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The calculated hydrological isolation appears to be very significant in predicting species occurrence, except when other 
site factors, such as wetness, were taken into account. An explanation might be the significance of wetness for predicting 
the occurrence of Scorpidium. The wet class represents irrigation and water overflow, which can be interpreted as a 
hydrological short cut, i.e. the surface water reaches a site in spite of the hydrological isolation. The added value of Hi for 
predicting species occurrence will, therefore, be minimal. The Hi model can become more complete and its usefulness in 
predicting species occurrence will probably increase when wetness is taken into account and spatia] information on 
evapotranspiration and infiltration becomes available for the entire area. Evapotranspiration is proportional to biomass and 
wetness that can be obtained by remote sensing. 
  
  
  
  
  
  
  
  
Site factor Erica Scorpidium 
Wetness Dry Wet 
Raft thickness Thick Thin 
Management Summer mown Ns 
  
Table 2. Significant site factor classes 
The significant site factors classes (Table 2) for both species agree closely with the environmental needs of Scorpidium and 
Erica found in literature. The results of the statistical test show that Erica occurs more often in areas where the raft is 
relatively thick, dry and the vegetation is summer mown. Erica was assumed to be indicative of base-poor and oligotrophic 
conditions. Base supply and surface water influence decrease with increasing thickness of the floating rafts. The results of 
the statistical tests show that Scorpidium occurs more often in areas where the floating raft is thin and wet. It is a species 
indicative of base-rich conditions and a beginning stage of terrestrialization. When the floating raft is thin and submerged, 
base supply will be relatively large. The predicted distribution of the two species was mutually exclusive, with Erica most 
common in a base-poor environment and Scorpidium in a base-rich environment. 
4 CONCLUSIONS 
Hydrological isolation represented the influence of the base-rich and eutrophic surface water. It was formulated as a 
mathematical model, based on the water balance and Darcy’s law, so that a GIS can handle it. Several parameters 
necessary to calculate Hi were supplied by remote sensing. Peat baulks (the hydrological barriers) and watercourses 
(hydrological source) were mapped by analogue aerial photo interpretation and used to calculate the distance measures that 
are part of the Hi model. Furthermore, digital image analyses offered information on wetness representing water overflow 
and irrigation. Irrigation decreases the hydrological isolation of sites regardless of their distance from the hydrological 
source and thus can be considered as a hydrological short cut. It is possible to refine the model in the future because remote 
sensing offers information on biomass and wetness that can be used to estimate differences in evapotranspiration. The 
remote sensing and GIS techniques turned out to be very suitable and promising for determining potential habitat for plant 
species; information can be used to optimise field sampling, for land use planning and evaluation, and in scenario studies. 
The model is suitable to prioritise management practices to support sustainable land use. The site factors indicate valuable 
sites with a high probability of plant species occurrence that are vulnerable. Identifying these vulnerable areas makes the 
method suitable to support sustainable land use planning. It is very important to study the possible impact of a new 
management regime before it is carried out; like digging ditches, change of water inlet, irrigation, mowing or cutting 
regime. To change for example from summer mowing to reed cutting might be such a scenario. GIS analyses of the results 
will lead to statements about the decrease or increase of potential habitat and their spatial changes, especially in relation to 
species whose distribution was well predicted by management type. The method should be applied to more plant species 
or species groups to become a more general applicable instrument. 
ACKNOWLEDGEMENTS 
I am grateful to Martien Molenaar, Bas Pedroli, Jan Clevers, Ruut Wegman and Han van Dobben for their support and 
valuable comments. 
REFERENCES 
Box, E.O., B.N. Holben & V. Kalb 1989. Accuracy of the AVHRR Vegetation Index as a predictor of biomass, primary 
productivity and net CO, flux. Vegetatio 80, pp 71-89. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1313