Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
- (1)
- Land use data. This study utilized remote sensing data from the Landsat-8 Operational Land Imager (OLI) sensor, selecting images from 2015 and 2020 with cloud cover below 5% and a resolution of 30 m. We performed radiometric calibration, atmospheric correction, cropping, supervised classification, and post-classification processing in ENVI 5.1. The supervised classification was conducted using the random forest classification method, categorizing the images into six land use types: farmland, forest land, grassland, built-up land, unused land, and water body (Figure 1). During the classification process, 50 region of interest (ROI) samples were selected for each land use type. To ensure the accuracy of the results, post-classification processing was carried out. The classification results were corrected through visual interpretation in conjunction with high-resolution Google imagery and remote sensing data from Landsat 8. Finally, we validated the classified results in ENVI, achieving an overall accuracy exceeding 90% (Table S1).
- (2)
- Driving factors. The driving factors for land use prediction are divided into natural and socioeconomic factors. The natural factors include slope and elevation, which derive from the digital elevation model (DEM), as well as climate data. The socioeconomic factors include gross domestic product (GDP) distribution data, population distribution data, and distance from railways, roads, built-up areas, and water bodies. Using ArcGIS 10.5’s resampling function, the resolution of the driving factor data is uniformly adjusted to 30 m × 30 m.
3. Methodology
3.1. FLUS Model
3.2. LULC Change Scenarios
- (1)
- No restriction (NR): a business-as-usual development scenario reflecting the historical trend of land use, with no policy planning to restrict the future development of land use.
- (2)
- Ecological conservation red line (EC): areas with crucial ecological functions that require strict protection. Xiamen has designated 303.69 km2 as ecological conservation red lines, including 216.01 km2 of terrestrial areas, encompassing key ecological areas like nature reserves, water source conservation areas, and forests. Under the EC scenario, the addition of new farmland and built-up land in these areas is forbidden.
- (3)
- Permanent basic farmland conservation red line (BF): farmland boundaries that cannot be occupied or developed, which require permanent protection. Xiamen has designated 68.87 km2 of permanent basic farmland. The BF scenario restricts the conversion of farmland to other uses.
- (4)
- Urban development boundary red line (UB): the boundary of areas designated for urban development and construction within a certain period, including both existing built-up areas and reserved space for future urban construction. Xiamen has designated 734.06 km2 for urban development. Under the UB scenario, new built-up land is prohibited outside the urban development boundary to prevent uncontrolled urban sprawl.
- (5)
- Integrated three control line scenarios (ITL): a comprehensive scenario that combines all three control lines (EC, BF, and UB). It reflects Xiamen’s territorial planning, integrating ecological protection, farmland preservation, and orderly urban development.
3.3. InVEST Model
3.4. Variation Partitioning Analysis
3.5. Hot Spot Analysis
4. Results
4.1. Future Land Use in Multi-Scenario
4.2. Carbon Storage Under Various Scenarios
4.3. Carbon Sequestration Under Various Scenarios
4.4. Spatial Patterns of Carbon Sequestration Hot/Cold Spots
4.5. Impact of Territorial Spatial Planning and Climate Change on Carbon Sequestration
5. Discussion
5.1. The Impact of Territorial Spatial Planning on Carbon Storage
5.2. The Impact of Climate Change on Carbon Storage
5.3. The Impact of Territorial Spatial Planning and Climate Change on Carbon Storage
5.4. Constructing Territorial Spatial Planning for Climate Change Adaptation
5.5. Limitations
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data | Source | Year | Resolution |
---|---|---|---|
Landsat 8 OLI/TIRS | Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 6 July 2024)) | 2015, 2020 | 30 m |
DEM | Geospatial Data Cloud (https://www.gscloud.cn/ (accessed on 7 July 2024)) | 2009 | 30 m |
Slope | Calculated from DEM data | 2009 | 30 m |
Elevation | Calculated from DEM data | 2009 | 30 m |
Railway and road data | National Catalogue Service For Geographic Information (https://www.webmap.cn/ (accessed on 12 July 2024)) | 2022 | − |
GDP | Resource and Environment Science and Data Center (https://www.resdc.cn/ (accessed on 19 July 2024)) | 2020 | 1 km |
Population | Resource and Environment Science and Data Center (https://www.resdc.cn/ (accessed on 19 July 2024)) | 2020 | 1 km |
RCP 4.5, RCP 8.5 | WorldClim Data Website (https://www.worldclim.org/ (accessed on 16 September 2021)) | − | 900 m |
The territorial spatial planning three control lines data | Xiamen Municipal Bureau of Natural Resources and Planning | 2019 | − |
NR | EC | BF | UB | ITL | |
---|---|---|---|---|---|
NC | NR_NC | EC_NC | BF_NC | UB_NC | ITL_NC |
RCP 4.5 | NR_RCP 4.5 | EC_RCP 4.5 | BF_RCP 4.5 | UB_RCP 4.5 | ITL_RCP 4.5 |
RCP 8.5 | NR_RCP 8.5 | EC_RCP 8.5 | BF_RCP 8.5 | UB_RCP 8.5 | ITL_RCP 8.5 |
Land Use Type | C_above | C_below | C_soil | C_dead |
---|---|---|---|---|
Farmland | 46.5 | 80.7 | 108.4 | 0 |
Forest land | 42.4 | 115.9 | 236.9 | 0 |
Grassland | 4.3 | 86.5 | 99.9 | 0 |
Built-up land | 1.2 | 0 | 71 | 0 |
Unused land | 0 | 0 | 50 | 0 |
Waterbody | 0 | 0 | 0 | 0 |
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Zhu, W.; Lan, T.; Tang, L. Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration. Remote Sens. 2025, 17, 273. https://doi.org/10.3390/rs17020273
Zhu W, Lan T, Tang L. Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration. Remote Sensing. 2025; 17(2):273. https://doi.org/10.3390/rs17020273
Chicago/Turabian StyleZhu, Wei, Ting Lan, and Lina Tang. 2025. "Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration" Remote Sensing 17, no. 2: 273. https://doi.org/10.3390/rs17020273
APA StyleZhu, W., Lan, T., & Tang, L. (2025). Impacts of Future Climate Change and Xiamen’s Territorial Spatial Planning on Carbon Storage and Sequestration. Remote Sensing, 17(2), 273. https://doi.org/10.3390/rs17020273