Modelling tree canopy cover and evaluating the driving factors based on remotely sensed data and machine learning

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dc.authorid0000-0002-1565-095X
dc.authorid0000-0002-6781-2658
dc.contributor.authorAkin, Anil
dc.contributor.authorCilek, Ahmet
dc.contributor.authorMiddel, Ariane
dc.date.accessioned2026-02-12T21:05:29Z
dc.date.available2026-02-12T21:05:29Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractQuantifying urban tree cover is important to ensure sustainable urban ecosystem. This study calculates urban percent tree cover (PTC) for Bursa city, Turkey from Sentinel-2 data and evaluates the driving factors of PTC using an Artificial Neural Network-Multi Layer Perception (ANN-MLP) approach. For the PTC calculation, a Regression Tree (RT) analysis was performed using several vegetation indices (NDVI, LAI, fCOVER, MSAVI2, and MCARI) to improve accuracy. Socio-economic, topographic, and biophysical variables were incorporated into the ANN-MLP approach to evaluate the factors that drive urban PTC. A PTC prediction map was generated with an accuracy of 0.95 and a coefficient of determination of 0.87. The ANN-MLP training process yielded a correlation coefficient value of 0.71 and an R-square of 0.82 was achieved between the predicted ANN-MLP and observed tree cover maps. A priority tree cover map was generated considering statistical relationships between the factors and the ANN-MLP prediction map in addition to visual interpretations at the urban scale. Results demonstrate that, unlike other urban forms, PTC has a statistically negative relationship with the gross dwelling density (R2 =0.31). Topographic variables including slope and DEM were positively correlated with PTC with the R2 value of 0.80 and 0.72 respectively. The integration of remote sensing data with vegetation indices and driving factors yielded accurate prediction for identifying and evaluating the variability in the urban PTC.
dc.identifier.doi10.1016/j.ufug.2023.128035
dc.identifier.issn1618-8667
dc.identifier.issn1610-8167
dc.identifier.scopus2-s2.0-85165918133
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.ufug.2023.128035
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6980
dc.identifier.volume86
dc.identifier.wosWOS:001051364300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Gmbh
dc.relation.ispartofUrban Forestry & Urban Greening
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectUrban Green
dc.subjectPercent tree cover
dc.subjectRegression Tree
dc.subjectArtificial Neural Network
dc.titleModelling tree canopy cover and evaluating the driving factors based on remotely sensed data and machine learning
dc.typeArticle

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