A spatiotemporal analysis of landscape change using an integrated Markov chain and cellular automata models

Abstract

Spatially land-cover models are necessary for sustainable land-cover planning. The expansion of human-built land involves the destruction of forests, meadows and farmlands as well as conversion of these areas to urban and industrial areas which will result in significant effects on ecosystems. Monitoring the process of these changes and planning for sustainable use of land can be successfully achieved by using the remote sensing multi-temporal data, spatial criteria and predictor models. In this study, land-cover change analysis and modeling was performed for our study area in central Germany. An integrated Cellular Automata–Markov Chain land change model was carried out to simulate the future landscape change during the period of 2020–2050. The predictive power of the model was successfully evaluated using Kappa indices. As a consequence, land change model predicts very well a continuing downward trend in grassland, farmland and forest areas, as well as a growing tendency in built-up areas. Hence, if the current trends of change continue regardless of the actions of sustainable development, drastic natural area decline will ensue. The results of this study can help local authorities to better understanding the current situation and possible future conditions as well as adopt appropriate strategies for management of land-cover. In this case, they can create a balance between urban development and environmental protection.