A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability

By Tirthankar Chakraborty, Xuhui Lee. International Journal of Applied Earth Observation and Geoinformation, 2019

388 citations recorded by the source corpus.

Abstract We develop a new algorithm, the simplified urban-extent (SUE) algorithm, to estimate the surface urban heat island (UHI) intensity at a global scale. We implement the SUE algorithm on the Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer (MODIS) images to calculate the UHI intensity for over 9500 urban clusters using over 15 years of data, making this one of the most comprehensive characterizations of the surface UHI to date. The results from this algorithm are validated against previous multi-city studies to demonstrate the suitability of the method. The dataset created is then filtered for elevation differentials and percentage of urban area and used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 °C during daytime and 0.55 °C at night. Cities in arid climate show distinct diurnal and seasonal patterns, with higher surface UHI during nighttime (compared to daytime) and two peaks throughout the year. The diurnal variability in surface UHI is highest for equatorial climate zone (0.88 °C) and lowest for arid zone (0.53 °C). The seasonality is highest in the snow climate zone and lowest for equatorial climate zone. While investigating the change in the surface UHI over a decade and a half, we find a consistent increase in the daytime surface UHI in the urban clusters of the warm temperate climate zone (0.04 °C/decade) and snow climate zone (0.05 °C/decade). Only arid climate zones show a statistically significant increase in the nighttime surface UHI intensity (0.03 °C/decade). Globally, the change is mainly seen during the daytime (0.03 °C/decade). Finally, the importance of vegetation differential between urban and rural areas on the spatiotemporal variability is examined. Vegetation has a strong control on the seasonal variability of the surface UHI and may also partly control the long-term variability. The complete UHI data are available through this website ( https://yceo.yale.edu/research/global-surface-uhi-explorer ) and allows the user to query the UHI of urban clusters using a simple interface.

Subfields: Environmental Engineering, Global and Planetary Change, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/c44e40e47b6c14bea22d0c8a4ba4fd39b42658c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. D. Manya
2. Angel Hsu
3. Glenn Sheriff
4. Tirthankar Chakraborty

In Global and Planetary Change

1. D. Manya
2. Angel Hsu
3. Glenn Sheriff
4. Tirthankar Chakraborty

In Health, Toxicology and Mutagenesis

1. D. Manya
2. Angel Hsu
3. Glenn Sheriff
4. Tirthankar Chakraborty

Citations in the corpus

• Disproportionately higher exposure to urban heat in lower-income neighborhoods: a multi-city perspective (2019, methodology): “In general, we see a negative correlation between UHI and ΔNDVI, since more vegetated neighborhoods have higher evaporative cooling, and thus, lower surface temperatures, as demonstrated at various scales (Rizwan et al 2008, Peng et al 2011, Chakraborty et al 2017, Chakraborty and Lee 2019).”