Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methodology
2.3.1. Improved CASA Model
2.3.2. Theil–Sen Median Trend Analysis and Mann–Kendall Significance Test
2.3.3. Rescaled Rang Analysis (R/S Analysis) and Hurst Index Method
2.3.4. Geographic Detectors Model
3. Results
3.1. Spatial and Temporal Variation of Vegetation NPP in Yangzhou City
3.2. Spatiotemporal Variation of Vegetation NPP in Yangzhou City
3.3. Prediction of Future Trends in the Evolution of Vegetation NPP in Yangzhou City
3.4. Analysis of Factors Driving the Variation of Vegetation NPP in Yangzhou City
4. Discussion
4.1. Validation of Improved CASA Model-Estimation Results
4.2. Spatiotemporal Evolution of NPP and Prediction of Future Trends
4.3. Selection and Analysis of Driving Factors
4.4. Uncertainty and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Data Type | Data Content | Spatial Resolution | Data Coordinate System | Data Sources |
---|---|---|---|---|
Climate data | Monthly temperature | 1 km | WGS84-UTM-50 | https://data.tpdc.ac.cn/ (accessed on 22 February 2023) |
Monthly rainfall | 1 km | WGS84-UTM-50 | https://data.tpdc.ac.cn/ (accessed on 22 February 2023) | |
Monthly solar radiation | 1 km | WGS84-UTM-50 | https://data.cma.cn/ (accessed on 22 February 2023) | |
Social data | GDP of Yangzhou City | 1 km | WGS84-UTM-50 | https://www.resdc.cn/ (accessed on 25 February 2023) |
POP of Yangzhou City | 1 km | WGS84-UTM-50 | https://www.resdc.cn/ (accessed on 25 February 2023) | |
Distance to road | 30 m | WGS84-UTM-50 | https://www.openstreetmap.org/ (accessed on 25 February 2023) | |
Land-use CLCD | 30 m | WGS84-UTM-50 | https://doi.org/10.5194/essd-13-3907-2021/ (accessed on 26 February 2023) | |
Ecological data | NDVI | 30 m | WGS84-UTM-50 | Google Earth Engine-based inversion |
NDBSI | 30 m | WGS84-UTM-50 | Google Earth Engine-based inversion | |
WET | 30 m | WGS84-UTM-50 | Google Earth Engine-based inversion | |
Other data | DEM data | 30 m | WGS84-UTM-50 | NASA DEM30 m Type Dataset |
Administrative division Map of Yangzhou City | — | WGS84-UTM-50 | https://www.resdc.cn/ (accessed on 22 February 2023) | |
Historical and cultural data of Yangzhou City | — | — | http://www.yangzhou.gov.cn/yangzhou/zrdl/ (accessed on 28 February 2023) and the annals of statistics |
No. | Land use | NDVIi,max | NDVIi,min | εmax |
---|---|---|---|---|
1 | Farmland | 0.634 | 0.023 | 0.604 |
2 | Forest | 0.676 | 0.023 | 1.295 |
3 | Water | 0.634 | 0.023 | 0.542 |
4 | Urban | 0.634 | 0.023 | 0.542 |
5 | Unused land | 0.634 | 0.023 | 0.542 |
The Value Range of β and Hurst Index | Future Trends |
---|---|
β > 0, H > 0.5 | Continuing Improvement |
β < 0, H > 0.5 | Continuing Decline |
β > 0, H < 0.5 | Improvement to Decline |
β < 0, H < 0.5 | Decline to Improvement |
Β = 0 | Remained Stable |
