landsat (USA) — the longest-running programme for satellite imagery of Earth; Landsat-1 launched 1972; current satellite: Landsat-9 (2021); 30 m resolution; archives are freely available since 2008, transforming environmental research globally. (2) Sentinel (ESA/EU) — part of the European Copernicus programme; Sentinel-1 (SAR, all-weather), Sentinel-2 (optical, 10-60 m, 5-day revisit), Sentinel-3 (ocean/land monitoring); data are free and open; widely used in India alongside IRS data. (3) MODIS (USA) — Moderate Resolution Imaging Spectroradiometer on NASA's Terra and Aqua satellites; 250-1000 m resolution; daily global coverage; crucial for fire detection, vegetation monitoring, and atmospheric studies. (4) Planet Labs — private company operating over 200 small satellites (Doves); provides daily coverage of the entire Earth at 3-5 m resolution; used commercially for agriculture, forestry, and intelligence. (5) Google Earth Engine — a cloud-based platform providing free access to petabytes of satellite imagery and tools for analysis; widely used by researchers and government agencies for large-scale environmental monitoring. International cooperation: India participates in the Committee on Earth Observation Satellites (CEOS), the Group on Earth Observations (GEO), and the International Charter on Space and Major Disasters (which provides free satellite imagery during disasters worldwide — ISRO has contributed data for numerous international disasters). India's small satellite launch capability (PSLV) has made it a major launcher of foreign Earth observation satellites, launching over 300 foreign satellites for 36 countries as of 2024.
Electromagnetic Spectrum and Spectral Signatures
Remote sensing depends fundamentally on the electromagnetic spectrum (EMS) — the full range of electromagnetic radiation from gamma rays to radio waves. Different portions of the EMS are used for different RS applications: (1) Visible Light (0.4-0.7 micrometres) — what human eyes can see; used in natural-colour imagery; blue (0.4-0.5 micrometre) for bathymetry and coastal mapping; green (0.5-0.6 micrometre) for vegetation vigour; red (0.6-0.7 micrometre) for plant species differentiation and soil boundaries. (2) Near-Infrared (NIR, 0.7-1.3 micrometre) — critical for vegetation studies; healthy vegetation strongly reflects NIR (high mesophyll reflectance) while stressed or dead vegetation reflects much less; water absorbs NIR strongly — used to delineate water bodies sharply; the NDVI (Normalized Difference Vegetation Index) uses NIR and Red bands: NDVI = (NIR-Red)/(NIR+Red); values range from -1 to +1; healthy vegetation: 0.6-0.9; bare soil: 0.1-0.2; water: negative values; NDVI is extensively used by ISRO's FASAL programme and IMD for drought monitoring. (3) Shortwave Infrared (SWIR, 1.3-3 micrometre) — sensitive to moisture content in vegetation and soils; used for mineral identification, snow-ice differentiation, and fire detection. (4) Thermal Infrared (TIR, 3-14 micrometre) — detects heat emitted by objects; used for measuring land surface temperature, urban heat island mapping, forest fire detection, and volcanic monitoring; INSAT-3D carries TIR sensors for weather forecasting. (5) Microwave (1 mm-1 m) — penetrates clouds, vegetation canopy, and shallow soil; used in RADAR (SAR) systems; RISAT satellites use C-band (5.35 GHz) microwave. Every object on Earth has a unique spectral signature — the characteristic pattern of reflectance and emission across the EMS — which allows RS to identify and classify land cover types (vegetation, water, urban, bare soil) without ground visits.
