A Loosely Coupled Model for Simulating and Predicting Land Use Changes
The analysis and modeling of spatial and temporal changes in land use can reveal changing urban spatial patterns and trends. In this paper, we introduce a linear transformation optimization Markov (LTOM) model that can be exploited to estimate the state transition probability matrix of land use, bui...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/12/1/189 |
| _version_ | 1827624021128118272 |
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| author | Jing Liu Chunchun Hu Xionghua Kang Fei Chen |
| author_facet | Jing Liu Chunchun Hu Xionghua Kang Fei Chen |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | The analysis and modeling of spatial and temporal changes in land use can reveal changing urban spatial patterns and trends. In this paper, we introduce a linear transformation optimization Markov (LTOM) model that can be exploited to estimate the state transition probability matrix of land use, building a loosely coupled ANN-CA-LTOM model for simulating and predicting land use changes. The advantages of this model are that it is flexible and high expansibility; it can maintain semantic coupling between the Artificial Neural Networks (ANN), Cellular Automata (CA), and LTOM model and enhance their functions; and it can break the limitation of requiring two periods of land use data when calculating the transition probability matrix. We also construct a suitability atlas of land use as the transition rules into the CA-LTOM model, taking into account the regional natural and socioeconomic driver factors, by exploiting the ANN model. The ANN-CA-LTOM model is employed to simulate the distribution of the three major types of land use, i.e., construction land, agricultural land, and unused land, in the Nansha District, China, in 2018 and 2020. The results show that the model performs well and the overall accuracy of the land use simulation was 97.72%, with a kappa coefficient of 0.962761. Furthermore, the simulated and predicted results of land use changes from 2021 to 2023 in Nansha District show changing trends in construction, agricultural, and unused land use. This study provides an approach for estimating a Markov transition probability matrix and a coupled mode of the models for simulating and predicting land use changes. |
| first_indexed | 2024-03-09T11:59:56Z |
| format | Article |
| id | doaj.art-bccdf5c867e0480aa5d1735bc6169fc5 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T11:59:56Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-bccdf5c867e0480aa5d1735bc6169fc52023-11-30T23:05:35ZengMDPI AGLand2073-445X2023-01-0112118910.3390/land12010189A Loosely Coupled Model for Simulating and Predicting Land Use ChangesJing Liu0Chunchun Hu1Xionghua Kang2Fei Chen3School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaGuangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510120, ChinaThe analysis and modeling of spatial and temporal changes in land use can reveal changing urban spatial patterns and trends. In this paper, we introduce a linear transformation optimization Markov (LTOM) model that can be exploited to estimate the state transition probability matrix of land use, building a loosely coupled ANN-CA-LTOM model for simulating and predicting land use changes. The advantages of this model are that it is flexible and high expansibility; it can maintain semantic coupling between the Artificial Neural Networks (ANN), Cellular Automata (CA), and LTOM model and enhance their functions; and it can break the limitation of requiring two periods of land use data when calculating the transition probability matrix. We also construct a suitability atlas of land use as the transition rules into the CA-LTOM model, taking into account the regional natural and socioeconomic driver factors, by exploiting the ANN model. The ANN-CA-LTOM model is employed to simulate the distribution of the three major types of land use, i.e., construction land, agricultural land, and unused land, in the Nansha District, China, in 2018 and 2020. The results show that the model performs well and the overall accuracy of the land use simulation was 97.72%, with a kappa coefficient of 0.962761. Furthermore, the simulated and predicted results of land use changes from 2021 to 2023 in Nansha District show changing trends in construction, agricultural, and unused land use. This study provides an approach for estimating a Markov transition probability matrix and a coupled mode of the models for simulating and predicting land use changes.https://www.mdpi.com/2073-445X/12/1/189land use changesimulation and predictionANN-CA-LTOM modelsuitability atlas |
| spellingShingle | Jing Liu Chunchun Hu Xionghua Kang Fei Chen A Loosely Coupled Model for Simulating and Predicting Land Use Changes Land land use change simulation and prediction ANN-CA-LTOM model suitability atlas |
| title | A Loosely Coupled Model for Simulating and Predicting Land Use Changes |
| title_full | A Loosely Coupled Model for Simulating and Predicting Land Use Changes |
| title_fullStr | A Loosely Coupled Model for Simulating and Predicting Land Use Changes |
| title_full_unstemmed | A Loosely Coupled Model for Simulating and Predicting Land Use Changes |
| title_short | A Loosely Coupled Model for Simulating and Predicting Land Use Changes |
| title_sort | loosely coupled model for simulating and predicting land use changes |
| topic | land use change simulation and prediction ANN-CA-LTOM model suitability atlas |
| url | https://www.mdpi.com/2073-445X/12/1/189 |
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