Cruise Planning -- The
by Kim Iles
One of the people at the recent OSU short course was not a regular
cruiser, and asked “what is the sequence of steps for carrying out a
Variable Plot cruise”. It’s a fair question, so here is one attempt to
In all cases, make sure your computer program can handle the approach
(and if not, lobby to fix it - or get another program). There is usually
some way to fool the computer into doing what you need, but it should
not be necessary. Computer people are supposed to make things easy for
you, not the other way around.
- What is the area to be cruised? A large tract probably means a
VP cruise, while a small one would suggest 3P sampling. With VP
cruising, you must measure the area that will be sampled. Any error
for stand area will cause a proportional error in the cruise.
- If the area can be broken down into strata that have different
characteristics (and can be recognized from the ground) you might
stratify the area to be cruised. This is really doing separate
cruises, so you can cruise the strata in different ways if you wish.
Sometimes you stratify just so you can report strata separately.
What people want to know, and how they might still change the
logging boundaries, is important to consider.
Taking out small blank areas and roads is quite possible with GIS
systems, but the “edge” this causes can be more trouble than it’s
worth. The areas removed may complicate the field sampling process
- Do you know how variable the stand is? If you have cruised
similar stands before, they can suggest a variability (measured by
CV) for both the tree count and VBAR. If you still measure every
tree on every plot, there is a CV that your cruise program should
show. For count/measure plots there is a similar approach called
“Johnson’s method” that gives you a single CV. The most efficient
way to do VP sampling is to do the counting for basal area
separately from the measuring that converts basal area to other
The CV of the Tree Counts is easy to get using a few tree counts in
the area, but is often in a narrow range you can find from your past
results. The average CV of VBAR is often about the same as the CV of
tree heights chosen with a prism multiplied by about 1.3 (check this
with your own data measurement methods).
In general, if you have CVs of 50% for Tree Count and 25% for VBAR
it suggests measuring 2 count plots for every measured tree. That
would be about every 10th tree. You might do them more frequently to
get better grade data, but this would be the right ratio for volume
Some folks visit the area to get this CV information before formal
cruising starts, and they can check access and other interesting
things at the same time.
- A) How much time and staff can you afford to spend on the
project? B) What SE% would that level of effort give you? C) Can you
live with that result?
If that SE% is much too big or too small, you can also compute a
sample size based on some SE% that you would like. One SE% is CV
divided by the square root of the number of plots (for basal area or
the number of measured trees for VBAR). The smart thing is probably
to use the STAR_BAR EXCEL
program to suggest the numbers to measure for each of these.
- Systematic sampling is always best, so to get the “square
spacing” between plots for that sample size, divide the area in
square feet (acres*43,560) by the number of plots, then take the
square root of that number to get the plot spacing.
(why not round to 100¸ or 1.5 chains?)
- Would you like to change the suggested sample sizes to make the
process more practical or easier? If so, use the second section of
the STAR_BAR program to
see if that answer gets you a result you can still live with. It
- Get the GPS system to choose a random point in the area, then
spread out the grid from that point in each direction. No need to
get too upset if that does not give you exactly the sample size you
were looking for.
Lines of plots are also acceptable, and sometimes cost-effective.
Use the random point to tell you where the first line should pass
through, and set the other lines off from that one. This is a more
correct method than using a “baseline” to lay out the sample lines.
- If you want to count an average of about 6 trees per point,
divide an approximate basal area of the stand by 6, and that gives
you the BAF to use. You can use any BAF (or your thumb) to get the
approximate Basal Area of the stand. Round the calculated BAF off to
a convenient number based on the instruments you have for tree
counts. An average of about 5-7 is most efficient, but use one where
you will not miss trees – even if you are getting an average Tree
Count of 1 or 2 in horrible brush.
If you want every 5th tree, you can measure them by groups on every
5th plot, or use a second BAF (5 times larger) to potentially choose
sample trees on each plot (this is the “Big BAF” method – which
spreads out the measurements, and is as effective as measuring about
twice as many sample trees in groups).
- Time to cruise. Correct for edge effect when you are near the
edge of the stand, and be careful about tree heights. Correct
diameters make little difference to the volume, but might affect
number of trees per acre, merchantable height, and grades - and DBH
is easy to do correctly.
We would suggest checking trees that are “borderline” unless you
have done something to prove that you can guess them really well.
Tree counts matter when calculating basal area, and directly affect
volume and many other results.
- Some people take the diameters of all the “in” trees. They then
assign approximate heights to calculate gross volume. They also get
a better DBH distribution in numbers of trees and logs. Just make
sure that your assignment of heights by DBH does not bias the volume
too much (see an earlier article in the newsletter called “Imaginary
Heights in Variable Plot Cruising”).
- Now examine the results. SE% not good enough? Really? Go back to
*BAR and see how many extra count plots would need to give you the
result you want. Those have to be spread over the whole area when
you go back, but perhaps it is worth it to you. Better yet, revisit
whether you can live with the result. You can also put in the same
mixture of plots you did before. An additional strip of plots to
make up the additional plots is probably OK too, if it is located
properly (see item 7).
- Do you want answers on specific groups of trees? You can break
down information in ways that the compiler does not normally report
by simply dropping trees that do not apply, and rerunning the data.
This also provides statistics for that computed group. When things
are entangled inside a tree, like grades, this is too complicated.
If you need statistics on combined grades, just call those grades
the same thing and get the statistics by rerunning the data with
that combined phony grade. There is no other simple way to correctly
combine the sampling error for grades (or combined species, in most
cases). Sometimes that information is important for marketing the
stand and you want to know how accurate some combinations might be.
- At some point, if the accounting system allows it, you want to
check the cutout for the cruise. Bear in mind that the Sampling
Error is actually less than about 1/3 of the “95% confidence
interval” often stated (for random samples), and it is even lower
when you use systematic sampling to intentionally spread out the
plots or tree measurements. The average cutout value should be zero
over time, if there is no bias involved.
If you are consistently getting more error in your cutouts than the
sampling error shows, then it is a bias in the measurements or
calculations - or an error in the accounting system. Go looking for
it. To change your cruising to meet cutouts that are bogus numbers
does not make much sense. The cruise says what is in the stand, not
what will be made from it. If you cannot identify the cause, there
is not much you can do about that except to additionally state that
correction when you report future results.
What matters in the cruise data?
If you do not know what affect an error or other decision will make, try
using old data that has all the trees measured. Make the change or
introduce the error, then rerun the data to see what results change. The
rules for “what matters” developed for fixed plot sampling in former
times are often no longer true.