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Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network

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dc.citedby15
dc.contributor.authorBao Pham Q.en_US
dc.contributor.authorAjim Ali S.en_US
dc.contributor.authorParvin F.en_US
dc.contributor.authorVan On V.en_US
dc.contributor.authorMohd Sidek L.en_US
dc.contributor.author?urin B.en_US
dc.contributor.authorCetl V.en_US
dc.contributor.author?amanovi? S.en_US
dc.contributor.authorNguyet Minh N.en_US
dc.contributor.authorid57208495034en_US
dc.contributor.authorid57208693930en_US
dc.contributor.authorid57210162430en_US
dc.contributor.authorid56803785700en_US
dc.contributor.authorid58617132200en_US
dc.contributor.authorid55596817500en_US
dc.contributor.authorid23471894000en_US
dc.contributor.authorid57190570766en_US
dc.contributor.authorid58591026000en_US
dc.date.accessioned2025-03-03T07:43:03Z
dc.date.available2025-03-03T07:43:03Z
dc.date.issued2024
dc.description.abstractThe integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asr.2024.03.027
dc.identifier.epage47
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85191613022
dc.identifier.spage17
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85191613022&doi=10.1016%2fj.asr.2024.03.027&partnerID=40&md5=a7d02e76db581da686e4571599a03c7d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36554
dc.identifier.volume74
dc.pagecount30
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleAdvances in Space Research
dc.subjectForecasting
dc.subjectForestry
dc.subjectLand use
dc.subjectMultilayer neural networks
dc.subjectRemote sensing
dc.subjectBinh duong province
dc.subjectLand cover maps
dc.subjectLand use/land cover
dc.subjectLand-use land-cover changes
dc.subjectLand-use prediction
dc.subjectLanduse change
dc.subjectMulti-spectral
dc.subjectRandom forests
dc.subjectRemote sensing and GIS
dc.subjectRemote-sensing
dc.subjectGeographic information systems
dc.titleMulti-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural networken_US
dc.typeArticleen_US
dspace.entity.typePublication
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