Estimation of stand type parameters and land cover using Landsat-7 ETM image: A case study from Turkey

Guenlue A., Sivrikaya F., Baskent E. Z., Keles S., Cakir G., Kadiogullari A. İ.

SENSORS, vol.8, no.4, pp.2509-2525, 2008 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 4
  • Publication Date: 2008
  • Doi Number: 10.3390/s8042509
  • Journal Name: SENSORS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2509-2525
  • Karadeniz Technical University Affiliated: Yes


Remote sensing has been considered a low-cost, large-area coverage forest information resource ideally suited to broad-scale forest inventory objectives. The objective of this study is to determine stand type parameters such as crown closure, development stage and stand types, and land cover obtained from Landsat 7 ETM image and forest cover type map ( stand type map). The research also focuses on classifying and mapping the stand parameters with the spatial analysis functions of GIS. In the study, stand parameters determined by forest cover type map and remote sensing methods were compared and contrasted to evaluate the potential use of the remote sensing methods. The result showed that development stage were estimated with Landsat 7 ETM image using supervised classification with a 0.89 kappa statistic value and 92% overall accuracy assessments. Among the features, development stages were the most successfully classified stand parameters in classification process. According to the spatial accuracy assessment results, development stages also had the highest accuracy of 72.2%. As can be seen in the results, spatial accuracy is lower than classification accuracy. Stand type had the lowest accuracy of 32.8. In conclusion, it could be stated that development stages, crown closure and land cover could be determined at an acceptable level using Landsat 7 ETM image. However, Landsat 7 ETM image do not provide means to map and monitor minor vegetation communities and stand types at stand level due to low spatial resolution. High resolution satellite images could be used either alone or with field survey data.