How has the DEM been derived from the LiDAR dataset?
DEM = Digital elevation models. The ground returns were identified using a groundfinding algorithm delveloped by EarthData Technologies.
LidAR data sets were used to produce canopy and ground DEMs using a linear interpolaion technique (0.20cm cell resolution).
How has the tree height/DSM information be retrieved from the LiDAR dataset?
The DEM ground datas substracted from first return LiDAR DEMs construct the forest height surface.
With using a „Tree Height Findeing Model“ the highest point of the trees could be detected
How has the vertical structure be classified into single- and multi-story areas?
„The two structure classes were based on the median value between the minimum tree height variance observed in the multistory plots (2.75 m)
and the maximum tree height variance observed in the single-story (1.21 m) plots. Thus, each 30.0-m cell in the tree height
variance data set was classified as single-story ( < 1.54 m) or multistory (>1.54 m)“ (S. 176)
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)?
CV is independent of sample size and can be used to campare samples of unequal sizes.
Which errors of the approach can be attributed to post spacing?
Some crowns have more than just one peak, so maybe they have been counted as two trees and conversly in tree clumps, some trees has been counted as one.