Image Interpretation and Digital Image Processing
Remote sensing imagery requires interpretation — either visual (manual) or digital (computer-based) — to extract useful information. Visual Interpretation uses recognition elements: (1) Tone/Colour — lighter tones indicate high reflectance (sand, concrete); darker tones indicate low reflectance (water, wet soil); in false-colour composites (FCC), vegetation appears red (NIR mapped to red channel). (2) Texture — smooth (calm water, runway) vs rough (forest canopy, rocky terrain). (3) Shape — regular shapes indicate human-made features (fields, buildings); irregular shapes suggest natural features. (4) Size — helps distinguish features of similar appearance (e.g., stadium vs house). (5) Pattern — regular arrangement indicates cultivation, orchards, or urban grid; random patterns indicate natural land cover. (6) Shadow — helps estimate height of objects (buildings, trees); also creates information loss. (7) Association — co-occurrence of features (e.g., parking lot near building indicates commercial area). (8) Site — topographic location (e.g., settlement near river). Digital Image Processing involves computer-based techniques: (a) Pre-processing — geometric correction (removing distortion), radiometric correction (calibrating sensor values), and atmospheric correction (removing haze and scattering effects). (b) Enhancement — contrast stretching, histogram equalization, edge enhancement, and band rationing to improve visual quality. (c) Classification — supervised (training the computer with known samples, e.g., Maximum Likelihood Classifier) or unsupervised (computer groups pixels by spectral similarity, e.g., K-means clustering); accuracy is verified against ground truth data. (d) Change Detection — comparing multi-date images to identify land use changes (deforestation, urban expansion, flood extent). ISRO's NRSC at Hyderabad processes all IRS satellite data and distributes it through the Bhoonidhi portal.
Spatial, Spectral, Temporal, and Radiometric Resolution
The quality and utility of remote sensing data depends on four types of resolution: (1) Spatial Resolution — the size of the smallest feature (pixel) that a sensor can detect on the ground; determines the level of detail visible in the image. Very High Resolution (<1 m): Cartosat-2 (0.65 m), WorldView-3 (0.31 m) — used for urban mapping, building identification, defense; High Resolution (1-10 m): IRS LISS-IV (5.8 m), Sentinel-2 (10 m) — used for agricultural field mapping, road detection; Medium Resolution (10-100 m): IRS LISS-III (23.5 m), Landsat (30 m) — used for regional land use mapping, watershed studies; Low/Coarse Resolution (>100 m): MODIS (250-1000 m), AWiFS (56 m) — used for continental/national-scale monitoring, daily vegetation tracking. Trade-off: higher spatial resolution means smaller swath width (area covered) and larger data volume. (2) Spectral Resolution — the number and width of spectral bands a sensor records. Panchromatic: single broad band (black-and-white); Multispectral: 3-10 bands (e.g., LISS-III has 4 bands); Hyperspectral: dozens to hundreds of narrow bands (e.g., HySIS has 55 bands in VNIR and 256 bands in SWIR) — enables identification of specific minerals, vegetation species, and soil types. (3) Temporal Resolution — how frequently a satellite revisits the same area. Daily revisit: MODIS, INSAT (geostationary); 5-day: Sentinel-2; 16-day: Landsat; 22-24 day: IRS LISS-III. Higher temporal resolution is critical for disaster monitoring (floods, fires) and crop growth tracking. (4) Radiometric Resolution — the sensitivity of the sensor to variations in reflected/emitted energy; measured in bits. 8-bit: 256 brightness levels (older satellites); 12-bit: 4,096 levels (modern satellites like Resourcesat-2); higher radiometric resolution means finer distinction between similar features (e.g., different vegetation types or soil moisture levels).
LiDAR and Drone-Based Remote Sensing
LiDAR (Light Detection and Ranging) is an active remote sensing technology that uses laser pulses to measure distances with extreme precision. A LiDAR system emits thousands of laser pulses per second, measures the time taken for each pulse to return after hitting an object, and calculates precise 3D coordinates. Types: (1) Airborne LiDAR — mounted on aircraft or helicopters; used for topographic mapping (producing Digital Elevation Models with 10-15 cm vertical accuracy), flood inundation modelling, forest canopy height measurement, power line mapping, and archaeological site discovery (can "see through" forest canopy to detect ground structures — used to discover ancient Mayan cities). (2) Terrestrial LiDAR — ground-based scanners; used for building surveys, heritage site documentation, and mining volume calculations. (3) Bathymetric LiDAR — uses green laser that penetrates water; used for shallow water depth measurement (up to 50 m) and reef mapping. In India, LiDAR is increasingly used by: Survey of India for high-precision topographic mapping; ISRO for terrain modelling; National Highways Authority for road corridor mapping; forest departments for canopy height and biomass estimation; archaeological surveys for heritage site documentation. Drone-Based Remote Sensing has transformed RS by making it accessible and cost-effective. India's Drone Rules 2021 and Drone Shakti initiative promote drone use. Applications include: precision agriculture (crop health monitoring at field level), disaster assessment (rapid post-flood or post-earthquake mapping), mining surveillance, urban planning, and infrastructure monitoring. SVAMITVA (Survey of Villages and Mapping with Improvised Technology in Village Areas) scheme uses drones to map rural residential land and issue property cards. The Digital Sky Platform is India's national unmanned traffic management system for drones.
