Remote Sensing and GIS - Memory Anchors
The human brain is wired for stories, patterns, and emotion, not dry lists of technical terms. For PRC board exam preparation, where you must recall dozens of definitions, sensor types, resolution categories, and GIS data models under pressure, these anchors serve as mental shortcuts. Each anchor creates a strong neural link between a trigger (something familiar) and the target concept (the exam answer). Use them actively: read the trigger, close your eyes, and reconstruct the concept before checking. The more vividly and repeatedly you use each anchor, the deeper it embeds into long-term memory.
Memory anchors
The four types of resolution in remote sensing: Spatial, Spectral, Radiometric, Temporal
Memory aid
Remember the acronym SSRT — 'Some Sensors Really Tire' — Spatial (pixel size), Spectral (number of bands), Radiometric (bit depth), Temporal (revisit time). Imagine a tired sensor sitting on a satellite, squinting at tiny pixels, counting rainbow bands on its fingers, measuring in bits, and checking its watch to see when it can come back.
Why it works
Acronyms compress a 4-item list into one pronounceable word-chunk. Adding a vivid image of a 'tired sensor' pins the emotion and humor, making the acronym stick.
Example usage
Question: 'What are the four types of resolution in remote sensing?' — Recall SSRT: Spatial, Spectral, Radiometric, Temporal. Then expand each definition.
Recall trigger
Think: 'Some Sensors Really Tire' whenever the exam asks about resolution types.
Spatial Resolution — the ground size of one pixel
Memory aid
Spatial resolution is like the size of the tiles on a bathroom floor. If the tiles are tiny (1 cm × 1 cm), you can see every crack and ant — high spatial resolution. If the tiles are huge (1 m × 1 m), you cannot see small objects — low spatial resolution. In remote sensing, a 1-m satellite gives you tile-sized pixels you can almost walk on; a 30-m Landsat pixel is the size of a basketball court.
Why it works
Everyday tiles are a universally understood reference. The contrast (tiny vs huge tiles) locks in the relationship between pixel size and detail level.
Example usage
Board question: 'Which has higher spatial resolution, a 1-m or 30-m sensor?' — Think tiles: smaller tile (1 m) = more detail = higher resolution.
Recall trigger
Visualize your bathroom floor tiles — smaller tiles = better detail = higher spatial resolution.
Spectral Resolution — number and width of bands a sensor measures
Memory aid
Spectral resolution is like the number of color crayons in your box. A panchromatic sensor has ONE big black crayon — it sees everything as gray. Landsat has a box of 7–8 crayons (multispectral). A hyperspectral sensor has 200+ crayons — it can identify every material by its exact color fingerprint. More crayons = finer spectral resolution = more materials you can distinguish.
Why it works
Crayons are a concrete, childhood-familiar metaphor. The progression (1 → 7 → 200+) is intuitive and memorable.
Example usage
If asked to distinguish between multispectral and hyperspectral: crayon box with 7 vs 200+ crayons. More crayons = hyperspectral.
Recall trigger
Think crayon box — how many colors can your sensor 'see'?
Radiometric Resolution — the number of digital levels (bit depth) a sensor can distinguish
Memory aid
Radiometric resolution is like the volume dial on an old radio. An 8-bit sensor has 256 volume levels (2⁸ = 256); a 12-bit sensor has 4,096 levels. The more levels on your dial, the more subtle differences in brightness you can detect. Low radiometric resolution = coarse volume dial, cannot tell if a whisper is a soft whisper or a medium whisper.
Why it works
Volume dial is tactile and familiar. Connecting bit depth to a dial with discrete steps makes the abstract concept concrete.
Example usage
Question: 'An 8-bit sensor can distinguish how many grey levels?' — Volume dial with 2⁸ = 256 levels.
Recall trigger
Think of a radio volume dial with numbered steps — more steps = higher radiometric resolution.
Temporal Resolution — how often a satellite revisits the same location
Memory aid
Imagine your nosy neighbor (the satellite) who walks past your house every day to check what you are doing. A satellite with 1-day revisit time is that super-nosy neighbor — high temporal resolution. One that passes only every 16 days (like Landsat) is the neighbor who goes on a long vacation — low temporal resolution. For monitoring typhoon floods in the Philippines, you want the nosy neighbor back DAILY.
Why it works
A neighbor analogy is culturally relatable in Filipino communities. The humor and cultural context make the concept unforgettable.
