title: Response of the regression tree model to high resolution remote sensing data for predicting percent tree cover in a Mediterranean ecosystem.
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/ETM data. 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.
authors.affiliation: Cukurova University, Adana, Turkey, cdonmez@cu.edu.tr.
authors.forename: Cenk
authors.initials: C
authors.lastname: Donmez
authors.forename: Suha
authors.initials: S
authors.lastname: Berberoglu
authors.forename: Mehmet Akif
authors.initials: MA
authors.lastname: Erdogan
authors.forename: Anil Akin
authors.initials: AA
authors.lastname: Tanriover
authors.forename: Ahmet
authors.initials: A
authors.lastname: Cilek
mesh_terms: D017753:Ecosystem; D004777:Environment; D004784:Environmental Monitoring / Q000379:methods*; D065928:Forests; D015233:Models, Statistical; D058998:Remote Sensing Technology; D063809:Satellite Imagery; D014197:Trees; D014421:Turkey