Addressing the impact of land use land cover changes on land surface temperature using machine learning algorithms

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 13, 2024

Over the past two and a half decades, rapid urbanization has led to significant land use cover (LULC) changes in Kabul province, Afghanistan. To assess impact of LULC on surface temperature (LST), province was divided into four classes applying Support Vector Machine (SVM) algorithm using Landsat satellite images from 1998 2022. The LST assessed data thermal band. Cellular Automata-Logistic Regression (CA-LR) model applied predict future patterns for 2034 2046. Results showed classes, as built-up areas increased about 9.37%, while bare soil vegetation decreased 7.20% 2.35%, respectively, analysis annual revealed that highest mean LST, followed by vegetation. simulation results indicate an expected increase 17.08% 23.10% 2046, compared 11.23% Similarly, indicated area experiencing class (≥ 32 °C) is 27.01% 43.05% 11.21% increases considerably decreases, revealing direct link between rising temperatures.

Язык: Английский

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Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

Remote Sensing, Год журнала: 2024, Номер 16(3), С. 454 - 454

Опубликована: Янв. 24, 2024

The pressing issue of global warming is particularly evident in urban areas, where thermal islands amplify the effect. Understanding land surface temperature (LST) changes crucial mitigating and adapting to effect heat islands, ultimately addressing broader challenge warming. This study estimates LST city Yazd, Iran, field high-resolution image data are scarce. assessed through parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter summer 2019 2020, then it estimated 2021. modeled using six machine learning algorithms implemented R software (version 4.0.2). accuracy models measured root mean square error (RMSE), absolute (MAE), logarithmic (RMSLE), standard deviation different performance indicators. results show that gradient boosting model (GBM) algorithm most accurate estimating LST. albedo NDVI features with greatest impact on both (with 80.3% 11.27% importance) winter 72.74% 17.21% importance). 2021 showed acceptable seasons. GBM each seasons useful modeling based learning, support decision-making related spatial variations temperatures. method developed can help better understand island mitigation strategies improve human well-being enhance resilience climate change.

Язык: Английский

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Evaluating the impact of landscape configuration, patterns and composition on land surface temperature: an urban heat island study in the Megacity Lahore, Pakistan

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(7)

Опубликована: Июнь 18, 2024

Язык: Английский

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Impact assessment of planned and unplanned urbanization on land surface temperature in Afghanistan using machine learning algorithms: a path toward sustainability

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 24, 2025

The increasing trend in land surface temperature (LST) and the formation of urban heat islands (UHIs) has emerged as a persistent challenge for planners decision-makers. current research was carried out to study use cover (LULC) changes associated LST patterns planned city (Kabul) unplanned (Jalalabad), Afghanistan, using Support Vector Machine (SVM) Landsat data from 1998 2018. Future LULC were predicted 2028 2038 Cellular Automata-Markov (CA-Markov) Artificial Neural Network (ANN) models. results clearly emphasize different between Kabul Jalalabad. Between 2018, built-up areas Jalalabad increased by 16% 30%, respectively, while bare soil vegetation decreased 15% 1% 4% 30% showed highest seasonal annual LST, followed vegetation. maximum occurred during summer both cities predictions that (48% 55% 2018) will increase approximately 59% 68% 79% Jalalabad, respectively. Similarly, simulations percentage with higher (> 35°C) would (0% 5% 22% 43% 2038, Kabul's shows lower than Jalalabad's city, primarily due urbanization greater center. Urban should limit development reduce potential impacts high temperatures.

Язык: Английский

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Impact assessment of land-use alteration on land surface temperature in Kabul using machine learning algorithm

Journal of Spatial Science, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Июнь 27, 2024

Язык: Английский

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Examining the impact of land use and land cover changes on land surface temperature in Herat city using machine learning algorithms

GeoJournal, Год журнала: 2024, Номер 89(5)

Опубликована: Сен. 28, 2024

Язык: Английский

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Synergistic biochar and Serratia marcescens tackle toxic metal contamination: A multifaceted machine learning approach

Journal of Environmental Management, Год журнала: 2024, Номер 370, С. 122575 - 122575

Опубликована: Сен. 20, 2024

Язык: Английский

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Spatiotemporal analysis of land surface temperature and land cover changes in Prešov city using downscaling approach and machine learning algorithms

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(2)

Опубликована: Янв. 3, 2025

Abstract In recent decades, global climate change and rapid urbanization have aggravated the urban heat island (UHI) effect, affecting well-being of citizens. Although this significant phenomenon is more pronounced in larger metropolitan areas due to extensive impervious surfaces, small- medium-sized cities also experience UHI effects, yet research on these rare, emphasizing importance land surface temperature (LST) as a key parameter for studying dynamics. Therefore, paper focuses evaluation LST cover (LC) changes city Prešov, Slovakia, typical European that has recently undergone LC changes. study, we use relationship between Landsat-8/Landsat-9-derived spectral indices Normalized Difference Built-Up Index (NDBI), Vegetation (NDVI), Water (NDWI) derived from Landsat-8/Landsat-9 Sentinel-2 downscale 10 m. Two machine learning (ML) algorithms, support vector (SVM) random forest (RF), are used assess image classification identify how different types selected years 2017, 2019, 2023 affect pattern LST. The results show several decisions made during last decade, such construction new fabrics roads, caused increase evaluation, based RF algorithm, achieved overall accuracies 93.2% 89.6% 91.5% 2023, outperforming SVM by 0.8% 2017 4.3% 2023. This approach identifies UHI-prone with higher spatial resolution, helping planning mitigate negative effects increasing LSTs.

Язык: Английский

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From Data to Insights: Modeling Urban Land Surface Temperature Using Geospatial Analysis and Interpretable Machine Learning

Sensors, Год журнала: 2025, Номер 25(4), С. 1169 - 1169

Опубликована: Фев. 14, 2025

This study introduces an innovative machine learning method to model the spatial variation of land surface temperature (LST) with a focus on urban center Da Nang, Vietnam. Light Gradient Boosting Machine (LightGBM), support vector machine, random forest, and Deep Neural Network are employed establish functional relationships between LST its influencing factors. The approaches trained validated using remote sensing data from 2014, 2019, 2024. Various explanatory variables representing topographical characteristics, as well landscapes, used. Experimental results show that LightGBM outperforms other benchmark methods. In addition, Shapley Additive Explanations utilized clarify impact factors affecting LST. analysis outcomes indicate while importance these changes over time, density greenspace consistently emerge most influential attained R2 values 0.85, 0.92, 0.91 for years 2024, respectively. findings this work can be helpful deeper understanding heat stress dynamics facilitate planning.

Язык: Английский

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A Remote Sensing Approach to Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use/Land Cover Change via Cloud Base and Machine Learning Methods, Case Study: Sari Metropolis, Iran

International Journal of Environmental Research, Год журнала: 2025, Номер 19(3)

Опубликована: Март 17, 2025

Язык: Английский

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