Calibrating Cellular Automata of Land Use/Cover Change Models Using a Genetic Algorithm (original) (raw)

MODELING AND PROJECTING LAND-USE AND LAND-COVER CHANGES WITH A CELLULAR AUTOMATON IN CONSIDERING LANDSCAPE TRAJECTORIES: AN IMPROVEMENT FOR SIMULATION OF PLAUSIBLE FUTURE STATES

The modeling and projecting of land use change is essential to the assessment of consequent en- vironmental impacts. In agricultural landscapes, land use patterns nearly always exhibit spatial autocorrelation, that is due in large part, to the clustered distribution of landscape features as hedgerows and wetlands, and also to the spatial interactions between land uses types itself. The importance of such structural spatial dependencies has to be taken into account when conducting land use projections, more especially as landscape features influence the precision of land use and land cover classifications of remote sensing imagery. The objective of this work is to improve land-use projections in considering landscape features in the modeling process. Cellular automata (CA), that provide a powerful tool for the dynamic modeling of land use changes, are a common method to take spatial interactions into account. They have been implemented in land use models that are able to simulate mul...

Improving the accuracy and reliability of land use/land cover simulation by the integration of Markov cellular automata and landform-based models

2018

Land use/land cover (LULC) is one of the important variables affecting human life and the physical environment. Modelling of change in LULC is an important tool for environmental management and for supporting spatial planning in environmentally important areas. In this study, a new approach was proposed to improve the accuracy and reliability of LULC simulation by integrating Markov cellular automata (Markov-CA) and landform-based models. Landform characteristics, positions and patterns influence LULC changes that are important in understanding the effects of environmental change and other physical factors. The results of this study showed that integration of Markov-CA and landform-based models increased correct rejection as a component of agreement and reduced incorrect hits and false alarms as components of disagreement for the percentage of the study area in each resolution (multiple of native pixel size). Correctly simulated hits as a component of agreement change also increased...

Modeling and projecting land-use and land-cover changes with Cellular Automaton in considering landscape trajectories

The modelling and projecting of land-use change is essential to the assessment of consequent environmental impacts. In agricultural landscapes, land-use patterns nearly always exhibit spatial autocorrelation, which is largely due to the clustered distribution of landscape features as hedge- rows and wetlands and also to the spatial interactions between land-use types themselves. The importance of such structural spatial dependencies has to be taken into account while conducting land-use projections. Also, land-use simulations have to be based on land-use and land-cover trends for two reasons: to identify the land-use and land-cover change processes and to be logical with the land-use and land-cover temporal dynamic. The objective of this work is to improve land- use projections in considering the influences of landscape features on land-use and land-cover change and in using long/short series of past observations in the modelling process. Cellular automata (CA) provide a powerful to...