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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Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 135, С. 103682 - 103682

Опубликована: Июль 23, 2024

Язык: Английский

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Evaluation of Land Use Land Cover Changes in Response to Land Surface Temperature With Satellite Indices and Remote Sensing Data

Rangeland Ecology & Management, Год журнала: 2024, Номер 96, С. 183 - 196

Опубликована: Авг. 2, 2024

Язык: Английский

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Monitoring and prediction of the LULC change dynamics using time series remote sensing data with Google Earth Engine

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2024, Номер 136, С. 103689 - 103689

Опубликована: Авг. 9, 2024

Язык: Английский

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Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

Hydrological Processes, Год журнала: 2024, Номер 38(8)

Опубликована: Авг. 1, 2024

Abstract Anthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional organic matter loss. In this study, we predicted the annual rate of loss in Salt Range, extending south from Pothohar plateau, Pakistan, using Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters probability zones were estimated remote sensing Geo‐Spatial methods. average rates calculated by considering five geo‐environmental factors, that is, slope length steepness (LS), rainfall erosivity (R), cover management (C), erodibility (K), conservation practice (P) range 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, 0.2–122 respectively. This research determined yearly SE Range varies over 50 above 350 . distribution area across different reveals a small portion (2.11%) is classified as High, moderate (7.13%) falls under category Moderate, while majority (90.7%) Low terms proneness towards erosion. devoid vegetation characterized steep slopes especially prone SE. highly vulnerable risk due climatic variations improper practices. result provides spatial salt range, utilized for planning processes at policy level among decision‐makers land‐use planners.

Язык: Английский

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The impact of cooling strategies on urban microclimates and building energy consumption: A study of a street canyon in Melbourne

Energy and Built Environment, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Leveraging urban AI for high-resolution urban heat mapping: Towards climate resilient cities

Environment and Planning B Urban Analytics and City Science, Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Urban heat island (UHI) effects are increasingly recognised as a significant challenge arising from urbanisation, leading to elevated temperatures within urban areas that pose risks public health and undermine the sustainability of cities. Effective UHI management requires high-resolution timely mapping temperature patterns guide interventions. Traditional methods for often lack spatial accuracy efficiency necessary detailed analysis, especially in complex environments. This study integrates artificial intelligence (Urban AI) by presenting U-Net model tailored metropolitan area Adelaide, South Australia. Trained on thermal data Australian Government Data Directory, captures pixel-level variations across diverse landscapes, including densely built areas, suburban zones, green spaces. Achieving low Mean Squared Error (MSE) 0.0029 processing each map less than 30 seconds, demonstrates exceptional computational efficiency. The model, an AI agent, offers scalable tool supporting real-time assessments facilitating targeted mitigation efforts. By bridging gap between advanced geospatial modelling practical planning, it enables data-driven decisions enhance climate resilience, optimise infrastructure, improve rapidly urbanising regions. approach highlights transformative potential addressing challenges, delivering precise actionable insights support sustainable climate-adaptive

Язык: Английский

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Low carbon solar-based sustainable energy system planning for residential buildings

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 207, С. 114942 - 114942

Опубликована: Окт. 4, 2024

Язык: Английский

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Refining urban morphology: An explainable machine learning method for estimating footprint-level building height

Sustainable Cities and Society, Год журнала: 2024, Номер 112, С. 105635 - 105635

Опубликована: Июль 1, 2024

Язык: Английский

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Predicting Land Use Land Cover Dynamics and Land Surface Temperature Changes Using CA-Markov-Chain Models in Islamabad, Pakistan (1992–2042)

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Год журнала: 2024, Номер 17, С. 16255 - 16271

Опубликована: Янв. 1, 2024

Язык: Английский

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