Recognition and understanding of landscape dynamics as a historical legacy of disturbances are necessary for sustainable management of forest ecosystems. This study analyzes spatial and temporal changes in land use and forest cover patterns in a typical mountain forest area in Rize Forest Enterprise of the Northeastern part of Turkey. The area is investigated by evaluated the temporal changes of spatial structure of forest conditions through spatial analysis of forest cover type maps from 1984 and 2007 using GIS and FRAGSTATS. The quantative evidences presented here showed that there were drastic changes in the temporal and spatial dynamics of land use/forest cover. As an overall change between 1984 and 2007, there was a net decrease of 2.30% in total forested areas. On one hand, productive forest areas decreased 12,506 ha, on the other hand, degraded forest areas increased 14,805 ha. In examining the changes of crown closure and development stages of forest ecosystem during the study period, the forest stand area with medium crown closures increased. Regenerated area increased while the other development stages were left to grow to mature development stages in the period. These results regarding to crown closure and development stage showed that forest quality has increased but total forest areas decreased. This is partially due to out-migration of rural population in Rize and Cayeli towns. In terms of spatial configuration, analysis of the metrics revealed that landscape structure in Study area had changed substantially over the 23-year study period, resulting in fragmentation of the landscape as indicated by the large patch numbers and the smaller mean patch sizes due to heavy timber subtraction, illegal cutting, and uncontrolled stand treatments.

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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.

Diameter at breast height (DBH) is the simplest, most common and most important tree dimension in forest inventory and is closely correlated with wood volume, height and biomass. In this study, a number of linear and nonlinear models predicting diameter at breast height from stump diameter were developed and evaluated for Oriental beech (Fagus orientalis Lipsky) stands located in the forest region of Ayancık, in the northeast of Turkey. A set of 1,501 pairs of diameter at breast height-stump measurements, originating from 70 sample plots of even-aged Oriental beech stands, were used in this study. About 80 % of the otal data (1,160 trees in 55 sample plots) was used to fit a number of linear and nonlinear model parameters; the remaining 341 trees in 15 sample plots were randomly reserved for model validation and calibration response. The power model data set was found to produce the most satisfactory fits with the Ad-

The objective of this study is to evaluate the relationships between stand parameters (stand volume, basal area and dominant height), and band reflectance values and six vegetation indices (VIs) obtained from pan-sharpened, IKONOS satellite image in Artvin-Genya Mountain located in northeastern part of Turkey. Multiple stepwise regression analysis is used to estimate the stand parameters. The results indicated that a linear combination of EVI and DVI for stand volume and basal area (adjusted R 2 =0.55; a root mean square error (RMSE)=153.53 m 3 ha -1 and adjusted R 2 =0.59; RMSE=12.37 m 2 ha -1 ), respectively, and a linear combination of SAVI, EVI and DVI for dominant height (adjusted R 2 =0.57; RMSE=3.80 m) were better predictors than a linear combination of IKONOS Band 1and Band 4 for stand volume and basal area, and the IKONOS Band 1 and Band 2 for dominant height (R 2 =0.41; RMSE=181.01 m 3 ha -1 , R 2 =0.43; RMSE=14.84 m 2 ha -1 and R 2 =0.45; RMSE=4.62 m), respectively. This study concludes that the regression models developed with IKONOS VIs were able to predict stand parameters better than do the IKONOS band reflectance values in Artvin-Genya Mountain forest areas.

The objective of this study is to estimate the leaf area index (LAI) of a forest ecosystem using two different satellite images, WorldView-2 and Aster. For this purpose, 108 sample plots were taken from pure Crimean pine forest stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. Each sample plot was imaged with hemispherical photographs with a fish-eye camera to determine the LAI. These photographs were analyzed with the help of Hemisfer Hemiview software program, and thus, the LAI of each sample plot was estimated. Furthermore, multiple regression analysis method was used to model the statistical relationships between the LAI values and band spectral reflection values and some vegetation indices (Vis) obtained from satellite images. The results show that the high-resolution WorldView-2 satellite image is better than the medium-resolution Aster satellite image in predicting the LAI. It was also seen that the results obtained by using the VIs are better than the bands when the LAI value is predicted with satellite images.

This study assessed the suitability of Landsat ETM+ and QuickBird digital number values and various vegetation indices for predicting some structural parameters of forests in western Turkey. The empirical relationships between the structural parameters such as stand volume, basal area, tree density and quadratic mean diameter, and Landsat ETM+ and QuickBird satellite images were estimated using stepwise multiple regression analysis. Results indicated weak relationships between forest structural parameters and Landsat ETM+ images. The adjusted R 2 values of the regression analysis using the spectral digital number values for stand volume, basal area, tree density and quadratic mean diameter were found to be 0.37, 0.32, 0.44 and 0.25, respectively. Based on the vegetation indices, the adjusted R 2 values of the regression analysis were attained as 0.36, 0.34, 0.28 and 0.17, respectively. However, the results demonstrated moderate relationships between the forest structural parameters and the QuickBird satellite image. The adjusted R 2 values from the regression analysis using the digital number values for stand volume, basal area, tree density and quadratic mean diameter were found as 0.57, 0.45, 0.29 and 0.30, respectively. Depending on the vegetation indices, the adjusted R 2 values from the regression analysis were obtained as 0.54, 0.41, 0.41 and 0.44, respectively. When the results from Landsat ETM+ and QuickBird satellite images are compared with each other, it could be stated that the QuickBird satellite images provide better representation of structural parameters of forests.

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 567.2 ha 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 262.5 ha, very moist site of 122.5 ha and highly moist site of 191.2 ha in direct method; sites I-II cover 38.9 ha, III 289.6 ha, IV-V 143.5 ha and treeless-degraded areas of 104.2 ha in indirect method; moist site of 203.5 ha, very moist site of 232.1 ha and highly moist site of 140.6 ha in remote sensing method. However, 104.2 ha treeless and degraded areas were not determined by indirect method, yet by the other methods. Secondly, forest site map for the whole area (5,980.8 ha) 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 134.1 ha, III 1,643.6 ha, IV-V 1,396.5 ha, treeless-degraded areas of 1,097.3 ha and settlement-agriculture areas of 1,709.3 ha in indirect method; moist site of 1,674.3 ha, very moist site of 853.6 ha, highly moist site of 1,729.6 ha and settlement-agriculture areas 1,723.3 ha in remote sensing method. Again the treeless- degraded areas of 1,097.3 ha were not determined by indirect method but by remote sensing method.