Abstract

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson’s correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan’s NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan.

Highlights

  • Vegetation is an important hub linking the atmosphere, hydrology, and soil; continuous monitoring of vegetation dynamics is imperative in the context of global climate change [1,2,3,4,5,6,7]

  • Krishnaswamy et al (2014) [18] found an elevation-sensitive interaction between temperature, rainfall, and vegetation in highaltitude mountainous areas based on AVHRR (Advanced Very-High-Resolution Radiometer) normalized difference vegetation index (NDVI) GIMMS (Global Inventory, Monitoring, and Modelling Studies) data

  • For the Break for Additive Seasonal and trend component (Trend) (BFAST) method, a minimal section size, h, is employed to distinguish between potentially detected breaks in the trend model given as a portion relative to the sample size

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Summary

Introduction

Vegetation is an important hub linking the atmosphere, hydrology, and soil; continuous monitoring of vegetation dynamics is imperative in the context of global climate change [1,2,3,4,5,6,7]. As vegetation is sensitive to climate change, and mountainous regions are experiencing more pronounced climate change impacts, many studies have investigated the relationship between vegetation and climatic factors from a vertical perspective [8,9,10,11,12,13]. Jump et al (2012) [16] investigated the biodiversity of Taiwan’s highaltitude areas, and found that the high-altitude boundary of mountain plant distribution has changed significantly with the warming of the climate. Krishnaswamy et al (2014) [18] found an elevation-sensitive interaction between temperature, rainfall, and vegetation in highaltitude mountainous areas based on AVHRR (Advanced Very-High-Resolution Radiometer) NDVI (normalized difference vegetation index) GIMMS (Global Inventory, Monitoring, and Modelling Studies) data. Chen et al (2014) [19] analyzed vegetation photosynthetic activity trends across the Asia-Pacific region, and found a significant increase in annual

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