Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem

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dc.authorid0000-0001-5267-9105en_US
dc.contributor.authorDonmez, Cenk
dc.contributor.authorBerberoglu, Suha
dc.contributor.authorErdogan, Mehmet Akif
dc.contributor.authorAkın Tanrıöver, Anıl
dc.contributor.authorCilek, Ahmet
dc.date.accessioned2021-03-20T20:15:15Z
dc.date.available2021-03-20T20:15:15Z
dc.date.issued2015
dc.departmentBTÜ, Orman Fakültesi, Peyzaj Mimarlığı Bölümüen_US
dc.description.abstractPercent tree cover is the percentage of the ground surface area covered by a vertical projection of the outermost perimeter of the plants. It is an important indicator to reveal the condition of forest systems and has a significant importance for ecosystem models as a main input. The aim of this study is to estimate the percent tree cover of various forest stands in a Mediterranean environment based on an empirical relationship between tree coverage and remotely sensed data in Goksu Watershed located at the Eastern Mediterranean coast of Turkey. A regression tree algorithm was used to simulate spatial fractions of Pinus nigra, Cedrus libani, Pinus brutia, Juniperus excelsa and Quercus cerris using multi-temporal LANDSAT TM/ETM data as predictor variables and land cover information. Two scenes of high resolution GeoEye-1 images were employed for training and testing the model. The predictor variables were incorporated in addition to biophysical variables estimated from the LANDSAT TM/ETMdata. Additionally, normalised difference vegetation index (NDVI) was incorporated to LANDSAT TM/ETM band settings as a biophysical variable. Stepwise linear regression (SLR) was applied for selecting the relevant bands to employ in regression tree process. SLR-selected variables produced accurate results in the model with a high correlation coefficient of 0.80. The output values ranged from 0 to 100 %. The different tree species were mapped in 30 m resolution in respect to elevation. Percent tree cover map as a final output was derived using LANDSAT TM/ETM image over Goksu Watershed and the biophysical variables. The results were tested using high spatial resolution GeoEye-1 images. Thus, the combination of the RT algorithm and higher resolution data for percent tree cover mapping were tested and examined in a complex Mediterranean environment.en_US
dc.description.sponsorshipScientific Projects Administration Unit of Cukurova UniversityCukurova University [ZF2011BAP19]en_US
dc.description.sponsorshipThis research has been supported by the Scientific Projects Administration Unit of Cukurova University (Project ID: ZF2011BAP19).en_US
dc.identifier.doi10.1007/s10661-014-4151-5en_US
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue2en_US
dc.identifier.pmid25604062en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttp://doi.org/10.1007/s10661-014-4151-5
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1169
dc.identifier.volume187en_US
dc.identifier.wosWOS:000349012200004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorAkın Tanrıöver, Anıl
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironmental Monitoring And Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRemote sensingen_US
dc.subjectPercent tree coveren_US
dc.subjectLandsaten_US
dc.subjectRegression treemodelen_US
dc.subjectGoksu watersheden_US
dc.titleResponse of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystemen_US
dc.typeArticleen_US

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