Variant B

Model answer

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

Model answer

• FAA automated system for sUAS ATC authorization in controlled airspace.

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• Chlorophyll absorbs red; leaf mesophyll scatters NIR strongly.

Model answer

• 919 km was Landsat 1–3; dropped to 705 km from L4 onward.

Model answer

• Landsat 8/9 OLI is the first Landsat pushbroom.

Model answer

AOI = Area of Interest. It’s like cropping a satellite image down to just the patch you care about. Could be a polygon, a rectangle, or a weird outline — whatever fits your study area.

Why bother? Two reasons:

• Speeds things up — the computer only crunches the area you specified, not the whole 185 km × 170 km Landsat scene.
• Keeps your training samples honest — you don’t want unrelated land-cover muddying your stats.

In ERDAS Imagine you draw it on the screen and the software respects it for whatever you do next.

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.

Model answer

The decision rule is the formula the computer uses to sort each pixel into a class.

Four common ones:

• Parallelepiped — pixel falls inside a box defined by class min/max. Fast but leaves gaps.
• Minimum Distance — pixel goes to whichever class mean is closest. Fast and never leaves anything unclassified, but ignores class shape.
• Maximum Likelihood — picks the class the pixel is most probably from, using class shape. Most accurate when classes are Gaussian.
• Mahalanobis — like Min Distance, but the distance is scaled by class shape.

The slower rules are smarter; the faster rules are dumber. Pick based on what you have time for and how Gaussian your classes look.

Model answer

Find the average color of each known class, then assign each pixel to whichever class average it’s closest to. That’s the whole rule.

Pros: - Fast. - Never leaves a pixel unclassified.

Cons: - Ignores class shape (a tight class and a loose class are treated identically). - Force-fits weird pixels that should probably be flagged as unclassified.

Good for a quick first pass. Use Max Likelihood if you want accuracy.

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).

Model answer

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

How it works

1. Find places you know — forest patches, water bodies, urban blocks.
2. Sample those — give the computer color signatures of each class.
3. Pick a decision rule — Max Likelihood is the most common.
4. Run it — every pixel in the scene gets a class label.
5. 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.

Model answer

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

How it works

1. Tell the computer how many clusters to look for (say, 30).
2. ISODATA (or K-means) iteratively groups similar pixels.
3. You inspect the result and label each cluster — “cluster 7 looks like wetland.”
4. 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.

Model answer

Moderate-resolution sensors — the workhorses of regional and global Earth observation. Roughly 10 m to 250 m per pixel.

Examples: Landsat (30 m), Sentinel-2 (10 m), MODIS (250 m+).

Advantages

• Wide swaths — one pass covers an entire state.
• Frequent revisit when you combine satellites — Landsat + Sentinel-2 ≈ every 2–3 days globally.
• Long free archives — Landsat back to 1972, Sentinel-2 since 2015.
• Solid band selection for vegetation indices, water indices, fire mapping.

Disadvantages

• Can’t see individual objects — buildings, trees, single fields blur out.
• Mixed pixels at edges — a 30 m pixel might be half-forest, half-grassland.
• Clouds wreck many scenes — even with 16-day repeat, a lot of acquisitions are unusable.
• Thermal is coarser still — Landsat thermal is 100 m, resampled to 30 m for display.

The trade-off: you sacrifice fine detail for wide coverage and frequent revisit. For continental-scale science, that’s the right trade.

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.