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

Abdulmana, Sahidan; Lim, Apiradee; Wongsai, Sangdao; Wongsai, Noppachai

doi:10.1016/j.rsase.2021.100636

Cited by 15 publications

(9 citation statements)

References 47 publications

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“…The overall mean daytime LST increased by 0.021 • C per decade. This finding is parallel with the previous analysis by Abdulmana et al [22]; Abdulmana et al [23], that Taiwan's daytime LST had increased. Moreover, our findings are consistent with the Council for Economic Planning and Development in Taiwan report [24], which reported that Taiwan's temperature in the years 1980-2009 had increased by 0.29 • C per decade.…”

Section: Discussionsupporting

confidence: 91%

The Influence of Elevation, Land Cover and Vegetation Index on LST Increase in Taiwan from 2000 to 2021

2023

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Land Surface Temperature (LST) is an important factor in ground surface energy balance and in universal climatology studies. Elevation, Land Cover (LC), and vegetation index are three factors that influence ground surface variation, and their influences vary depending on geography. This study aimed to: (i) investigate the seasonal patterns and trends of daytime LST, and (ii) examine the influence of elevation, LC, and vegetation index on daytime LST increase in Taiwan from 2000 to 2021. LST, vegetation, and LC data were downloaded from the Moderate Resolution Imaging Spectroradiometer (MODIS) website, and elevation data were downloaded from the United States Geological Survey (USGS) website. The natural cubic spline method was applied to investigate annual seasonal patterns and trends in daytime LST. Linear regression modeling was applied to investigate the influence of elevation, LC, and vegetation index on daytime LST increases. The results showed that the average increase in daytime LST per decade in Taiwan was 0.021 °C. Elevation, LC, and vegetation had significantly affected the daytime LST increase, with R2 of 32.5% and 28.1% for the North and South parts of the country, respectively. The daytime LST increase in the North at elevations higher than 1000 m had an increasing trend, while in the South the increasing trend was found at elevations higher than 350 m above sea level. All types of forest and urban areas in the North had a higher daytime LST increase than the average, while in the South, the areas with water, closed shrubland, and urban parts had a higher daytime LST increase than the average.

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Section: Discussionsupporting

confidence: 91%

The Influence of Elevation, Land Cover and Vegetation Index on LST Increase in Taiwan from 2000 to 2021

2023

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“…Additionally, the positive trend in NDVI found at 0-500 m is consistent with the findings of Abdulmana et al (2021) [59], who studied NDVI based on satellite images across Taiwan from 2000 to 2020; the authors reported a significant mean increase in decadal trends of NDVI (95% confidence interval: 0.013-0.025 unit/decade). As the effects of land use/cover would interact with the mesoscale meteorological simulation, understanding the exchange mechanism of heat and momentum plays an important role in predictive models [62,63] that can be applied to future climate change mitigation.…”

Section: Temporal Variations Of Ndvi Rainfall and Temperature In Taiwan At Varied Elevationssupporting

confidence: 85%

“…The increasing rainfall pattern may be associated with the positive trend found in extreme rainfall reported by Henny et al (2021) [58], based on a 56-year dataset across Taiwan in different seasons and geographic regions; however, the slightly decreasing average temperature and maximum temperature differed from the findings of other studies [59,60]; these discrepancies may result from the different datasets and study periods.…”

Section: Temporal Variation Of Ndvi Rainfall and Temperature In Taiwanmentioning

confidence: 70%

Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

2021

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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.

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“…where a is constant, b0, b2, …, bk are the coefficients from the model, c1, c2, …, ck are the coefficients derived from cubic spline function with k = 3 as we assigned six knots to the data, more details are explained elsewhere (Abdulmana et al, 2021). Then, the air pollutant concentration data were seasonally adjusted by subtracting the fitted values from observed air pollutants in each time series observation and adding the mean of the seasonal component.…”

Section: Discussionmentioning

confidence: 99%

Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018

Sukkhum

Lim

Ingviya

et al. 2022

Aerosol Air Qual. Res.

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The seasonal patterns and trends of air pollutants can be used in assessments of air quality, facilitating authorities' decisions on policies for monitoring and managing. The objective of this study was to investigate the seasonal patterns and trends of air pollutants in the upper northern Thailand (UNT) from 2004 to 2018. The hourly air pollutant concentration data including carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NOx), sulfur dioxide (SO2), ozone (O3), and particulate matter ≤ 10 µm (PM10) recorded from 6 monitoring stations in the UNT were obtained from the Pollution Control Department, Ministry of Natural Resources and Environment of Thailand. Cubic splines were used to assess seasonal patterns and trends of the air pollutants. Linear regression was used to estimate the average increase in concentrations of air pollutants at each monitoring station. The results exhibited seasonal patterns for CO, NOx, NO2, O3 and PM10, in all stations while SO2 exhibited seasonal patterns only in one station in Lampang and all stations in Chiangmai. The concentrations of these pollutants rose during August and September and reached peak levels in March. In the past 15 years, the levels of overall CO, O3 and SO2 in the UNT had significantly increased, on average by 0.015 ppm, 0.012 ppb and 0.015 ppb, respectively. In contrast, NO2, NOx and PM10 had significantly decreased on average of -0.010 ppb, -0.008 ppb and -0.011 µg m -3 , respectively. The results provide information to the authorities for setting up proper measures and policies to control and mitigate air quality impacts especially in the dry season.

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

Cited by 15 publications

References 47 publications

The Influence of Elevation, Land Cover and Vegetation Index on LST Increase in Taiwan from 2000 to 2021

The Influence of Elevation, Land Cover and Vegetation Index on LST Increase in Taiwan from 2000 to 2021

Vertical Differences in the Long-Term Trends and Breakpoints of NDVI and Climate Factors in Taiwan

Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018

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