Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019
Abstract
:1. Introduction
2. Study Area and Materials
2.1. Study Area
2.2. Materials
2.2.1. NDVI Dataset
2.2.2. Climate Dataset
2.2.3. Extreme Climate Dataset
2.2.4. Land Cover Dataset
3. Methods
3.1. Mann–Kendall Trend Analysis
3.2. Hurst Exponent
3.3. Correlation Analysis
3.4. Residual Analysis
4. Results
4.1. Spatio-Temporal Variations of Vegetation
4.2. Future Trend of Vegetation Dynamics Based on the Hurst Exponent
4.3. Analysis of the Factors Influencing Vegetation Growth
4.3.1. Relationships between Climatic Factors and Vegetation Dynamics
4.3.2. Relationships between Climate Extremes and Vegetation Dynamics
4.3.3. Relationships between Human Activities and Vegetation Dynamics
5. Discussion
6. Conclusions
- (1)
- The NDVI of vegetation in China showed an overall increasing trend from 1998 to 2019, with a slight fluctuation in the interannual variability. The areas with significant increases in the NDVI are located on the North China Plain, the Loess Plateau, and in the Qinling Mountains–Huaihe River area, while the areas with significant decreases in NDVI are located in the Jungar Basin, around the Northeast Plain, and in several economically developed cities.
- (2)
- According to the Hurst exponent analysis results, the anti-continuity of the NDVI change is greater than the continuity. The predicted vegetation growth will remain consistent with past trends in 44.16% of the area.
- (3)
- The impact of climate factors on the NDVI showed significant spatial variation. The correlation between the NDVI and precipitation was overall higher than that with temperature. Areas showing significant positive correlations between the NDVI and precipitation are located on the Inner Mongolia Plateau, the North China Plain, and the Loess Plateau. Areas showing significant positive correlations between the NDVI and temperature are located on the southeast coast, in the north of the Qinghai–Tibet Plateau, and in the Qaidam Basin. Extreme temperatures and precipitation have spatially different impacts on vegetation dynamics. The NDVI of most vegetation types showed positive correlations with extreme precipitation, while showing negative correlations with extreme temperature.
- (4)
- The residual trend provides a preliminary investigation about the driving factors of vegetation dynamics in addition to the precipitation and temperature. While human activities are likely to contribute significantly to it, it was found that the residual trend in some areas could be explained by human activities but could not in other places.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Indices | Indices Name | Definition | Units | |
---|---|---|---|---|
Temperature Extremes Indices | DTR | Diurnal temperature range | Annual mean difference between daily max. and min. temperature | °C |
TXx | Hottest day | Monthly and annual highest value of daily max. temperature | °C | |
TNx | Warmest night | Monthly and annual highest value of daily min. temperature | °C | |
TXn | Coldest day | Monthly and annual lowest value of daily max. temperature | °C | |
Precipitation Extremes Indices | Rx1day | Max. 1 day precipitation amount | Monthly and annual maximum 1-day precipitation | mm |
Rx5day | Max. 5-day precipitation amount | Monthly and annual maximum consecutive 5-day precipitation | mm |
−20 < Zc < −1.96 | 1.96 < Zc < 20 | |
---|---|---|
H < 0.5 | Reduction–increase trend | Increase–reduction trend |
H > 0.5 | Continuously reducing status | Continuously increasing status |
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Chang, J.; Liu, Q.; Wang, S.; Huang, C. Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019. Remote Sens. 2022, 14, 3390. https://doi.org/10.3390/rs14143390
Chang J, Liu Q, Wang S, Huang C. Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019. Remote Sensing. 2022; 14(14):3390. https://doi.org/10.3390/rs14143390
Chicago/Turabian StyleChang, Jiahui, Qihang Liu, Simeng Wang, and Chang Huang. 2022. "Vegetation Dynamics and Their Influencing Factors in China from 1998 to 2019" Remote Sensing 14, no. 14: 3390. https://doi.org/10.3390/rs14143390