• How has the DEM (digital elevation model) been derived from the LiDAR dataset?

Canopy and ground DEMs were produced from LiDAR data sets using a linear interpolation technique.

  • How has the tree height/DSM (digital surface model) information been retrieved from the LiDAR dataset?

It has been retrieved in a three-step-process:

1. The ground (DEM ?) is subtracted from the first return LiDAR DEM in order to yield forrest height surface

2. The LiDAR return of the peaks of tree crowns is detected, gathering that peaks represent indivitudal crowns .

3. Data for indivitudal trees of plot is extracted (using information about location of individual trees ?).

  •  How has the vertical structure been classified into single- and multi-story areas?

Trees < 1.54 m were classified single story, trees > 1.54 are classified multistory, based on the median values of tree heights variance observed in plots.

  • What is the principle difference between the single-/multi-storage data set and the characterization of the forest areas using the coefficient of variation approach (section 4)?

The single-/multistorage dataset, classifies a forrest in only two classes single or multistorage. The CV does not characterize forrest by classes but calculates a unitless ratio between the standard deviation and mean of tree heights. It it thus independent of sample size and can be used to compare samples of unequal sizes.

  • Which errors of the approach can be attributed to post spacing?

Tree heights measurements can be incorrect and therefore influence tree heights variances: inflate single-story tree heights variance and reduce multi-story tree heights variance.


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