4.1. Direct Method
Direct method states that the site productivity depends not only on the soil factors but also on topography (aspect, altitude, slope and landform) and climatic data. Thus, forest site classification was conducted by combining edaphic, physiographic and climatic factors. As far as landform or physiographic classification is concerned, the research area was classified into five different elevation-climate zones (500-600m, 600-700m, 700-800m, 800-900m and 900-970m). Digital Elevation Models (DEM) contains the elevation of the terrain over a specified area, usually at a fixed grid, displaying slope, aspect and landform. The source of the DEM data (at 10×10 m pixel resolution) comes from the contour line map with 10 m intervals digitized from digital topographic maps, registered with 6–8 m root mean square (RMS) error with 3D modeling in GIS. The sample points in each of these zones were distributed according to physiographic structure of the area. The landform was stratified by slope and aspect subzones. East, north, north-west and north- east aspects were included in north aspect sub zone and the rest was grouped as south aspect subzone. The landscape surface was stratified into five surface subzones; ridge, top hillside, mid hillside, sub hillside and base land. The sample points were also grouped according to the physiographic stratification.
Soil classification was carried out in each elevation-climate zone using soil sample data Therefore, the soil was classified by bedrock type (clay stone), soil deepness [deep (75-100 cm) and quite deep (100-125 cm)], rockiness (sparsely distributed rocks and densely packed rocks) and soil texture, which describes the proportions of sand, silt and clay particles in the soil. The terms sand, silt and clay refer to different size fractions of the soil's mineral content. Texture is quoted as the percentage of each of these components or, more generally, by internationally recognized shorthand of terms such as loam, sandy clay loam or silt clay. Soil samples were classified as clay and heavy clay. Moreover, water holding capacity of soil samples was calculated using field capacity and wilting point for each horizon.
Two methods were used to classify and map forest sites. The first method is based on soil nutrient regime (SNR) where water deficit is nonexistent in summer months [
12]. The second method uses soil moisture regime (SMR), where water deficit exists in summer months [
13-
15]. There were no meteorological stations in the study area to measure and gather the weather data specifically for each plot or stand. The climate related information of each sample area is harmonized according to its relative spatial location to the Sinop meteorological station, approximately 90 kilometers away from the study site. The temperature generally decreases about 0.6 °C in summer and 0.4 °C in winter for each 100 meter altitude, with an average decrease of 0.5 °C [
16]. The mean temperature change of 0.5 °C was taken into consideration to determine the temperature for each sample plot in the study area. Furthermore, the average annual precipitation was calculated for each elevation-climate zone using the Shreiber formula as suggested by [
17]:
Where Yh: Annual mean precipitation value calculated for the area without meteorological station; Y
o: Annual mean precipitation value (mm) at meteorological station; h: Difference (hectometer) between the altitude of meteorological station and the mean altitude of area whose precipitation value is to be calculated.
The research area was classified into five different elevation-climate zones with an average altitude. The precipitation and temperature data were adopted for each elevation-climate zone and climate analyses was carried out according to Thornthwaite method [
18]. The analysis indicated that there was water shortage in each elevation climate zone in summer months. Therefore, SMR is used to classify the forest sites in the study area. The water deficit of the second elevation-climate zone was illustrated in
Figure 2.
Later, the following eqution was used to calculate drought index of each elevation climate zone in the study area.
where Im: drought index, AET
m: monthly actual evapotranspiration (mm), Tom: total monthly maximum temperature (°C), and 12: annual coefficient. With
equation 3, monthly index calculated and then annual index determined by averaging monthly index values. According to these index values, forest sites were determined (
Table 1).
In direct method, the available water holding capacity of the soils is mainly taken into account as it is a very important parameter in forest sites; but by it is not adequate for mapping and distinguishing sites. Other factors such as altitude, aspect, slope and landform have to be taken into account. Therefore, main rock, soil type, soil skeleton with physiological soil depth as the most effective parameters were taken into consideration for forest site classification. Ecological soil units are determined using main rock, soil type, soil skeleton and physiological soil depth of sample plots. They were identifiable parameters having similar soil and productivity conditions because of similar climatic and geologic processes (
Figure 3). The sample plots with similar properties were grouped into ecological soil units and average water holding capacity for each ecological unit was calculated based on horizon thickness, the amount of fine soil, field capacity and wilting point for each horizon. Total water holding capacity of soil profile was calculated by simply summing available water holding capacity of each horizon [
19]. Forest sites were finalized and mapped using climate data in each elevation climate zones, mean water holding capacity of each ecological soil unit and topographic features.
The drought index values calculated for each elevation climate zone are between 15 and 23, indicating the moderate fresh base site. Since this classification does not take aspect, slope and altitude into consideration, the drought index values, thus the base site, calculated according to ridge flat were adjusted by the spatial factors. Therefore, the adjustment method developed by Kantarci [
19] for various base sites were used to determine forest sites across the forest landscape (
Figure 4). For example, if the sample point is at the top of the hillside on a south-facing aspect, then the site is dry forest site according to the ridge-flat base site scheme in
Figure 4.
The scheme in
Figure 4 has been developed based on an ecological understanding of forest sites. Basically, the north and south aspect groups differ from each other in terms of water economics. Since the areas in south aspect group gets more direct and longer solar radiation in northern hemisphere, the areas are drier than those with similar ecological attributes [
17]. This fact can be clearly seen in
Figure 4. For example, while an area, which is located in the north aspect group, at top hillside is classified as moderate fresh forest site, the same area located in south aspect group would be classified as dry forest site.