Example usage
Board question: 'Which temporal resolution is better for disaster monitoring?' — The daily nosy neighbor: shorter revisit = higher temporal resolution = better for time-sensitive events.
Recall trigger
Nosy neighbor walking past — how often does it come back?
Passive vs Active sensors
Memory aid
P-A-S-S-I-V-E = 'Please Allow Sunshine — Sensor Is Very Exhausted (without it)'. A passive sensor NEEDS the sun (or thermal emission) to work — no sun, no image. A-C-T-I-V-E = 'Always Carries Torch In Very Extreme nights'. An active sensor (radar/LiDAR) CARRIES ITS OWN energy — torch in the dark — so it works day, night, and through clouds. Picture a passive sensor curled up sleeping in the dark, while an active sensor walks boldly with a flashlight.
Why it works
The contrasting images (sleeping vs carrying a torch) create a vivid before-and-after mental picture. The acronym reinforces the definition.
Example usage
Exam: 'Which sensor type can map a flood at 2 AM during a typhoon?' — Active (SAR/radar) — it carries its own torch, penetrates clouds, works at night.
Recall trigger
Torch in the dark = ACTIVE; needs sunshine = PASSIVE.
Radar/SAR is active, penetrates clouds, works day and night
Memory aid
During Typhoon Ondoy (2009), the Philippines was covered in thick clouds for days. Optical satellites (passive) were blind — nothing but gray cloud images. But a SAR satellite (active) flew overhead, sent its own microwave pulses, and received the bounced signals back through the clouds — producing clear flood maps that saved lives. SAR = the hero that works when all the lights go out.
Why it works
A Philippine disaster reference creates emotional resonance. Connecting SAR to a real event the students know makes the concept personally significant.
Example usage
Question: 'A coastal area is perpetually cloudy. Which sensor?' — SAR/radar (active) — the typhoon hero that sees through clouds.
Recall trigger
Typhoon Ondoy, thick clouds, SAR hero — active, all-weather, all-night.
Spectral signatures — each material reflects energy differently across the spectrum
Memory aid
A spectral signature is like a fingerprint or a barcode on a grocery item. Every item in a sari-sari store has a unique barcode — the scanner reads it and knows exactly what it is. Similarly, healthy green vegetation has a unique 'barcode' of reflectance values across visible and infrared bands — the sensor reads it and classifies it as 'vegetation'. Bare soil, water, concrete — each has its own barcode.
Why it works
Barcodes in a sari-sari store are familiar to every Filipino. The scan-and-identify action maps perfectly to how sensors classify surfaces.
Example usage
Question: 'Why can a multispectral sensor distinguish vegetation from bare soil?' — Each has a unique spectral signature (barcode) in the near-infrared band.
Recall trigger
Sari-sari store barcode scanner — each land cover has its own reflectance barcode.
Image classification — assigning pixels to land-cover classes
Memory aid
Image classification is like sorting M&Ms by color. Each pixel is an M&M; its reflectance values across bands are its color. Supervised classification = you first train the machine by showing it labeled samples ('this red M&M is vegetation, this blue one is water') — then it sorts the rest. Unsupervised classification = the machine groups M&Ms by color on its own, and you name the groups afterward.
Why it works
Sorting candy is a playful, hands-on analogy. The supervised/unsupervised distinction is captured naturally by 'train first vs sort then name'.
Example usage
Board question: 'In supervised classification, what is required before running the algorithm?' — Training samples (labeled M&Ms to teach the machine).
Recall trigger
M&M sorting — supervised = label first, unsupervised = sort then name.
GIS — links location (where) to attributes (what)
Memory aid
GIS is like Google Maps plus a barangay census database stapled together. Google Maps tells you WHERE every house is (location). The census tells you WHO lives there, how many people, income bracket (attributes). GIS staples both together so you can ask: 'Show me all houses within 500 m of a river that have 5+ residents' — that is spatial analysis on linked location + attribute data.
Why it works
Google Maps + barangay census is an immediately relatable Filipino civic reference. The 'stapling together' action concretizes the linkage between spatial and attribute data.
Example usage
Exam: 'What is the defining function of a GIS?' — It links geographic location to attribute data, enabling spatial queries and analysis.
Recall trigger
Google Maps + barangay census = GIS.
