Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia

ABSTRACT

In 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.

KEYWORDS:

• Multi-temporal remotely sensed data
• CA-Markov model
• dynamics land use change

Acknowledgments

This paper is a part of the research activities entitled ‘The utilization of remote sensing data for disaster mitigation in Indonesia’. This research was funded by the budget of DIPA LAPAN activities in 2017, Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN) and supported by the Program of National Innovation System Research Incentive (INSINAS) in 2018, Ministry of Research Technology and Higher Education Republic of Indonesia. Thanks go to colleagues at the Remote Sensing Application Center, LAPAN for discussions and suggestions, and two anonymous reviewers were very helpful in improving our manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Fajar Yulianto http://orcid.org/0000-0002-3084-6694

Data availability statement

Landsat 5 TM and SRTM30 DEM were provided by the U.S. Geological Survey (USGS). Landsat 7 ETM+, Landsat 8 OLI/TIRS, and SPOT 6 images were provided by Remote Sensing Technology and Data Center, LAPAN. Data and Analysis Center Development, Regional Development Planning Agency (Bappeda), West Java Province, Indonesia for discussion, collaboration, field surveys, and sharing spatial data to support this research.