70 cards showing.
UAV vs UAS — difference?
maybe dronePart 107 regulations talk in terms of systems, not just vehicles.
Wien’s Displacement Law — formula and the two peak wavelengths to know?
essential emrHot things glow short, cool things glow long. That’s the whole law in plain terms.
That’s exactly why we use VIS/NIR sensors to capture reflected sunlight, and thermal infrared sensors to capture Earth’s own heat — at night, in the dark, all the time.
λ_max = 2897.8 / T (µm, T in K)
Hotter → shorter peak wavelength.
Hot = short, cool = long. Sun hot → 0.48 µm (visible). Earth cool → 9.66 µm (thermal IR). That's why we use VIS/NIR for reflected solar and thermal IR for emitted Earth.
Stefan–Boltzmann Law?
essential emrHow brightly something glows depends on its temperature to the fourth power — meaning a small temp jump produces a huge brightness jump.
That’s why a campfire feels exponentially warmer than warm tea, and why thermal satellite sensors can detect 1°C differences across a landscape — the signal isn’t subtle once you know to look at the right wavelength.
M_λ = σ · T⁴ — total emitted power per m² from a blackbody.
ε · σ · T⁴ (ε = emissivity)SB = Super Boost. T to the FOURTH — double the temp, 16× the energy. That's why a 300 K Earth emits radiation at all.
Three types of atmospheric scattering?
essential emrClassified by particle size relative to wavelength:
Rayleigh → blue sky (short wins). Mie → haze (particles match wavelength). Non-selective → white clouds (big droplets scatter ALL colors equally = white).
Active vs passive remote sensing?
likely emrMnemonic: passive listens; active shouts and listens.
Passive = listens. Active = shouts and listens. Active works at night (radar/LiDAR don't need the sun).
Atmospheric windows — which spectral regions transmit well?
likely emrBands where the atmosphere lets EM radiation through to the sensor:
⚠️ Blocked regions are dominated by H₂O and CO₂ absorption.
Photo scale formula for an aerial photograph?
likely airbornePhoto scale is just focal length divided by flying height.
Same trade-off as your phone camera — wide-angle covers more, telephoto zooms in. Aerial cameras are the same physics, just a few thousand feet up.
📐 S = f / H
Three ways to express scale: - 🗣️ Verbal: “1 cm = 1 km” - 🔢 Ratio (RF): 1:100 000 - 📏 Graphic bar: drawn on the map
⚠️ Larger denominator = smaller scale. A 1:100 000 map shows less detail than 1:10 000.
Vertical vs oblique aerial photography — the 3° rule?
likely airborneMnemonic: nadir is dead-center, oblique is at an angle.
True-color composite on Landsat TM — band-to-color-gun mapping?
likely airborneBand Combination 3-2-1 on Landsat TM:
Each band feeds the matching color gun → image looks roughly natural.
⚠️ On Landsat 8/9 OLI (numbering shifted by 1 because coastal/aerosol = Band 1): - 🟥 R ← Band 4 (Red) - 🟩 G ← Band 3 (Green) - 🟦 B ← Band 2 (Blue)
True color = 3-2-1 countdown (on TM). Each band matches its real color. OLI just shifts everything up by 1.
False-color (CIR) composite on Landsat TM — band-to-color-gun mapping?
likely airborneBand Combination 4-3-2 (color-infrared, CIR) on Landsat TM:
Each band feeds the next display gun up — one color shifted.
⚠️ On Landsat 8/9 OLI: R=B5, G=B4, B=B3.
🔎 Field check: if vegetation looks bright red, you’re looking at CIR.
False color = 4-3-2 (one higher than true color). NIR → Red gun = vegetation glows BRIGHT RED. If veg is red in your image, it's CIR.
Landsat 9 — launch date, altitude, sensors?
essential landsat705 km, 2021. OLI-2 + TIRS-2 (the '-2' matches Landsat-9). With L8 flying opposite, combined revisit ≈ 8 days.
Landsat TM — 7 bands and their resolutions?
essential landsatTM has 7 spectral bands. All 30 m except thermal at 120 m.
⚠️ Numbering oddity: 1-2-3-4-5-7-6. Band 7 was added later and squeezed in spectrally between 5 and 6 but numbered last.
Bands 1-2-3-4-5-7-6. Yes, 7 before 6. All 30 m except thermal band 6 at 120 m. Watch for 'Band 4 = NIR' (on TM) vs 'Band 5 = NIR' (on OLI).
Landsat WRS-2 — path/row grid specs?
likely landsatLandsat 1–3 orbit — altitude + revisit?
maybe landsat⚠️ Orbit was lowered to 705 km at Landsat 4 for better spatial resolution. That’s why later Landsats have 16-day revisit instead of 18.