NPP Level | 2000 | 2005 | 2010 | 2015 | 2020 | |||||
---|---|---|---|---|---|---|---|---|---|---|
Area/km2 | % | Area/km2 | % | Area/km2 | % | Area/km2 | % | Area/km2 | % | |
Worst | 345.07 | 5.26% | 472.15 | 7.20% | 469.75 | 7.17% | 496.23 | 7.57% | 545.52 | 8.32% |
Poor | 478.12 | 7.29% | 555.04 | 8.47% | 805.80 | 12.29% | 515.31 | 7.86% | 599.44 | 9.15% |
Medium | 3341.66 | 50.98% | 1963.81 | 29.96% | 3470.35 | 52.95% | 2663.92 | 40.64% | 2816.01 | 42.96% |
Better | 2388.46 | 36.44% | 3562.32 | 54.35% | 1807.41 | 27.57% | 2877.85 | 43.91% | 2592.19 | 39.55% |
Best | 1.22 | 0.02% | 1.22 | 0.02% | 1.22 | 0.02% | 1.22 | 0.02% | 1.37 | 0.02% |
District | 2000 NPP | Rank | 2005 NPP | Rank | 2010 NPP | Rank | 2015 NPP | Rank | 2020 NPP | Rank | Mean of NPP 2000–2020 | Rank |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Hanjiang | 422.72 | 5 | 418.92 | 6 | 409.22 | 5 | 377.34 | 5 | 399.53 | 5 | 405.55 | 5 |
Jiangdu | 467.45 | 2 | 459.86 | 3 | 450.15 | 1 | 423.35 | 3 | 455.82 | 1 | 451.32 | 2 |
Yizheng | 446.49 | 3 | 463.93 | 2 | 440.99 | 3 | 435.40 | 2 | 446.83 | 3 | 446.73 | 3 |
Guangling | 439.91 | 4 | 424.14 | 4 | 422.65 | 4 | 381.25 | 4 | 410.08 | 4 | 415.61 | 4 |
Baoying | 475.10 | 1 | 484.33 | 1 | 443.05 | 2 | 441.23 | 1 | 455.42 | 2 | 459.82 | 1 |
Gaoyou | 415.92 | 6 | 418.45 | 5 | 402.62 | 6 | 343.27 | 6 | 365.99 | 6 | 389.25 | 6 |
Yangzhou | 449.20 | / | 451.84 | / | 432.02 | / | 403.02 | / | 424.17 | / | 432.05 | / |
Class | Grade | 2000–2005 | 2005–2010 | 2010–2015 | 2015–2020 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Grade Area /km2 | Class Area /km2 | Percentage% | Grade Area /km2 | Class Area /km2 | Percentage% | Grade Area /km2 | Class Area /km2 | Percentage% | Grade Area /km2 | Class Area /km2 | Percentage% | ||
Degradation | 1 | 180.74 | 1086.45 | 16.58 | 840.19 | 2950.33 | 45.01 | 267.94 | 1148.54 | 17.52 | 563.29 | 2710.47 | 41.35 |
2 | 905.71 | 2110.13 | 880.60 | 2147.18 | |||||||||
NO change | 3 | 1977.28 | 1977.28 | 30.17 | 2065.09 | 2065.09 | 31.51 | 2057.48 | 2057.48 | 31.39 | 2724.99 | 2724.99 | 41.57 |
Improvement | 4 | 2255.65 | 3490.81 | 53.26 | 1165.48 | 1539.13 | 23.48 | 1843.32 | 3348.52 | 51.09 | 1117.65 | 1119.08 | 17.07 |
5 | 1235.16 | 373.65 | 1505.21 | 1.43 |
Driving Factors | Ecological Factors | Climatic Factors | Social Factors | |||||||
---|---|---|---|---|---|---|---|---|---|---|
NDVI | WET | NDBSI | Rainfall | TEM | RAD | LUCC | DTR | POP | GDP | |
q statistic | 0.7287 | 0.3923 | 0.2085 | 0.0163 | 0.0343 | 0.0124 | 0.5601 | 0.0266 | 0.0651 | 0.0275 |
p-value | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Land-Use Type | 2000 | 2005 | 2010 | 2015 | 2020 |
---|---|---|---|---|---|
Mean values of NPP | 449.2002 | 451.8444 | 432.0215 | 403.0195 | 424.1662 |
Farmland | 456.5331 | 455.7672 | 438.6139 | 407.8482 | 411.9065 |
