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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 5. House shadows are sometimes misclassified as grey areas. 
Figure 6. Tree shadows are sometimes mistaken as grey areas (far 
and middle left), and other times they block grey areas (far and 
middle right). 
Tree shadows are sometimes classified as grey areas, other 
times they block grey areas (Figure 6). In both cases, the 
shadows need to be detected and removed. The tree height is 
not readily available, but one can make a few guesses and see if 
one of the heights matches the shadow length fairly well. 
For both tree shadows and building shadows, the shadow 
outline must be extracted, and the intensity values inside the 
shadow increased to the level outside the shadow. 
Shadows aside, there are many more segmentation issues to 
solve. The most important shortcoming of the current 
segmentation approach is that no prior information is used. By 
including outlines of buildings, roads, rivers and lakes from a 
digital map, the outlines could be used to guide the 
segmentation step so that the outlines from the map were 
preferred to some extent. In some cases, there might be 
coregistration errors in the order of 1-2 m between the GIS and 
the Quickbird image. Ideally, the segmentation algorithm 
should be aware of this uncertainty and allow that a, say, house 
be moved 1-3 pixels. 
6.2 Time series of chlorophyll or NDVI 
An entirely different approach than the current could be to use 
time series of medium or low resolution satellite images to 
directly measure the variation from year to year in chlorophyll, 
which is often estimated from the so-called normalized 
difference vegetation index, NDVI. The NDVI for a pixel (x, y) 
is computed from the near infrared (NIR) spectral band and the 
red (R) spectral band as 
NDVI{x,y) = 
NIR(x,y)-R(x,y) 
NIR(x, y) + R(x, y) 
By using 250 meter resolution images from MODIS, or even 1 
km resolution images from AVHRR, one obtains average 
values, in which a decrease in chlorophyll in one small area 
may be cancelled by an increase in another small area within 
the same pixel. However, the general trend can be monitored, 
since these images are captured daily. 
The Norwegian Computing Center has developed time series 
analysis algorithms for vegetation monitoring in other projects 
(Salberg, 2010; Aurdal et al., 2005). These algorithms could be 
modified for use on monitoring of green structure in urban and 
suburban areas. The time series analysis algorithm models 
change on three scales: 
1. Daily variations due to imaging conditions 
2. Phenological variation during one year 
3. Changes from year to year. 
During one year, the green vegetation goes through one cycle, 
which has nearly the same shape from one year to another, but 
with variations in the start and end dates of the summer season, 
as well as the strength of the peak of the cycle (Huseby et al., 
2005). By eliminating the modeled changes on the daily, 
seasonal and yearly scale, one can detect statistically significant 
changes in individual pixels, and detect areas in which the 
green structure has been reduced or improved. 
7. CONCLUSION 
In the present work, Defmiens Developer was used for 
segmentation and classification of a Quickbird scene from 
2008. The result is validated in the present paper, and the 
conclusion is that this is a good starting point for further 
improvements of the method. The most striking problems are 
related to the segmentation. Object contours are often ragged, 
and do not follow the true boundaries of houses and roads very 
well. Another difficulty is shadows from buildings and trees, 
resulting in frequent misclassifications of whatever happens to 
be in the shadow areas. 
REFERENCES 
Aurdal, L., Huseby, R. B., Eikvil, L., Solberg, R., Vikhamar, 
D., Solberg, A. H. S., 2005. Use of hidden markov models and 
phenology for multitemporal satellite image classification - 
applications to mountain vegetation classification. In Proc. Int. 
Workshop Analysis Multi-Temporal Remote Sensing Images, 
Biloxi, Mississippi, USA, May 16-18, 2005, pp. 220-224. 
Defmiens Developer 7, User Guide, 2007. Defmiens AG, 
Munich, Germany. 
Huseby, R. B., Aurdal, L., Eikvil, L., Solberg, R., Vikhamar, 
D., Solberg, A. H. S., 2005. Alignment of growth seasons from 
satellite data. In Proc. Int. Workshop Analysis Multi-Temporal 
Remote Sensing Images, Biloxi, Mississippi, USA, May 16-18, 
2005, pp. 213-216. 
Salberg, A. B., 2010. Land cover classification of cloud- 
contaminated multi-temporal high-resolution images. Revised 
version submitted to IEEE Transactions on Geoscience and 
Remote Sensing. 
Trier, 0. D., 2009. Urban green structure - validation of 
automatic classification. Norwegian Computing Center, Note 
No. SAMBA/39/09, 52 pp., http://publ.nr.no/5159.