LESSON 15.2 — Remote Sensing, Spatial Database Creation & Planning Applications

A. Standard Map

Topic	Governing Source	Exam Focus
Remote sensing — definition	Acquisition of data without physical contact	Definition
EM spectrum in RS	Visible, NIR, SWIR, TIR, microwave	Bands + applications
Resolution types	Spatial, spectral, radiometric, temporal	Definitions + trade-offs
Passive vs active sensors	Solar (passive) vs emitted energy (active)	Distinction + examples
Major satellite programmes	Landsat, Sentinel, IRS, MODIS, Cartosat	Programme + country
Spatial database creation	Workflow: capture → edit → attribute → topology	Steps
Planning applications	Land use; disaster; infrastructure; environment	Application → method
NDVI	Normalised Difference Vegetation Index	Formula + use
TRAC / NRSC / Bhuvan	Indian RS institutions	Bodies + roles

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B. Why It’s Used

Paper II §15 of the TGPSC syllabus closes with “Remote Sensing and GIS in spatial planning, infrastructure planning and disaster management.” Remote sensing (RS) is the principal source of spatial data for modern planning — satellite imagery is cheaper, more current, and more consistent than ground surveys for many applications. The exam tests EM spectrum bands (visible, NIR, SWIR, TIR, microwave — which for what), four resolution types (spatial, spectral, radiometric, temporal), major satellite programmes, NDVI computation, and planning applications of RS+GIS. Telangana-specific: TRAC (Telangana State Remote Sensing Applications Centre), NRSC (National Remote Sensing Centre — ISRO’s Hyderabad-headquartered centre), Bhuvan (ISRO’s geoportal).

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C. Mechanism in Words

1. Remote sensing is the acquisition of information about an object or phenomenon without physical contact — typically via sensors on satellites, aircraft, drones, or hand-held devices. The most common form is satellite remote sensing — sensors on satellites orbiting the Earth capture electromagnetic radiation reflected or emitted from the surface, transmit the data to ground stations, and the data is processed into usable imagery. The key advantage of RS over ground survey: synoptic view (large areas in one image), repeatability (regular revisits), accessibility (remote, hazardous, or restricted areas), and multispectral capture (bands beyond visible light). The principal limitation: cloud cover (for optical sensors), and the need for skilled interpretation.

2. The electromagnetic (EM) spectrum is the basis of remote sensing. Sensors capture radiation in different bands (wavelength ranges), each revealing different features. Visible (0.4–0.7 μm) — what the human eye sees; blue, green, red sub-bands; useful for basic land cover (vegetation looks green, water looks blue/black). Near-Infrared (NIR) (0.7–1.4 μm) — beyond human vision; strongly reflected by healthy vegetation (used in NDVI); absorbed by water. Shortwave Infrared (SWIR) (1.4–3.0 μm) — sensitive to moisture content; useful for soil moisture, snow, cloud vs snow discrimination. Thermal Infrared (IR) (8–15 μm) — emitted heat; useful for temperature mapping (urban heat islands, forest fires, sea surface temperature). Microwave (1 mm – 1 m) — used in radar (active remote sensing); penetrates cloud cover, day/night; useful for terrain, soil moisture, ocean surfaces.

3. Resolution in remote sensing has four dimensions, often confused but distinct. Spatial resolution — the size of the smallest feature the sensor can distinguish (the pixel size on the ground). Landsat: 30 m; Sentinel-2: 10 m; WorldView: 0.5 m. Finer spatial resolution = more detail but larger file sizes and higher cost. Spectral resolution — the number and width of spectral bands. A panchromatic sensor captures one broad band; a multispectral sensor captures 4–10 narrow bands; a hyperspectral sensor captures hundreds of very narrow bands. Radiometric resolution — the sensor’s ability to distinguish small differences in reflected/emitted energy, typically expressed in bits (8-bit = 256 levels; 11-bit = 2,048 levels). Temporal resolution — the time between successive images of the same location (revisit time). Landsat: 16 days; Sentinel-2: 5 days; MODIS: 1–2 days (daily global coverage at coarse resolution). The four resolutions trade off — finer spatial resolution usually means lower temporal resolution (because the satellite has to point precisely and can’t cover the whole Earth as fast).