Landsat 7 ETM+ — what it added vs. TM?
maybe landsatTwo upgrades over the older TM:
Landsat MSS band numbering — why does it differ across satellites?
maybe landsatMSS flew on Landsat 1/2 and Landsat 4/5 — but the bands got renumbered. Same wavelengths, new numbers:
⚠️ MSS has no blue band — visible spectrum starts at green. That’s why MSS imagery can’t be made into natural-color composites.
Radiometric resolution bit depths?
maybe landsatMore bits → finer tonal detail in shadows + highlights.
The three types of optical remote sensing?
essential sensorsMulti = few (tens), Hyper = many (hundreds), Thermal = heat only. 'Hyper' literally means MORE — that's the clue.
Across-track vs along-track scanners — examples?
essential sensorsTwo scanning geometries used by multispectral sensors:
Whiskbroom WIGGLES a mirror (Landsat 1–7). Pushbroom PUSHES a linear array straight ahead (SPOT, IKONOS, Landsat 8/9 OLI). No wiggle, no breaking part.
IKONOS — altitude, bands, radiometric resolution?
essential sensorsIKONOS = 1 meter pan, 4 meter MS, 11-bit, at 681 km. Sub-meter commercial pioneer. Tilts ±45° (more than SPOT's ±27°).
SPOT 1–3 — altitude, bands, orbit?
essential sensors⚠️ No blue band on SPOT 1–3 → can’t make natural-color composites.
SPOT 1-3: 832 km (higher than IKONOS 681), 10 m pan / 20 m MS, no blue (Green-Red-NIR only). Off-nadir ±27°. French (CNES).
Hyperion (on EO-1) — specs?
likely sensors⚠️ The hallmark of hyperspectral: hundreds of narrow contiguous bands (vs. multispectral’s handful of broad bands).
Pushbroom over whiskbroom — four advantages + one disadvantage?
likely sensors✅ Advantages - ⚙️ No moving mirror → more reliable, longer mission life - ⏱️ Longer dwell time per pixel → better signal-to-noise - 📦 CCDs are smaller, lighter, lower power than scanning optics - 🎯 More accurate radiometry per detector
❌ Disadvantage - 🎚️ Calibrating thousands of detectors uniformly is hard — each CCD has slightly different gain/offset
What does a sun-synchronous orbit guarantee?
likely sensorsA near-polar orbit tuned so the orbital plane precesses at Earth’s orbital rate around the Sun.
SPOT HRV off-nadir capabilities?
likely sensorsIKONOS does the same but with a wider tilt range (±45°).
SPOT 1–3 MS bands?
maybe sensorsAll multispectral bands at 20 m:
Also: 🩶 Pan 0.51–0.73 µm at 10 m.
⚠️ No blue band on SPOT 1–3 → no natural-color composites.
WGS 84 — key facts?
maybe sensorsWGS 84 — coordinate origin accuracy?
probably not sensorsCoordinate origin (Earth’s center of mass) is defined to within about 2 cm.
IKONOS successors — the current sub-meter commercial satellites?
probably not sensorsAll inherit IKONOS’s tasked sub-meter commercial model.
SPOT launches + retirements?
probably not historyFrench/European Earth-observation series, run by CNES.
⚠️ SPOT 1–3 have no blue band — important quirk on the older satellites.
What happened to Landsat 6?
probably not historyLaunch failure in 1993. Failed to reach orbit due to a propellant leak. First gap in Landsat continuity.
First IKONOS image — where and when?
probably not historyWashington, D.C., late 1999 (shortly after the Sept 24, 1999 launch). A landmark — sub-meter civilian imagery had been restricted to defense until then.
IKONOS — six product levels?
probably not historySold by square kilometer.
First vegetation index — history?
probably not historySimple Ratio (SR) = NIR / Red. Originally used by Jordan (1969); Cohen (1991) called it the first true vegetation index. NDVI (Rouse 1974) fixed SR’s unbounded range.
Anderson LULC classification — year?
probably not historyBayesian decision rule — citation?
probably not historyHord (1982). The Bayesian variant of Max Likelihood lets you enter prior probabilities instead of assuming equal priors for all classes.
Rouse et al. (1974) NDVI — what data was it developed on?
probably not historyAVHRR imagery of the US Great Plains. The normalized-difference form kept values bounded (unlike SR) and reduced illumination variability — became the canonical VI.
