**Analyst Example**

Congratulations! Your program manager has promoted you from field
personnel to an office analyst (although some might consider this a
demotion). Your program manager is facing increasing pressure to get
quality data to state X in a short time-frame with an overworked staff.
As soon as you walk in the door, the program manager assigns you the
task of analyzing DWM data. You have a couple days to provide your boss
with estimates of fuels per acre for various forest types in state X.
The database code that provides you fuel estimates from raw data is
producing really weird results, they are twice the amount that state X
expects in its forests. You need to quickly take some raw data and do
hand calculations to see what's up. You pick one plot from a common
forest type and estimate the tons per acre for the following fuel
classes: duff, litter, small/medium/large FWD, and CWD.

**Formulae**

Duff (tons/ac) = {\[(sum of duff depths/number of samples) x 3630\] x
18} / 2000

Litter (tons/ac) = {\[(sum of litter depths/number of samples) x 3630\]
x 4} / 2000

FWD (tons/ac) = \[(11.64) x (FWDCount) x (d^2^) x (s) x (a) x
(c)\]/\[Total FWD tran length\]

*For FWDCount assume the total count across all transects for size
class*

Small FWD: d^2^ =0.015

s=0.48

a=1.13

c=1.08

Medium FWD: d^2^ =0.289

s=0.48

a=1.13

c=1.08

Large FWD: d^2^ =2.76

s=0.40

a=1.13

c=1.08

CWD (tons/ac) = \[(11.64) x (Σd^2^) x (s) x (a) x (c)\]/\[Total CWD
transect length\]

*For s assume the specific gravity for decay class 3 ...0.32*

s=0.32

a=1.0

c=1.08

Σd^2^=square each transect diameter then sum for entire plot

**Raw Data Tables for One Plot**

**Transect**

![](media/image1.wmf){width="6.625694444444444in"
height="2.222916666666667in"}

**Duff and Litter**

![](media/image2.wmf){width="5.972916666666666in"
height="1.9013888888888888in"}

**Fine Woody Debris**

![](media/image3.wmf){width="6.248611111111111in"
height="0.6291666666666667in"}

**Coarse Woody Debris**

![](media/image4.wmf){width="5.997222222222222in"
height="0.9652777777777778in"}

The corresponding P2 data indicate only one condition class for this P3
plot...a forested condition

**Example Answer Key**

***Duff***

Scenario 1: Assuming data "as is" with duff was sampled at only nine
locations estimate of tons/acre is **62.8**.

Scenario 2: Assuming duff was sampled at nine locations and using data
with alterations to depth estimates once it has been determined that
field crew entered duff depths (6 and 9 inches) to the incorrect decimal
place (0.6 and 0.9 inches) tons/acre is **13.8**.

Scenario 3: Same as scenario two except that subplot 3 is found to be
the same forested condition as the rest of the subplots so that the
estimate of duff tons/acre is **10.3**.

***Litter***

Scenario 1: Assuming data "as is" with litter sampled at nine locations
the estimate of tons/acre is **48.2**.

Scenario 2: Assuming litter was sampled at only nine locations and using
data with alterations to depth estimates once it has been determined
that field crew incorrectly entered litter depths (30 and 20 inches) and
meant to enter 3 and 2, the estimate of tons/acre is **11.9**.

Scenario 3: Same as scenario two except that subplot 3 is found to be
the same forested condition as the rest of the subplots so that the
estimate of litter tons/acre is **8.9**.

***Fine Woody Debris***

Scenario 1: Assuming the data "as is" with small, medium, and large fine
woody debris tons/acre estimated on only 2 sub-plots to be: **0.85**,
**6.7** and **15.7** respectively.

Scenario 2: Assuming that there was no FWD on subplot 4 and "0" values
were accidentally not entered by crew the estimates would be **0.57**,
**4.5**, and **10.5** respectively.

Scenario 3: Assuming that FWD was measured on all 4 forested sub-plots
and crew/data mgmt error led to an extraneous conditions class (2) and
no data values for subplot 4 the estimates would be: **0.43**, **3.36**,
and **7.84.**

***Coarse Woody Debris***

Scenario 1: Data is accepted "as is" with tons per acre estimates equal
to **346.5**.

Scenario 2: Field crews accidentally entered transect diameter to the
tenth of inches with 40, 60, and 140 being 4, 6, and 14 respectively.
Using these corrected transect diameters the estimated tons/acre of CWD
is **3.9**.

**Classroom Notes**

First have trainees try to determine the estimates on their own for up
to 30 minutes. There is no really correct answer since there are
errors/edit problems with the data. Once trainees have gotten frustrated
or are confident on their estimates go over them as a class. If
questions arise over constants in the equations they are defined as:
d=diameter of DWM piece, s=specific gravity, a=non-horizontal lean angle
correction, c=slope correction. The equations have been greatly
simplified using Brown's estimators to enable field crews to quickly
estimate DWM attributes with no previous instruction.

For both litter and duff the estimates can change drastically due to
errors with the digits entered for depths. Additionally, there might
have been an error with measuring a "0" depth on subplot 3, but not
including the 0 value, zeroes are important. The difference in duff and
litter depths across scenarios means that simple data errors can reduce
estimate by 80%, easily. Finally, duff weighs much more than litter (the
18 versus the 4 in the estimation formulae). Duff errors can have a huge
effect on total tonnage estimates for a plot.

For fine woody debris, errors can arise from missing transect
information. In this classroom example the fine woody estimates were
reduced in half as the number of subplots sampled goes from 2 to 4.

For coarse woody debris, incorrectly entering the decimal place of
diameters is a huge problem. In this example field crews accidentally
entered the diameter of some CWD pieces to the tenth of an inch.
However, because PDR's don't accept that diameter precision, the
transect diameter of 3 CWD pieces is 10x what they should be. The error
in tonnage/acre estimates is over 300 tons.

This exercise serves as a segue way into the "Part 3" of the PowerPoint
lectures talking about data quality. The issues in this exercise
highlight CWD transect errors, duff and litter depth errors, not
including zero values (the variable was measured, it was 0, but it was
not recorded), and mismatching data such as indicating a condition class
2 in P3 data when P2 data says there is no condition class 2.