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Article

Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County

by 1,2,3, 1,2,3,*, 1,2,3, 1,2,3, 1,2,3, 4, 1,2,3 and 1,2,3
1
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China
2
Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China
3
School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
4
The College of Forestry, Beijing Forestry University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(12), 718; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120718
Received: 28 October 2020 / Revised: 22 November 2020 / Accepted: 30 November 2020 / Published: 2 December 2020
Simulating spatiotemporal land use and land cover change (LUCC) data precisely under future climate scenarios is an important basis for revealing the carbon cycle response of forest ecosystems to LUCC. In this paper, a coupling model consisting of a back propagation neural network (BPNN), Markov chain, and cellular automata (CA) was designed to simulate the LUCC in Anji County, Zhejiang Province, under four climate scenarios (Representative Concentration Pathway (RCP) 2.6, 4.5, 6.0, 8.5) from 2024 to 2049 and to analyze the temporal and spatial distribution of bamboo forests in Anji County. Our results provide four outcomes. (1) The transition probability matrices indicate that the area of bamboo forests shows an expansion trend, and the largest contribution to the expansion of bamboo forests is the cultivated land. The Markov chain composed of the average transition probability matrix could perform excellently, with only small errors when simulating the areas of different land-use types. (2) Based on the optimized BPNN, which had a strong generalization ability, a high prediction accuracy, and area under the curve (AUC) values above 0.9, we could obtain highly reliable land suitability probabilities. After introducing more driving factors related to bamboo forests, the prediction of bamboo forest changes will be more accurate. (3) The BPNN_CA_Markov coupling model could achieve high-precision simulation of LUCC at different times, with an overall accuracy greater than 70%, and the consistency of the LUCC simulation from one time to another also had good performance, with a figure of merit (FOM) of approximately 40%. (4) Under the future four RCP scenarios, bamboo forest evolution had similar spatial characteristics; that is, bamboo forests were projected to expand in the northeast, south, and southwest mountainous areas of Anji County, while bamboo forests were projected to decline mainly around the junction of the central and mountainous areas of Anji County. Comparing the simulation results of different scenarios demonstrates that 74% of the spatiotemporal evolution of bamboo forests will be influenced by the interactions and competition among different land-use types and other driving factors, and 26% will come from different climate scenarios, among which the RCP8.5 scenario will have the greatest impact on the bamboo forest area and spatiotemporal evolution, while the RCP2.6 scenario will have the smallest impact. In short, this study proposes effective methods and ideas for LUCC simulation in the context of climate change and provides accurate data support for analyzing the impact of LUCC on the carbon cycle of bamboo forests. View Full-Text
Keywords: land use and land cover change; bamboo forest; cellular automata (CA); Markov chain; back propagation neural network (BPNN); RCP scenarios land use and land cover change; bamboo forest; cellular automata (CA); Markov chain; back propagation neural network (BPNN); RCP scenarios
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MDPI and ACS Style

Huang, Z.; Du, H.; Li, X.; Zhang, M.; Mao, F.; Zhu, D.; He, S.; Liu, H. Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County. ISPRS Int. J. Geo-Inf. 2020, 9, 718. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120718

AMA Style

Huang Z, Du H, Li X, Zhang M, Mao F, Zhu D, He S, Liu H. Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County. ISPRS International Journal of Geo-Information. 2020; 9(12):718. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120718

Chicago/Turabian Style

Huang, Zihao, Huaqiang Du, Xuejian Li, Meng Zhang, Fangjie Mao, Di’en Zhu, Shaobai He, and Hua Liu. 2020. "Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County" ISPRS International Journal of Geo-Information 9, no. 12: 718. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9120718

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