The sub-pixel urban land cover has been proved to have obvious correlations with land surface temperature (LST). Yet
these relationships have seldom been used to simulate LST. In this study we provided a new approach of urban LST
simulation based on sub-pixel land cover modeling. Landsat TM/ETM+ images of Xiamen city, China on both the
January of 2002 and 2007 were used to acquire land cover and then extract the transformation rule using logistic
regression. The transformation possibility was taken as its percent in the same pixel after normalization. And cellular
automata were used to acquire simulated sub-pixel land cover on 2007 and 2017. On the other hand, the correlations
between retrieved LST and sub-pixel land cover achieved by spectral mixture analysis in 2002 were examined and a
regression model was built. Then the regression model was used on simulated 2007 land cover to model the LST of
2007. Finally the LST of 2017 was simulated for urban planning and management. The results showed that our method is
useful in LST simulation. Although the simulation accuracy is not quite satisfactory, it provides an important idea and a
good start in the modeling of urban LST.
The simulation of urban expansion or land cover changes is a major theme in both geographic information science and landscape ecology. Yet till now, almost all of previous studies were based on grid computations at pixel level. With the prevalence of spectral mixture analysis in urban land cover research, the simulation of urban land cover at sub-pixel level is being put into agenda. This study provided a new approach of land cover simulation at sub-pixel level. Landsat TM/ETM+ images of Xiamen city, China on both the January of 2002 and 2007 were used to acquire land cover data through supervised classification. Then the two classified land cover data were utilized to extract the transformation rule between 2002 and 2007 using logistic regression. The transformation possibility of each land cover type in a certain pixel was taken as its percent in the same pixel after normalization. And cellular automata (CA) based grid computation was carried out to acquire simulated land cover on 2007. The simulated 2007 sub-pixel land cover was testified with a validated sub-pixel land cover achieved by spectral mixture analysis in our previous studies on the same date. And finally the sub-pixel land cover of 2017 was simulated for urban planning and management. The results showed that our method is useful in land cover simulation at sub-pixel level. Although the simulation accuracy is not quite satisfactory for all the land cover types, it provides an important idea and a good start in the CA-based urban land cover simulation.
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