Full text: Resource and environmental monitoring

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or both sur- 
C, solid line: 
If some regions of interest have already been defined (in the 
so called training of the data set or by an initial unsuper- 
vised classification) it may be useful to take the distribition 
of temperature values into account for classification purposes 
(Holbe & Luvall 1989). We will show one possibility to use 
this source of information. 
We derive characteristic features of vegetational and non- 
vegetational surface types from remotely sensed temperature 
data which allow a grouping of the thermal image data into 
these two categories. In order to assess classification accuracy 
we compare our results with NDVI based vegetation detec- 
tion. 
2 DATA SET 
The analyzed imagery was recorded by a DAEDALUS AADS 
1268 line scanner on board a Dornier Do 228 aircraft yielding 
a multispectral data set consisting of ten reflective bands 
and one band in the thermal infrared. The experiment took 
place at the 26 August, 1997, in late summer. We chose 
parts of the city of Nuremberg, Germany, as test site. The 
flight altitudes of 300 m and 900 m yielded geometric ground 
resolutions of about one and three meters, respectively. The 
data was recorded at 4:30, 8:30, 11:30 and 12:30 (MET), 
in order to measure a characteristic part of the diurnal 
temperature curve. 
Atmospheric corrections were performed by using the SEN- 
SAT/MODTRAN code (Richter 1992) relying on standard 
atmospheric parameters supplemented by ground measure- 
ments of meteorological data. In this paper we only use 
brightness temperatures, because no correction for emissiv- 
ities is possible (and required) to perform an unsupervised 
classification. Having this simplification in mind, we use the 
notion of temperature for abbreviation purpose. From the 
data set a small scene showing the airport of Nuremberg 
was extracted for every overflight. The early morning im- 
age was taken as reference on which the images were reg- 
istered using Akima local quintic polynomial interpolation 
(Wiemker 1996). This scene is well suited for our work be- 
cause it contains different types of concrete and roofs, dense 
and light grass areas and trees. The four-dimensional tem- 
perature data set resulting from the four overflights provides 
the basis for the investigations described below. 
3 THERMAL PROPERTIES OF DIFFERENT 
SURFACE TYPES 
In this section we briefly describe two kinds of information 
contained in the data set that was used for classification. 
3.1 TEMPERATURE CURVES 
To take a first look at the data set described in Section 2 
we defined several regions of interest from aerial photographs 
representing different surface types. The corresponding av- 
eraged temperatures of each region are plotted in Figure 2. 
Grassland and concrete are represented by two curves each to 
describe a brighter and darker region in the aerial photographs 
respectively (corresponding to a brighter resp. darker col- 
ored concrete and a thicker resp. more sparsely vegetated 
grassland). For illustration purpose the two-dimensional his- 
  
  
  
  
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= 407 7 
Em [4] | 
Us 
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o 
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Ob que NN 
4 6 8 10 12 14 
Time (MET) 
Figure 2: Temperature curves averaged over the regions of 
various surface types ([1]: trees, [2]: grass (dense), [3]: grass 
(sparse), [4]: concrete (bright), [5]: concrete (dark), [6]: various 
roofs) 
tograms of early morning and noon temperatures of the same 
regions are shown in Figure 3. 
With regard to a classification application based solely upon 
the temperature data Figure 2 shows some remarkable fea- 
tures: 
e as expected from model runs trees and thick vegetated 
grassland have flatter temperature curves with rela- 
tively low noon values corresponding to high thermal 
inertia values. It should be easy to separate these sub- 
stances from other materials, 
e roofs have the steepest curve and highest noon tem- 
peratures, 
e bright concrete and sparse grassland have nearly the 
same heating behaviour and are expected to be hardly 
distinguishable. 
So temperature curves alone will not provide enough infor- 
mation for a proper separation of surface types. 
3.2 TEMPERATURE DISTRIBUTIONS 
A second source of information based on thermal measure- 
ments are the temperature distributions of various surface 
types. On the right side of Figure 2 some examples corre- 
sponding to the temperature curves on the left are shown. 
It can be seen that roofs have temperature values of about 
20° to 80° (of course several roofs were put together in 
this region of interest) and so have the broadest temperature 
histograms while concrete surfaces have the smallest. 
To draw surface specific information from thermal infrared 
images (Holbe & Luvall 1989) have modeled surface tempera- 
ture distributions of forest landscapes using a two-dimensional 
beta probability distribution. For our purposes the standard 
Intemational Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 229 
  
  
  
 
	        
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