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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
3. RESULTS AND DISCUSSION 
3.1 Body temperature modelling 
Independent observations (N=31) of lizard's body temperature 
were collected to validate the body temperature model. The 
observations were compared with model simulations and the 
root mean square error (RMSE) of the temperature prediction 
was calculated (figure 2). The result showed that by a bio- 
physical model, the body temperature of the animal can be 
accurately modeled, and therefore, the model can be integrated 
into the cellular automaton model to simulate the animal 
movement. 
  
  
  
  
  
  
  
  
  
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Observed Body Temperature (C) 
Figure 2. The simulated vs. observed body temperature of a 
lizard 
32 Thermal habitat occupancy 
The integrated model was run to simulate the thermal habitat 
use by a lizard during a 96-hour experiment. The results of this 
simulation are illustrated in Figure 5 in a spatially explicit way. 
The cell colour on the map represents the total time the lizard 
spent in a particular cell. 
For validation, the observatory lizard tracking data was 
aggregated, classified and compared with the simulation. The 
accuracy of the simulated thermal habitat use was calculated 
using a confusion matrix, in which the different levels of 
occupancy between simulation and actual observation were 
summarised. The result showed an overall accuracy of 75.796 
(Table 1). It also showed the simulation slightly over-estimated 
the activity of the lizard. 
Table 1. Confusion matrix of the simulated vs. observed 
microhabitat occupancy (unit: pixel) 
  
  
  
Obs. 
high Moderate low Row total 
Sim. 
high 28 16 4 48 
Moderate 9 29 32 70 
low 47 78 2 650 
Column overall 
total 84 123 se accuracy: 
75.7% 
  
This result revealed that the thermal environment and the 
behavioural thermoregulation can be mapped to explain the 
lizard’s microhabitat use. Furthermore, using a cellular 
automaton algorithm, the spatial pattern of microhabitat 
occupancy can be simulated solely based on an energy point of 
view (figure 3). 
   
   
5 0 45 20 25 © M tn 
Figure 3. The simulated vs observed thermal habitat occupancy 
of a lizard 
3.3 Thermal roughness index 
Although testing the accordance between the habitat 
occupancies derived from CA model and the thermal roughness 
index map is an ongoing work, and need rigorous repetitions 
under different thermal conditions, from the results available, 
we report that as much as 70% of lizard habitat occupancy 
modelled by the CA model can be explained by the thermal 
roughness index. More results will be reported as we simulate 
different thermal habitat at different spatial scales in the future. 
4. CONCLUSION 
For the micro-habitat occupancy prediction, an overall accuracy 
of 75.7% was obtained. The results suggest that the integrated 
model of the lizard’s body temperature and CA algorithm may 
accurately predict thermal habitat use by lizards in a controlled 
environment. For the thermal habitat at larger scales, a newly 
proposed index: thermal roughness index which has a 
computational advantage can also be utilized to predict the 
occupancy of animal thermal habitat as the index provides 
similar results to the CA model. 
References: 
Barnosky, A.D., Hadly, E.A., Maurer, B.A. and Christie, M.I., 
2001. Temperate Terrestrial Vertebrate Faunas in 
North and South America: Interplay of Ecology, 
Evolution, and Geography with Biodiversity. 
Conservation Biology, 15(3), pp. 658-674. 
Chen, J. et al., 2002. Assessment of the urban development plan 
of Beijing by using a CA-based urban growth model. 
Photogrammetric Engineering and Remote Sensing, 
68(10), pp. 1063-1071. 
Fei, T. et al, 2011. A body temperature model for lizards as 
estimated from the thermal environment. Journal of 
Thermal Biology, pp. In press.