In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
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Figure 1. Study area. © SWISSTOPO. 
2.2 Ground truth 
The ground truth data to validate the tree species classifications 
was collected in the natural environment to be representative. 
Two ground surveys were carried out in summer 2008 and 2009 
focusing on the most frequent tree species (at least 10% 
coverage in Switzerland) which were also visible in the aerial 
images. For a total of 230 sampled trees we recorded the 
species (table 1) and delineated in the field the crowns of all 
visited trees on the corresponding aerial images. This 
information was used as reference to digitize the corresponding 
tree crowns on the ADS40 RGB images. 
Scientific tree species name 
(common tree species name) 
Num. of 
samples 
Species 
prop. 
Acer sp. (maple) 
28 
< 10% 
Fagus sylvatica (beech) 
42 
20% 
Fraxinus excelsior (ash) 
32 
15% 
Abies alba (white fir) 
28 
15% 
Larix decidua (larch) 
20 
<10% 
Picea abies (Norway spruce) 
56 
25% 
Pinus sylvestris (Scots pine) 
24 
<10% 
Table 1. Tree species sampled with number of samples. 
Species proportion is based on estimates by an expert during the 
field surveys 
2.3 Remotely sensed data 
2.3.1 Airborne Digital Sensor Data (ADS40): First 
generation ADS40-SH40 and second generation ADS40-SH52 
images Level 1 (Leica Geosystems AG, Switzerland) were 
used in this study (for further details on the sensor see e.g. 
Reulke et al. (2006). The main drawback of the first-generation 
ADS40-SH40 is that the NIR line CCD is placed 18° forward 
from the nadir RGB CCDs which makes it difficult to combine 
all four lines. The second generation ADS40-SH52 provides the 
NIR band in the same nadir position as the RGB bands. Three 
Digital Surface Models (DSMs) were generated automatically 
from the above images with a spatial resolution of 0.5 m using 
modified strategies of NGATE of SOCET SET 5.4.1 (BAE 
Systems). Prior to the DSM generation, a Wallis filter was 
applied to enhance contrast, especially in shadow regions, and 
to equalize radiometrically the images for matching. 
Sensor 
ADS40-SH40 
ADS40-SH52 
Acquisition date 
24/05/2007 & 
18/08/2008 
Focal length 
13/07/2007 
62.8 mm 
62.8 mm 
Spectral 
Red: 610-660 
Red: 608-662 
resolution (nm) 
Green: 535-585 
Green: 533-587 
Blue: 430-490 
Blue: 428-492 
Ground pixel size 
~25 cm 
NIR: 833-887 
~25 cm 
Orthoimage 
25 cm 
25 cm 
Radiometric 
11 bit 
11 bit 
resolution 
Table 2. Summary of characteristics of the image data used 
2.3.2 LiDAR: National LiDAR digital terrain data (DTM) 
produced by the Swiss Federal Office of Topography 
(SWISSTOPO) for the study area (acquisistion date: March 
2002, reflown March 2003 leaves-off) were used. The data were 
acquired by Swissphoto AG / TerraPoint using a TerraPoint 
ALTMS 2536 system with an average flying height above 
ground of 1200 m. The DTM has an average point density of 
0.8 points / m 2 height accuracy (1 sigma) of 0.5 m (Artuso et 
al., 2003) and was interpolated to a regular grid with 0.25 m. 
3. METHODS 
3.1 Variables derived from ADS40 imagery 
To extract tree area and classify tree species, several variables 
(geometric and spectral signatures) were derived from the 
remote sensing data using standard digital image processing 
methods as described in e.g. Gonzales and Woods (2002). 
Details about extraction of geometric and spectral explanatory 
variables derived from airborne remote sensing data are 
described in Waser et al. (2007, 2008a and 2008b). A good fit 
to the given (training) data is not a sufficient condition for good 
predictive models. To obtain good predictions, a small set of 
powerful variables has to be selected. 
Therefore stepwise variable selection (AIC, both directions, 
Akaike, 1973) was applied using the defaults of R version 2.9.1. 
A separate stepwise selection was performed for each tree 
species. The variables were ranked according to their 
contribution to the model. 
The input variables used in this study consist of four commonly 
used geometric parameters derived from the CHMs (slope, 
curvature, and two local neighborhood functions). For further 
details, see Burrough (1986) and Moore et al. (1991). Spectral 
variables were derived from each of the three images. This 
includes for each set of variables the mean and standard 
deviations of: 3 x 3 original bands of ADS40-SH40 RGB and 
ADS40-SH52 RGB and CIR images and the colour 
transformation from RGB and CIR (only from the 2008 images) 
to IHS into the 3 channels intensity (I), hue (H), and saturation 
(S). 
3.2 Image segmentation 
Homogenous image segments of individual tree crowns or tree- 
clusters are needed to classify tree species (see below). Both the 
ADS40-SH40 and /ADS40-SH52 orthoimages were therefore 
subdivided into patches by a multi-resolution segmentation 
using the Definiens 7.0 software (Baatz & Schape, 2000). 
Segmentation was iteratively optimized using several levels of 
detail and adapted to shape and compactness parameters. The 
final segmentation provided groups of trees and individual trees