397 
2.6 Regression analysis 
Regression functions for estimation of stand 
characteristics from spectral signatures, were 
constructed by means of regression analysis of the 
NFI-data, described in section 2.3, and corre 
sponding Landsat TM spectral signatures. The 
statistical software package Minitab (Version 
6.1.1 for VAX/VMS) was used for the analysis. 
Regression functions were constructed for the 
following stand characteristics: Volume/hectare, 
mean diameter, mean age, and tree species mixture 
(relative proportions of species). The selection 
of predictor variables to be included in the 
models was based on residual studies and the 
adjusted coefficient of determination (R-sq(adj)). 
The regression models used were of the type shown 
in equation 3. 
Log(y)=b 0 +b 1 *x 1 +b2*x 2 ...+b n *x n (3) 
where: y = volume/hectare, age, diameter, etc. 
x^ = band intensities, band products, band 
ratios, etc. 
The accuracies of t-ratio segmentation and manual 
delineation were found to be very similar for most 
stand variables except for mean diameter 
(Figure 3). However, the average region size was 
10% smaller for the segmentation result. The CFU- 
time (VAX 8530) needed for the t-ratio segmen 
tation of 1100 hectares was less than two minutes. 
The quality of the results obtained by computer 
segmentation was also confirmed by field inspec 
tion. 
STAND VARIABLES 
Correction factors for logarithmic bias were 
calculated for all regression functions as the 
ratio of: mean of real observed values divided by 
the mean of real predicted values (Equation 4). 
Mean of observed values 
Correction factor (4) 
Mean of predicted values 
Standwise estimates of characteristics, for any 
stand within the satellite scene, may be calcu 
lated by applying the regression functions to 
individual pixel signatures within the stand and 
multiplying the averages obtained with the correc 
tion factors for logarithmic bias. 
3 RESULTS 
3.1 Stand delineation 
3.1.1 Segmentation. The result of a computer 
segmentation using the t-ratio algorithm described 
in section 2.5, was compared to the manual deli 
neation of test site no. 1, obtained by visual 
interpretation of aerial photos and field check 
ing. 
Three bands of composite SPOT XS,PAN data were 
used as feature bands. The overlay defining diff 
erent land use and administrative regions was 
digitized from public maps at scale of 1:10 000. 
Both individual pixels and results of low level 
segmentation (Directed trees algorithm) were 
tested as definitions of initial regions. If the 
average size of input regions was fairly small 
(i.e. less than 10-15 pixels), more or less equi 
valent results were obtained with the two app 
roaches. The appropriate level for the t-ratio 
threshold was simply determined on a trial and 
error basis. 
The accuracy was quantified as the preportion of 
stand characteristic variance explained by the 
delineation (R-sq, ANOVA) The statistics were 
calculated from the sample plot grid of test site 
no. 1. Only plots with forest older than 15 years 
were used in the analysis. 
Figure 3. Relative effectiveness of stand deline 
ation (ANOVA, R-sq %), for t-ratio segmentation of 
composite SPOT XS,PAN data compared to visual 
interpretation of black and white aerial photos at 
scale 1:30 000 and field checking. 
3.1.2 Interactive editing. A raster based 
system for interactive editing on an image back 
ground, has been developed at the Remote Sensing 
laboratory in Umea. The software is written in 
Pascal and presently runs on a VAX-hosted image 
processing system (Gould IP8500). The editing 
system has a very user-friendly menu interface 
(Eliasson, 1989). 
3.2 Estimation of stand characteristics 
Estimates of stand characteristics were calculated 
for each of the 80 reference stands described in 
section 2.2.2. The estimates were obtained by 
applying regression functions constructed from NFI 
and landsat data to all pixel intensities within a 
stand, except for the edge pixels. Stand edge 
pixels were excluded in order to avoid mixed 
signatures and to reduce the effects of geometric 
distortions due to topography. 
The precision of estimates obtained from satellite 
and NFI-data were found to be comparable to these 
obtained by subjective field inventory, except for 
tree species mixture. A substantial reduction of 
estimate error was found when combined estimates 
were calculated, using both satel 1 ite-NFI plot 
regression functions and subjective field inven 
tory estimates. (Figure 4).