NDVI — formula and range?
essential vegPlants pull a sneaky trick with light: chlorophyll drinks red light for photosynthesis but bounces back invisible near-infrared. NDVI just compares the two bands — the bigger the gap, the healthier the vegetation. Numbers land between −1 (water / shadow) and +1 (lush rainforest). The middle is bare ground.
It’s basically a ‘how green is this place’ score that NASA, USDA, and every satellite ag company uses every day.
NDVI = (NIR − Red) / (NIR + Red). Range −1 to +1. Rouse et al. (1974).
NDVI = Normalized to −1..+1. Unlike SR which has no upper bound, and unlike EVI which adds the Blue band.
Simple Ratio (SR) — formula and who’s credited?
essential vegThe first vegetation index. Divide NIR by Red — see how much more NIR came back. A jungle gives you 30+, a parking lot gives you 1. Simple but the number has no upper limit, so you can’t easily compare scenes against each other.
That’s why NDVI replaced it for most modern work — same idea, but locked between −1 and +1.
SR = NIR / Red. Cohen (1991) identified it as the first true vegetation index. On Landsat TM: Band 4 / Band 3.
Simple Ratio = Simple division. No subtraction, no normalization — just NIR/Red. Unbounded. Cohen 1991 (not Rouse, that's NDVI).
EVI — formula and coefficients?
essential vegThink of EVI as NDVI’s smarter cousin. It does two extra tricks:
Use EVI when NDVI flatlines — i.e., over thick rainforest canopy where every leaf already maxes NDVI out. EVI keeps climbing where NDVI gets stuck.
EVI = [(NIR − Red) / (NIR + C₁·Red − C₂·Blue + L)] · (1 + L)
C₁ = 6.0, C₂ = 7.5, L = 1.0 (MODIS standard). Corrects NDVI for atmosphere (blue band) and soil (L).
EVI = Enhanced with the Blue band. Coefficients 6-7-1 (six-seven-one). Doesn't saturate in dense canopy like NDVI does.
Spectral signature of healthy vegetation?
essential vegThat contrast is why NDVI, SR, and EVI all work.
Leaves DRINK red (chlorophyll eats it), BOUNCE NIR. That 'red dip, NIR jump' is THE vegetation fingerprint.
NDVI typical value ranges by land cover?
likely vegPhenology — definition and why it matters for RS?
likely vegPhenology — periodic biological phenomena tied to annual climate (🌱 planting → 🌿 greening → 🌾 senescence → 🚜 harvest).
Why it matters for remote sensing: - 📅 Imaging date changes what you see - 🌾 Peak biomass maximizes vegetation / soil contrast - 📊 Multi-date NDVI series separates crops that overlap when green
NDVI of AVHRR imagery — what does it show?
maybe vegContinental US NDVI through the seasons (Jan → Dec):
🛰️ AVHRR on NOAA polar orbiters, ~1 km resolution.
Why does EVI need blue, C1, C2, and L?
maybe vegResult: EVI doesn’t saturate in high-biomass canopies the way NDVI does.
Beyond standard VIs — two linear transforms?
maybe vegSupervised vs. unsupervised classification — key differences?
essential classificationTwo camps of pixel classification:
After unsupervised you still label the clusters by hand — that’s where Recode comes in.
Supervised needs a TEACHER (training samples). Unsupervised is CLUSTERING — the computer invents the classes, you label afterward.
ISODATA — three parameters the user must set?
essential classificationN-T-M: Number (clusters), Threshold (convergence %), Max-iterations. Terminates on whichever hits first: T reached or M hit.
Maximum Likelihood classifier — how it works and its main assumption?
essential classificationPicture each land-cover class (forest, water, city) as a fuzzy cloud floating in spectral space. Max Likelihood asks the question:
“Which cloud is this pixel most likely sitting inside?”
Crucially, it considers each cloud’s shape (covariance), not just its center. Min Distance only looks at the center — it’s like deciding which city you’re closest to without checking which one’s borders you’re inside.
That’s why ML usually wins on accuracy: it understands a tightly-grouped class is more confident, while a wide loose class lets in more variety.
For each pixel, compute the probability it belongs to each class. Assign to the most likely class.
ML uses **mean + covariance**, not just the mean. Min Distance ignores shape (just the mean). Mahalanobis adds shape. Max Likelihood adds priors on top. The full discriminant formula lives in the long-form review — the *concept* lives here.
Supervised classification — three-step procedure?
essential classificationTrain → Stats → Classify. You TEACH the computer with samples, it LEARNS signatures (mean + covariance), then APPLIES a decision rule to every pixel.
