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Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

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dc.citedby13
dc.contributor.authorPande C.B.en_US
dc.contributor.authorSrivastava A.en_US
dc.contributor.authorMoharir K.N.en_US
dc.contributor.authorRadwan N.en_US
dc.contributor.authorMohd Sidek L.en_US
dc.contributor.authorAlshehri F.en_US
dc.contributor.authorPal S.C.en_US
dc.contributor.authorTolche A.D.en_US
dc.contributor.authorZhran M.en_US
dc.contributor.authorid57193547008en_US
dc.contributor.authorid57221943932en_US
dc.contributor.authorid57193546415en_US
dc.contributor.authorid56763877500en_US
dc.contributor.authorid58617132200en_US
dc.contributor.authorid57224683617en_US
dc.contributor.authorid57208776491en_US
dc.contributor.authorid57198446685en_US
dc.contributor.authorid57553459500en_US
dc.date.accessioned2025-03-03T07:41:37Z
dc.date.available2025-03-03T07:41:37Z
dc.date.issued2024
dc.description.abstractLand use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30�m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of�Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes. ? The Author(s) 2024.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo84
dc.identifier.doi10.1186/s12302-024-00901-0
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85191348820
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85191348820&doi=10.1186%2fs12302-024-00901-0&partnerID=40&md5=15694a845ecc055096119507fec3f391
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36226
dc.identifier.volume36
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleEnvironmental Sciences Europe
dc.subjectIndia
dc.subjectMaharashtra
dc.subjectClassification (of information)
dc.subjectEngines
dc.subjectForestry
dc.subjectImage enhancement
dc.subjectLand use
dc.subjectMachine learning
dc.subjectSatellite imagery
dc.subjectVegetation mapping
dc.subjectChange detection
dc.subjectEnergy
dc.subjectGoogle earth engine
dc.subjectGoogle earths
dc.subjectLand cover maps
dc.subjectLand use and land cover
dc.subjectLand-use and land-cover classifications
dc.subjectRandom forests
dc.subjectRemote-sensing
dc.subjectSDG
dc.subjectAnthropocene
dc.subjectcloud cover
dc.subjectdata set
dc.subjectdetection method
dc.subjectimage classification
dc.subjectland cover
dc.subjectland use change
dc.subjectmachine learning
dc.subjectremote sensing
dc.subjectsatellite imagery
dc.subjectSustainable Development Goal
dc.subjectRemote sensing
dc.titleCharacterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementationen_US
dc.typeArticleen_US
dspace.entity.typePublication
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