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

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 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.

Language: 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, Journal Year: 2024, Volume and Issue: 135, P. 103682 - 103682

Published: July 23, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 96, P. 183 - 196

Published: Aug. 2, 2024

Language: Английский

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

Hydrological Processes, Journal Year: 2024, Volume and Issue: 38(8)

Published: Aug. 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.

Language: 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, Journal Year: 2024, Volume and Issue: 136, P. 103689 - 103689

Published: Aug. 9, 2024

Language: 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, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 207, P. 114942 - 114942

Published: Oct. 4, 2024

Language: Английский

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

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 112, P. 105635 - 105635

Published: July 1, 2024

Language: Английский

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Phytoremediation Potential of Urban Trees in Mitigating Air Pollution in Tehran

Forests, Journal Year: 2024, Volume and Issue: 15(8), P. 1436 - 1436

Published: Aug. 15, 2024

The rapid urbanization and growing number of factories, human population, motor vehicles have led to a drastic increase in the concentration air pollutants. This smog is one most important disturbances city planning. Urban trees play vital role improvement quality. selection high-potential capture pollutants provides an attractive route for mitigation urban smog. current study explored purification potential four abundant trees, i.e., white mulberry (Morus alba L.), plane tree (Platanus orientalis European ash (Fraxinus excelsior Tehran pine (Pinus eldarica Ten.)], as phytoremediators grown three parks located regions with low, moderate, high levels pollution on hazardous gases (O3, NO2, CO, SO2) altering content respiratory (CO2 O2). measurement gas was carried out September–October, from 1.30 1.50 m above ground. measured by ambient assessment device (Aeroqual). Broad-leaf deciduous species had greater ability mitigate O3, CO2, SO2 concentrations than needle-leaf evergreen species. lowest O3 CO were found around P. (0.035 0.044 ppm, respectively), whereas O2 highest atmosphere this (20.80 ppm). NO2 (0.081 ppm) (0.076 determined vicinity M. F. excelsior, respectively. Among studied species, proved be best phytoremediation, effectively mitigating more other Conversely, not recommended phytoremediation green spaces. Future research should focus exploring wider range their diverse settings across different seasons climatic conditions.

Language: Английский

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Nonlinear impacts of landscape and climatological interactions on urban thermal environment during a hot and rainy summer

Ecological Indicators, Journal Year: 2024, Volume and Issue: 166, P. 112551 - 112551

Published: Aug. 31, 2024

Investigating the nonlinear impacts of urban landscape and climatic parameters on temperatures, a critical issue within climatology. Chengdu, characterized by its hot, rainy summers rapid development, serves as an ideal model to illustrate these dynamics. Our investigation utilizes advanced analytical methods such Random Forests (RF), SHapley additive explanation (SHAP), Partial Dependence Plots (PDP) analyze how factors influence air temperature (AT) land surface (LST). Significant findings reveal profound thermal heterogeneity across Chengdu's fabric, underscored spatially distinct phenomena where some regions exhibit strong contrasts in due varying factors. The study identifies relative humidity rainfall key drivers variations during summer months, reflecting specific idiosyncrasies. These insights are critical, they highlight planning green infrastructure can be strategically used mitigate adverse effects. research not only enhances understanding complex interplays microclimates but also offers new perspectives heat management. It contributes scientific community providing evidence-based strategies for planners counter island effect enhance resilience against climate change. This comprehensive analysis underscores importance incorporating multiple variables into models, lays groundwork more refined environmental policies practices.

Language: Английский

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