In this study, a highly differenciated land use 
classification had been calculated with radiation 
corrected data (Parlow, 1988, 1991, 1992) and was 
found to be coherent with the field mapping realized by 
the author (Storl, 1992). 
For a modelling of sensible heat flux on micro scale, the 
intraclass variance of roughness length has to be taken 
into account and must be adecuately parametrized. 
The author could dispose of a PAR-albedo dataset 
(Photosynthetically Active Radiation albedo). PAR- 
albedo is the albedo for radiation in the 
photosynthetically active region of the spectra, and 
therefore an excellent biomass parameter that reflects 
vegetation density much better than NDVI, as has been 
shown by Parlow (Parlow, 1988). 
In can be assumed, that PAR-albedo is correlated to 
stand height within a certain vegetation class, as 
absorption of photosynthetically active radiation is related 
to biomass, and more biomass means higher plants 
within one vegetation class in general. 
As a first approximation, a linear relation was assumed 
between plant height and PAR-albedo within the classes. 
In order to obtain a roughness length value for each 
30x30m pixel, the information about intraclass variance 
variation inherent in the PAR-albedo dataset was 
integrated with the land use classification. 
An image processing procedure (CSCALE, Storl, 1992, 
1993, 1994) was written in order to scale each value of 
PAR-albedo according to its land use class. Therefore, 
representative values for roughness length and its 
standard deviation for each vegetation class were taken 
from the data accumulated at the Royal Academy of 
Science in Abisko. 
Tab.1 shows the medium values and standard deviation 
of roughness length used in meters. 
  
  
  
  
  
  
  
  
  
very dry heath |dry heath moist heath meadow | snow swamp | salix 
0,010 0,010 0,020 0,008 0,001 0,010 0,040 
0,005 0,005 0,005 0,002 0,000 0,000 0,020 
old birch forest | heath birch forest | moist heath birch forest | constr. water 
0,500 0,400 0,600 0,900 0,001 
0,200 0,200 0,200 0,000 0,000 
  
  
  
  
  
  
Tab. 1. Mean roughness length and standard deviation for the land use classes 
Conditioned scaling was applied to the PAR-albedo 
dataset, with the land use classes as conditioning 
parameters. For each land use class, CSCALE 
calculated the medium value and the standard deviation 
of PAR-albedo. In a second pass through the images, 
CSCALE transformed the PAR-albdo values in a linear 
way accordingly, assigning pixels with mean PAR-albedo 
value for the respective land use class the mean 
roughness length of the class, and transforming PAR- 
albedo values that varied about standard deviation from 
the mean values into roughness length values within the 
roughness lenth statistics of the respective class. 
7.2 Surface temperature 
The thermal channel 6 of Landsat TM with a spatial 
resolution of 120m had been calibrated by Parlow 
(Parlow, 1988) to radiance surface temperature over the 
sea surface of lake Abisko using a calibration formula 
suggested by Schott and Volchok (Schott/Volchok, 
1985). The atmospheric influence was eliminated using 
an improved version of the WINDOW model of J. Price, 
NASA (WINDHA, Price, 1987 / ATMKOR, Scherer, 1987) 
7.2.1 Resolution enhancement 
The channels 1-5 and 7 of Landsat-TM have a spatial 
resolution of 30x30m, whereas the thermal channel 6 is 
restricted to a resolution of 120x120m. 
Scherer (Scherer, 1987) developed a method to improve 
the spatial resolution of thermal data, integrating data 
sets of higher resolution. He obtained an enhanced 
spatial resolution of remotely sensed thermal data, using 
a multiple linear regression model, which he derived from 
the energy budget equation. The energy budget equation 
under equilibrium conditions can be written as: 
ESL (oo) * EN - Fit * oO "H*LE-O (9) 
a  .. integral albedo 
Es ... short wave solar irradiation 
Ej .. long wave atmospheric counter irradiation 
Ej .. long wave terrestrial radiation 
G  .. soil heat flux 
H  .. sensible heat flux 
LE ... latent heat flux (evapotranspiration) 
Scherer showed, that equation (9) can be transformed 
with permissible approximations, representing long wave 
radiation as a linear combination of short wave 
irradiation, terrain elevation and the percentage of 
different land use classes in a pixel. He assumed a 
constant ratio between sensible and latent heat flux 
(Bowen Ratio) within each class. Hence, for each 
120x120m thermal pixel of Landsat-TM, the following 
equation can be written (Scherer, 1987): 
n 0 
Ep =a Es) +bh+E cip; + d (10) 
i=1 
Es .. short wave solar irradiation 
h .. terrain elevation 
662 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996 
  
ua - ea gemmam " 
l2, 0 Dll DU a D aD hn gg w= rv i 
cn 
<<‘ OEY (9 — "TITS ee