4. Sensors are classified as passive or active. Passive sensors rely on external energy (typically sunlight) — they record radiation reflected from the Earth’s surface. Examples: Landsat, Sentinel-2, MODIS. Passive sensors cannot operate at night (no sunlight) and are blocked by cloud cover. Active sensors emit their own energy and measure what is reflected back — they operate day/night and through cloud cover. Examples: Synthetic Aperture Radar (SAR) satellites (Sentinel-1, RISAT-1); LiDAR (Light Detection and Ranging — laser-based, used from aircraft and drones). Active sensors are more complex and expensive but enable applications passive cannot (e.g., cloud-penetrating forest monitoring, ocean surface wind measurement).

5. Major satellite programmes the planner should know. Landsat (NASA/USGS, USA, 1972–) — the oldest civilian RS programme; Landsat 8 (2013) and 9 (2021) currently operational; 30 m multispectral, 16-day revisit; free data globally. Sentinel (ESA, EU, 2014–) — Copernicus programme; Sentinel-1 (SAR, 10 m), Sentinel-2 (multispectral, 10 m, 5-day revisit), Sentinel-3 (ocean/land), Sentinel-5P (atmospheric); free data globally. IRS (Indian Remote Sensing, ISRO, 1988–) — Indian RS satellites; Resourcesat, Cartosat (high-resolution, 1 m panchromatic), RISAT (radar), Oceansat (ocean colour). MODIS (NASA, USA) — daily global coverage at 250–1000 m resolution; widely used for vegetation, fire, atmosphere. WorldView / GeoEye (Maxar, USA, commercial) — sub-metre resolution; expensive but high detail. Bhuvan (ISRO, 2009–) — India’s geoportal providing satellite imagery and GIS data free for Indian users.

6. The Normalized Difference Vegetation Index (NDVI) is the most widely used RS-derived index in planning and environmental analysis. Formula: NDVI = (NIR − Red) / (NIR + Red), where NIR is the near-infrared reflectance and Red is the red-band reflectance. NDVI values range from −1 to +1: negative values = water; 0 to 0.1 = bare soil, rocks, snow; 0.2 to 0.5 = sparse vegetation (grasslands, shrubs, crops); 0.6 to 1.0 = dense vegetation (forest). Planners use NDVI to map forest cover, monitor deforestation, assess agricultural health, identify green spaces in cities, and track urbanisation (urban areas show lower NDVI than vegetation). The index works because healthy vegetation strongly reflects NIR (internal leaf structure) and strongly absorbs Red (chlorophyll for photosynthesis) — making the NIR–Red difference large for healthy vegetation.

7. Spatial database creation follows a standard workflow. (1) Data capture — sources include satellite imagery (raster), digitising from existing maps (vector), GPS field surveys (vector points/lines), and tabular data (attributes). (2) Editing and cleaning — fixing geometric errors (overshoots, undershoots, gaps in polygons); establishing topology (adjacency, connectivity). (3) Attribute data entry — linking each spatial feature to its non-spatial attributes via a unique identifier. (4) Georeferencing — ensuring all data is in a common coordinate system (typically UTM). (5) Quality control — checking accuracy (positional, attribute, logical consistency). (6) Storage — typically in a spatial database (PostGIS, Oracle Spatial, file geodatabase). (7) Documentation — metadata (source, accuracy, date) per ISO 19115 or national standards. Modern Indian practice increasingly follows URDPII 2015 / NNRMS (National Natural Resources Management System) standards.

8. Planning applications of RS+GIS are numerous. Land use / land cover mapping — most Master Plans now use RS-based land use surveys instead of ground surveys; faster, more current, more consistent. Urban growth monitoring — multi-date imagery shows city expansion over time; useful for plan evaluation. Disaster management — flood mapping (radar imagery through clouds); cyclone damage assessment; landslide-prone area identification (slope + lithology); forest fire detection (MODIS thermal). Infrastructure planning — road network extraction from high-resolution imagery; optimal alignment for new highways (terrain + slope + land use); utility corridor planning. Environmental monitoring — forest cover (NDVI); wetland mapping; pollution (air quality from Sentinel-5P); watershed analysis. Agriculture — crop health; irrigation monitoring; yield forecasting. Property tax mapping — building footprints from high-resolution imagery for tax base expansion. Telangana-specific: TRAC supports HMDA’s land use updates; NRSC’s National Land Use/Cover mapping provides state-level data.

