Variant C

Variant C — mandatory Section II: Recode + Landsat; choose 2 more from Inquire Box / IKONOS / SPOT. Mandatory Section III: Unsupervised classification; choose 1 from Supervised / Drone / Landsat applications.

Total: 140 points. Star (⭐) any question you want to carry into the final cram.

Section I — Multiple choice / matching

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2 pts LAANC stands for…

○ a. Low Altitude Authorization and Notification Capability
○ b. Large Aircraft Altitude Notification Center
○ c. Licensed Airspace And No-fly Coordination
○ d. Landsat Atlas And Navigation Catalog

Reveal answer

Correct: Low Altitude Authorization and Notification Capability

Model answer

FAA automated system for sUAS ATC authorization in controlled airspace.

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2 pts Landsat MSS, TM, and ETM+ use which scanning geometry?

○ a. Across-track (whiskbroom, discrete detectors + mirror)
○ b. Along-track (pushbroom, linear array)
○ c. Staring array
○ d. Conical scan

Reveal answer

Correct: Across-track (whiskbroom, discrete detectors + mirror)

Model answer

Landsat 8/9 OLI is the first Landsat pushbroom.

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2 pts Part 107 minimum flight visibility is…

○ a. 3 statute miles
○ b. 1 statute mile
○ c. 5 statute miles
○ d. 10 statute miles

Reveal answer

Correct: 3 statute miles

Model answer

Measured at a slant from the control station.

💡

3 SM visibility. Pair with 500 ft below + 2000 ft horizontal cloud clearance — the '3-5-2' rule.

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2 pts In ISODATA, the **T** parameter is…

○ a. A convergence threshold — % of pixels unchanged between iterations.
○ b. Maximum number of iterations.
○ c. Number of clusters.
○ d. Spectral distance metric.

Reveal answer

Correct: A convergence threshold — % of pixels unchanged between iterations.

Model answer

N = max clusters, M = max iterations, T = convergence threshold.

💡

N-T-M: Number, Threshold, Max-iterations. T is a PERCENT (e.g., 95% pixels stable). M is an iteration COUNT. Don't swap T and M.

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2 pts Stefan–Boltzmann: total emitted radiation from a blackbody scales with…

○ a. T⁴
○ b. T
○ c. T²
○ d. T³

Reveal answer

Correct: T⁴

Model answer

M = σT⁴. Small temperature changes = big radiance changes.

💡

Stefan has 4 letters. T⁴. Or: FOURTH power. Double temp → 16× radiance.

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2 pts What is the formula for NDVI?

○ a. (NIR − Red) / (NIR + Red)
○ b. NIR / Red
○ c. (Red − NIR) / (Red + NIR)
○ d. (NIR + Red) / (NIR − Red)

Reveal answer

Correct: (NIR − Red) / (NIR + Red)

Model answer

NDVI = (NIR − Red) / (NIR + Red). Range −1 to +1.

💡

NIR comes FIRST (it's bigger for vegetation). Difference on top, sum on bottom. NIR/Red is the Simple Ratio — a different index.

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2 pts TM's Band 6 (thermal) has a spatial resolution of…

○ a. 120 m
○ b. 30 m
○ c. 60 m
○ d. 100 m

Reveal answer

Correct: 120 m

Model answer

TM thermal was 120 m; ETM+ improved to 60 m; TIRS is 100 m.

💡

Thermal got BETTER over time: TM 120 → ETM+ 60 → TIRS 100 (resampled to 30). Don't confuse 30 m (VSWIR bands) with the much coarser thermal.

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2 pts The Sun's peak emission wavelength (Wien's Law, T ≈ 6000 K) is approximately…

○ a. 0.48 µm
○ b. 9.66 µm
○ c. 1.0 µm
○ d. 0.10 µm

Reveal answer

Correct: 0.48 µm

Model answer

λ_max = 2897.8 / 6000 ≈ 0.483 µm (visible green-blue).

💡

Sun peak = 0.48 µm (visible). Earth peak = 9.66 µm (thermal). Don't swap them. Big 9.66 goes with COOL Earth.

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2 pts Supervised classification REQUIRES…

○ a. A priori knowledge of land-use types and sample locations.
○ b. No training data — the computer infers classes.
○ c. Only an image and a decision rule.
○ d. A DEM and atmospheric profile.

Reveal answer

Correct: A priori knowledge of land-use types and sample locations.

Model answer

Unsupervised (ISODATA) is the one that needs no a priori knowledge.

💡

Supervised = teacher needed (training samples). If the question says 'needs NO training data' that's UNSUPERVISED (ISODATA).

