Land surface temperature and vegetation cover changes and their relationships in Taiwan from 2000 to 2020

Remote Sensing Applications: Society and Environment(2021)

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摘要
This study reported decadal trends and acceleration patterns of surface temperatures and vegetation conditions in Taiwan from 2000 to 2020. Time series data on Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Land Cover (LC) types were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Seasonal patterns, decadal trends, and acceleration rates for each LU type were evaluated using the natural cubic spline method and multivariate linear regression. Acceleration patterns were then analyzed using the correlations between derived trends and acceleration rates. Results are provided for the entire island, for sub-regions, and for the smallest area elements of 1 km2. Taiwan has had a significant mean increase in decadal trends of NDVI (95%CI: 0.013–0.025 unit/dec), but not of LST (95%CI: −0.018-0.302 °C/dec). Negative correlation between LST and NDVI was stronger in green coverage areas than in built-up areas. On sub-regional level, a larger increase in surface temperature and tremendous vegetation loss were observed at high elevations in the southern regions of the Central Mountain Range. Meanwhile, the central-western lowlands faced moderately warming temperatures and slight greening. At the pixel level, urbanization and agricultural expansion have caused rising surface temperatures, while afforestation contributes cooling. The rise in temperature in accelerating in the different LC types throughout Taiwan. Despite that, the acceleration patterns of the vegetation index have remained stable in most parts of the island. Although the rates of vegetation growth have not yet declined, practitioners and policymakers need to consider a balanced view of green cities to keep pace with the accelerating temperature conditions.
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关键词
Subtropical island,Land surface warming,NDVI,Cubic spline function,Time series analysis,Multivariate regression model
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