Land surface temperature responses to land use dynamics in urban areas of Doha, Qatar

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 104, P. 105273 - 105273

Published: Feb. 13, 2024

Rapid urbanization primarily converts naturally vegetated areas and pervious surfaces into impervious built-up areas, significantly transforming microclimates ecological dynamics. The surfaces, marked by their higher thermal conductivity, disrupt surface energy balance accumulate solar heat, subsequently elevating the land temperatures (LSTs). This study investigates impact of use cover changes on summer winter LSTs in Doha Al Dayeen municipalities Qatar, spanning from years 2000 to 2023, using remote sensing techniques Geographic Information Systems (GIS). analysis reveals a remarkable 343.16% increase area at expense previously existing desert lands water bodies. While Qatar's has high temperature, substituting such with exhibits notable rise temperatures. Additionally, reclamation also results elevated LSTs. LST data derived sources demonstrates an upward trend for contrasting winter. Specifically, mean increases 7.64°C (0.34°C annually), decreases 4.87°C (0.22°C annually). Notably, consistently recorded highest both seasons all observed years. A strong correlation was between patterns Normalized Difference Vegetation Index (NDVI), Water (NDWI), Built-up index (NDBI) Barrenness (NDBal). imply negative influence climate change urgent need urban planning mitigation measures counteract adverse effects increasing LSTs, particularly months, ensure human well-being resilience environments.

Language: 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, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 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.

Language: Английский

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Urban planning impact on summer human thermal comfort in Doha, Qatar

Building and Environment, Journal Year: 2024, Volume and Issue: 254, P. 111374 - 111374

Published: March 1, 2024

Urbanization entails extensive construction and substantial land use alterations, converting natural areas into residential, commercial, mixed-use industrial. These alterations disrupt the surface energy, impacting temperatures (LSTs). Elevated LSTs affect thermal comfort of urban residents exerting pressure on environment ecosystems. This study investigates repercussions elevated LST human comfort, focusing Doha municipality in Qatar for 2002–2003, 2013, 2022, with a specific focus summer temperatures. Utilizing remote sensing Geographical Information Systems (GIS), we conducted an in-depth investigation, employing Landsat data along GIS tools to create maps pattern establish correlation between LST. The findings reveal notable increase built-up area municipality, predominantly at expense desert water bodies. increases consistently by 0.65 °C annually, shifting from moderate heat stress 2002 2013 2023 all neighborhoods Doha. Our comparison different indicates lower near bodies suburban developments higher downtown areas. Suburban exhibit favourable impact compared compact developments. methodology developed this exhibits potential adaptation various settings regionally.

Language: Английский

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Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105654 - 105654

Published: July 9, 2024

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly rapidly developing regions with harsh climates. This study examines the seasonal variation of Doha, Qatar, exploring interconnectedness land use/land cover (LULC) socio-demographic characteristics household consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation Bootstrap analysis) advanced geostatistical models, including Geographically Weighted Regression (GWR) Multiscale (MGWR), to analyze monitor spatial variations The methods involved assessing relationship between surface temperature (LST), water-electricity consumption, analyzing impact demographic variables. Key findings indicate significant spatiotemporal influenced by changes LULC such as size structure. highlight need for integrated planning energy policies that consider impacts enhance efficiency sustainability settings. Furthermore, results underscore importance addressing complex interplay development resource policy-making.

Language: Английский

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Fine-scale satellite-based monitoring of temperature and vegetation cover in microclimates, distribution ranges, and landscape connectivity for Neurergus kaiseri (Kaiser’s mountain newt) during the breeding season

Ecological Indicators, Journal Year: 2025, Volume and Issue: 170, P. 113077 - 113077

Published: Jan. 1, 2025

Language: Английский

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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 318 - 318

Published: Jan. 17, 2025

Addressing global warming and adapting to the impacts of climate change is a primary focus adaptation strategies at both European national levels. Land surface temperature (LST) widely used proxy for investigating climate-change-induced phenomena, providing insights into radiative properties different land cover types impact urbanization on local characteristics. Accurate continuous estimation across large spatial regions crucial implementation LST as an essential parameter in mitigation strategies. Here, we propose deep-learning-based methodology using multi-source data including Sentinel-2 imagery, cover, meteorological data. Our approach addresses common challenges satellite-derived data, such gaps caused by cloud image border limitations, grid-pattern sensor artifacts, temporal discontinuities due infrequent overpasses. We develop regression-based convolutional neural network model, trained ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment Space Station) mission which performs pixelwise predictions 5 × patches, capturing contextual information around each pixel. This method not only preserves ECOSTRESS’s native resolution but also fills enhances coverage. In non-gap areas validated against ground truth model achieves with least 80% all pixel errors falling within ±3 °C range. Unlike traditional satellite-based techniques, our leverages high-temporal-resolution capture diurnal variations, allowing more robust time periods. The model’s performance demonstrates potential integrating urban planning, resilience strategies, near-real-time heat stress monitoring, valuable resource assess visualize development use changes.

