Land-cover change prediction from landsat data using markov chain analysis

Urbanization has brought socioeconomic benefits to people. It has also caused environmental issues such as loss of agricultural land, pollution and habitat destruction. Understanding dynamic mechanism of these changes in landscape from a spatio-temporal perspective is important for urban policy making. This study analysed the land-cover change and predicted the urban development using Landsat data in Guatemala during the three periods (1993-1994, 1996-1998, and 2000-2004). Image classification detect the land cover change. The overall accuracy were 90.95%, 96.79% and 96.28, respectively. The present study used the Markov chain method to simulate the spatial distribution of future urban growth. Prediction of urban growth was projected for 2023. The results showed that the area of urban will be increased. The methods used in this study could be extended to other regions for urbanization monitoring and landscapes planning.