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SAF Certified Forester Forest GIS, GNSS & LiDAR Analytics: practice questions

SAF Certified Forester (CF) · USA · Society of American Foresters

Original practice questions written to the published syllabus · not past papers
6worked here, free
18written to this exam in this subject
9syllabus 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 18 questions fall

How our questions for SAF Certified Forester spread across the units of Forest GIS, GNSS & LiDAR Analytics. Difficulty: 3 easy, 9 medium, 6 hard.

Forest stand and compartment databases4
Spatial decision support and multi-criteria analysis3
Spatial analysis: overlay, buffer, terrain analysis2
GNSS data collection and accuracy2
Area-based and individual-tree LiDAR inventory2
Terrestrial and mobile laser scanning2
GIS concepts: vector and raster data, projections, datums1
Open data and cloud platforms for forest monitoring1
Map production and cartographic standards1

6 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

Two layers of the same forest are overlaid and the roads are offset by about 150 m in a consistent direction. The most likely cause is:

  1. One layer was digitised from a larger-scale map sheet than the other
  2. The raster layer has a cell size that is too coarse
  3. The layers use different datums with no transformation applied
  4. The attribute tables use different primary key fields
Answer and working

C. The layers use different datums with no transformation applied

A consistent shift of tens to hundreds of metres is typical of a datum mismatch. Scale differences cause irregular, not uniform, offsets; attribute keys do not move geometry.

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

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

Before deriving a stream network from a DEM, small depressions are filled. Why?

  1. Spurious sinks would stop flow and break the network
  2. Filling smooths the slopes so that aspect can then be calculated
  3. Depressions make the DEM file too large to process
  4. Filling converts the DSM into a bare-earth DTM
Answer and working

A. Spurious sinks would stop flow and break the network

Artificial pits trap flow, so accumulation stops and streams are fragmented. Filling does not remove vegetation or change a DSM into a DTM.

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

3 · GNSS data collection and accuracy · medium

Which field practice most improves GNSS plot-centre accuracy under dense canopy?

  1. Taking a single fix as soon as the receiver locks
  2. Disabling all constellations except one
  3. Setting the elevation mask to zero degrees
  4. Logging many fixes with an antenna raised on a pole
Answer and working

D. Logging many fixes with an antenna raised on a pole

Averaging many fixes and raising the antenna above understorey reduce error. Using fewer constellations reduces visible satellites; a zero mask admits low, noisy signals.

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

4 · Area-based and individual-tree LiDAR inventory · medium

Which trees are most often missed by individual-tree detection from airborne LiDAR?

  1. Tall emergent conifers with clear apexes
  2. Dominant trees along stand edges
  3. Suppressed and understorey trees
  4. Isolated trees in open parkland
Answer and working

C. Suppressed and understorey trees

Lower trees hidden under the upper canopy cause omission errors; tall, distinct crowns are detected best.

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

5 · Spatial decision support and multi-criteria analysis · hard

Why do adjacency (green-up) constraints make harvest scheduling much harder to solve?

  1. They link decisions on neighbouring stands into integer choices
  2. They remove the need to project stand growth through the planning horizon
  3. They require every stand to be cut in the first period
  4. They make the objective function nonlinear in prices
Answer and working

A. They link decisions on neighbouring stands into integer choices

Adjacency rules turn the problem into a large integer programme in which each stand's decision depends on its neighbours; heuristics such as simulated annealing or tabu search are often used.

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

6 · Map production and cartographic standards · hard

Why should red and green not be the only contrast distinguishing two categories on a field map?

  1. Common colour-vision deficiencies make them hard to tell apart
  2. Red and green inks fade in sunlight within a few hours of field use
  3. International rules ban red on forest maps
  4. Green is reserved for water bodies
Answer and working

A. Common colour-vision deficiencies make them hard to tell apart

Red–green colour deficiency is common, especially among men; adding differences in lightness, pattern or symbol shape keeps the map readable.

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

Ten here, 18 in the app. The Laisha app has every practice question we have written to SAF Certified Forester in this subject, marked as you go, with timed papers, a jump-to-unanswered list and a certificate when you pass at 70 per cent.

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Read the subject

Forest GIS, GNSS & LiDAR AnalyticsThe full study manual, free SAF Certified ForesterFormat, sections and how it has changed

More SAF Certified Forester practice

Forest Inventory & Sampling DesignForest Certification & Sustainable Forest ManagementProfessional Forestry Practice, Ethics & RegistrationStand Tending, Thinning & PruningGrowth & Yield Modelling

Forest GIS, GNSS & LiDAR Analytics for other exams

ICAR AICE-JRF/SRF · IndiaGIS Professional · International

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