Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem
dc.authorid | 0000-0001-5267-9105 | en_US |
dc.contributor.author | Donmez, Cenk | |
dc.contributor.author | Berberoglu, Suha | |
dc.contributor.author | Erdogan, Mehmet Akif | |
dc.contributor.author | Akın Tanrıöver, Anıl | |
dc.contributor.author | Cilek, Ahmet | |
dc.date.accessioned | 2021-03-20T20:15:15Z | |
dc.date.available | 2021-03-20T20:15:15Z | |
dc.date.issued | 2015 | |
dc.department | BTÜ, Orman Fakültesi, Peyzaj Mimarlığı Bölümü | en_US |
dc.description.abstract | Percent 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.sponsorship | Scientific Projects Administration Unit of Cukurova UniversityCukurova University [ZF2011BAP19] | en_US |
dc.description.sponsorship | This research has been supported by the Scientific Projects Administration Unit of Cukurova University (Project ID: ZF2011BAP19). | en_US |
dc.identifier.doi | 10.1007/s10661-014-4151-5 | en_US |
dc.identifier.issn | 0167-6369 | |
dc.identifier.issn | 1573-2959 | |
dc.identifier.issue | 2 | en_US |
dc.identifier.pmid | 25604062 | en_US |
dc.identifier.scopusquality | Q2 | en_US |
dc.identifier.uri | http://doi.org/10.1007/s10661-014-4151-5 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12885/1169 | |
dc.identifier.volume | 187 | en_US |
dc.identifier.wos | WOS:000349012200004 | en_US |
dc.identifier.wosquality | Q3 | en_US |
dc.indekslendigikaynak | Web of Science | en_US |
dc.indekslendigikaynak | Scopus | en_US |
dc.indekslendigikaynak | PubMed | en_US |
dc.institutionauthor | Akın Tanrıöver, Anıl | |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartof | Environmental Monitoring And Assessment | en_US |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Remote sensing | en_US |
dc.subject | Percent tree cover | en_US |
dc.subject | Landsat | en_US |
dc.subject | Regression treemodel | en_US |
dc.subject | Goksu watershed | en_US |
dc.title | Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem | en_US |
dc.type | Article | en_US |