Vector data model — points, lines, polygons representing discrete features
Memory aid
Vector = Vertices, Edges, Contours, Topology, Objects, Relations. More simply: picture a BARANGAY MAP drawn by hand on paper — individual lot corners are POINTS, the roads between them are LINES, and the cadastral lots themselves are POLYGONS. Everything is discrete, has a precise shape, and has an ID number linked to an attribute table. Vector = the hand-drawn precise map of individual land parcels.
Why it works
Cadastral parcels are the core professional context for Filipino geodetic engineers. The hand-drawn map image is concrete and professionally meaningful.
Example usage
Question: 'Which GIS data model represents cadastral parcel boundaries?' — Vector (polygons with precise boundaries and linked attribute records).
Recall trigger
Hand-drawn barangay cadastral map — points, lines, polygons.
Raster data model — a grid of cells representing continuous surfaces
Memory aid
Raster = rice field seen from above. A rice field divided into equal square paddies — each paddy (cell) holds one value (elevation, rainfall, temperature). The surface is continuous — no gaps. SRTM DEM, rainfall maps, slope maps — all are rasters. Now zoom in: each paddy-cell has ONE value and covers a fixed ground area (the spatial resolution). The whole field together is the raster layer.
Why it works
Rice fields (palayan) are a quintessential Philippine landscape. Every student has seen them. The grid of equal paddies is geometrically identical to a raster grid.
Example usage
Exam: 'Which data model is best for a terrain elevation surface?' — Raster (each cell holds one elevation value over a continuous surface).
Recall trigger
Palayan (rice field) seen from above = raster grid.
Vector vs Raster — which to use for what
Memory aid
V-R Rule: 'Vector = Very Razor-sharp (precise boundaries, discrete objects); Raster = Really Smooth Surfaces (continuous data)'. A memory hook: V points like an arrow to a precise corner of a lot (discrete). R is round — it flows, like a topographic surface flowing across a landscape. If you can count or measure the feature's boundary exactly — Vector. If it flows continuously — Raster.
Why it works
Shape associations (V = arrow/precise, R = round/flowing) create a visual link between the letter and the concept's nature.
Example usage
Question: 'Road centerlines and a rainfall surface — vector or raster?' — Roads = Vector (discrete lines); Rainfall = Raster (continuous surface).
Recall trigger
V = arrow to a precise point; R = smooth flowing surface.
All GIS layers must share a common datum and projection for overlay
Memory aid
Imagine a geodetic engineer stacking transparent map sheets on a light table. Sheet 1 is in PRS92 / PPCS Zone III. Sheet 2 is in WGS84 / UTM Zone 51N. When stacked, the Pasig River on Sheet 2 runs through the middle of Makati City on Sheet 1 — nothing aligns! The engineer throws his hands up: 'Magkamali tayo ng projection!' He re-projects everything to PPCS Zone III, stacks again — and every road, river, and parcel snaps perfectly into place.
Why it works
A concrete Filipino scenario (Pasig River, Makati, PPCS) makes the abstract 'coordinate consistency' rule immediately tangible and professionally relevant.
Example usage
Exam: 'Why must GIS layers share a common coordinate system?' — Layers in different projections will not overlay correctly — features will be spatially displaced.
Recall trigger
Pasig River running through Makati — mismatched projections = misaligned layers.
Pixels-per-swath formula: N = Swath Width / Spatial Resolution
Memory aid
Think of tiling a floor with square tiles. The hallway is 185 m wide; each tile is 30 cm wide. How many tiles fit across? 185 / 0.30 ≈ 617 tiles. Now scale up: a 185-km swath with 30-m pixels — same math, just bigger numbers: 185,000 m / 30 m ≈ 6,167 pixels. The formula N = W / r (Number = Width / resolution) is just counting tiles across a hallway.
Why it works
Tile-counting is a familiar home-improvement task. The identical structure of the calculation at two scales reinforces the formula's universality.
Example usage
Board problem: '290-km swath, 10-m resolution. Pixels per line?' — N = 290,000 / 10 = 29,000 pixels.
Recall trigger
Count floor tiles across a hallway — N = Width / resolution.
LiDAR — active sensor, uses laser pulses, produces point clouds and DEMs
Memory aid
LiDAR is like a bat using echolocation in the dark. The bat (sensor) emits ultrasonic pulses (laser pulses) and listens for the echo. From the time delay it calculates the exact distance to objects — trees, buildings, the ground. Thousands of pulses per second create a dense 3D 'point cloud' — a bat-made 3D map of the cave (terrain). In the Philippines, LiDAR was used after Typhoon Haiyan (Yolanda) to map coastal terrain for rebuilding.