Language: Английский

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Spatiotemporal Dynamics and Response of Land Surface Temperature and Kernel Normalized Difference Vegetation Index in Yangtze River Economic Belt, China: Multi-Method Analysis

Land, Journal Year: 2025, Volume and Issue: 14(3), P. 598 - 598

Published: March 12, 2025

As global climate change intensifies, its impact on the ecological environment is becoming increasingly pronounced. Among these, land surface temperature (LST) and vegetation cover status, as key indicators, have garnered widespread attention. This study analyzes spatiotemporal dynamics of LST Kernel Normalized Difference Vegetation Index (KNDVI) in 11 provinces along Yangtze River their response to based MODIS Terra satellite data from 2000 2020. The linear regression showed a significant KNDVI increase 0.003/year (p < 0.05) rise 0.065 °C/year 0.01). Principal Component Analysis (PCA) explained 74.5% variance, highlighting dominant influence urbanization. K-means clustering identified three regional patterns, with Shanghai forming distinct group due low variability. Generalized Additive Model (GAM) analysis revealed nonlinear LST–KNDVI relationship, most evident Hunan, where cooling effects weakened beyond threshold 0.25. Despite 0.07 increase, high-temperature areas Chongqing Jiangsu expanded by over 2500 km2, indicating limited mitigation. reveals complex interaction between KNDVI, which may provide scientific basis for development management adaptation strategies.

Language: Английский

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Exploration of Influencing Factors of Land Surface Temperature in Cities Within the Beijing–Tianjin–Hebei Region Based on Local Climate Zone Scheme

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 9728 - 9744

Published: Jan. 1, 2024

Clarifying the factors that influence land surface temperature (LST) is crucial for proposing specific LST mitigation strategies. This study focuses on Beijing-Tianjin-Hebei (BTH) Region and investigates influencing of various local climate zone (LCZ) built types from perspectives urban morphology, cover, human activity. The results suggest areas LCZ vary across cities within BTH Region, attributed to differences in city size Gross Domestic Product (GDP). area Beijing Tianjin, with significantly high sizes GDP, exceeds 2000 km2. In contrast, Qinhuangdao, Zhangjiakou Chengde, which have relatively low this less than 500 However, main same type are highly consistent. Building coverage ratio (BCR), average building height (ABH) pervious fraction (PSF) three most important factors. correlation between BCR mainly concentrated compact high-rise open types, Pearson coefficient (r) ranging 0.2 0.44; ABH high-rise, mid-rise, mid-rise r -0.2 -0.52; PSF almost all -0.56. By integrating these findings features each strategies were further proposed. can help develop context Coordinated Development thereby promoting healthy sustainable development region.

Language: Английский

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Investigating the Impact of Various Vegetation Scenarios on Outdoor Thermal Comfort in Low-Density Residential Areas of Hot Arid Regions

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 3995 - 3995

Published: May 10, 2024

In hot, arid regions, outdoor spaces suffer from intense heat. This study explores how vegetation can improve thermal performance for pedestrians in low-density residential areas. Specifically, it seeks to identify the best combination of grass and trees optimal comfort. Four scenarios were simulated using ENVI-met software, varying proportions three tree types: 50% grass, with 25% trees, 75% trees. A reference scenario no was also investigated. The outputs encompassed air temperature (Ta), mean radiant (Tmrt), relative humidity (RH), physiologically equivalent (PET). findings show that a higher percentage exhibited reduction temperature, ranging 0.2 k 0.92 k. Additionally, inclusion resulted substantial improvement performance, an average 7.5 degrees PET. Among evaluated scenarios, one comprising exhibits most noteworthy enhancement. underscores significance strategically positioning coincide prevailing wind patterns, thereby enhancing convective cooling mechanisms improving overall comfort levels. These insights offer valuable implications urban planning development sustainable design strategies.

Language: 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, Journal Year: 2024, Volume and Issue: 89(5)

Published: Sept. 28, 2024

Language: Английский

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