Remote Sensing for Agriculture — FASAL, CHAMAN, and Precision Farming
Agriculture is the largest application area for remote sensing in India, directly impacting food security and farmer welfare. Key programmes: (1) FASAL (Forecasting Agricultural output using Space, Agro-meteorology and Land-based observations) — flagship programme of the Ministry of Agriculture; uses IRS satellite data (AWiFS for national level, LISS-III for district level) along with meteorological data and ground truth to generate pre-harvest crop production estimates for 11 major crops (rice, wheat, cotton, sugarcane, rapeseed-mustard, jute, potato, rabi pulses, soybean, kharif pulses, maize); estimates are provided at district, state, and national levels; accuracy exceeds 95% for rice and wheat; used by the Commission for Agricultural Costs and Prices (CACP) for MSP decisions and by the Food Corporation of India for procurement planning. (2) CHAMAN (Coordinated Horticulture Assessment and Management using geoiNformatics) — uses RS for mapping and forecasting horticultural crop production (mango, banana, citrus, potato, onion, chilli, tomato); supports export planning and price stabilization. (3) PMFBY (Pradhan Mantri Fasal Bima Yojana) — crop insurance scheme uses satellite data and drone imagery for crop loss assessment (Crop Cutting Experiments replaced by technology-based assessment); smart sampling methodology reduces fraudulent claims. (4) Soil Health Mapping — RS-based soil mapping (using SWIR bands for organic carbon, moisture, and mineralogy) supports the Soil Health Card Scheme. (5) Precision Agriculture — site-specific management of inputs (water, fertilizer, pesticides) based on RS data; variable rate technology guided by satellite-derived crop health maps; ISRO's Bhuvan Geoportal provides crop-specific advisories. (6) Drought Assessment — ISRO's Drought Assessment module uses NDVI, soil moisture index, and rainfall departure to classify drought severity at district level; feeds into crisis management protocols of the National Disaster Management Authority.
RS and GIS for Forest and Environmental Monitoring
Remote sensing has transformed forest management and environmental monitoring in India: (1) Forest Survey of India (FSI) — publishes the India State of Forest Report (ISFR) biennially using IRS satellite data; classifies forest cover into Very Dense Forest (canopy >70%), Moderately Dense Forest (40-70%), Open Forest (10-40%), and Scrub (<10%); ISFR 2021 reported total forest cover of 713,789 sq km (21.71% of geographic area) and tree cover of 28,540 sq km (0.87%); Madhya Pradesh has the largest forest area; Mizoram has the highest forest cover as percentage of geographic area. (2) Fire Detection — ISRO operates a near-real-time forest fire monitoring system using MODIS and VIIRS satellite data; fires detected within 4-6 hours; alerts sent to state forest departments via FSI's Forest Fire Alert System; the system detects about 20,000-30,000 fire points annually across India. (3) Mangrove Monitoring — satellite-based mapping tracks changes in mangrove extent; the Sundarbans is monitored for erosion, accretion, and cyclone damage. (4) Wildlife Habitat Mapping — RS provides habitat suitability maps for endangered species (tiger, elephant, one-horned rhino); used in Project Tiger for core zone delineation and corridor identification; All-India Tiger Estimation uses camera traps + RS-based habitat classification. (5) Air Quality — satellite-based aerosol optical depth (AOD) measurements complement ground-based monitoring; ISRO's satellite data is used alongside SAFAR (System of Air Quality and Weather Forecasting and Research) for air pollution mapping. (6) ISRO's National Carbon Project — uses RS for estimating carbon stocks in Indian forests, contributing to India's climate change reporting under the UNFCCC. (7) Wetland Mapping — ISRO has mapped all wetlands larger than 2.25 hectares using IRS satellite data; this inventory supports Ramsar site management and the National Plan for Conservation of Aquatic Ecosystems (NPCA).
