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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.