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2 pts Healthy vegetation is characterized by…

○ a. Low red reflectance, high NIR reflectance
○ b. High red reflectance, low NIR reflectance
○ c. Low red reflectance, low NIR reflectance
○ d. High red reflectance, high NIR reflectance

Reveal answer

Correct: Low red reflectance, high NIR reflectance

Model answer

Chlorophyll absorbs red; leaf mesophyll scatters NIR strongly.

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2 pts Class C airspace typically extends to…

○ a. 4 000 ft AGL
○ b. 10 000 ft MSL
○ c. 2 500 ft AGL
○ d. 1 200 ft AGL

Reveal answer

Correct: 4 000 ft AGL

Model answer

Inner 5 NM ring SFC–4000 AGL; outer 10 NM shelf 1200–4000 AGL.

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2 pts On Landsat TM, Band 4 corresponds to…

○ a. NIR (0.76–0.90 µm)
○ b. Red (0.63–0.69 µm)
○ c. SWIR (1.55–1.75 µm)
○ d. Thermal (10.4–12.5 µm)

Reveal answer

Correct: NIR (0.76–0.90 µm)

Model answer

TM band order: 1 Blue, 2 Green, 3 Red, 4 NIR, 5 SWIR-1, 7 SWIR-2, 6 Thermal.

💡

TM Band 4 = NIR (for NDVI: (B4−B3)/(B4+B3)). Don't confuse with OLI Band 4 = Red (OLI shifted numbering by 1).

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2 pts IKONOS radiometric resolution is…

○ a. 11-bit (2 048 grey levels)
○ b. 8-bit (256 grey levels)
○ c. 12-bit (4 096 grey levels)
○ d. 14-bit (16 384 grey levels)

Reveal answer

Correct: 11-bit (2 048 grey levels)

Model answer

Delivered as 16-bit unsigned with 5 bits of zero padding.

💡

IKONOS = ELEVEN bits (2048). SPOT/Landsat 5-7 = 8-bit (256). L8 = 12-bit. L9 = 14-bit. IKONOS was best-in-class for its era.

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2 pts Part 107 cloud clearance requirements are…

○ a. 500 ft below + 2 000 ft horizontal
○ b. 500 ft above + 2 000 ft horizontal
○ c. 1 000 ft below + 1 SM horizontal
○ d. 500 ft below + 1 SM horizontal

Reveal answer

Correct: 500 ft below + 2 000 ft horizontal

Model answer

Reduces the chance of a manned aircraft exiting a cloud on a collision course.

💡

500 BELOW, 2000 HORIZONTAL. Drones fly UNDER clouds (500 below), well AWAY from edges (2000). Not ABOVE, not 1 SM.

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2 pts On a sectional chart, Class B airspace is marked by…

○ a. Solid blue lines
○ b. Solid magenta lines
○ c. Blue dashed lines
○ d. Magenta dashed lines

Reveal answer

Correct: Solid blue lines

Model answer

Memorize: B = Big, Busy, solid Blue.

💡

B = Blue, solid. C = magenta, solid. D = blue, DASHED. E surface = magenta, dashed. B/C solid, D/E-surface dashed.

Section II — Short answer

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10 pts mandatory Recode — what it does and when it's used.

Reveal answer

Model answer

After classification, your image often has more spectral classes than meaningful ones. Recode lets you collapse them.

Example: ISODATA finds 30 clusters. You inspect them and realize 5 are all just different shades of forest, 4 are all urban, 3 are all water. Use Recode to merge them down to a handful of real classes — Forest, Urban, Water, Crop — for a clean final map.

Cleanup tool. Always used after classification, never before.

🔬 Show the science / technical version

Post-classification tool that reassigns class values in a thematic raster.
Renumber the New Value column to merge or rename classes (e.g., 3 forest subclasses → 1 Forest class).
Runs after a classification to simplify the final map.
In ERDAS Imagine: Raster → Thematic → Recode.

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10 pts mandatory Landsat — altitude, repeat, sensors, band cheat sheet.

Reveal answer

Model answer

Landsat is the granddaddy of Earth observation — running continuously since 1972, longer than any other civilian satellite series.

It captures the whole Earth every 16 days (or every 8 days now, with Landsat 8 + 9 working together) at moderate detail (30 m). The data is free, which is why it’s the backbone of every long-term land-change study you’ve heard of: Amazon deforestation, glacier retreat, urban sprawl, agriculture monitoring.

Modern sensors are OLI-2 (visible/NIR/SWIR) and TIRS-2 (thermal infrared).

