Modelling microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms

Saha, Milan; Kafy, Abdulla Al; Bakshi, Arpita; Faisal, Abdullah-Al-; Almulhim, Abdulaziz I.; Rahaman, Zullyadini A.; Rakib, Abdullah Al; Fattah, Md. Abdul; Akter, Kaniz Shaleha; Rahman, Muhammad Tauhidur; Zhang, Maomao; Rathi, R.

doi:10.1016/j.enbuild.2022.112452

Cited by 45 publications

(10 citation statements)

References 82 publications

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“…To develop the transition potential model, 3000 samples were randomly selected using a random sampling technique. Considering the literature, the neighborhood rule was limited to three pixels, and the model’s learning rate was set to 0.100 [ 45 ]. The simulation consisted of 500 iterations to capture the shifting pattern for the year 2010.…”

Section: Methodsmentioning

confidence: 99%

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

Khachoo

Cutugno

Robustelli

et al. 2023

Sensors

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Continuous and unplanned urbanization, combined with negative alterations in land use land cover (LULC), leads to a deterioration of the urban thermal environment and results in various adverse ecological effects. The changes in LULC and thermal characteristics have significant implications for the economy, climate patterns, and environmental sustainability. This study focuses on the Province of Naples in Italy, examining LULC changes and the Urban Thermal Field Variance Index (UTFVI) from 1990 to 2022, predicting their distributions for 2030. The main objectives of this research are the investigation of the future seasonal thermal characteristics of the study area by characterizing land surface temperature (LST) through the UTFVI and analyzing LULC dynamics along with their correlation. To achieve this, Landsat 4-5 Thematic Mapper (TM) and Landsat 9 Operational Land Imager (OLI) imagery were utilized. LULC classification was performed using a supervised satellite image classification system, and the predictions were carried out using the cellular automata-artificial neural network (CA-ANN) algorithm. LST was calculated using the radiative transfer equation (RTE), and the same CA-ANN algorithm was employed to predict UTFVI for 2030. To investigate the multi-temporal correlation between LULC and UTFVI, a cross-tabulation technique was employed. The study’s findings indicate that between 2022 and 2030, there will be a 9.4% increase in built-up and bare-land areas at the expense of the vegetation class. The strongest UTFVI zone during summer is predicted to remain stable from 2022 to 2030, while winter UTFVI shows substantial fluctuations with a 4.62% decrease in the none UTFVI zone and a corresponding increase in the strongest UTFVI zone for the same period. The results of this study reveal a concerning trend of outward expansion in the built-up area of the Province of Naples, with central northern regions experiencing the highest growth rate, predominantly at the expense of vegetation cover. These predictions emphasize the urgent need for proactive measures to preserve and protect the diminishing vegetation cover, maintaining ecological balance, combating the urban heat island effect, and safeguarding biodiversity in the province.

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Section: Methodsmentioning

confidence: 99%

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

Khachoo

Cutugno

Robustelli

et al. 2023

Sensors

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“…Urbanization and development can significantly impact the climate and energy exchange in an area, resulting in a phenomenon known as an urban heat island (UHI) [1]. Another relevant aspect of the temperature increase is the study of surface temperature with remote sensing, known as a surface urban heat island (SUHI) [2], also associated with land use and land cover [3]. In recent years, habitability problems like energy consumption [4], thermal comfort [5], and health [6,7] were associated with the UHI phenomenon.…”

Section: Introductionmentioning

confidence: 99%

From Block to City Scale: Greenery’s Contribution to Cooling the Urban Environment

Licón-Portillo,

Martínez-Torres,

Chung-Alonso

et al. 2024

Urban Science

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Urban greenery is a strategy to improve the thermal environment in urban areas affected by heat islands and global warming. These phenomena can harm the citizens’ quality of life. Researchers have investigated the thermal benefits of urban vegetation, but only a few have explored its complexities across diverse urban scales. Understanding these variations is critical for precise analysis, customized solutions, efficient resource allocation, and enhancing urban living quality while promoting sustainability and climate resilience. This paper reviews 250 scientific articles about the relationship between greenspace and the urban thermal environment published between 2010 and 2023 through urban scales. It summarizes the parameters and findings of greenery’s contribution to cooling the urban environment. The data reveal that most studies concentrated on the block scale, public open spaces, neighborhoods, parks, grouped vegetation, mixed arrangements, high vegetation, spatial parameters, and the use of air temperature data to report their findings. The cooling-effect evidence shows that the block scale has an average mitigation range of 0.7–2.7 °C, the neighborhood scale of 1.1–2.9 °C, and the city scale of 0.5–2.2 °C. Furthermore, it is critical to define reliable research methods and perform thorough software validation to assess model performance and establish guidelines for urban-landscape design accurately.

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“…The NDVI is widely considered to be the main remote-sensing index regulating the change in LST (Chen et al 2006 ; Mohammad and Goswami 2022 ; Saha et al 2022 ). The improvement in air pollution and the increase in humidity mainly reinforce the strength of the relationship between LST and NDVI (Govil et al 2019 , 2020 ).…”

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 microscale impacts assessment of urban expansion on seasonal surface urban heat island intensity using neural network algorithms

Cited by 45 publications

References 82 publications

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

Unveiling the Dynamics of Thermal Characteristics Related to LULC Changes via ANN

From Block to City Scale: Greenery’s Contribution to Cooling the Urban Environment

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

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