Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Journal of Landscape Ecology, Journal Year: 2023, Volume and Issue: 16(1), P. 1 - 18

Published: March 8, 2023

Abstract The land use and cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because the city’s ongoing industrialization urbanisation processes. These shifts can be directly attributed to human actions. Thermal variation in study area necessitates LULC analysis. Landsat Sentinel satellite data for 2011 2021 were used map LULC, estimate surface temperature (LST) analysis spatial autocorrelation among variables using ArcGIS software Google Earth Engine (GEE) cloud platform. A sharp descent is observed cropland while built-up has increased during period. With increase area, ambient temperatures also from 18.70 °C 21.81 leading urban heat island effect. At all scales, a characteristic property most ecological parameters. clustering LST an ecosystem play crucial role determining dynamics LULC.The Moran’s, I show that there considerable level values highly clustered pattern both years. Monitoring understanding thermal environment discerning causes climate change.

Language: Английский

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Characterizing Land Surface Temperature (LST) through Remote Sensing Data for Small-Scale Urban Development Projects in the Gulf Cooperation Council (GCC)

Published: Jan. 2, 2024

Given the context of global climate change, a worldwide increase in land surface temperature (LST) is anticipated, leading to exacerbation and broadening its impacts. This could jeopardize environmental conditions countries with predominantly hot harsh climate, such as Bahrain, one Cooperation Countries (GCC) nations. Conversely, Bahrain currently experiencing significant population growth, surge demand for accommodate construction additional residential developments. circumstance allows investigation potential impact use cover alterations on variation Land Surface Temperature (LST). In order accomplish this objective, development project was executed within timeframe spanning from 2013 2023. Four sets Landsat 8 OLI/TIRS remote sensing datasets were selected, each set corresponding four seasons. Each consisted two images: capturing study area before commencement process other depicting after completion development. The analyzed by extracting (LST), normalized difference vegetation index (NDVI), built-up (NDBI) various dates. Subsequently, correlation regression analysis employed examine interrelationships among these three variables. findings demonstrated notable rise mean throughout spring autumn seasons following conclusion activities. indicate positive robust association between LST NDBI across all Moreover, relationship strengthened activities area. there negative NDVI prior region's development, which transformed into post-development. These results provide empirical support notion that small-scale developments contribute LST, primarily driven expansion impervious surfaces areas. can potentially formulation localized adaptation strategies projects.

Language: Английский

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Simulating the effects of planning strategies on urban heat island and air pollution mitigation in an urban renewal area

Journal of Geographical Systems, Journal Year: 2024, Volume and Issue: 26(3), P. 329 - 350

Published: Jan. 22, 2024

Language: Английский

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Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India

Regional Sustainability, Journal Year: 2024, Volume and Issue: 5(2), P. 100138 - 100138

Published: June 1, 2024

Rapid urbanization creates complexity, results in dynamic changes land and environment, influences the surface temperature (LST) fast-developing cities. In this study, we examined impact of use/land cover (LULC) on LST determined intensity urban heat island (UHI) New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series Landsat multi-spectral thermal bands. This study used maximum likelihood algorithm for image classification other methods like correlation analysis hotspot (Getis–Ord Gi* method) examine LULC environment. noticed that area percentage built-up increased rapidly 21.91% 45.63% during 1991–2021, with a positive change negative sparse vegetation. The mean significantly period (1991–2021), 16.31°C 22.48°C winter, 29.18°C 34.61°C summer, 19.18°C 27.11°C autumn. result showed impervious surfaces contribute higher LST, whereas vegetation helps decrease it. Poor ecological status has been found land, excellent water body. hot spot cold areas shifted their locations every decade due random changes. Even after became city, high observed. Overall, indicated patterns can influence appropriate planning is needed reduce LST. help policy-makers create sustainable

Language: Английский

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A decision-making framework for promoting the optimum design and planning of Nature-based Solutions at local scale

Urban forestry & urban greening, Journal Year: 2023, Volume and Issue: 84, P. 127945 - 127945

Published: April 20, 2023

Language: Английский

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