Why it works
Bat echolocation is a well-known natural analogy for active sensing. The Philippine Yolanda reference adds emotional relevance.
Example usage
Question: 'What data product results from airborne LiDAR?' — A dense 3D point cloud processed into a Digital Elevation Model (DEM).
Recall trigger
Bat in the dark with laser pulses = LiDAR.
Image rectification — correcting geometric distortions in raw imagery
Memory aid
Raw satellite imagery is like a photograph taken with a tilted, warped funhouse mirror — features are stretched, rotated, or displaced. Rectification is ironing out a crumpled map — you take known Ground Control Points (GCPs), like fixed barangay corner monuments with PRS92 coordinates, and use them to 'iron' the image flat so every pixel aligns to its true ground position.
Why it works
Ironing a crumpled map is a physical, tactile metaphor. GCPs as known monuments connects to the geodetic engineer's professional practice of ground control.
Example usage
Exam: 'What is the purpose of image rectification?' — To remove geometric distortions and register the image to a known coordinate system using GCPs.
Recall trigger
Iron out the crumpled photo using known ground control points.
GIS overlay analysis — combining multiple layers to produce new spatial information
Memory aid
Walk through DENR office halls in your mind. Room 1: a map of flood-prone areas. Room 2: a map of protected forest zones. Room 3: a map of cadastral lots. At each room, you overlay the maps on the light table. At the end of the hall, the combined map shows which cadastral lots are BOTH flood-prone AND inside a protected zone — that is overlay analysis. Each room = one layer; the final desk = the overlay result.
Why it works
Method of Loci attaches data layers to physical rooms in a familiar professional building. The walk creates a memorable spatial journey through the analysis.
Example usage
Question: 'How does GIS overlay help in land use planning?' — Multiple thematic layers (flood, forest, cadastre) are combined to identify areas meeting all criteria simultaneously.
Recall trigger
Walking through DENR rooms stacking maps on a light table.
Buffering in GIS — creating a zone of specified distance around a feature
Memory aid
Buffering is like dropping a stone into a still fishpond (bangus pond). The stone is the feature (a road, river, or point). The ripples spreading outward are the buffer zones — a 50-m buffer, a 100-m buffer. In Philippine land law, PD 1529 and DENR rules require buffer zones along rivers and forest edges — GIS buffering automates this: draw the river line, apply a 3-m buffer (for easement), and identify all lots that encroach.
Why it works
Bangus pond ripples are a vivid, familiar Filipino image. Connecting buffering to PD 1529 easement rules shows real-world professional relevance.
Example usage
Question: 'How is GIS buffering used in easement analysis under PD 1529?' — Buffer the riverbank by the required easement distance; parcels within the buffer are flagged for review.
Recall trigger
Stone in a bangus pond — ripples = buffer zones.
Electromagnetic spectrum regions used in remote sensing: Visible, NIR, SWIR, TIR, Microwave
Memory aid
Remember 'Very Nice Sunsets Turn Marvelous' — Visible, Near-Infrared, Short-Wave Infrared, Thermal Infrared, Microwave. Visualize standing on a Manila Bay sunset: you SEE the colors (Visible), feel the warmth beyond the red (NIR), the heat on your skin (SWIR), the scorching ground radiating heat at night (TIR), and a radar tower blinking through the fog (Microwave). Each sensation = one spectral region.
Why it works
Manila Bay sunset is a culturally beloved Filipino image. Attaching each spectral region to a physical sensation (sight, feel, heat, radar) uses multi-sensory encoding for deeper memory traces.
Example usage
Board question: 'Which EM region is used by thermal infrared sensors?' — TIR — the heat radiating from the ground at night (the 4th sensation in the Manila Bay scene).
Recall trigger
Manila Bay sunset — each physical sensation = one EM band.
Revision game
I am a satellite that never sleeps. I work at night, through typhoon clouds, using my own microwave energy. I helped map floods during Ondoy. What kind of sensor am I?
Answer
Active sensor (SAR / Synthetic Aperture Radar)
Memory link
A6 and A7 — Torch-in-the-dark and Typhoon Ondoy SAR hero story
I have 256 different shades of grey. My bit depth is 8. Use the light-switch rule to figure out: 2 to the power of what equals 256?
