American Journal of Engineering and Applied Sciences

MODELING OF FUTURE LAND COVER LAND USE CHANGE IN NORTH CAROLINA USING MARKOV CHAIN AND CELLULAR AUTOMATA MODEL

Mohammad Sayemuzzaman and Manoj K. Jha

DOI : 10.3844/ajeassp.2014.295.306

American Journal of Engineering and Applied Sciences

Volume 7, Issue 3

Pages 295-306

Abstract

State wide variant topographic features in North Carolina attract the hydro-climatologist. There is none modeling study found that predict future Land Cover Land Use (LCLU) change for whole North Carolina. In this study, satellite-derived land cover maps of year 1992, 2001 and 2006 of North Carolina were integrated within the framework of the Markov-Cellular Automata (Markov-CA) model which combines the Markov chain and Cellular Automata (CA) techniques. A Multi-Criteria Evaluation (MCE) was used to produce suitability future images. The Markov Chain and MCE analyses provided transition probability area and suitable images, respectively which were then dynamically adjusted through the Multi-Objective Land Allocation and CA spatial filter. Two stages of validation procedures were adopted in this study: 1. The Relative Operating Characteristics was used to validate suitability images and 2. The Kappa index of agreement was used to validate the overall LCLU changed simulated map. LCLU prediction of North Carolina for year 2030 shows 20% increase of built up land, 17% decrease of forest land while comparing that with year 1992. About 7% agricultural land was found to decrease in 2030 when compared with 2001 data. No significant changes were found for water body and other land category coverage. Much of the built-up land (urban expansion) was found to be in the southern, mid and mid-eastern portion of North Carolina. Loss of forest area was predicted mostly in western and mid-western part.

Copyright

© 2014 Mohammad Sayemuzzaman and Manoj K. Jha. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.