Classifying oriental beech (Fagus orientalis Lipsky.) forest sites using direct, indirect and remote sensing methods: A case study from Turkey

Guenlue A., Baskent E. Z., Kadiogullari A. İ., Ercanli I.

SENSORS, vol.8, no.4, pp.2526-2540, 2008 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 4
  • Publication Date: 2008
  • Doi Number: 10.3390/s8042526
  • Journal Name: SENSORS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2526-2540
  • Karadeniz Technical University Affiliated: Yes


Determining the productivity of forest sites through various classification techniques is important for making appropriate forest management decisions. Forest sites were classified using direct and indirect ( site index) and remote sensing ( Landsat 7 ETM and Quickbird satellite image) methods. In the direct method, forest site classifications were assigned according to edafic ( soil properties), climate ( precipitation and temperature) and topographic ( altitude, slope, aspect and landform) factors. Five different forest site classes ( dry, moderate fresh, fresh, moist and highly moist) were determined. In the indirect method, the guiding curve was used to generate anamorphic site index ( SI) equations resulting in three classes; good ( SI=I-II), medium ( SI= III) and poor ( SI= IV-V). Forest sites were also determined with a remote sensing method ( RSM) using supervised classification of Landsat 7 ETM and Quickbird satellite images with a 0.67 kappa statistic value and 73.3% accuracy assessments; 0.88 kappa statistic value and 90.7% accuracy assessments, respectively. Forest sites polygon themes obtained from the three methods were overlaid and areas in the same classes were computed with Geographic Information Systems ( GIS). The results indicated that direct and SI methods were consistent as a 3% dry site ( 19.0 ha) was exactly determined by both the direct and SI methods as a site class IV. Comparison of SI and RMS methods indicated a small difference as the area was highly homogeneous and unmanaged. While 15.4 ha area ( open and degraded areas) was not determined by SI but RSM. A 19.0 ha ( 100%) poor site was determined by the SI method, 14.9 ha ( 78%) poor site was in Landsat 7 ETM satellite image and 17.4 ha ( 92%) poor site in Quickbird satellite image. The relationship between direct and SI methods were statistically analyzed using chi-square test. The test indicated a statistically significant relationships between forest sites determined by direct method and Quicbird satellite image (chi(2) = 36.794; df = 16; p = 0.002), but no significant relationships with Landsat 7 ETM satellite image (chi(2) = 22.291; df = 16; p = 0.134). Moderate association was found between SI method and direct method (chi(2) = 16.724; df = 8; p = 0.033).