Changing pattern of urban landscape and its impact on thermal environment of Lahore; Implications for climate change and sustainable development

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(2)

Published: Jan. 9, 2025

Language: Английский

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Machine learning-based spatial-temporal assessment and change transition analysis of wetlands: An application of Google Earth Engine in Sylhet, Bangladesh (1985–2022)

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102075 - 102075

Published: March 24, 2023

Language: Английский

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Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020)

Environmental Impact Assessment Review, Journal Year: 2023, Volume and Issue: 105, P. 107396 - 107396

Published: Dec. 20, 2023

Language: Английский

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Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 366, P. 121787 - 121787

Published: July 8, 2024

Language: Английский

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Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e21245 - e21245

Published: Oct. 24, 2023

Land use land cover change (LULC) significantly impacts urban sustainability, planning, climate change, natural resource management, and biodiversity. The Chattogram Metropolitan Area (CMA) has been going through rapid urbanization, which impacted the LULC transformation accelerated growth of sprawl unplanned development. To map those sprawls resources depletion, this study aims to monitor using Landsat satellite imagery from 2003 2023 in cloud-based remote sensing platform Google Earth Engine (GEE). classified into five distinct classes: waterbody, build-up, bare land, dense vegetation, cropland, employing four machine learning algorithms (random forest, gradient tree boost, classification & regression tree, support vector machine) GEE platform. overall accuracy (kappa statistics) receiver operating characteristic (ROC) curve have demonstrated satisfactory results. results indicate that CART model outperforms other models when considering efficiency designated region. analysis conversions revealed notable trends, patterns, magnitudes across all periods: 2003–2013, 2013–2023, 2003–2023. expansion unregulated built-up areas decline croplands emerged as primary concerns. However, there was a positive indication significant increase vegetation within area over 20 years.

Language: Английский

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Climate change, heat stress and the analysis of its space-time variability in european metropolises

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 425, P. 138892 - 138892

Published: Sept. 22, 2023

Global warming is a pressing problem that necessitates immediate action. This phenomenon particularly affecting the quality of life in larger cities due to population growth and human mobility. Understanding space-time variability heat stress various locations will face future therefore crucial for us. Taking into account aforementioned facts, current study examined evolution Hi index four European capitals - Berlin, Madrid, Paris, Rome during months July, August, September between 2008, 2012, 2017. The Space Agency (ESA) UrbClim climate model was used collect environmental data. Furthermore, Local Climatic Zones (LCZ) classifications land use/cover change (LULC) coverages were improve evaluation extrapolation results. According findings, studied areas experienced significant increases temperatures 2008 cities' average increase 0.31 °C per decade, with southern experiencing greater intensity northern less intensity. When comparing spatiotemporal different zones, discovered more impervious fewer green are vulnerable potential stress. As result, urban developments can be able create spaces resistant stress, improving people's life.

Language: Английский

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Advancing flood susceptibility prediction: A comparative assessment and scalability analysis of machine learning algorithms via artificial intelligence in high‐risk regions of Pakistan

Journal of Flood Risk Management, Journal Year: 2024, Volume and Issue: 18(1)

Published: Nov. 24, 2024

Abstract Flood susceptibility mapping (FSM) is crucial for effective flood risk management, particularly in flood‐prone regions like Pakistan. This study addresses the need accurate and scalable FSM by systematically evaluating performance of 14 machine learning (ML) models high‐risk areas The novelty lies comprehensive comparison these use explainable artificial intelligence (XAI) techniques. We employed XAI to identify significant conditioning factors at both model training prediction stages. were assessed accuracy scalability, with specific focus on computational efficiency. Our findings indicate that LGBM XGBoost are top performers terms accuracy, also excelling achieving a time ~18 s compared LGBM's 22 random forest's 31 s. evaluation framework presented applicable other highlights superior accuracy‐focused applications, while optimal scenarios constraints. this can assist different scaling up analysis larger geographical region which could better decision‐making informed policy production management.

Language: Английский

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Exploring Mangrove Complexity with Gate-Based Fractal Analysis Through AND Circuitry

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 419 - 430

Published: Jan. 1, 2025

Language: Английский

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Tracking land use land cover changes in the twin cities of Odisha, India using a machine learning based Google Earth Engine approach

Urban Water Journal, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 22

Published: Jan. 30, 2025

The current study is based on analyzing the land use cover (LULC) changes and its corresponding effects water surface temperature (LST) twin cities of Odisha, i.e. Bhubaneswar Cuttack using a machine learning Google Earth Engine (GEE) platform. A random forest (RF) classification model was adopted due to simplicity high popularity for providing accurate results. For study, Landsat 8 (OLI/TRIS) Sentinel 2 were accessed via GEE. With an overall accuracy about 99% RF algorithm, results indicate alarming situation cities, especially where there has been reduction in by 59% response increments built-up area 90% LST 1.5%. expanding city radius, faced 28% increase 17% 3.4%. respectively.

Language: Английский

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Spatiotemporal Analysis of Land Use Change and Urban Heat Island Effects in Akure and Osogbo, Nigeria Between 2014 and 2023

Climate, Journal Year: 2025, Volume and Issue: 13(4), P. 68 - 68

Published: March 26, 2025

Rapid urbanization and climate impacts have raised concerns about the emergence aggravation of urban heat island effects. In Africa, studies focused more on big cities due to their growing populations high impact, while mid-sized remain under-studied, with limited comparative insights into distinct characteristics. This study therefore provided a spatiotemporal analysis land use cover change (LULCC) surface islands (SUHI) effects in Nigerian Akure Osogbo from 2014 2023. used Landsat 8 9 imagery (2014 2023) analyzed data via Google Earth Engine ArcGIS Pro 3.4. Results showed that Akure’s built areas increased significantly 164.026 km2 224.191 witnessed smaller expansion 41.808 58.315 areas. identified Normalized Difference Vegetation Index (NDVI) emissivity patterns associated vegetation thermal emissions positive association between LST urbanization. The findings across established LULCC has different SUHI As result, evidence city might not be extended other similar size socioeconomic characteristics without caution.

Language: Английский

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