Answer
8 bits (2⁸ = 256 grey levels) — this is the radiometric resolution
Memory link
A4 — Volume dial analogy and 2^b formula mnemonic
A barangay captain wants to store land parcel boundaries, road centerlines, and individual survey monuments in GIS. Name the correct vector geometry type for each.
Answer
Parcel boundaries = Polygon; Road centerlines = Line; Survey monuments = Point
Memory link
A11 and Quick Recall Chain: PLP — Point, Line, Polygon
I link WHERE something is to WHAT it is. Without me, a map is just a picture with no data behind it. I am the foundation of modern spatial analysis. What am I?
Answer
GIS — Geographic Information System
Memory link
A10 — Google Maps + barangay census stapled together
A sensor has a 185-km swath and 30-m spatial resolution. How many pixels span one across-track scan line? Use the tile-counting formula.
Answer
N = 185,000 m / 30 m ≈ 6,167 pixels
Memory link
A15 — Tile-counting hallway analogy and N = W / r formula
Two GIS layers refuse to overlay correctly — the Pasig River appears to flow through Makati's CBD. What is the most likely cause?
Answer
The two layers use different coordinate systems / projections (e.g., one in PRS92/PPCS, another in WGS84/UTM). They must be re-projected to a common datum and projection.
Memory link
A14 — Mismatched projections micro-story: Pasig River through Makati
I am the best model for a continuous terrain surface (DEM), a rainfall distribution map, and a slope analysis layer. I look like a palayan viewed from above. What GIS data model am I?
Answer
Raster data model
Memory link
A12 — Palayan (rice field) visual association for raster grids
Name the four types of resolution using the SSRT mnemonic. For each, state what it actually measures.
Answer
S = Spatial (pixel ground size); S = Spectral (number/width of bands); R = Radiometric (bit depth, grey levels); T = Temporal (revisit time)
Memory link
A1 — 'Some Sensors Really Tire' acronym + tired satellite image
Formula mnemonics
N = W / r, where N = number of pixels per line, W = swath width (m), r = spatial resolution (m)
Mnemonic
N-W-R = 'Number of tiles = Width of hallway / tile Resolution (size)'. Say it: 'N equals W over R — count the tiles across the floor.'
When to use
Use this formula whenever a board problem gives you a swath width and spatial resolution and asks for the number of pixels per scan line, or rearranges to find W or r.
What each part means
N = number of pixels across one scan line; W = swath width of the sensor in metres; r = spatial resolution (ground size of one pixel) in metres.
Number of grey levels = 2^b, where b = number of bits (radiometric resolution)
Mnemonic
'2 to the Bits = Grey Levels' — think of a light switch (2) raised to the power of how many bits your sensor has. An 8-bit sensor: 2⁸ = 256 grey levels. A 12-bit sensor: 2¹² = 4,096 grey levels.
When to use
Use when asked: 'How many grey levels / digital numbers can an n-bit sensor record?' or when comparing radiometric sensitivity of two sensors.
What each part means
2 = binary base (each bit is a 0 or 1); b = radiometric resolution in bits; result = number of discrete digital numbers (DN) the sensor can record.
Ground Pixel Size = (altitude × IFOV) where IFOV is in radians
Mnemonic
'Altitude times Angle = Pixel on the Ground' — Picture shining a flashlight (IFOV cone) from a height H. The wider the cone (larger IFOV) or the higher you are (larger H), the bigger the circle of light on the ground (larger pixel). Pixel = H × IFOV.
When to use
Use when given sensor altitude and IFOV to compute spatial resolution, or when asked why flying higher increases pixel size.
What each part means
H = sensor altitude above ground (m); IFOV = Instantaneous Field of View in radians (angular size of one detector element); product = ground-projected pixel size in metres.
Quick recall chains
Four Resolutions of Remote Sensing (SSRT)
Recall test
Without looking: name all four resolution types and what each measures. Start with S... S... R... T...
Memory chain
A tired satellite named SSRT (Some Sensors Really Tire) orbits the Earth. It squints to measure PIXEL SIZE (Spatial), counts its CRAYON BANDS (Spectral), checks its BIT METER (Radiometric), and looks at its WATCH to see when it comes back (Temporal). Every orbit, it chants: S-S-R-T.
