Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh

作者：Islam Kamrul^*; Rahman Md Farhadur; Jashimuddin Mohammed

来源：Ecological Indicators, 2018, 88: 439-453.

DOI：10.1016/j.ecolind.2018.01.047

• Chunati Wildlife Sanctuary
• Markov chain
• Cellular Automata
• Artificial Neural Network
• Binary logistic regression
• land use and land cover change

Land use changes generally affect the integrity of an ecosystem. The effect of this change can be very severe if the conversion disrupts a crucial habitat of major plants and animals. The degraded Chunati Wildlife Sanctuary is one such area of Bangladesh which is facing a serious problem of rapid land use change. In this study, the future trend of land use change of the area was modelled using Artificial Neural Network. Several driver variables were also incorporated to determine their effect on land use change. Binary logistic regression was used to assess the significance of the drivers of land use change for this region. The analysis shows that nearly 76% of the total land area (8258 ha) was covered by vegetation during 2005. After 2005, that was reduced to 61% (6637 ha) in 2015, a 15% decline from 2005. On the other hand, the coverage of vacant land increased from nearly 10% in 2005 to 22% in 2015. This is indeed a matter of real concern. The critical analysis suggests that Cellular Automata is not a good fit to simulate the future land uses as it misdirects the analysis both spatially and numerically. The incorporation of driver variables gives strength to the Artificial Neural Network to predict the future. The chisquare value for the prediction of land use of the area found from the neural network was 7.815 which was greater than the critical value (3.316). The neural network was found to be a good fit for future land use prediction. The kappa index of variation shows that the overall accuracy of the prediction using neural network was above 90%. Elevation, slope, and distance to the road were the three driver variables which were found statistically significant while predicting the probability of forest land use change. The accuracy of the binary logistic regression was about 61% which was quite satisfactory. The simulation result shows that almost 5732 ha of the total land will be in the forest category of land use during 2020 and it will be further decreased to 5128 ha in 2025. The vacant area will increase from 24% to 31% from 2020 to 2025. Based on the findings and simulated land use map of 2020-2025, the study will help the management authority of this critical habitat to take proper action before further degradation occurs.

• 出版日期2018-5