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D. Core Concept Explanations

C1. EM spectrum bands

Band	Wavelength (μm)	Use
Visible	0.4–0.7	Basic land cover; human interpretation
Near-Infrared (NIR)	0.7–1.4	Healthy vegetation (NDVI)
Shortwave Infrared (SWIR)	1.4–3.0	Soil moisture; snow
Thermal Infrared (TIR)	8–15	Temperature; forest fires
Microwave	1 mm – 1 m	Radar; cloud penetration; terrain

C2. Four resolution types

Type	Definition	Examples
Spatial	Smallest feature distinguishable (pixel size)	Landsat 30 m; WorldView 0.5 m
Spectral	Number + width of bands	Panchromatic (1); multispectral (4–10); hyperspectral (100s)
Radiometric	Sensitivity to energy differences	8-bit (256 levels); 11-bit (2,048)
Temporal	Time between revisits	Landsat 16 days; Sentinel-2 5 days

C3. Major satellite programmes

Programme	Country / Agency	Resolution	Revisit	Note
Landsat (8, 9)	USA / NASA-USGS	30 m MS; 15 m PAN	16 days	Free; oldest civilian RS
Sentinel-1 (SAR)	EU / ESA	10 m	6 days	Radar; cloud-penetrating
Sentinel-2 (MS)	EU / ESA	10 m MS	5 days	Free; widely used
MODIS	USA / NASA	250–1000 m	1–2 days	Daily global; vegetation, fire
IRS / Cartosat / Resourcesat	India / ISRO	1 m PAN; 5 m MS	Variable	Indian RS constellation
RISAT	India / ISRO	Radar	Variable	All-weather; Indian radar
WorldView / GeoEye	USA / Maxar	0.5 m	Variable	Commercial high-res

C4. NDVI computation

• Formula: NDVI = (NIR − Red) / (NIR + Red)
• Range: −1 to +1
• Negative: water
• 0–0.1: bare soil, rocks, snow
• 0.2–0.5: sparse vegetation (grass, crops)
• 0.6–1.0: dense vegetation (forest)

C5. Indian RS institutions

Body	Role
ISRO (Indian Space Research Organisation)	Indian space agency; develops satellites
NRSC (National Remote Sensing Centre, Hyderabad)	ISRO’s RS operations; data dissemination
NNRMS (National Natural Resources Management System)	National RS coordination
Bhuvan	ISRO’s geoportal; free Indian imagery + GIS
TRAC (Telangana State Remote Sensing Applications Centre)	State-level RS support
APSRAC (Andhra Pradesh State Remote Sensing Applications Centre)	State-level (post-bifurcation)

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E. Worked Numericals and Parameter Tables

E1. NDVI — worked

A pixel has NIR = 0.50 and Red = 0.10 (typical for healthy vegetation):

• NDVI = (0.50 − 0.10) / (0.50 + 0.10) = 0.40 / 0.60 = 0.67 → dense vegetation

A pixel has NIR = 0.20 and Red = 0.18 (typical for bare soil):

• NDVI = (0.20 − 0.18) / (0.20 + 0.18) = 0.02 / 0.38 = 0.05 → bare soil

A pixel has NIR = 0.05 and Red = 0.10 (water — absorbs NIR):

• NDVI = (0.05 − 0.10) / (0.05 + 0.10) = −0.05 / 0.15 = −0.33 → water

E2. Revisit computation

A satellite has a 16-day repeat cycle. In a year (365 days), there are 365/16 ≈ 23 revisits. With 50% cloud-free probability, only ~12 useful images per year.

E3. Spatial database workflow

For a town of 50,000 population: capture land use from 0.5 m imagery (cost ~₹2 lakh); digitise parcels (50 hours @ ₹1,000/hr = ₹50,000); attribute linkage (ULB records → 200 hours @ ₹500/hr = ₹1 lakh); total cost ~₹3.5 lakh — a one-time investment that pays off in decades of plan-making and property tax use.

E4. Land cover change

A region’s NDVI-derived vegetation cover declined from 60% to 45% over 20 years. Vegetation loss = 15 percentage points. If the region is 1,000 sq km, the lost vegetation = 150 sq km — equivalent to substantial urbanisation or deforestation.

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F. Design Criteria

Parameter	Standard / Typical value	Source
Landsat multispectral resolution	30 m	NASA / USGS
Landsat panchromatic	15 m	NASA / USGS
Sentinel-2 multispectral	10 m	ESA
WorldView panchromatic	0.5 m	Maxar
Landsat revisit	16 days	NASA
Sentinel-2 revisit	5 days	ESA
MODIS revisit	1–2 days	NASA
NDVI range	−1 to +1	Convention
NDVI dense vegetation	0.6 to 1.0	Convention
NRSC location	Hyderabad	ISRO
Bhuvan launch	2009	ISRO

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G. Application Zones

1. Land use mapping — Master Plans; URDPFI standards.
2. Urban growth monitoring — multi-date change analysis.
3. Disaster management — flood (SAR), cyclone, landslide, fire.
4. Infrastructure planning — road alignment; utility corridors; site suitability.
5. Environment — forest cover (NDVI); wetlands; air quality (Sentinel-5P).
6. Property tax mapping — building footprints from high-res imagery.

