Abstract: 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 (χ2 = 36.794; df = 16; p = 0.002), but no significant relationships with Landsat 7 ETM satellite image (χ2 = 22.291; df = 16; p = 0.134). Moderate association was found between SI method and direct method (χ2 = 16.724; df = 8; p = 0.033).
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Günlü, A.; Baskent, E.Z.; Kadiogullari, A.İ.; Ercanli, İ. Classifying Oriental Beech (Fagus orientalis Lipsky.) Forest Sites Using Direct, Indirect and Remote Sensing Methods: A Case Study from Turkey. Sensors 2008, 8, 2526-2540.
Günlü A, Baskent EZ, Kadiogullari Aİ, Ercanli İ. Classifying Oriental Beech (Fagus orientalis Lipsky.) Forest Sites Using Direct, Indirect and Remote Sensing Methods: A Case Study from Turkey. Sensors. 2008; 8(4):2526-2540.
Günlü, Alkan; Baskent, Emin Z.; Kadiogullari, Ali İ.; Ercanli, İlker. 2008. "Classifying Oriental Beech (Fagus orientalis Lipsky.) Forest Sites Using Direct, Indirect and Remote Sensing Methods: A Case Study from Turkey." Sensors 8, no. 4: 2526-2540.