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Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton

1
Graduate School for International Development and Cooperation, Hiroshima University, 1-5-1 Kagamiyama, Higashi-Hiroshima 739-8529, Hiroshima, Japan
2
Environmental and Forestry Research and Development Agency of Aek Nauli, Sibaganding, Simalungun 21174, North Sumatra, Indonesia
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(11), 3024; https://doi.org/10.3390/su11113024
Received: 16 April 2019 / Revised: 24 May 2019 / Accepted: 24 May 2019 / Published: 28 May 2019
(This article belongs to the Section Sustainable Urban and Rural Development)
Land use and land cover (LULC) form a baseline thematic map for monitoring, resource management, and planning activities and facilitate the development of strategies to balance conservation, conflicting uses, and development pressures. In this study, changes in LULC in North Sumatra, Indonesia, are simulated and predicted using an artificial-neural-network-based cellular automaton (ANN-CA) model. Five criteria (altitude, slope, aspect, distance from the road, and soil type) are used as exploratory data in the learning process of the ANN-CA model to determine their impacts on LULC changes between 1990 and 2000; among the criteria, altitude and distance from the road have strong impacts. Comparison between the predicted and the real LULC maps for 2010 illustrates high agreement, with a Kappa index of 0.83 and a percentage of correctness of 87.28%. Then, the ANN-CA model is applied to predict LULC changes in 2050 and 2070. The LULC predictions for 2050 and 2070 demonstrate high increases in plantation area of more than 4%. Meanwhile, forest and crop area are projected to decrease by approximately 1.2% and 1.6%, respectively, by 2050. By 2070, forest and crop areas will decrease by 1.2% and 1.7%, respectively, indicating human influences on LULC changes from forest and cropland to plantations. This study illustrates that the simulation of LULC changes using the ANN-CA model can produce reliable predictions for future LULC. View Full-Text
Keywords: land use; land cover; cellular automata; artificial neural network; LULC prediction; North Sumatra; Indonesia land use; land cover; cellular automata; artificial neural network; LULC prediction; North Sumatra; Indonesia
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MDPI and ACS Style

Saputra, M.H.; Lee, H.S. Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton. Sustainability 2019, 11, 3024. https://doi.org/10.3390/su11113024

AMA Style

Saputra MH, Lee HS. Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton. Sustainability. 2019; 11(11):3024. https://doi.org/10.3390/su11113024

Chicago/Turabian Style

Saputra, Muhammad H.; Lee, Han S. 2019. "Prediction of Land Use and Land Cover Changes for North Sumatra, Indonesia, Using an Artificial-Neural-Network-Based Cellular Automaton" Sustainability 11, no. 11: 3024. https://doi.org/10.3390/su11113024

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