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H. Common Confusions

Confusion	Reality
“RS and GIS are the same.”	No — RS is data acquisition; GIS is data analysis. They work together but are distinct.
“Active sensors need sunlight.”	False — active sensors (radar, LiDAR) emit their own energy and work day/night.
“Thermal and microwave are the same band.”	No — thermal is emitted heat (8–15 μm); microwave is radar (1 mm–1 m) — very different.
“Higher spatial resolution always means better data.”	Not always — depends on the application. Coarse resolution (MODIS 250 m) is fine for global vegetation; fine resolution (WorldView 0.5 m) is needed for parcel mapping.
“NDVI ranges 0 to 1.”	False — NDVI ranges −1 to +1. Negative values indicate water.
“Bhuvan is a private portal.”	No — Bhuvan is ISRO’s geoportal (free Indian imagery and GIS data).
“Landsat revisit is 5 days.”	False — Landsat revisit is 16 days; Sentinel-2 is 5 days.

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I. Compare & Contrast

I1. Passive vs active sensors

Dimension	Passive	Active
Energy source	External (sun)	Self-emitted
Day/night operation	Day only	Day/night
Cloud penetration	Blocked by clouds	Penetrates clouds
Examples	Landsat, Sentinel-2, MODIS	Sentinel-1 SAR; RISAT; LiDAR

I2. Four resolution types

Type	What it measures	Examples
Spatial	Pixel size	0.5 m to 1 km
Spectral	Number + width of bands	1 (pan); 4–10 (MS); 100s (hyperspectral)
Radiometric	Energy sensitivity	8-bit; 11-bit
Temporal	Revisit time	1–2 days to 16 days

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J. Memory Hooks

• “Vis-NIR-SWIR-TIR-Microwave” — five EM bands in order of wavelength.
• “Passive sees reflected; Active emits and listens” — sensor distinction.
• “Landsat 30 m; Sentinel-2 10 m; WorldView 0.5 m” — three resolutions.
• “Spatial-Spectral-Radiometric-Temporal” — four resolution types.
• “NDVI = (NIR − Red) / (NIR + Red); range −1 to +1”.
• “Healthy vegetation: high NIR reflectance, low Red absorption” — the basis of NDVI.
• “ISRO + NRSC + Bhuvan + TRAC” — Indian RS institutions.

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K. Revision Ladder

Order	Item	Time
1	Memorise EM spectrum bands with uses	30 min
2	Memorise four resolution types with examples	30 min
3	Memorise passive vs active sensors	20 min
4	Memorise major satellite programmes	45 min
5	Memorise NDVI formula + interpretation	20 min
6	Practise NDVI computation	20 min
7	Memorise spatial database workflow (7 steps)	20 min
8	Memorise Indian RS institutions (ISRO, NRSC, Bhuvan, TRAC)	15 min
9	Map Telangana-specific RS use (TRAC, NRSC Hyderabad)	30 min

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L. Exam Traps

Trap	Correct response
Question pairs RS with GIS as the same.	False — RS is data acquisition; GIS is analysis.
Question pairs active sensors with sunlight.	False — active sensors work day/night (emit own energy).
Question lists NDVI range as 0 to 1.	False — NDVI range is −1 to +1.
Question pairs Landsat with 5-day revisit.	False — Landsat revisit is 16 days; Sentinel-2 is 5 days.
Question pairs Bhuvan with commercial provider.	False — Bhuvan is ISRO’s geoportal.
Question lists thermal and microwave as the same band.	False — thermal (8–15 μm) vs microwave (1 mm – 1 m) are very different.
Question lists spatial resolution as “the number of bands.”	False — that’s spectral resolution. Spatial is pixel size.

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M. Answer-Writing Cues

• For RS definition questions, give the key features: “Remote sensing is the acquisition of information about an object or phenomenon without physical contact, typically via satellite sensors — providing synoptic view, repeatability, accessibility, and multispectral capture.”
• For resolution questions, give type + definition + example: “Spatial resolution is the size of the smallest feature distinguishable — Landsat’s 30 m multispectral vs WorldView’s 0.5 m panchromatic.”
• For NDVI questions, give formula + range + interpretation: “NDVI = (NIR − Red) / (NIR + Red); values range from −1 (water) to +1 (dense vegetation); used for vegetation mapping, urban growth monitoring, and environmental assessment.”
• For planning applications, give application + RS/GIS technique: “Land use mapping uses RS imagery (Sentinel-2 at 10 m) as the base; GIS overlay enables Master Plan preparation per URDPFI 2015 standards.”

