What is it about?

Accurate estimation of the microenvironment and radiative susceptibility, as well as forecasting of climate and weather scenarios in urban contexts, are critical for achieving sustainable development goals (SDGs). Rapid urbanization has a substantial impact on both the local and global atmosphere, resulting in Urban Heat Islands (UHIs). This study is the first to attempt to analyze spatiotemporal space-borne sensor datasets in the Indian Himalayan foothills using multifractal detrended fluctuation analysis (MDFA). The coefficients derived through MDFA such as holder exponent (h), spectral width (α), irregularity, truncation spectra, and spatial distribution are analyzed for various land features to illustrate the dynamic patterns and corroborate the multifractality behavior of the land surface temperature (LST). The negative correlation between LST and normalized difference vegetation index (NDVI) suggests that vegetated land can assist to mitigate the effects of UHIs, whereas the positive correlation between LST and normalized difference built-up index (NDBI) suggests that urbanization can aggravate the effects of UHIs. The obtained result encourages to implementation of the suggested framework to address anthropogenic heat and the transportation of surface heat fluxes, as well as empowers to work on non-linear dynamic climate models, which are critical for building real-time resilient infrastructure to meet environmental sustainability.

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Why is it important?

The challenge of forecasting climate catastrophes can be addressed by performing critical analyses and modeling basic climatic factors, including air density, precipitation, ambient temperature, humidity, and others, in conjunction with specific geographical features. Unbalanced environments influence every aspect of temperature, wind, humidity, and air quality that directly affects living things [1], [2]. Studies on climate change and other topics related to the earth sciences mainly rely on these crucial characteristics and their spatiotemporal variability [3], [4]. The built-up area, forest, flora, and existing water bodies, which control heat flow in a certain landscape, have an impact on the weather and climate variance [5], [6], [7]. The microclimate is adversely affected by growing urbanization, especially in the Himalayan foothills to middle ridges. Rapid urbanization causes ecological imbalance, weather extremes, and imbalanced management of air and water consumption [8]. Urban planners and climatologists should investigate the causes of unfavorable microclimatic effects such as rising summer and winter temperatures [9]. The topography of the Himalayan region is very intricate, and anthropogenic effects have a significant impact on the climate and hydrological cycle. Additionally, the transition of land usage has an impact on biodiversity, raising environmental hazards.

Perspectives

The suggested framework to address anthropogenic heat and the transportation of surface heat fluxes, as well as empowers to work on non-linear dynamic climate models, which are critical for building real-time resilient infrastructure to meet environmental sustainability.

Vaseem Akram Shaik

Read the Original

This page is a summary of: Spatio-temporal fluctuations analysis of land surface temperature (LST) using Remote Sensing data (LANDSAT TM5/8) and multifractal technique to characterize the urban heat Islands (UHIs), Sustainable Energy Technologies and Assessments, February 2023, Elsevier,
DOI: 10.1016/j.seta.2022.102956.
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