SAF Certified Forester (CF) · USA · Society of American Foresters
Original practice questions written to the published syllabus · not past papersPlanning and running forest inventories: kinds of enumeration, sampling designs and plot types, sample size, estimation and error, from a timber cruise to a national forest inventory.
In the exams: ICAR AICE-JRF 59 Unit 4 (forest inventory, kinds of enumeration, sampling methods, sample plots); ICAR AIEEA-PG Group 09 (forest inventory); IFoS Forestry Paper II Section A; SAF Certified Forester; Indonesia timber-cruiser certification
SAF Certified Forester; US and Canadian professional forester examinations
Designing and analysing a timber cruise: mean, standard deviation, CV, standard error, confidence interval, sample size, stratified estimates, fixed-area and point sampling, and interpretation of FIA data.
How our questions for SAF Certified Forester spread across the units of Forest Inventory & Sampling Design. Difficulty: 1 easy, 18 medium, 7 hard.
| Simple random and systematic sampling | 4 | |
| Sample size and sampling error | 4 | |
| Stratified sampling | 3 | |
| Sample plots: fixed-area, variable-radius, permanent | 3 | |
| Point, line and strip cruising | 3 | |
| Cluster, multistage and multiphase sampling | 2 | |
| National forest inventories and continuous forest inventory | 2 | |
| Inventory of non-timber resources and biodiversity | 2 | |
| Inventory data processing and reporting | 2 | |
| Objectives and kinds of enumeration: complete and partial | 1 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
A crew chooses plots near the road that 'look typical' of the stand. What is the main problem?
C. No valid sampling error exists for it, and the estimate is likely biased
Subjective selection is non-probability sampling: there is no basis for an error estimate and roadside or 'typical' plots are rarely representative. The other statements are false.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Eight plots have a mean of 160 m³/ha and a standard deviation of 20 m³/ha. What is the standard error of the mean (ignore the finite population correction)?
A. 7.07 m³/ha
SE = SD ÷ √n = 20 ÷ √8 = 20 ÷ 2.828 = 7.07. Dividing by n gives 2.5; 20 is the SD itself; 50 is 400 ÷ 8, the variance of the mean before taking the square root.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Ten plots give a mean of 210 m³/ha with a standard error of 8.16 m³/ha. Using t = 2.262 (9 df), what is the 95 per cent confidence interval?
B. 191.5 to 228.5 m³/ha
Half-width = 2.262 × 8.16 = 18.5; interval 210 ± 18.5 = 191.5 to 228.5. Using ±1 SE gives 201.8–218.2; using 1.96 gives 194.0–226.0; multiplying 2.262 by the SD instead of the SE gives an interval far too wide.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
A sale cruise must achieve an allowable error of 5 per cent at 95 per cent confidence (take t = 2). A pilot cruise shows a CV of 30 per cent. How many plots are required (large population)?
B. 144 plots
n = 2² × 30² ÷ 5² = 4 × 900 ÷ 25 = 144. 36 omits t²; 72 uses t instead of t²; 12 omits all squaring (2 × 30 ÷ 5).
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Plots of 0.1 ha are spaced 100 m apart on cruise lines 100 m apart. What is the cruise intensity?
B. 10 per cent
Each plot represents 100 × 100 m = 1 ha; intensity = 0.1 ÷ 1 = 10%. The other values are decimal slips.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Point cruising with an angle gauge is particularly efficient for estimating timber volume because:
C. Large, valuable trees are sampled with higher probability and each point is quick to measure
Probability proportional to basal area concentrates effort on the trees that carry most volume and value, and points are quick. Point sampling has no fixed radius, still needs diameters and heights for volume, and hidden and borderline trees are its known problems.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Which choice of basal area factor is generally recommended for a point cruise?
D. One that gives a moderate count of trees at most points
Too large a BAF gives many zero counts and high variance; too small a BAF counts distant, hidden and borderline trees and slows the work. A moderate count balances efficiency and accuracy.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
The US Forest Inventory and Analysis programme uses a national hexagonal grid with one field plot per roughly:
A. 2,400 ha (6,000 acres), each plot with four subplots
FIA's phase 2 grid has about one plot per 6,000 acres (about 2,400 ha), each with four subplots. The other densities are far off.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Heights not measured on every tree in a cruise are usually filled in by:
C. A local height-diameter equation fitted to the sample trees
A local height-diameter relationship predicts heights from diameters. National means, maximum heights or random values would add bias or noise.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
Which is part of quality assurance in a forest inventory?
C. A share of plots remeasured blind by an independent check crew
Blind check plots, written rules, calibration and archived raw data are QA practices. The other actions undermine quality and reproducibility.
Further reading: Kershaw et al., Forest Mensuration (5th ed., 2016); Shiver & Borders, Sampling Techniques for Forest Resource Inventory (1996); de Vries, Sampling Theory for Forest Inventory (1986)
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