RS and GIS for Disaster Management
Remote sensing and GIS are indispensable for disaster management across all phases — preparedness, early warning, response, and recovery: (1) Floods — ISRO's Decision Support Centre (DSC) at NRSC provides near-real-time flood inundation maps using RISAT (SAR) and Resourcesat data within 24 hours of a major flood event; SAR is preferred because it can image through monsoon clouds; in 2018 Kerala floods, ISRO provided continuous flood maps that aided rescue operations; Sentinel-1 SAR data (free) complements Indian data. (2) Cyclones — INSAT-3D and INSAT-3DR geostationary satellites provide continuous imaging of the Indian Ocean region; thermal IR data tracks cyclone eye position, movement, and intensity; IMD uses Dvorak technique (satellite-based intensity estimation) to classify cyclones; Scatterometer satellite (Oceansat-2/3) measures ocean surface winds around cyclones. (3) Earthquakes — while satellites cannot predict earthquakes, post-earthquake damage assessment uses high-resolution optical imagery (Cartosat-2) and SAR (RISAT) for building collapse detection and landslide mapping; InSAR (Interferometric SAR) measures ground deformation of millimetres over large areas — used to map fault displacement after the 2015 Nepal earthquake. (4) Landslides — ISRO has created a national landslide susceptibility map using DEM (slope, aspect), geology, land cover, and rainfall data in GIS; 12.6% of India identified as landslide-prone; early warning uses rainfall thresholds derived from satellite-based precipitation estimates (GPM satellite). (5) Drought — integrated drought severity index uses satellite-derived NDVI, soil moisture (SMAP/SMOS), rainfall departure (satellite rain estimation), and surface water extent; India Meteorological Department uses these inputs for drought declaration. (6) International Charter on Space and Major Disasters — India (through ISRO) is a member; provides free satellite data during any country's disaster; India has contributed data for numerous international disasters and activated the charter during the 2004 Indian Ocean Tsunami, 2013 Uttarakhand floods, and 2015 Nepal earthquake.
RS for Urban Planning and Smart Cities
Remote sensing and GIS are core tools for urban planning, smart city development, and land administration in India: (1) Master Plan Preparation — high-resolution satellite imagery (Cartosat-2: 0.65 m) provides the base layer for city master plans; land use classification (residential, commercial, industrial, open space, water body, agricultural, wasteland) is done through supervised classification of multispectral imagery; temporal analysis reveals urban expansion trends. (2) Unauthorized Construction Detection — comparison of approved building plans (GIS layers) with high-resolution satellite imagery identifies unauthorized construction; several municipal corporations use this for encroachment monitoring. (3) Urban Heat Island (UHI) Mapping — thermal RS (Landsat thermal band, INSAT TIR) measures land surface temperature variation within cities; concrete and asphalt areas are 3-8 degrees Celsius warmer than green areas; UHI maps guide urban forest planning and cool-roof programmes. (4) Smart Cities Mission — all 100 Smart Cities use GIS-based Integrated Command and Control Centres (ICCCs) for real-time monitoring of traffic, water supply, waste collection, and emergency services; GIS-based property tax mapping has improved municipal revenue. (5) SVAMITVA Scheme — uses drone surveys (RS technology) to map every residential property in Indian villages; provides legal ownership cards (property cards) to village household owners; covers 6.62 lakh villages; the scheme enables villagers to use property as collateral for bank loans — a transformative land governance reform. (6) National Land Records Modernization Programme (now DILRMP — Digital India Land Records Modernization Programme) — integrates RS, GIS, and GPS for computerization of land records, spatial data of maps, and survey/settlement work; aims to create conclusive land titling system. (7) AMRUT Cities — use satellite-based land use maps for Service Level Improvement Plans; GIS-based asset management for water and sewerage networks.
