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Analysis and Projection of Land-Use/Land-Cover Dynamics through Scenario-Based Simulations Using the CA-Markov Model: A Case Study in Guanting Reservoir Basin, China
| Content Provider | MDPI |
|---|---|
| Author | Ruben, Gebdang B. Zhang, Ke Dong, Zengchuan Xia, Jun |
| Copyright Year | 2020 |
| Description | Understanding the rate and process of land-use/land-cover (LULC) change in a watershed is essential for managing natural resources and achieving sustainable development. Therefore, this study aims to analyze historical LULC change from 1980 to 2010 and project future changes in 2030, 2060, and 2090 in the Guanting Reservoir Basin (GRB), China, a critical water-supplying watershed for China’s capital Beijing, through scenario-based simulations. Two LULC scenarios, ‘business-as-usual’ and ‘governance’ (Gov), were projected using the Cellular Automata-Markov (CA–Markov) model. Historical LULC trend analysis shows that built-up land increased from 2.6% in 1980 to 5.26% in 2010, while cropland, grassland, and water body decreased. LULC conversion analysis indicates that, in general, grassland, cropland, and woodland were converted to built-up area from 1980 to 2010. The BAU scenario projects a dramatic increase in built-up area, rising from 2296.98 km2 (5.26%) in 2010 to 11,757.35 km2 (26.93%) in 2090 at the expense of cropland and grassland areas. Conversely, the Gov scenario predicts an increase in water body, woodland, and grassland, encouraging sustainable development. Overall, these results provide useful inputs to the LULC planners and water resources managers to elaborate on eco-friendly policies and regulations for GRB. |
| Starting Page | 3747 |
| e-ISSN | 20711050 |
| DOI | 10.3390/su12093747 |
| Journal | Sustainability |
| Issue Number | 9 |
| Volume Number | 12 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-05-05 |
| Access Restriction | Open |
| Subject Keyword | Sustainability Remote Sensing Land-use/land-cover Change Guanting Reservoir Basin Ca–markov Prediction |
| Content Type | Text |
| Resource Type | Article |
