Forest site classification using Landsat 7 ETM data: A case study of Macka-Ormanuestue forest, Turkey

Aforestation activities, silvicultural prescription, forest management decisions and land use planning are based on site information to develop appropriate actions for implementation. Forest site classification has been one of the major problems of Turkish forestry for long time. Both direct and indirect methods can be used to determine forest site productivity. Indirect methods are usually reserved for practical applications as they are relatively simple, yet provide less accurate site estimation. However, direct method is highly time-demanding, expensive and hard to conduct, necessitating the use of information technologies such as Geographic Information Systems (GIS) and Remote Sensing (RS). This study, first of all, generated a forest site map using both direct and indirect methods based on ground measurements in 567.2ha sample area. Then, supervised classification was conducted on Landsat 7 ETM image using forest site map generated from direct method as ground measurements to generate site map. The classification resulted in moist site of 262.5ha, very moist site of 122.5ha and highly moist site of 191.2ha in direct method; sites I-II cover 38.9ha, III 289.6ha, IV-V 143.5ha and treeless-degraded areas of 104.2ha in indirect method; moist site of 203.5ha, very moist site of 232.1ha and highly moist site of 140.6ha in remote sensing method. However, 104.2ha treeless and degraded areas were not determined by indirect method, yet by the other methods. Secondly, forest site map for the whole area (5,980.8ha) was generated based on the site map generated by the direct method for sampled area. The Landsat 7 ETM image was classified based on the forest site map of sample area. The site index (SI) map for the whole area was generated using conventional inventory measurements. The classification resulted in sites I-II cover 134.1ha, III 1,643.6ha, IV-V 1,396.5ha, treeless-degraded areas of 1,097.3ha and settlement-agriculture areas of 1,709.3ha in indirect method; moist site of 1,674.3ha, very moist site of 853.6ha, highly moist site of 1,729.6ha and settlement-agriculture areas 1,723.3ha in remote sensing method. Again the treeless- degraded areas of 1,097.3ha were not determined by indirect method but by remote sensing method.