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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- 作者：Cenk Donmez ; Suha Berberoglu…
- 关键词：Remote sensing ; Percent tree cover ; Landsat ; Regression tree model ; Goksu watershed
- 刊名：Environmental Monitoring and Assessment
- 出版年：2015
- 出版时间：February 2015
- 年：2015
- 卷：187
- 期：2
- 全文大小：2,058 KB
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16. Helsel, D.R. and Hirsch, R.M. (2002). Statistical methods in water resources, USGS Techniques of Water Resources Investigations, Book 4, Chapter A3, 510 - 作者单位：Cenk Donmez (1)
Suha Berberoglu (1)
Mehmet Akif Erdogan (1)
Anil Akin Tanriover (2)
Ahmet Cilek (1)
1. Cukurova University, Adana, Turkey
2. Bursa Technical University, Bursa, Turkey - 刊物类别：Earth and Environmental Science
- 刊物主题：Environment
Monitoring, Environmental Analysis and Environmental Ecotoxicology
Ecology
Atmospheric Protection, Air Quality Control and Air Pollution
Environmental Management - 出版者：Springer Netherlands
- ISSN：1573-2959
文摘
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.