MARV4/2 fotogrammetric inventory 2007

Learning objectives

The exercise will expose the student to the following - learning by doing :
  1. 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.

  2. 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.

  3. Model errors, analysis of their impact, calibration of biased model estimates

    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.

  4. Airborne 3D single-tree remote sensing - potential and weaknesses of it

    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. 

  5. Mapping of trees in the field using a simple trilateration-triangulation technique

    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.

  1. 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 positioning accuracy.

  2. 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.