Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement

Mustak, Sk.; Baghmar, Naresh Kumar; Singh, Sudhir Kumar; Srivastava, Prashant K.

doi:10.1080/10106049.2022.2138983

Cited by 4 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Urbanization is a growing challenge for city planners and policymakers who are continuously focusing on computer-based statistical models, and machine learning for a sustainable and livable city (Mustak et al, 2022). Some models of land use and urban expansion have been studied and used to allocate land-use changes explicitly on maps by using the prediction method over the past two decades.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

Nguyen,

Nguyen

2023

Environ. Nat. Resour. J.

View full text Add to dashboard Cite

The lack of ability to control human activities led to changes of land use/land cover (LULC) in Dalat City where rapid urbanization and the demand to expand agricultural land have resulted in dramatic forest reductions. This study assessed the rate and extent of LULC changes over the past 12 years and simulated future scenarios in Dalat City, Lam Dong Province, Vietnam by using an integrated model of Markov chain and logistics regression. Three land-use maps used to analyze land-use change were extracted from satellite images in 2010, 2016, and 2022 by classification approach. The outcome of this process indicates a significant increase in agricultural and built-up land of 48.22 km2 and 9.36 km2, respectively; a decrease in forest land of 55.61 km2, and a minor change in water bodies and bare land in the 2010-2022 period. Prediction maps of land-use change in 2028 and 2034 are generated after the model is validated by comparing the actual map with the prediction map of LULC in 2022 using Kappa statistics. Transition of forest area to other land use types, especially land for expansion of built-up and agricultural land is the crucial trend of land-use change in the future according to the forecast model. Compared to 2022, forest area in 2034 will decrease by 60.65 km2 while built-up and agricultural land will increase by 14.07 km2 and 43.61 km2, respectively. The research results provide valuable information as a foundation for land-use policy planning and local urban development to ensure sustainable development objectives.

show abstract

Section: Introductionmentioning

confidence: 99%

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

Nguyen,

Nguyen

2023

Environ. Nat. Resour. J.

View full text Add to dashboard Cite

show abstract

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin

Singh

Behera

et al. 2023

Environ Sci Pollut Res

View full text Add to dashboard Cite

Analyzing the effect size of urban growth driving factors: application of multilayer-perceptron Markov-chain model for the Riyadh city

Al-Shaar

2023

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement

Cited by 4 publications

References 59 publications

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

Monitoring and Modeling of Spatio-Temporal Urban Expansion and Land Use/Land-Cover Change in Mountain Landscape: A Case Study of Dalat City, Vietnam

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin

Analyzing the effect size of urban growth driving factors: application of multilayer-perceptron Markov-chain model for the Riyadh city

Contact Info

Product

Resources

About