Category Archives: VP-PS 2014 Summer

Module “Advanced Physical Geography”, summer term 2014.

VPPS-SS14-W09-1-LiDARgeil

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.

VPPS-SS14-W09-1-GISdur


 

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”?

Sources:
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.