Forest | 478.0433 | 489.3618 | 461.6351 | 449.2652 | 472.9257 |
Water | 292.51 | 263.4293 | 284.5288 | 158.4521 | 156.6578 |
Urban | 242.5894 | 317.584 | 301.0946 | 257.5867 | 290.9038 |
Unused land | 435.5329 | 260.052 | 362.058 | 276.519 | 223.492 |
2000–2020 | Farmland | Forest | Water | Unused Land | Urban |
---|---|---|---|---|---|
Farmland | 4564.5777 | 1.71 | 188.4123 | 0.0108 | 456.525 |
Forest | 6.5232 | 6.0462 | 1.629 | 0.0000 | 0.7218 |
Water | 108.9126 | 0.423 | 707.8401 | 0.0036 | 20.0673 |
Urban | 1.5939 | 0.0000 | 3.5703 | 0.0000 | 505.5921 |
q | LUCC | NDVI | WET | NDBSI | Rainfall | TEM | RAD | DTR | POP | GDP |
---|---|---|---|---|---|---|---|---|---|---|
LUCC | 0.5601 | |||||||||
NDVI | 0.7777 | 0.7287 | ||||||||
WET | 0.6269 | 0.7491 | 0.3923 | |||||||
NDBSI | 0.6283 | 0.7327 | 0.6467 | 0.2085 | ||||||
Rainfall | 0.5742 | 0.7343 | 0.4145 | 0.2872 | 0.0163 | |||||
TEM | 0.5820 | 0.7407 | 0.4243 | 0.2913 | 0.0688 | 0.0343 | ||||
RAD | 0.5871 | 0.7405 | 0.4166 | 0.2652 | 0.0918 | 0.0481 | 0.0124 | |||
DTR | 0.5714 | 0.7348 | 0.4101 | 0.2642 | 0.0582 | 0.0849 | 0.0896 | 0.0266 | ||
POP | 0.5740 | 0.7348 | 0.4354 | 0.2882 | 0.0900 | 0.0904 | 0.0775 | 0.0953 | 0.0651 | |
GDP | 0.5678 | 0.7334 | 0.4173 | 0.2847 | 0.0361 | 0.0570 | 0.0469 | 0.0484 | 0.0707 | 0.0275 |
Pearson Correlation | NPP | NDVI | WET | NDBSI | Rainfall | TEM | RAD | DTR | POP | GDP |
---|---|---|---|---|---|---|---|---|---|---|
NPP | 1.000 ** | |||||||||
NDVI | 0.854 ** | 1.000 ** | ||||||||
WET | −0.385 ** | −0.237 ** | 1.000 ** | |||||||
NDBSI | −0.484 ** | −0.709 ** | −0.424 ** | 1.000 ** | ||||||
Rainfall | 0.017 ** | −0.012 | −0.382 ** | 0.375 ** | 1.000 ** | |||||
TEM | −0.108 ** | −0.116 ** | −0.249 ** | 0.372 ** | 0.804 ** | 1.000 ** | ||||
RAD | 0.095 ** | 0.099 ** | 0.272 ** | −0.363 ** | −0.857 ** | −0.914 ** | 1.000 ** | |||
Distance to road | −0.017 ** | 0.008 | 0.163 ** | −0.162 ** | −0.341 ** | −0.564 ** | 0.587 ** | 1.000 ** | ||
POP | 0.003 | −0.061 ** | −0.202 ** | 0.244 ** | 0.534 ** | 0.529 ** | −0.539 ** | −0.443 ** | 1.000 ** | |
GDP | −0.048 ** | −0.103 ** | −0.204 ** | 0.289 ** | 0.606 ** | 0.621 ** | −0.640 ** | −0.423 ** | 0.921 ** | 1.000 ** |
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Zhou, Y.; Shao, M.; Li, X. Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China. Sustainability 2023, 15, 14518. https://doi.org/10.3390/su151914518
Zhou Y, Shao M, Li X. Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China. Sustainability. 2023; 15(19):14518. https://doi.org/10.3390/su151914518
Chicago/Turabian StyleZhou, Yinqiao, Ming Shao, and Xiong Li. 2023. "Temporal and Spatial Evolution, Prediction, and Driving-Factor Analysis of Net Primary Productivity of Vegetation at City Scale: A Case Study from Yangzhou City, China" Sustainability 15, no. 19: 14518. https://doi.org/10.3390/su151914518