Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait

Al-Dousari, Ahmad; Kafy, Abdulla Al; Saha, Milan; Fattah, Md. Abdul; Almulhim, Abdulaziz I.; Faisal, Abdullah-Al-; Rakib, Abdullah Al; Jahir, Dewan Md. Amir; Rahaman, Zullyadini A.; Bakshi, Arpita; Shahrier, Mahir; Rahman, Md. Mijanur

doi:10.1016/j.scs.2022.104107

Cited by 55 publications

(14 citation statements)

References 57 publications

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“…The spatiotemporal distribution of LST and LU/LC is predicted using multiple models, including ANN, CA, MC, and regression approaches [45]. For this study, the MOLUSCE plugin tool in QGIS software was applied to forecast the 2031 LST and LU/LC distribution, which is regarded as one of the finest prediction models [46]. MOLUSCE is a software tool that analyses, displays, and simulates changes.…”

Section: Simulation Of Lst and Lu/lc Using Ca-ann Algorithmmentioning

confidence: 99%

Automated Mapping of Urban Heat Island to Predict Land Surface Temperature and Land use/cover Change Using Machine Learning Algorithms: Mansoura City

2022

IJG

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Urban heat island (UHI) frequency and emergence are strongly associated with variations in land use/land cover (LU/LC) and land surface temperature (LST). This study investigates the impact of LU/LC class changes on LST based on the distribution of UHI spot maps in Mansoura city, Egypt, using Landsat satellite images from 1991 to 2021. Based on these estimated LU/LC and LST maps, machine learning algorithms, cellular automata, and artificial neural network approaches were used to predict future changes in LU/LC and LST for 2031. The influence of UHI may be quantified using the urban thermal field variance index (UTFVI). The geographic information system (GIS) add-in UHI calculator ArcGIS tool was created because we considered the spatial consequences of employing remote sensing data. This tool includes all the methods and procedures for calculating LST and UHI. The analysis revealed a positive correlation between LST and normalized difference built-up index and a negative correlation between LST and normalized difference vegetation index. The forecasted results for 2031 also show that the built-up area will grow roughly 20%, with a considerable drop in vegetation by 18%. If the city's fast urbanization continues, more than 40% of Mansoura will have land surface temperatures above 45°C by 2031. Avoiding dense built-up areas and growing vegetation spaces remain efficient means of minimizing the influence of UTFVI in urban construction practice. Therefore, this research will help to achieve sustainable development by providing essential insights into the complicated interplay between diverse aspects of urban settings and promoting city competency.

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Section: Simulation Of Lst and Lu/lc Using Ca-ann Algorithmmentioning

confidence: 99%

Automated Mapping of Urban Heat Island to Predict Land Surface Temperature and Land use/cover Change Using Machine Learning Algorithms: Mansoura City

2022

IJG

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“…It can be stated that cities have a pivotal role in both mitigation [5,6] and adaptation activities [7], aiming to reduce the adverse effects of climate change by taking well-targeted and effective actions [8,9]. In order to provide detailed information to local decision-makers, addressing micro and mesoscale urban climate patterns is essential by analyzing different aspects of local features, such as LULC patterns [10], urban heat island (UHI) characteristics [11], the role of green areas regarding cooling benefits [12] through the normalized difference vegetation index [13], urban hotspots [14], or the presence of surface urban heat island [15,16]. For these purposes, remote sensing data are commonly used [17,18] along with GISsupported analyses [19].…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Buzási,

Beszedics-Jäger,

Hortay

2024

Environ. Res. Commun.

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Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use – land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.

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“…This metric will serve as a proxy because it would not be able to characterize changes in cover directly but will help us make the charts and graphs, we need to learn more about it. Prospective policymakers and city planners can reduce the effects of heat stress and make cities sustainable by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI (AlDousari et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale

Kikon

Kumar

Ahmed

2023

Environ Sci Pollut Res

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Due to expanding populations and thriving economies, studies into the built environment’s thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3 °C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST’s effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30 °C and 29.91 °C, respectively, with a mean LST of 25.1 °C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31 °C and 25.35 °C, respectively, with a mean LST of 21.33 °C. There was a 3.8 °C drop in the LST of this grid. The minimum and maximum NDVI were also − 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

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Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait

Cited by 55 publications

References 57 publications

Automated Mapping of Urban Heat Island to Predict Land Surface Temperature and Land use/cover Change Using Machine Learning Algorithms: Mansoura City

Automated Mapping of Urban Heat Island to Predict Land Surface Temperature and Land use/cover Change Using Machine Learning Algorithms: Mansoura City

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale

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