Prediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov-FLUS Model

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dc.contributor.authorInalpulat, Melis
dc.date.accessioned2025-01-27T21:04:04Z
dc.date.available2025-01-27T21:04:04Z
dc.date.issued2024
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractGreenhouses (GHs) are important elements of agricultural production and help to ensure food security aligning with United Nations Sustainable Development Goals (SDGs). However, there are still environmental concerns due to excessive use of plastics. Therefore, it is important to understand the past and future trends on spatial distribution of GH areas, whereby use of remote sensing data provides rapid and valuable information. The present study aimed to determine GH area changes in an agricultural hotspot, Serik, T & uuml;rkiye, using 2008 and 2022 Landsat imageries and machine learning, and to predict future patterns (2036 and 2050) via the Markov-FLUS model. Performances of random forest (RF), k-nearest neighborhood (KNN), and k-dimensional trees k-nearest neighborhood (KD-KNN) algorithms were compared for GH discrimination. Accordingly, the RF algorithm gave the highest accuracies of over 90%. GH areas were found to increase by 73% between 2008 and 2022. The majority of new areas were converted from agricultural lands. Markov-based predictions showed that GHs are likely to increase by 43% and 54% before 2036 and 2050, respectively, whereby reliable simulations were generated with the FLUS model. This study is believed to serve as a baseline for future research by providing the first attempt at the visualization of future GH conditions in the Turkish Mediterranean region.
dc.identifier.doi10.3390/su16198456
dc.identifier.issn2071-1050
dc.identifier.issue19
dc.identifier.scopus2-s2.0-85206441492
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su16198456
dc.identifier.urihttps://hdl.handle.net/20.500.12428/27541
dc.identifier.volume16
dc.identifier.wosWOS:001332967100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20250125
dc.subjectFLUS model
dc.subjectgreenhouse
dc.subjectLandsat
dc.subjectmachine learning
dc.subjectMarkov
dc.subjectsimulation
dc.titlePrediction of Greenhouse Area Expansion in an Agricultural Hotspot Using Landsat Imagery, Machine Learning and the Markov-FLUS Model
dc.typeArticle

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