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N. PYQ Integration

Pattern questions only:

Pattern question 1 — NDVI

Q. The Normalized Difference Vegetation Index (NDVI) formula is:
– (A) (NIR − Red) / (NIR + Red) ✓
– (B) (NIR + Red) / (NIR − Red)
– (C) (Red − NIR) / (NIR + Red)
– (D) NIR / Red

Ans: (A). Range: −1 to +1.

Pattern question 2 — Bands

Q. Which EM band is most useful for monitoring healthy vegetation?
– (A) Visible red
– (B) Thermal infrared
– (C) Near-infrared (NIR) ✓
– (D) Microwave

Ans: (C). Healthy vegetation strongly reflects NIR (basis of NDVI).

Pattern question 3 — Resolution

Q. The “size of the smallest feature the sensor can distinguish” is called:
– (A) Spatial resolution ✓
– (B) Spectral resolution
– (C) Radiometric resolution
– (D) Temporal resolution

Ans: (A).

Pattern question 4 — MSQ

Q. Which of the following are remote sensing satellites/programmes?
– (A) Landsat ✓
– (B) Sentinel ✓
– (C) Cartosat ✓
– (D) Global Positioning System

Ans: (A), (B), (C). GPS is a positioning/navigation system, not an imaging satellite.

Pattern question 5 — Numerical

A pixel has NIR reflectance 0.50 and Red reflectance 0.10. The NDVI is:
– (A) 0.20
– (B) 0.50
– (C) 0.67 ✓
– (D) 5.00

Ans: (C). NDVI = (0.50 − 0.10) / (0.50 + 0.10) = 0.40 / 0.60 = 0.67 — dense vegetation.

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O. Mini-Check — Lesson 15.2

1. Define remote sensing and state its four key advantages over ground survey.
2. List the five EM spectrum bands with their wavelength ranges and uses.
3. State the four resolution types with definitions.
4. Distinguish passive from active sensors with examples.
5. Name five major satellite programmes with their countries/agencies.
6. State the NDVI formula and its interpretation for water, bare soil, and dense vegetation.
7. List the seven steps of spatial database creation workflow.
8. Name the principal Indian RS institutions.
9. State the revisit times of Landsat, Sentinel-2, and MODIS.
10. List four planning applications of RS+GIS.

Answers:
1. Remote sensing = acquisition of information without physical contact (typically via satellite sensors). Four advantages: synoptic view, repeatability, accessibility, multispectral capture.
2. Visible (0.4–0.7 μm): basic land cover. NIR (0.7–1.4): healthy vegetation. SWIR (1.4–3.0): soil moisture, snow. TIR (8–15): temperature, fires. Microwave (1 mm – 1 m): radar, cloud penetration.
3. Spatial: smallest feature distinguishable (pixel size). Spectral: number + width of bands. Radiometric: sensitivity to energy differences (bits). Temporal: revisit time.
4. Passive uses external energy (sunlight); day-only; blocked by clouds (Landsat, Sentinel-2). Active emits own energy; day/night; cloud-penetrating (Sentinel-1 SAR, RISAT, LiDAR).
5. Landsat (NASA-USGS, USA); Sentinel (ESA, EU); MODIS (NASA, USA); IRS/Cartosat/Resourcesat (ISRO, India); WorldView/GeoEye (Maxar, USA commercial).
6. NDVI = (NIR − Red) / (NIR + Red). Water: negative. Bare soil: 0 to 0.1. Dense vegetation: 0.6 to 1.0.
7. (1) Data capture; (2) Editing/cleaning; (3) Attribute data entry; (4) Georeferencing; (5) Quality control; (6) Storage; (7) Documentation (metadata).
8. ISRO (Indian Space Research Organisation); NRSC (National Remote Sensing Centre, Hyderabad); NNRMS (National Natural Resources Management System); Bhuvan (ISRO’s geoportal); TRAC (Telangana State RS Applications Centre).
9. Landsat: 16 days; Sentinel-2: 5 days; MODIS: 1–2 days.
10. Land use / land cover mapping; urban growth monitoring; disaster management (flood, cyclone, fire); infrastructure planning (road alignment, utility corridors); environmental monitoring (forest cover, wetlands, air quality); property tax mapping. Any four.

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