Four common decision rules for supervised classification?
likely classificationThe four ways the computer assigns a pixel to a class, ranked by sophistication:
Minimum Distance classifier — advantages and disadvantages?
likely classification✅ Advantages - ⚡ No unclassified pixels (every pixel has some nearest mean) - 🚀 Very fast decision rule
❌ Disadvantages - 🎯 Force-fits outlier pixels that should be flagged unclassified - 📐 Ignores class variability — treats tight clusters and loose clusters equally
Min Distance: everyone gets a class (no gaps) but weird pixels get force-fit. Ignores shape. Opposite of Parallelepiped (which leaves gaps).
Parallelepiped classifier — advantages and disadvantages?
likely classification✅ Advantages - ⚡ Very simple, very fast - 🎯 Good first-pass broad classification
❌ Disadvantages - 🕳️ Gap regions — pixels outside all boxes stay unclassified - ↗️ Corner overlaps — pixels in two boxes get assigned ambiguously
Boxes. Pixel in a box → class. Pixel in NO box → unclassified (gap). Pixel in 2+ boxes (corner overlap) → wrong class. Fast but sloppy.
Anderson (1976) LULC — nine Level I classes?
likely classification📚 Level II adds 37 subclasses total — used as the standard hierarchical scheme for RS data.
Covariance between two bands — formula?
maybe classificationMeasures how Band Q and Band R vary together around their means. The full covariance matrix is what Max Likelihood and Mahalanobis use.
ISODATA convergence threshold T — worked example (10 pixels, 3 changed)?
maybe classificationIf user set T = 95%, 70% is not enough → run another iteration.
METAR field order?
likely weatherExample: KDKB 151235Z AUTO 01011KT 10SM OVC110 02/M01 A3013 RMK AO2
Cloud base estimate from temp/dewpoint spread?
likely weather📐 Rule of thumb: cloud base (ft AGL) ≈ (T − Td) °C × 400
METAR gives you T/Td as 02/M01 (M = minus). Compute the spread, multiply by 400.
Convective SIGMET — what is it and when is it issued?
maybe weatherSignificant Meteorological advisory for aviation: - Severe thunderstorms / lines of thunderstorms / embedded storms - Hail ≥ ¾ inch, tornadoes - Issued by Aviation Weather Center, CONUS - Valid ~2 hours
If active in your area — do not fly.
Part 107 weather minimums?
essential part1073-5-2: three statute miles visibility, five hundred feet below clouds, two thousand feet horizontal.
Part 107 maximum altitude for sUAS?
essential part107400 ft AGL above ground, or within 400 ft of a structure (above the structure’s height).
Max ground speed: 100 mph.
Four hundred and no more — unless you're inspecting a tall structure, then stay within 400 ft of it.
LAANC — what is it?
maybe part107Low Altitude Authorization and Notification Capability — FAA’s automated system for sUAS operators to get near-real-time authorization to fly in controlled airspace (B/C/D/E-surface).
Accessed via FAA-approved apps like Aloft and Airmap.
Class A airspace — altitude?
essential airspaceFL 180 – FL 600 MSL (18 000 ft to 60 000 ft MSL). IFR only. Not a drone concern — sUAS capped at 400 ft AGL.
A = Airliners. Above FL180. Never a drone's problem.
Class B airspace — altitudes, sectional marking, drone rule?
essential airspaceB = Big, Busy, Blue.
B = Big, Busy, Blue. Solid Blue lines. Wedding-cake tiers to 10 000 MSL.
Class C airspace — altitudes, sectional marking, drone rule?
essential airspaceC = Solid magenta (think 'Cs = solid'). 4000 AGL ceiling. Rings 5/10 NM.
Class D airspace — altitudes, sectional marking?
essential airspaceD = Dashed blue Donut.
D = Dashed blue Donut. Small 4-5 NM cylinder, 2500 AGL.
Class E airspace — three common variants and their sectional markings?
essential airspaceE = Everywhere-else controlled. Three flavors:
⚠️ Only the surface-designated version requires LAANC.
E = Everywhere-else controlled. Dashed magenta = surface E (authorization). Shaded magenta = 700 AGL floor. Shaded blue = 1200 AGL floor.
MSL vs AGL — definitions and sectional chart rule?
essential airspace(12) means 1 200 ft AGL.Parens = AGL. No parens = MSL. Always add 00. So 70/30 = 7000/3000 MSL; (12) = 1200 AGL.
GOES satellites — orbit and primary instrument?
likely airspace⚠️ Don’t confuse with Landsat (705 km, polar). Geostationary = 0° inclination, very high altitude.
GOES parks over the equator at ~36 000 km. Don't confuse with Landsat (705 km, polar). Geostationary = zero inclination, matches Earth's rotation.
CTAF, ATIS, AWOS, ASOS — what are they?
maybe airspace