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Artificial neural network–cellular automata model has been applied successfully in land use change simulation. However, it has rarely been integrated with landscape pattern indices (LPIs), the embodiment of the spatial heterogeneity of land use. This paper proposed to integrate LPIs as the parameters of artificial neural network–cellular automata model. Subsequent to a description of the principles and implementation of the model, a case study was presented in Changping district. In the case study, two LPIs, the landscape similarity index and patch density, along with 10 other spatial variables, were selected as the influencing factors of land use change. Based on land use maps in years 1988 and 1998, a land use map in 2008 was simulated by the proposed model. Comparing with the actual land use map in 2008 and the simulated result of artificial neural network–cellular automata model, the results showed that the proposed model is more applicable for simulating land use change in the study area; the limitation of this model is the spatial scale and calculation method of LPIs. [ABSTRACT FROM PUBLISHER]

A remote sensing approach is proposed in this paper for monitoring instantaneous underwater light climate in Dahuchi Lake, China. Water constituents that are optically significant to the lake water can be retrieved through this method based on an artificial neural network (ANN) algorithm with a satisfactory accuracy. In this study, the ANN was trained by spectral simulations which were generated by a water colour simulation model (WASI). An underwater light attenuation model has been built by fitting the field measurements and applied to calculate photosynthetically available radiation (PAR) at the bottom of the lake from the inputs of hyperspectral reflectance above the water surface and the water depth. Results show that this method can predict the concentration of suspended particle matter (SPM) with a high accuracy and explain more than half of the underwater PAR variation with a standard deviation of 94.8 μmol s-1 m-2. In order to test the portability of the algorithm, it has also been extended to three bands of Landsat TM image. Compared with hyperspectral inputs, ANN with broadband inputs has lower but still acceptable accuracy on SPM retrieval. The advantage of this method lies in its time-efficient and cost-efficient monitoring of underwater light climate, i.e. the simulations generated by a semi-physical model can be used for ANN training, instead of using a large number of field measurements. [ABSTRACT FROM AUTHOR]