Land Use and Carbon Storage Evolution Under Multiple Scenarios: A Spatiotemporal Analysis of Beijing Using the PLUS-InVEST Model

Sustainability, Год журнала: 2025, Номер 17(4), С. 1589 - 1589

Опубликована: Фев. 14, 2025

The carbon stock in terrestrial ecosystems is closely linked to changes land use. Understanding how use alterations affect regional stocks essential for maintaining the balance of ecosystems. This research leverages and driving factor data spanning from 2000 2020, utilizing Patch-generating Land Use Simulation (PLUS) model alongside InVEST ecosystem services examine temporal spatial storage across Beijing. Additionally, four future scenes 2030—urban development, natural cropland protection, as well eco-protection—are explored, with PLUS models employed emulate dynamic corresponding variations. results show that following: (1) Between resulted a significant decline storage, total reduction 1.04 × 107 tons. (2) From agricultural, forest, grassland areas Beijing all declined varying extents, while built-up expanded by 1292.04 km2 (7.88%), minimal observed water bodies or barren lands. (3) Compared distribution 2030 urban development scenario decreased 6.99 106 tons, highlighting impact rapid urbanization expansion on storage. (4) In ecological protection scenario, optimization structure an increase 6.01 105 tons indicating allocation this contributes restoration enhances sink capacity ecosystem. study provides valuable insights policymakers optimizing perspective offers guidance achievement “dual carbon” strategic objectives.

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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Reconstructing daytime and nighttime MODIS land surface temperature in desert areas using multi-channel singular spectrum analysis

Ecological Informatics, Год журнала: 2024, Номер unknown, С. 102830 - 102830

Опубликована: Сен. 1, 2024

Язык: Английский

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Surface energy balance-based surface urban heat island decomposition at high resolution

Remote Sensing of Environment, Год журнала: 2024, Номер 315, С. 114447 - 114447

Опубликована: Окт. 5, 2024

Язык: Английский

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Development of downscaling technology for land surface temperature: A case study of Shanghai, China

Urban Climate, Год журнала: 2025, Номер 61, С. 102412 - 102412

Опубликована: Апрель 10, 2025

Язык: Английский

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Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature

Scientific Data, Год журнала: 2025, Номер 12(1)

Опубликована: Май 6, 2025

Global warming and urbanization serve as critical research themes in fine-scale climate studies, particularly developed cities. This study aims to provide a high spatiotemporal resolution dataset of near-surface air temperatures for densely urban areas. The comprises daily maximum, minimum, mean the summer months (June August) from 2019 2023, at spatial 100 m, across Jiangbei zone China. We applied Convolutional Long Short-Term Memory (ConvLSTM) deep learning model with multi-source data, including ERA5 temperature topography, landcover vegetation fraction cover. Model evaluation indicates accuracy, absolute errors (MAE) ranging 0.564 0.784 °C, root square (RMSE) 0.733 1.027 coefficients determination (R2) 0.892 0.943. Our is distinguished by m inclusion recent data 2023 scale. work valuable or inner-urban studies on heatwave mitigation policies adaptation strategies.

Язык: Английский

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Is 3D building morphology really related to land surface temperature? Insights from a new homogeneous unit

Building and Environment, Год журнала: 2024, Номер unknown, С. 112101 - 112101

Опубликована: Сен. 1, 2024

Язык: Английский

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

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Дек. 9, 2024

In recent decades, global climate change and rapid urbanisation 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 focusses evaluation LST land cover (LC) changes city Prešov, Slovakia, typical European that has recently undergone LC changes. study, we use relationship between Landsat-8/-9 derived spectral indices NDBI, NDVI, NDWI from Sentinel-2 downscale 10 m. Two machine learning (ML) algorithms, Support Vector Machine (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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Enhancing spatial resolution of Landsat derived land surface temperature: A novel downscaling approach using an extreme learning machine

Journal of Earth System Science, Год журнала: 2024, Номер 134(1)

Опубликована: Дек. 10, 2024

Язык: Английский

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