Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Dataset and Preprocessing
2.3. Methods
2.3.1. Spatial Transfer Rule and Convolutional Neural Network
2.3.2. Quantity Transfer Rules and Markov Chain Model
2.3.3. Cellular Automata Model
2.3.4. CNN-CA-MC Model and Accuracy Assessment
- Each grid with a spatial resolution of 30 × 30 m represents and stores the land use state, forming the cells. These cells collectively constitute the cellular space, distributed in spatial space.
- Each grid has an attribute known as the state, representing the land use type.
- The neighborhood concept employs a 5 × 5 Moore-type configuration, including the central cell and its 24 surrounding cells. It considers the influence of the surrounding cells on the attributes of the central cell.
- The transition rule governs the state of the neighborhood at the subsequent time step based on the current state of the cell and the condition of the neighborhood. It is defined as follows:
- 5.
- The CA model evolves in discrete time steps. In this context, discrete time refers to the iteration count or time interval at which the CA model progresses. For this specific case, the iteration count aligns with the 10-year interval between the utilized basic data, resulting in a specified iteration count corresponding to a 10-year interval.
3. Results
3.1. CNN-CA-MC Simulation
3.2. Policies-Driven Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Type | Year | Resolution | Resource | |
---|---|---|---|---|---|
1 | Land use dataset | Raster | 2000, 2010, 2020 | 30 m | https://www.resdc.cn/ (accessed on 15 December 2023) |
2 | GDEM V3 | Raster | 2000~2013 | 30 m | https://www.earthdata.nasa.gov/ (accessed on 15 December 2023) |
3 | Distance to water | Raster | 2000, 2010, 2020 | 30 m | Calculated from Land use dataset |
4 | Reservoir | Raster | 2000, 2010, 2020 | 30 m | Reclass from Land use dataset |
5 | Slope | Raster | 2000~2013 | 30 m | Calculated from GDEM V3 |
6 | Policy constraint factors | Raster | 2000~2013 | 30 m | Manual statistics |
Name | Input 1 | Input 2 | Input 3 | Input 4 | |
---|---|---|---|---|---|
Input 1-1 | Input 1-2 | ||||
Dataset A | 2000_11 and 2010_6 | 2010_11 | 2000_11 and 2010_6 | 2010_6 | 2020_6 |
Dataset B | 2010_11 and 2020_6 | 2020_11 | 2010_11 and 2020_6 | 2020_6 | - |
Farmland | Forest | Grassland | Water | Built-Up Area | Undeveloped Land | |
---|---|---|---|---|---|---|
Farmland | 1,325,715.1 (76.6%) | 103,360.7 (6.0%) | 112,233.5 (6.5%) | 30,448.6 (1.8%) | 63,766.6 (3.7%) | 94,599 (5.5%) |
Forest | 53,769.8 (20.2%) | 178,813.1 (67.3%) | 17,693.0 (6.7%) | 3536.3 (1.3%) | 2540.9 (1%) | 9290.9 (3.5%) |
Grassland | 111,277.9 (24.7%) | 15,258.0 (3.4%) | 243,024.4 (54%) | 1513.6 (0.3%) | 3055.1 (0.7%) | 76,267.3 (16.9%) |
Water | 8085.2 (4.2%) | 7820.8 (4.1%) | 7061.5 (3.7%) | 99,482.2 (52.1%) | 1411.1 (0.7%) | 66,974.9 (35.1%) |
Built-up area | 39,601.8 (23.1%) | 2257.0 (1.3%) | 2758.9 (1.6%) | 333.9 (0.2%) | 122,435.9 (71.4%) | 4087.4 (2.4%) |
Undeveloped land | 115,745.3 (10.9%) | 8682.5 (0.8%) | 121,081 (11.4%) | 34,216.3 (3.2%) | 11,981.4 (1.1%) | 774,841.1 (72.7%) |
Farmland | Forest | Grassland | Water | Built-up Area | Undeveloped Land | |
---|---|---|---|---|---|---|
Farmland | 1458 (81.2%) | 180.1 (10.0%) | 27.5 (2.5%) | 12.6 (0.7%) | 64.4 (3.6%) | 41.5 (2.3%) |
Forest | 68.4 (21.4%) | 238.8 (74.8%) | 2.3 (0.7%) | 4.3 (1.4%) | 1.1 (0.3%) | 4.4 (1.4%) |
Grassland | 68.2 (13.7%) | 11.1 (2.2%) | 387.3 (77.8%) | 7.6 (1.5%) | 3.8 (0.8%) | 20.1 (4.0%) |
Water | 6.4 (3.0%) | 3.2 (1.5%) | 2.4 (1.1%) | 165.5 (77.3%) | 0.3 (0.1%) | 36.4 (17.0%) |
Built-up area | 26.3 (12.9%) | 2.3 (1.1%) | 1.5 (0.7%) | 5.8 (2.8%) | 164.7 (80.9%) | 2.9 (1.4%) |
Undeveloped land | 119.5 (10.8%) | 27.5 (2.5%) | 67.3 (6.1%) | 8.0 (2.5%) | 11.9 (1.1%) | 854.9 (77.1%) |
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Wen, S.; Wang, Y.; Song, H.; Liu, H.; Sun, Z.; Bilal, M.A. Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin. Atmosphere 2024, 15, 288. https://doi.org/10.3390/atmos15030288
Wen S, Wang Y, Song H, Liu H, Sun Z, Bilal MA. Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin. Atmosphere. 2024; 15(3):288. https://doi.org/10.3390/atmos15030288
Chicago/Turabian StyleWen, Shibo, Yongzhi Wang, Haohang Song, Hengxi Liu, Zhaolong Sun, and Muhammad Atif Bilal. 2024. "Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin" Atmosphere 15, no. 3: 288. https://doi.org/10.3390/atmos15030288
APA StyleWen, S., Wang, Y., Song, H., Liu, H., Sun, Z., & Bilal, M. A. (2024). Integrated Predictive Modeling and Policy Factor Analysis for the Land Use Dynamics of the Western Jilin. Atmosphere, 15(3), 288. https://doi.org/10.3390/atmos15030288