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ICAR AICE-JRF/SRF Forest GIS, GNSS & LiDAR Analytics: practice questions

ICAR AICE-JRF/SRF (PhD) — Subject 59 Forestry · India · National Testing Agency for ICAR

Original practice questions written to the published syllabus · not past papers
10worked here, free
28written to this exam in this subject
10syllabus units

What this subject covers

Spatial data and analysis for forest management: GIS data models, spatial analysis, GNSS, airborne and terrestrial LiDAR, and the digital tools of precision forestry. General remote sensing and GIS for land and water is in the Agricultural Engineering track (remote-sensing-gis).

In the exams: ICAR AICE-JRF 59 Unit 4 (GIS and remote sensing concept and scope); GISP and ASPRS certifications; growing share of SAF and FPBC professional practice

Where the 28 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Forest GIS, GNSS & LiDAR Analytics. Difficulty: 3 easy, 15 medium, 10 hard.

Area-based and individual-tree LiDAR inventory6
Terrestrial and mobile laser scanning4
Spatial analysis: overlay, buffer, terrain analysis3
GNSS data collection and accuracy3
Airborne LiDAR: point clouds, canopy height models3
Spatial decision support and multi-criteria analysis3
GIS concepts: vector and raster data, projections, datums2
Open data and cloud platforms for forest monitoring2
Forest stand and compartment databases1
Map production and cartographic standards1

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · GIS concepts: vector and raster data, projections, datums · hard

Why is the spherical Web Mercator projection used by many online base maps unsuitable for measuring forest area at high latitudes?

  1. It shrinks areas strongly as latitude increases towards the poles
  2. It inflates areas strongly as latitude increases
  3. It cannot represent polygons, only raster tiles
  4. It preserves area but distorts every angle
Answer and working

B. It inflates areas strongly as latitude increases

Mercator-type projections enlarge areas towards the poles (the scale factor rises with the secant of latitude), so areas far from the equator are overstated. It is conformal, not equal-area.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

2 · Spatial analysis: overlay, buffer, terrain analysis · medium

A classified Landsat image has 12,000 forest cells of 30 m × 30 m. The forest area is:

  1. 360 ha
  2. 108 ha
  3. 1,080 ha
  4. 10,800 ha
Answer and working

C. 1,080 ha

Each cell = 900 m² = 0.09 ha; 12,000 × 0.09 = 1,080 ha. 360 multiplies by 30 m instead of 900 m²; 108 and 10,800 are factor-of-ten slips.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

3 · Spatial analysis: overlay, buffer, terrain analysis · medium

In the D8 flow-direction method, each DEM cell drains to:

  1. All lower neighbours in proportion to their drop
  2. The neighbour with the highest elevation
  3. The steepest downslope of its eight neighbours
  4. The nearest stream cell by straight-line distance
Answer and working

C. The steepest downslope of its eight neighbours

D8 assigns all flow to the single steepest of eight neighbours. Splitting flow among lower neighbours describes multiple-flow-direction methods such as D-infinity or MFD.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

4 · Spatial analysis: overlay, buffer, terrain analysis · hard

Why do slopes computed from a 30 m satellite DEM tend to be lower than those from a 1 m LiDAR DTM on the same rugged terrain?

  1. Satellite DEMs measure the ground beneath canopy
  2. Coarse cells average out short steep features
  3. LiDAR DTMs exaggerate elevation by the canopy height
  4. Slope algorithms only work correctly on fine grids
Answer and working

B. Coarse cells average out short steep features

Large cells smooth the surface, so short steep pitches disappear. Satellite DEMs from radar or stereo imagery include canopy effects rather than the bare ground, and a LiDAR DTM excludes the canopy.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

5 · GNSS data collection and accuracy · medium

Which error source is especially severe for GNSS under a forest canopy?

  1. Satellite clock drift caused by the canopy shading the antenna
  2. Ionospheric delay produced by the leaves
  3. Multipath from signals reflected by trunks and branches
  4. Rounding of coordinates by the receiver display
Answer and working

C. Multipath from signals reflected by trunks and branches

Under canopy, reflected and attenuated signals (multipath) dominate. Satellite clock and ionospheric errors exist everywhere and are not caused by the forest.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

6 · Airborne LiDAR: point clouds, canopy height models · medium

Why can airborne LiDAR map terrain beneath a closed forest when optical imagery cannot?

  1. Laser light passes straight through leaves without reflection
  2. LiDAR measures heat emitted by the soil surface
  3. LiDAR uses microwaves that penetrate the canopy
  4. Pulses passing through canopy gaps return from the ground
Answer and working

D. Pulses passing through canopy gaps return from the ground

Small footprints find gaps between leaves and crowns; last returns from the ground are classified to build the DTM. Laser light does reflect from leaves, and LiDAR uses near-infrared or green light, not microwaves.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

7 · Area-based and individual-tree LiDAR inventory · easy

In individual-tree detection, treetops are most commonly located as:

  1. Local minima in the digital terrain model
  2. Points with the highest return intensity
  3. Local maxima in a smoothed canopy height model
  4. Cells where last returns equal first returns
Answer and working

C. Local maxima in a smoothed canopy height model

Treetops are local height maxima; crowns are then delineated by watershed segmentation or region growing.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

8 · Terrestrial and mobile laser scanning · medium

Why are multi-scan TLS designs used in forest plots instead of a single scan?

  1. To increase the scanner's beam divergence
  2. To avoid the need for any ground classification
  3. To reduce occlusion of stems hidden behind others
  4. To convert phase-shift data into time-of-flight data
Answer and working

C. To reduce occlusion of stems hidden behind others

Stems behind nearer stems are invisible from one position; registering scans from several positions fills those shadows.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

9 · Open data and cloud platforms for forest monitoring · medium

GEDI, operating on the International Space Station, provides:

  1. Spaceborne LiDAR samples of canopy height in 25 m footprints
  2. Continuous wall-to-wall 1 m canopy height maps of the whole globe
  3. C-band radar images that see through cloud
  4. Daily 250 m optical images for fire detection
Answer and working

A. Spaceborne LiDAR samples of canopy height in 25 m footprints

GEDI samples footprints of about 25 m between about 51.6° N and S; maps require combining with imagery. Sentinel-1 is the C-band radar; MODIS gives daily 250 m–1 km data.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

10 · Map production and cartographic standards · medium

Cartographic generalisation for a 1:250,000 overview map includes:

  1. Adding every stand boundary from the 1:5,000 database
  2. Selecting, simplifying and aggregating features
  3. Increasing positional accuracy beyond that of the source
  4. Reprojecting the data into geographic coordinates
Answer and working

B. Selecting, simplifying and aggregating features

Generalisation reduces detail to suit the scale; it cannot improve accuracy and is independent of projection.

Further reading: Longley P; Bolstad P; Wulder M; Maltamo M

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Read the subject

Forest GIS, GNSS & LiDAR AnalyticsThe full study manual, free ICAR AICE-JRF/SRFFormat, sections and how it has changed

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Forest GIS, GNSS & LiDAR Analytics for other exams

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