Full text: Remote sensing for resources development and environmental management (Volume 1)

Photo 6 
Photo 7 
2.2 From higher altitudes 
From 1000 m and 3000 m altitude only groups of trees 
or stands can be detected. With increasing pixel size 
one has more problems in finding satisfactory homo 
geneous training areas for the computer classifica 
tion,especially in forests in Germany. 
From 1000 m altitude individual tree^shadow, slades 
between trees, roads etc. can be distiguished with 
some acuracy, but from 3000 m altitude and from the 
higher satellite data the contrast of these features 
will decrease and so the quality of class seperation 
decreases. But this effect can also be an advantage. 
Dead trees which are normally salvaged rapidly in 
German forests do not appear in the commonly used 
statistics of forest damage inventories. But as more 
trees are removed from a stand ground vegetation or 
soil is increasingly detected. This causes a higher 
reflection and a higher standart deviation of the 
pixels to represent this stand and so more pixels will 
be classified in a higher damage class. This classi 
fication might give more accurate information than 
the ground inventories which do not count salvaged 
In pict. 1 the results of photointerpretation and 
computer classification for the same test sites are 
(Picture 1 ) 
In dense old stands good correspondence between CIR- 
Photointerpretation and the computer classification 
was found. Worse results were found in steep slopes 
of mountains and on stands of lower density. 
The following photos show the results of computer 
classifications from different altitudes on a testsite 
in Southemwest of Germany. 
3 Conclusion 
After many computer classifications of daimaged fo 
rests one can say that it is possible to classify 
with good accuracy healthy and severely damaged fo 
rest stands from 1000 and 3000 m with an airborne 
scanner and with less accuracy from satellite data. 
From satellite data only two classes can be sepera- 
ted: healthy and severely damaged forests, and these 
only when larger areas are affected. Significant 
problems exist in the middle damaged class S2 (26 to 
60% needleloss). The wide distribution of this class 
makes it difficult to define exact class boundaries, 
so quite often pixels will be classified to the 
neighbor classes SO-1 or S3. For better results 
additional information should be included in the 
computer classification process, for example texture, 
terrain models, stand density etc. to minimize the 
still existing misclassifications. 
In the continuing project it is planned to investi 
gate in this. 
The authors thank Mr. H.P. Kienzle, H. Schneider and 
R. Waltenspiel of the Department of Photointerpre 
tation and Remote Sensing at the University of Frei 
burg for developing special software. 
The authors also thank Mr. V. Amann from the DFVLR 
Oberpfaffenhofen for aquiring the airborne scanner 
A. Kadro. Investigation of spectral signatures of 
differently damaged trees and forest stands using 
airborne multispectral data. 
Proceedings of IGARSS'84 Syrnp. , Strassburg, 27. - 
30. Aug. 1984. 
A. Kadro, S. Kuntz, C. Kim. Entwicklung eines Ver 
fahrens zur Waldschadensinventur durch multispek 
trale Fernerkundung. 
1. Statuskolloquium des PEF vom 5. - 7. Maerz 1985, 
A. Kadro. Investigation of Spectral Reflectance Pro 
perties of Forest Damage Using Multispectral Data. 
3rd Int. Colloquium; Spectral Signatures of Objects 
in Remote Sensing, Les Arcs, 16. - 20. Dec. 1985. 
G. Hildebrandt, A. Kadro, S. Kuntz. Entwicklung ei 
nes Verfahrens zur Waldschadensinventur durch mul 
tispektrale Fernerkundung. 
Zwischenbericht fuer das 2. Statuskolloquium des 
PEF, 4. - 7. Maerz 1986 in Karlsruhe. 
A. Kadro. Determination of Spectral Signatures of 
Different Forest Damages from Varying Altitudes of 
Multispectral Scanner Data. 
Int. Symp. on Remote Sensing, 25. - 29. Aug. 1986, 

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