Items to remember
- Spatial — pixel ground size
- Spectral — number/width of bands
- Radiometric — bit depth / grey levels
- Temporal — revisit time
Steps in Remote Sensing Image Processing
Recall test
Name all 5 steps of image processing in order. What happens to the raw image at step 2? What does the QC officer do at step 5?
Memory chain
The acronym PRECV — 'PRocess Every Class Validly': P = Pre-processing, R = Rectification (combined into step 2), E = Enhancement, C = Classification, V = Validation. Imagine a factory assembly line: raw image enters, gets IRONED flat (rectification), POLISHED (enhancement), SORTED into bins (classification), and finally INSPECTED by a QC officer (validation).
Items to remember
- 1. Data acquisition (sensor records EM energy)
- 2. Preprocessing / Rectification (geometric and radiometric correction)
- 3. Enhancement (contrast stretch, filtering)
- 4. Classification (assign pixels to land-cover classes)
- 5. Validation / Accuracy assessment (compare with ground truth)
GIS Core Analysis Types
Recall test
Name the 5 GIS analysis types. What does a geodetic engineer use when routing the shortest survey path between monuments? (Network analysis)
Memory chain
A GIS analyst named OBNIs (O-B-N-I-S) works at NAMRIA: She Overlays maps, Buffers rivers, does Network routing, Interpolates rainfall, and runs Spatial statistics. OBNIs = Overlay, Buffer, Network, Interpolate, Statistics. Remember: 'Our Best NAMRIA Inspectors Solve (spatial problems).'
Items to remember
- Overlay analysis
- Buffering
- Network analysis
- Interpolation
- Spatial statistics
Vector Feature Types in GIS
Recall test
A barangay boundary, a road centerline, and a survey monument — match each to Point, Line, or Polygon.
Memory chain
PLP = 'Point, Line, Polygon' — a geodetic engineer's survey day: she drives to a POINT (monument), walks the LINE (traverse), then closes the POLYGON (lot boundary). PLP = her whole day's work.
Items to remember
- Point — single location (e.g., survey monument, well)
- Line — connected vertices with length (e.g., road, river, traverse leg)
- Polygon — closed area (e.g., cadastral lot, lake, municipal boundary)
Active Sensor Examples vs Passive Sensor Examples
Recall test
Is Sentinel-2 active or passive? Is LiDAR active or passive? What makes them different?
Memory chain
ACTIVE team carries TORCHES: SAR (microwave torch), LiDAR (laser torch), Radar Altimeter (pulse torch). PASSIVE team needs SUNLIGHT: Landsat, MODIS, Sentinel-2 all wait for the sun. Remember: if the sensor name has 'Radar' or 'LiDAR' — it is ACTIVE with its own torch.
Items to remember
- Active: SAR (Synthetic Aperture Radar)
- Active: LiDAR (Light Detection and Ranging)
- Active: RADAR altimeter
- Passive: Landsat OLI (optical)
- Passive: MODIS (optical/thermal)
- Passive: Sentinel-2 (multispectral optical)
Diagrams
Remote Sensing and GIS — Complete Concept Map
A hierarchical mind map of all major Remote Sensing and GIS concepts covered in the chapter. Use this as a visual index: before studying, see the big picture; after studying, test yourself by covering branches and recalling details.
Choosing the Right Sensor: Active vs Passive Decision Tree
A decision flowchart for selecting the appropriate sensor type based on conditions (cloud cover, time of day) and output requirements (spectral detail vs 3D point cloud). Follow the branches from top to bottom to determine the correct sensor for any board exam scenario.
Vector vs Raster Data Model Selection Guide
A decision flowchart for choosing between vector and raster data models in GIS. Start with the nature of the feature (discrete or continuous), then follow branches to the correct model and geometry type with Philippine-relevant examples.
Remote Sensing Technology Timeline — Key Milestones
A chronological timeline of key remote sensing technology milestones. Useful for contextualizing when each sensor type was developed and for any board questions on the history or generation of remote sensing platforms.
Distribution of Remote Sensing Bands: Landsat-8 Spectral Coverage
A pie chart showing the band distribution of Landsat-8 across the electromagnetic spectrum. Useful for visualizing how a multispectral sensor allocates its 'crayons' (bands) across different spectral regions. Landsat-8 has 11 bands total: 3 visible, 1 NIR, 2 SWIR, 1 pan, 1 cirrus, 2 TIR.