All the sample points are evaluated likewise and recorded in a database to delineate the forest sites across the research area using GIS functions. The study resulted in five different forest sites; dry, moderate fresh, fresh, moist and highly moist forest site (
Figure 5-a). The dry sites are located in very steep slope on south aspect hillside. Water is at a low level, not enough to meet the need of forest trees over the growing period. In moderate fresh site, water deficit can occur or reach a level to cause droughtness in years with low precipitation. Even though this site is at the boundary of drought in water holding capacity, they are at steep slope on north aspect hillside. Because of local spatial factors, they are not taking water leakage from neighbor site. In fresh forest site, after rains, since the soil is fulfilling, water level at field capacity is held, spare of water has flown. They have location to not reinforcement of water and available water in soil is adequate amount at an important part of growing period. In moist forest site, if the moderate fresh and fresh forest sites can get reinforcement of water from at sub hillside, water collection line, valley and base, they are called “moist”. In highly moist forest site, this site areas which has continuous and height base water and which is side source and watery stream.
4.2. Indirect (SI) method
Practical foresters have used the relationship between tree height and age to quantify site productivity for different tree species. One way of doing this is to determine the SI, defined as the dominant height of a population of trees at a specific base or reference age. Although forest site productivity may not be fully represented by SI, this is the most widely used method for estimating the site productivity of even-aged forests [
2]. In this study, forest site quality was estimated by the SI method.
It is commonly assumed that the influence of genetic and silvicultural factors on forest height growth remains low in single-species, even-aged, closed plantations [
20]. In each sample plot, height and age were measured in free-growing dominant and co-dominant trees (100 dominant and co-dominant highest trees per hectare, for example, 4 highest trees in a 0.04 ha plot) which are the strongest competitors for light, moisture, nourishment and growing space without any obvious signs of growth abnormalities or damage [
21]. The height was measured with a Sunto hypsometer, and the age at breast height (1.30 m above ground level) was measured with an increment borer. SI was calculated for each stand at the reference age of 100 years by site index curves developed by Carus [
22] for
Fagus orientalis.
According to indirect method, SI curves were generated by the guide-curve method. These curves are developed by first fitting an average height-over-age guide curve to these data and then constructing a series of higher or lower curves with the same shape as the guide curve. Such a process is called anamorphic curving [
23]. Guide curves are first used to generate anamorphic SI equations, and then build SI curves. The site productivity was classified as poor = IV-V; medium = III and good = I-II. The SI map was created with GIS functions (
Figure 5-b). The SI value calculated for each sample plot was related to the location of the plot and the borders of the sites were then delineated using the spatial analysis functions (proximity, nearest neighborhood) of GIS. The geographically adjacent (contiguous) sample plots with the same SI values were then combined using reclassification functions (eliminate) of the GIS to produce a map of forest SI values throughout the study area.
4.3. Remote Sensing Method
The data used in this research are forest cover type map at 1/25.000 scale for year 1999, Landsat 7 ETM satellite image acquired on May 3, 2000 and Quickbird satellite image acquired on June 18, 2004. The forest cover type map used as ground truthing was originally generated from both the stereo interpretation of infrared aerial photos with an average 1/16.000 scale and ground measurements of sample points distributed by 300×300 m grids in 1999.
Data processing involved a number of steps. First of all, the forest cover type map (1999) was drafted and spatial database was build with Arc/Info 9.2 [
24]. Second, Landsat 7 ETM and Quickbird satellite images were rectified and classified using forest sites obtained from direct method. Third, Forest site polygon themes obtained from direct method, indirect method and RSM were overlaid and areas that are in the same classes in direct method, SI method and RSM were computed with GIS.
The site classification was carried out using ERDAS Imagine 9.0™ [
25] using image processing. Subsets of satellite images were rectified using 1/25.000 scale Topographical Maps with UTM projection (ED 50 datum) using first order nearest neighbor rules. A total of 24 ground points were used to register.
In this study, we used 5, 4, 3 spectral bands for Landsat 7 ETM image and 3, 2, 1 spectral bands for Quickbird image combination in image classification. Ground reference data were gathered from more than 50 points as signatures for each satellite image. The training points were equally distributed to each forest site type with at least 10 points per forest site type. For the supervised classification of Landsat 7 ETM image, the forest sites map was used to create ground signatures. In order to classify forest site types from the images, signatures were taken from these ground corrected forest site map. Furthermore, these signatures were further controlled with image enhancement techniques such as Transformed Vegetation Index and Principle Components Analysis-PCA. These signatures were then used in a supervised maximum likelihood classification algorithm. Our classification is composed of five classes; dry forest site, moderate forest site, fresh site, moist and highly moist forest site. We used equal number of control points with at least 30 points for each class. The accuracy assessment of the image is checked and accepted if the accuracy is higher than 80%. After accuracy assessment, the image was clumped and eliminated 2×1 pixels for Landsat 7 ETM satellite image and 10×10 pixels for Quickbird satellite image and vectorized into polygon coverage. Before the classification, Quickbird satellite image was resembled 5×5 meters because of data dimension classified into five forest site classes successfully for areas which are determined according to direct method. Whole areas were classified into five classes (dry, moderate fresh, fresh, moist and highly moist forest site). As a result of image classification, values of accuracy assessment and Kappa statistics (Conditional Kappa for each category) respectively 73.3% and 0.67 for Landsat 7 ETM, 90.7% and 0.88 for Quickbird image. Similarly, Gunlu et al. [
9] classified forest site classes in Maçka Ormanüstü Forest Planning Unit with 84.44% accuracy 0.77 Kappa statistics. The maps were generated supervised of classification were given in (
Figures 5-c and 5-d).