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errors observed for the land covers grass and field are important. We are currently wondering about the kind of qualitative 
labels to create. We also suppose a confusion between these two classes because of their similar visible and near infra-red 
behaviors which have been used for the unmixing process of NOAA data. This is illustrated on figure 6 by displaying 
the NDVI (Normalized Derived Vegetation Index) curves estimated from the visible and near infra-red NOAA/AVHRR 
channels (N.DVI — (nir — vis)/(nir -- vis)): the curves are quite similar and that surely explains the difficulty to 
discriminate grass and fields in the visible and near infra-red channels. A future task is then to compute a differentiation 
  
Figure 6: Temporal profiles of NDVI, for field and grass. 
criterion between these two land covers, in order to minimize this confusion. To do this, we propose to make use of the 
thermal channels and to compute proportions by considering the physical based mixture model that has been discussed 
in (Lahoche et al., 2000). 
5 CONCLUSION 
We describe a method for land use classification at meso-scale using coarse spatial resolution remotely sensed data ac- 
quired by the NOAA/AVHRR sensors. We define a model for the pixels’ content and a process allowing to compute the 
individual proportions of the land covers for each pixel. This process exploits the temporal information of the NOAA data 
and is based on inverting a linear mixture model of reflectance, we call it the *unmixing process". The result provides a 
description in terms of land covers percentage within each NOAA pixel. Results evaluation is carried out on a test site and 
show that the method gives a global idea about land cover proportion inside the NOAA pixels. If this method seems to 
be promising, it must still be improved: define which types of land cover can be discriminated, and with which precision 
level. On another hand, for the inversion of the linear mixture model, we have considered a determinist resolution. A new 
formulation based on a probabilistic framework is under investigation, in order to estimate proportion values and also the 
associated likelihood. 
6 ACKNOWLEDGMENTS 
This research is made within the context of the European INCO-PED IWRMS project (Integrated Water Resources Man- 
agement System http://www.iwrms.uni-jena.de/) funded by European Commission under the contract ERBIC18CT97044. 
REFERENCES 
Bouzidi, S., Berroir, J.-P. and Herlin, L, 1997a. Simultaneous use of SPOT and NOAA/AVHRR data for vegetation 
monitoring. In: proceedings of the 10th Scandinavian Conference on Image Analysis. 
Bouzidi, S., Berroir, J.-P. and Herlin, L, 1997b. Subpixel mixture modeling applied for vegetation monitoring. In: 
proceedings of the International Symposium on Environmental Software Systems. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 211