Driving forces and prediction of urban open spaces morphology: The case of Shanghai, China using geodetector and CA-Markov model

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102763 - 102763

Published: Aug. 11, 2024

Urban open spaces offer both environmental and social benefits. However, comprehensive studies that integrate quantitative qualitative evaluations of the factors driving change in these their long-term predictions are lacking. Most existing concentrate on land-use development rather than conducting empirical research specific to urban Shanghai. This study addresses this gap by employing a geographic detector (geodetector) analyze influence various open-space changes. These were then used as weight values multicriteria CA-Markov model simulate predict Shanghai's 2050. The advantage analyzing forces lies ability capture multifactor synergy influencing spaces, aligning with aim quantitatively evaluate interaction between natural, climatic, socioeconomic factors. Additionally, semi-structured interviews conducted 10 policymakers planners assess reliability predictions. results indicate primary drivers spaces. Specifically, normalized difference vegetation index (NDVI) population density (PD) emerged most influential variables. For prediction outcomes, unconstrained scenario predicts decrease area from 5610.94 km2 2020 5124.36 planning intervention anticipates minimal changes total almost no floating economic rapid decline Experts evaluated three scenarios confirmed accuracy models. methods findings can support zoning for systems other cities regions.

Language: Английский

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Comparative analysis of land use changes modeling based-on new hybrid models and CA-Markov in the Urmia Lake basin

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(8), P. 3749 - 3764

Published: July 6, 2024

Language: Английский

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Assessing urban forest decline and predicting future expansion: a spatial analysis and modeling approach in João Pessoa City, Brazil

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(3), P. 3923 - 3940

Published: April 7, 2024

Language: Английский

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Spatio-Temporal Dynamics of Carbon Storage in Rapidly Urbanizing Shenzhen, China: Insights and Predictions

Land, Journal Year: 2024, Volume and Issue: 13(10), P. 1566 - 1566

Published: Sept. 26, 2024

Rapid urbanization in developing countries leads to significant land-use and land-cover change (LULCC), which contributes increased carbon dioxide (CO2) emissions the degradation of storage. Studying spatio-temporal changes storage is crucial for guiding sustainable urban development toward neutrality. This study integrates machine-learning random forest algorithm, CA–Markov, InVEST models predict distribution Shenzhen, China, under various scenarios. The findings indicate that, over past two decades, Shenzhen has experienced changes. transformation from high- low-carbon-density land uses, particularly conversion forestland construction land, primary cause loss. Forestland mainly influenced by natural factors, such as digital elevation model (DEM) precipitation, while other (LULC) types are predominantly affected socio-economic demographic factors. By 2030, projected vary significantly across different scenarios, with greatest decline expected scenario (NDS) least ecological priority (EPS). RF-CA–Markov outperforms traditional CA–Markov accurately simulating use, small scattered types. Our conclusions can inform future low-carbon city optimization.

Language: Английский

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Wetland key habitat functional areas in China informed by flagship waterbirds: The past change, present status and future trend with modeling scenarios

Geography and sustainability, Journal Year: 2025, Volume and Issue: unknown, P. 100284 - 100284

Published: March 1, 2025

Language: Английский

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Optimized deep learning based classification and prediction of land use/land cover changes from satellite images

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(5)

Published: March 21, 2025

Language: Английский

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Prediction of Land Use and Land Cover Changes from 2018 to 2042 Using CA-Markov: A Case Study from Türkiye

Kastamonu University Journal of Forestry Faculty, Journal Year: 2025, Volume and Issue: 25(1), P. 34 - 52

Published: March 24, 2025

Aim of study: To determine the potential changes that may occur in land use classes Akyazı Forest Enterprise for 2030 and 2042. Area was selected as study area. Material method: In this study, Coordination Information on Environment (CORINE) cover (LULC) datasets years 2006, 2012 2018 were used. The Markov model derived transition area probability matrices (TPM) based LULC maps from CORINE 2006 2012. These used to predict through a 10-year simulation using CA-Markov module. Main results: A comparison made between projected map class data, similarity rate 91.1% found. For 24 2042, total forest is predicted increase by 3.8% or 581.5 ha. Research highlights: forecasted outcomes acquired future can aid developing revised management strategies, particularly ensuring long-term viability ecosystems.

Language: Английский

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Evaluating ecosystem services under various trajectories and land use/land cover changes in a densely populated area, Iran

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(2)

Published: April 10, 2025

Language: Английский

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Analysis of urban sprawl dynamics using machine learning, CA-Markov chain, and the Shannon entropy model: a case study in Mbombela City, South Africa

ENVIRONMENTAL SYSTEMS RESEARCH, Journal Year: 2024, Volume and Issue: 13(1)

Published: May 27, 2024

Abstract Over half of the world’s population resides in urban areas. We anticipate that this pattern will become more evident, notably South Africa. Therefore, research on spirals, both past and projected, is necessary for efficient land use planning management. This study aims to assess spatio-temporal sprawl dynamics from 2003 2033 Mbombela, employed robust approaches such as machine learning, cellular automata-Markov chain, Shannon entropy model look at how changes over time using Landsat 4–5 Thematic Mapper 8 Operational Land Imagers. conducted bridge gaps existing research, which primarily focuses current growth trends rather than future trends. The findings indicated coverage built-up areas vegetation has expanded by 1.98 km 2 13.23 between years 2023. On other hand, amount continues decrease -12.56 − 2.65 annually, respectively. an increase area a total 7.60 0.57 , respectively, year 2033. annual decline -7.78 0.39 water bodies open coverage, work potential assist planners policymakers improving sustainable land-use planning.

Language: Английский

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Predicting land use/land cover changes using CA-Markov and LCM models in the metropolitan area of Mashhad, Iran

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Language: Английский

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