🔬 Show the science / technical version

Longest continuous EO record (1972–present, USGS / NASA).
Orbit 705 km (L4 onward), sun-synchronous, 16-day repeat (L8 + L9 combined ≈ 8-day).
Modern sensors: OLI-2 (9 bands @ 30 m, pan @ 15 m) + TIRS-2 (2 thermal @ 100 m).
Heritage TM/ETM+ numbering: 1 Blue, 2 Green, 3 Red, 4 NIR, 5 SWIR-1, 7 SWIR-2, 6 Thermal.
NDVI: TM B4/B3 → OLI B5/B4 (numbering shifted by 1).

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10 pts choice Inquire Box — what it is and how it differs from the Inquire Cursor.

Reveal answer

Model answer

The Inquire Box is a draggable rectangle in ERDAS Imagine. You drag it over the part of the image you want, then use the Subset tool to save just that crop as a smaller file.

Different from the Inquire Cursor — that one’s a single-point query (tells you the values at one pixel). The Box is for cropping; the Cursor is for spot-checking.

🔬 Show the science / technical version

Resizable rectangle drawn on an image in ERDAS Imagine.
Used to subset the scene (Subset & Chip → From Inquire Box).
Different from the Inquire Cursor, which reports pixel values + geographic coordinates at one point.

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10 pts choice IKONOS — operator, altitude, bands, radiometric resolution.

Reveal answer

Model answer

IKONOS was the first U.S. commercial sub-meter satellite — launched 1999, retired 2015. Sub-meter means you can pick out individual cars and rooftops from space.

Big deal because before IKONOS, that resolution was military-only.

It carries a high-detail black-and-white panchromatic band plus four lower-detail color bands, which you fuse together (“pan-sharpening”) to get color at the pan resolution.

It paved the way for today’s commercial high-res constellation — WorldView, GeoEye, Pléiades, SkySat.

🔬 Show the science / technical version

First commercial sub-meter satellite (Space Imaging, Sept 1999 – Mar 2015).
Altitude ~681 km, sun-synchronous.
Bands: Pan 0.45–0.90 µm at 1 m; 4 MS bands (B/G/R/NIR) at 4 m.
Radiometric 11-bit → 2 048 levels.
Off-nadir up to ±45°, swath ~11–13 km, revisit 1–3 days.

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10 pts choice SPOT — nation, orbit altitude, band/resolution, notable feature.

Reveal answer

Model answer

SPOT is the French Earth-observation satellite series (“Satellite Pour l’Observation de la Terre”).

Think of it as a smaller Landsat with two cool tricks:

Tilting cameras — the sensor can swing sideways to image areas off the satellite’s path, which gets you stereo pairs (3D!) and faster revisit.
Finer detail — 10 m panchromatic vs. Landsat’s 30 m.

Quirk to remember: SPOT 1, 2, and 3 have no blue band — only green, red, and NIR. So you can’t make a natural-color image from those satellites.

Series ran from 1986 (SPOT 1) to current SPOT 6/7.

🔬 Show the science / technical version

French/European series from CNES; SPOT = Satellite Pour l’Observation de la Terre.
SPOT 1–3 altitude 832 km sun-synchronous; SPOT 6/7 altitude 694 km.
HRV bands: green, red, NIR (+SWIR on SPOT-4+); 10 m pan / 20 m MS.
Off-nadir pointing (±27°) → stereo imagery + shorter revisit (1–4 days).

Section III — Explanation / essay

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15 pts mandatory Unsupervised classification — details, advantages, disadvantages.

Reveal answer

Model answer

Unsupervised classification — let the computer find natural groupings, label them after.

How it works

Tell the computer how many clusters to look for (say, 30).
ISODATA (or K-means) iteratively groups similar pixels.
You inspect the result and label each cluster — “cluster 7 looks like wetland.”
Often merge redundant clusters with the Recode tool.

Strengths

No training data required — fast first look at unfamiliar imagery.
Reveals natural groupings the analyst didn’t predict.
Useful as a starter before doing supervised classification, or for stratified sampling.

Weaknesses

Clusters don’t always match meaningful categories — one class may split across multiple clusters, one cluster may contain several classes.
Sensitive to K and initial seeds — different runs give different results.
Post-classification cleanup is still required — labeling, merging, re-running with different K.

🔬 Show the science / technical version

Procedure (ISODATA / K-means)

Choose number of clusters.
Iterate: assign pixels to nearest centroid → recompute centroids → repeat until convergence.
Analyst labels each cluster post-hoc from reference data.

Advantages

No training data required — fast first pass.
Reveals natural spectral groupings the analyst might not have predicted.
Useful as preprocessing for supervised classification or for stratified sampling.

Disadvantages

Clusters don’t always map to meaningful classes (one class may split; one cluster may contain several classes).
Sensitive to K and initial seeds — different runs give different results.
Post-classification labeling/merging (often via Recode) still required.

