Abstract
ABSTRACTIn this research, the integration of remotely sensed data and Cellular Automata-Markov model (CA-Markov) have been used to analyze the dynamics of land use change and its prediction for the next year. Training phase for the CA-Markov model has been created based on the input pair of land use, which is the result of land use mapping using Maximum Likelihood (ML) algorithm. Three-map comparison has been used to evaluate process accuracy assessment of the training phase for the CA-Markov model. Furthermore, the simulation phase for the CA-Markov model can be used to predict land use map for the next year. The analyze of the dynamics of land use change and its prediction during the period 1990 to 2050 can be obtained that the land serves as a water absorbent surfaces such as primary forest, secondary forest and the mixed garden area continued to decline. Meanwhile, on build land area that can lead to reduced surface water absorbing tends to increase from year to year. The results of this research can be used as input for the next research, which aims to determine the impact of land use changes in hydrological conditions against flooding in the research area.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