RS for Water Resources and Groundwater Management
Remote sensing provides critical inputs for water resource assessment and management: (1) Watershed Delineation — Digital Elevation Models (DEMs) from Cartosat or SRTM (Shuttle Radar Topography Mission) are used in GIS to automatically delineate watershed boundaries, stream networks, and drainage patterns; this is the foundation for the Integrated Watershed Management Programme (IWMP) and Pradhan Mantri Krishi Sinchayee Yojana (PMKSY). (2) Groundwater Prospects Mapping — ISRO has mapped groundwater prospects for the entire country using RS and GIS integration of geology (lithology), geomorphology, land use, lineaments (fractures visible on satellite images), drainage density, slope, and rainfall; these maps guide borewell siting and are available on Bhuvan; the National Aquifer Mapping Programme (NAQUIM) by CGWB uses these maps. (3) Reservoir Monitoring — satellite-based monitoring of water spread area in major reservoirs provides storage estimates; India-WRIS (Water Resources Information System) tracks 91 major reservoirs weekly using satellite data; critical for irrigation scheduling, hydropower generation, and flood management. (4) Irrigation Command Area Monitoring — RS verifies actual irrigated area vs planned area in canal command areas; identifies seepage, waterlogging, and salinity problems. (5) Glacier Monitoring — Indian Himalayan glaciers (source of perennial rivers) are monitored using multi-temporal satellite imagery; Gangotri Glacier has retreated about 1.5 km over the last century; ISRO's Space Applications Centre (Ahmedabad) monitors all major Himalayan glaciers; glacier lake outburst flood (GLOF) risk areas are identified using SAR and optical imagery. (6) Wetland Monitoring — satellite data tracks seasonal variation in wetland extent; used for Ramsar site management; ISRO's National Wetland Atlas mapped 757,060 wetlands using IRS data. (7) Snow Cover Monitoring — ISRO provides weekly snow cover maps of the Himalayas using MODIS and AWiFS data; critical for Indus and Ganga water availability forecasting.
RS and GIS for Mining, Geology, and Infrastructure
Remote sensing and GIS serve critical roles in geological exploration, mining regulation, and infrastructure development: (1) Mineral Exploration — hyperspectral RS (HySIS satellite, 2018) identifies mineral deposits by detecting unique spectral signatures of minerals in SWIR region; iron oxides, clays, carbonates, and sulphates each have diagnostic absorption features; Geological Survey of India (GSI) uses RS for reconnaissance-level mineral targeting before field surveys; major discoveries include kimberlite pipes (diamond) in Andhra Pradesh using lineament analysis. (2) Illegal Mining Detection — multi-temporal high-resolution imagery identifies unauthorized mining operations by detecting changes in land cover, excavation pits, and overburden dumps; Supreme Court-mandated satellite monitoring of mining leases; ISRO provides regular monitoring reports for districts with mining activity. (3) Environmental Impact Assessment (EIA) — RS-based land use/land cover change analysis is mandatory for EIA of mining and industrial projects; pre-project baseline and post-project monitoring using satellite data. (4) Road and Railway Corridor Planning — GIS-based route optimization considers terrain (DEM), land use (satellite classification), geology (landslide risk), water bodies, settlements, and protected areas; the Mumbai-Ahmedabad Bullet Train corridor alignment used GIS analysis; Bharatmala highway corridors were planned using satellite-based terrain and traffic data. (5) Power Line Routing — LiDAR surveys provide precise terrain data for transmission line routing, ensuring minimum ground clearance and identifying vegetation encroachment on existing lines. (6) Pipeline Routing — GIS multi-criteria analysis optimizes pipeline routes for oil, gas, and water, minimizing environmental impact and construction cost. (7) Telecom Tower Planning — GIS-based viewshed analysis ensures optimal tower placement for maximum coverage; BharatNet optical fibre route planning uses satellite data for terrain and land use assessment.
