he 
om- 
2 MSS 
le- 
d. 
en was 
Kuhmo 
vari- 
ver- 
r, but 
ns. The 
multi- 
for 
"tral 
ichr. 
- 1781 - 
The aircraft scanner data were acquired by operating a leased ll- 
channel scanner on board the twin-engine airplane .of the Finnish 
National Board of Survey. Taken at two altitudes and in very 
clear weather conditions the data provide good basis for studying 
the effect of spectral and spatial resolution on vegetation 
classification. The parameters of this data are summarized in 
Appendix 1. 
3. DATA PROCESSING AND ANALYSIS METHODS 
3.1 Numerical interpretation and coordinate transformation of 
digital multispectral data 
A supervised pattern recognition program installed in a general 
purpose computer is being used for the numerical interpretation 
of digital multispectral data. The color enhancement and certain 
interactive image processing phases are done in a dedicated mini- 
computer and color display system. The use of this system is 
expanding along with the software development. The color display 
of the classification results is done by a color jet plotter and, 
in the near future, by a film drum scanner. 
In order to present the resulting thematic maps in the Finnish 
topographic map coordinate system, an affine coordinate transfor- 
mation was developed for Landsat-2-data. The underlying poly- 
nomial transformation assumes the form 
3 
+ B,u + C:,Vv + D u? + E,uv + F y? + Hu + 
x = A, i 1 1 1 1 1 al 
2 2 3 
y A, + B,v + Cou ih Dv + Ejuv * Fou + Hv "ul. 
The coefficients are determined on the basis of control points 
known in both coordinate systems involved. A least square fitting 
is calculated for the control points,and the coefficients resulted 
are used for coordinate transformation. An example of the Land- 
sat data transformation is shown in Fig. 1. It is a special case 
for presenting classification results as line printer output at 
a scale of 1:10 000. The interpretation of classification values 
among transferred pixels was done by nearest neighbor rule. 
3.2 Timber volume estimation 
Timber volume functions are being derived by regression analysis 
where the responses, or their transformations, of the Landsat MSS 
channels are used as independet variables. The functions give 
the estimates of timber volume in each pixel. In the procedure, 
water bodies are eliminated by using channel 7, and open land 
areas by using channel 5. The remaining timber lands comprise 
mainly pine stands which eliminates most of the variance caused 
by different tree species, thus simplifying the problem. Ina 
sample survey, channel 4 was highly correlated with the volume. 
Quite a few functions derived explain the bulk of the variance 
of volume. A common problem is the overestimation of the volume 
in seedling and young thinning stands. The best function to handle