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15 pts choice Supervised classification — details, advantages, disadvantages.

Reveal answer

Model answer

Supervised classification — you teach the computer what each class looks like, then it sorts every pixel.

How it works

Find places you know — forest patches, water bodies, urban blocks.
Sample those — give the computer color signatures of each class.
Pick a decision rule — Max Likelihood is the most common.
Run it — every pixel in the scene gets a class label.
Check accuracy — compare to ground-truth points you didn’t use for training (confusion matrix, kappa).

Strengths

Higher accuracy than unsupervised when your training data is good.
You control the class scheme — useful when you have a specific question.
Works well when classes are spectrally distinct.

Weaknesses

Training-sample collection is slow — fieldwork, photo interpretation, ground truth.
Bias risk — if you forget a class, the computer can’t predict it.
Max Likelihood assumes Gaussian classes — fails on weird-shaped distributions.

🔬 Show the science / technical version

Procedure

Define training sites (AOIs) for each known class.
Compute class signatures (mean + covariance per band).
Apply a decision rule — Min Distance, Mahalanobis, Max Likelihood, Parallelepiped — to every pixel.
Accuracy assessment (confusion matrix, kappa).

Advantages

Analyst controls the class scheme — matches the question.
Higher accuracy than unsupervised when training data is good.
Works well when classes are spectrally distinct + well-sampled.

Disadvantages

Labor-intensive — good ground truth takes time.
Bias risk — unrepresented classes won’t be predicted.
Max Likelihood assumes multivariate-normal class distributions.

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15 pts choice Advantages and disadvantages of drone (UAS) technology, with examples.

Reveal answer

Model answer

Drones (UAS) — strengths and weaknesses for remote sensing.

Strengths

Centimeter-level detail — way beyond any satellite.
Fly on demand — your schedule, under cloud cover, on any day.
Cheap per flight — orders of magnitude less than tasking a satellite or chartering a plane.
Swap sensors freely — RGB, thermal, multispectral, LiDAR, hyperspectral.
Perfect for small AOIs — a farm field, a construction site, a wildlife habitat.

Weaknesses

Tiny footprint — covers only a few acres at a time, so a regional project means many flights.
Heavy regulation — FAA Part 107: stay under 400 ft, in line-of-sight, away from controlled airspace, daylight only.
Weather-dependent — wind grounds you, rain damages the gear, cold kills the battery.
Short flights — typically 20–40 minutes per battery.
Big files — gigapixel orthomosaics fill drives fast.
Privacy / safety — public reactions are mixed.

🔬 Show the science / technical version

Advantages

Cm-level spatial resolution — far beyond any satellite.
On-demand scheduling, under clouds, on your schedule.
Low cost per flight vs. aircraft or satellite tasking.
Flexible payloads — RGB, multispectral, thermal, LiDAR, hyperspectral.
Ideal for small AOIs: field-scale ag, construction, infrastructure, wildlife.

Disadvantages

Regulatory burden — FAA Part 107, altitude / line-of-sight / no-fly zones.
Weather-limited — wind, rain, cold batteries.
Small footprint → many flights to cover a region.
Battery endurance ~20–40 min per flight.
Data management — gigapixel orthomosaics strain storage & processing.
Privacy and public-safety concerns.

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15 pts choice Applications of the Landsat satellite series, with examples.

Reveal answer

Model answer

What Landsat is good for — basically watching the Earth’s surface change, decade after decade.

Forests — tracking deforestation in the Amazon, Borneo, Congo. Map every year, see what’s gone.
Cities — urban growth, sprawl, paving over farmland.
Agriculture — crop type maps, yield forecasts (track how green fields get during the season).
Water — reservoir levels, glacier retreat, algal blooms in the Great Lakes.
Disasters — wildfire burn scars, flood extent, volcanic activity.
Climate — long-term surface-temperature trends, snow-cover decline.

Why it works: Landsat has been running since 1972, the imagery has been FREE since 2008, and a single scene covers ~185 km × 170 km. Hard to find a serious land-change study that doesn’t lean on Landsat somewhere.

🔬 Show the science / technical version

Continuity since 1972 — decadal change detection unmatched by any other system.
Land-cover / land-use change — Amazon deforestation, urban growth, wetland loss.
Agriculture — crop-type mapping, yield forecasting via NDVI, irrigation monitoring.
Water — reservoir extent, glacier retreat, algal blooms (thermal + VSWIR).
Disaster response — wildfire burn scars, flood extent, volcanic activity.
Climate — long-term surface-temperature records, snow-cover trends.
Free & open data policy (since 2008) — the single biggest multiplier of Landsat’s scientific impact.

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