The exercise will expose the student to the following - learning
by doing :
- Systematic sampling and uncertainty of inventory results due
to sampling errors
The 56-hectare area was covered by a 59 plot (2.36 ha, 4%) sample, which
induces an error. We will estimate it. All inventories contain this element.
- Measurement errors, analysis of their impact, possible calibration
of bias in measurements
Airborne and field observations are subject to imprecision and bias. Bias
remains in the estimates unless it's effect can be calibrated. We can also
try to lower the imprecision of some STRS measurements by using the field
data for learning.
- Model errors, analysis of their impact, calibration of biased
We will be using allometric models for predicting stem diameter from tree
height, crown width and the species information. Also, allometric volume
functions or taper curves will be used for calculating single tree volumes
and volumes of timber sortiments. These models are not perfect.
- Airborne 3D single-tree remote sensing - potential and weaknesses
In the field we will notice that some of the trees are not in the position
that was measured from the images or they are not of the species that was
interpreted. Also, we will notice that not all trees can be measured from
images and LiDAR.
- Mapping of trees in the field using a simple trilateration-triangulation
In the field we will have a map of the photo-measured trees.
For these trees we know the XY-coordinates and hence - intertree distances
and azimuths. With a precision compass (bussoli) and a laser rangefinder
we can then position the unseen trees with respect to the photo-measured
trees and get a full map of the trees belonging to the circular plot.
- Use of GPS in the field.
We will be using a GPS for a rough location of our plots. The inaccuracy
will be quite large and "the last meters" are squeezed using the tree map. We will learn that different GPS-instruments
provide different accuracy and that the canopy exersises an effect on the
- Fixed-area circular plots
Bitterlich plots are commonly used in forest inventory as they fulfill the
principle of PPS-sampling, probability proportional to size. We will find
out that STRS-observations favor big trees as well, but not quite in the
same manner as the angle gauge.