Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Presence Data
2.2. Environmental Variables
2.3. Methodology
2.3.1. Principal Component Analysis
2.3.2. Maxent Model
2.3.3. Evaluation of Model Accuracy
2.3.4. Spatial Geometric Center Analysis
3. Results
3.1. Model Accuracy
3.2. The Spatial and Temporal Dynamics of Potential Habitats of S. rostratum
3.2.1. Current Climate Scenarios
4.2. The Spatial Distribution of S. rostratum Is Influenced by Multiple Factors
4.3. The Future Development of Potential Suitable Habitats for S. rostratum
4.4. The Advantages of Using the PCA Method in Species Distribution Modeling
4.5. Limitations and Suggestions
4.5.1. Limitations
4.5.2. Suggestions
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Number of Features | Year | Remark | Source |
---|---|---|---|---|
Climatic variables | 19 | Current:1970–2020; Future:2021–2040, 2041–2060. | 19 bioclimatic factors | WorldClim database (https://www.worldclim.org/ (accessed on 3 August 2023)) |
Terrain variables | 3 | / | Elevation, slope, and aspect | WorldClim database (https://www.worldclim.org/ (accessed on 3 August 2023)) |
Soil variables | 98 | 2010–2018 | Grid data for various soil properties at different depths | National Tibetan Plateau Data Center (http://data.tpdc.ac.cn (accessed on 3 August 2023)) |
Humanistic variables | 5 | 2020 | GDP, population, distances to roads and water bodies, building density | GDP, population: Resource and Environmental Science and Data Center (https://www.resdc.cn/Default.aspx (accessed on 3 August 2023)) Roads and water bodies: OpenStreetMap (https://www.openstreetmap.org (accessed on 3 August 2023)) Building: Geoservice of the Earth Observation Center (EOC) of the German Aerospace Center (DLR) (https://download.geoservice.dlr.de/WSF2019 (accessed on 3 August 2023)) |
Spatial variables | 2 | / | Longitude and latitude | Extracted from Elevation |
Simulation Scenario | Number of Input Variables | Number of New Variables | PCs Name |
---|---|---|---|
current 1 (1970–2020) | 127 a | 11 | pc1, pc2, pc3……, pc11 |
current 2 (1970–2020) | 24 b | 4 | pc1, pc2, pc3, pc4 |
future 1 (2021–2040: SSP1-2.6, SSP5-8.5) | 24 b | 4 | pc1, pc2, pc3, pc4 |
future 2 (2041–2060: SSP1-2.6, SSP5-8.5) | 24 b | 4 | pc1, pc2, pc3, pc4 |
Cluster Names | Contribution Rate (%) | Variables | Cluster Names | Contribution Rate (%) | Variables |
---|---|---|---|---|---|
C1 | 40.99 | total potassium density, total potassium, total nitrogen, total nitrogen density, total phosphorus, total phosphorus density | C7 | 8.80 | soil organic carbon, soil organic carbon density |
C2 | 12.70 | coarse fragment content, sand (0.05–2 mm) | C8 | 2.15 | cation exchange capacity |
C3 | 7.08 | pH | C9 | 2.05 | geographical longitude, geographical latitude |
C4 | 7.14 | bio3, bio6, bio11, bio12, bio13, bio15, bio16, bio18 | C10 | 1.27 | distance from building ups, gross domestic product (GDP) within grid, population count within each grid cell (1 square kilometer) |
C5 | 4.53 | bio4, bio5, bio7, bio10 | C11 | 1.06 | elevation, aspect, slope |
C6 | 4.12 | bulk density, thickness |
ID | The Types of Input Environmental Variables | Training AUC | Test AUC | Mean AUC | Remark |
---|---|---|---|---|---|
1 | A+B+C+D+E | 0.950 | 0.980 | 0.941 | current 1 |
2 | A+B+D+E | 0.943 | 0.895 | 0.934 | |
3 | A+B+E | 0.934 | 0.929 | 0.925 | current 2 |
4 | A+B+C+E | 0.944 | 0.956 | 0.935 | |
5 | B+C+D+E | 0.948 | 0.971 | 0.933 | |
6 | A+B+C+D | 0.945 | 0.939 | 0.934 |
ID | Method | Number of input Variables | Number of Variables after Decorrelation | Training AUC | Test AUC | Mean AUC | Remark |
---|---|---|---|---|---|---|---|
1 | PCA | 127 | 11 | 0.950 | 0.980 | 0.941 | / |
2 | Spearman Correlation | 127 | 73 | 0.811 | 0.874 | 0.763 | 0.75 [29] |
3 | 70 | 0.897 | 0.911 | 0.861 | 0.6 | ||
4 | 62 | 0.889 | 0.947 | 0.866 | 0.4 |
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Huang, T.; Yang, T.; Wang, K.; Huang, W. Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis. Remote Sens. 2024, 16, 271. https://doi.org/10.3390/rs16020271
Huang T, Yang T, Wang K, Huang W. Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis. Remote Sensing. 2024; 16(2):271. https://doi.org/10.3390/rs16020271
Chicago/Turabian StyleHuang, Tiecheng, Tong Yang, Kun Wang, and Wenjiang Huang. 2024. "Assessing the Current and Future Potential Distribution of Solanum rostratum Dunal in China Using Multisource Remote Sensing Data and Principal Component Analysis" Remote Sensing 16, no. 2: 271. https://doi.org/10.3390/rs16020271