Monthly Archives: June 2014



How has the DEM been derived from the LiDAR dataset?

The DEM (Digital Elevation Model) contains only the topography. In order to construct the DEM “ground returns were identified using a groundfinding algorithm developed by EarthData Technologies. LiDAR data sets were used to produce a ground DEM using a linear interpolation technique (0.20 m cell resolution)“ (Zimble et al. 2003, p. 174).

How has the tree height/DSM information be retrieved from the LiDAR dataset?

The tree height/DSM information is retrieved form the LiDAR dataset within two major steps. First step is to subtract the ground from first return LiDAR DEMs. This leads to tree height LiDAR DEM containing the forest height.
The aim of the following step is to retrieve the peak of each individual tree based on the forest height surface. Therefore a tree height-finding model is used (ib., p. 175).

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

The classification in single- or multi-story areas is accomplished in reference cells of 30 m. The variance of tree heights was calculated for each cell. The threshold variance is 1.54 m. There a plot is defined as a single- (< 1.54 m) or multi-story (> 1.54 m) forest. The threshold value is “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” (ib., p. 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 (coefficient of variation) data set is achieved by “dividing the tree height standard deviation data set by the mean tree height data set and multiplying by 100” (ib., p. 179). The difference to the single- and multi-storage data set is that CV characterizes the “vertical forest structure as a continuous surface” (ib., 179) and therefore represents a normalized measure of dispersion.

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

Since there is a higher probability missing the treetop (ib., p. 178), large post spacings can inflate the tree height variance in single-story forests and decrease it in multi-story forests.

Question: What exactly is “post spacing”?

Zimble, D. A., Evans, D. L., Carlson, G. C., Parker, R. C., Grado, S. C. & P. D. Gerard (2003): Characterizing vertical forest structure using small-footprint airborne LiDAR. –- Remote Sensing of Environment 87 (2003): 171 –- 182.




1. The DEM is used to show the last returns of a LiDAR dataset and contains the topography. LiDAR used a linear interpolation technique to generate the dataset.

2. The DSM is used to show the first returns of a LiDAR dataset and contains amonght others tree canopies and buildings. To generate this datset, the DEM data was substracted by complete dataset of LiDAR.

3. A value as a parting line between single- and multi-story areas has been calculated. All values under this value have benn allocated to the single-story areas and all the other values above have been allocated to the multi-story areas.

4. The single-/multi-storage data set classifies in two classes, single or multistorafe. The CV calculates a ratio between standard deviation and mean tree height.

5. There are tree crowns with more then 1 peak witch can be counted as two trees. Trees with low height can also be not detectet as an own tree in the dataset.