India's Launch Vehicles and Space Infrastructure for RS
India's ability to deploy and maintain a large constellation of Earth observation satellites depends on its indigenous launch vehicle capabilities and ground infrastructure: (1) PSLV (Polar Satellite Launch Vehicle) — India's most reliable launch vehicle; has launched most IRS satellites into polar sun-synchronous orbits (SSO) at 600-900 km altitude; SSO ensures that the satellite passes over any given latitude at the same local solar time — critical for consistent RS data; PSLV has over 50 successful consecutive missions; commercial success: launched 328 foreign satellites from 36 countries by 2023, earning foreign exchange and demonstrating ISRO's cost-effectiveness; a typical PSLV launch costs about Rs 200 crore (compared to SpaceX Falcon 9 at $67 million). (2) GSLV (Geosynchronous Satellite Launch Vehicle) — launches heavier satellites into geostationary orbit (36,000 km); used for INSAT weather and communication satellites; GSLV Mk III (LVM3) can place 4,000 kg in GTO; used for Chandrayaan-2 and will be used for Gaganyaan (human spaceflight). (3) SSLV (Small Satellite Launch Vehicle) — designed for quick, low-cost launches of small Earth observation satellites (up to 500 kg to SSO); first successful launch in 2023; enables responsive RS — rapid replacement of failed satellites or quick deployment for disaster monitoring. (4) Ground Segment — NRSC (National Remote Sensing Centre, Hyderabad) is the central facility for satellite data reception, processing, archiving, and dissemination; Shadnagar Earth Station receives data from all IRS satellites; ISTRAC (ISRO Telemetry, Tracking and Command Network) at Bengaluru controls satellites; Indian Deep Space Network at Byalalu (near Bengaluru) communicates with interplanetary missions. (5) NewSpace India Limited (NSIL) — ISRO's commercial arm since 2019; markets satellite data and launch services; enables private sector participation in India's space programme; IN-SPACe (Indian National Space Promotion and Authorisation Centre) authorizes and regulates private space activities under the Indian Space Policy 2023.
Photogrammetry and Survey of India
Photogrammetry is the science of making measurements from photographs, especially for creating accurate maps and 3D models. It is closely linked to RS and predates satellite RS: (1) Aerial Photogrammetry — uses overlapping aerial photographs (taken from aircraft) to create topographic maps with contour lines; stereoscopic viewing (two overlapping photos) allows measurement of heights and creation of DEMs; Survey of India (SoI, established 1767) has been India's principal mapping agency; SoI produces topographic maps at scales from 1:25,000 to 1:1,000,000 using photogrammetry and satellite imagery; the Open Series Maps (OSM) at 1:50,000 scale are available for public use. (2) Satellite Photogrammetry — Cartosat-1 (2005) was India's first satellite designed specifically for cartographic applications; it carries two panchromatic cameras with 2.5 m resolution looking forward (+26 degrees) and backward (-5 degrees) to create stereo pairs; these stereo images enable generation of Digital Elevation Models (DEMs) for the entire country at 10 m accuracy; Cartosat-2 series (0.65 m) provides imagery for large-scale urban mapping. (3) Survey of India Modernization — SoI is transitioning from conventional surveying to satellite-based mapping; the Continuously Operating Reference Stations (CORS) network provides centimetre-level accuracy GPS corrections across India; SoI maintains the Great Trigonometrical Survey benchmarks (height references); the Indian Geodetic Datum has been updated to be compatible with the global WGS-84 datum used by GPS/NavIC. (4) National Map Policy 2005 — defines two series of maps: Defence Series Maps (restricted, classified) and Open Series Maps (unclassified, available for development); the 2021 geospatial data liberalization further opened mapping to the private sector. (5) Digital India initiatives include SoI's Manchitra (online map viewer) and the National Topographic Database being converted to a spatial database format for GIS integration.
Relevant Exams
Remote sensing and GIS are increasingly tested in competitive exams, especially UPSC. Questions cover principles of RS (active vs passive, spectral signatures), ISRO satellites (IRS, Cartosat, RISAT), NavIC vs GPS, GIS applications in governance, and Bhuvan. SSC/RRB exams test factual recall — satellite names, ISRO milestones, NavIC features, and basic RS concepts. The 2021 geospatial data liberalization, FASAL programme, SVAMITVA drone surveys, and ISRO's disaster management applications are current-affairs-heavy areas. UPSC Mains GS-III frequently asks about technology in agriculture and disaster management where RS is a key enabler.