Alkan Günlü

Spatial and temporal dynamics of land use pattern response to urbanization in Kastamonu

TM . Urban settlements account for only two percent of the Earth’s land surface. However, over half of the world’s population resides in cities (United Nations, 2001). The quantitative evidences presented here showed that there were drastic changes in the temporal and spatial dynamics of land use/land cover. As an overall change between 1987 and 2000, there was a net increase of 28.96% in total settlement areas. On one hand, forest areas increased to 111 466 ha and settlement areas increased to 1 440 ha, while on the other hand, open areas decreased to 112 888 ha. This is partially due to migration of rural population in Kastamonu regional directorate of forestry. In terms of spatial configuration, analysis of the metrics revealed that landscape structure in study area had changed substantially over the 13-year study period, resulting in fragmentation of the landscape as indicated by the small patch numbers and the large mean patch sizes due to immigration of rural population to urban population.

Spatiotemporal Changes in Landscape Pattern in Response to Afforestation in Northeastern Turkey: A Case Study of Torul

Abstract Monitoring and understanding of land use/forest cover dynamics as well as the factors affecting the changes in land use/forest cover are necessary for the sustainable management of natural resources. This study analyzes the spatial and temporal changes in land use and forest cover patterns in a typical mountain forest area in the Torul State Forest Enterprise area of Northeastern Turkey. The investigation evaluates the temporal changes of the spatial structure of forest conditions through the spatial analysis of forest-cover type maps from 1984 and 2005 using GIS and FRAGSTATS. The evidence presented here showed that there were drastic changes in the temporal and spatial dynamics of land use/forest cover, and between 1984 and 2005 there was a net increase of 19.9% in total forested area. Productive forest area increased by 3161 ha, although degraded forest area increased 9216 ha. In examining the changes of crown closure and development stages of the forest ecosystem during the study period, the forest stand area with low crown closures increased. The regenerated area increased because of regeneration activities while the other development stages were left to grow to older development stages in the period. These results regarding the crown closure and development stage show that forest quality has increased, and this is partly due to emigration of the rural population in Torul. In terms of spatial configuration, the landscape structure in Torul, the State Forest Enterprise area, changed substantially over the 21-year study period, resulting in fragmentation of the landscape as indicated by large patch numbers and smaller mean patch sizes, owing to heavy timber subtraction, illegal cutting, and uncontrolled stand treatments.

Relationship between site index of oriental spruce [Picea orientalis (L.) Link] and ecological variables in Maçka, Turkey

Abstract Oriental spruce [Picea orientalis (L.) Link] is a species native to Turkey, covering about 350,000 ha. The species is of great importance to the production of timber as well as to the creation of social welfare in eastern Black Sea region. There are few studies about ecological variables affecting oriental spruce site productivity. Based on data from 90 sample plots, multiple regression models were developed using topographic, edaphic, soil nutrients, climatic subgroups and all factors integrated. Within the different subgroups of site factors, the integrated model with all ecological variables provided the best statistical results, which explained 77% of index variation. Other topographic, edaphic, soil nutrients and climatic subgroup models were enumerated by the success of modelling site index of oriental spruce from the related site variables. The results show that the integrated model is more successful than the models developed separately for each ecological variable. However, the integrated model included 12 different variables and thus the assessment of some of these model variables, e.g. element content of the soil (Ca2+, Mg2+, etc.), require potentially laborious soil analyses and costly applications. Therefore, the topographic model with only three variables (R 2=0.656), which can be assessed directly from digital maps by Geographical Information System (GIS), seems to be more promising and convenient, since the model can be easily applied to large areas. The results can be translated into practical recommendations to forest managers for selecting suitable site conditions for oriental spruce.

Estimation of Crown Closure and Tree Density Using Landsat TM Satellite Images in Mixed Forest Stands

The objective of this study was to investigate the relationship between crown closure and tree density in mixed forest stands using Landsat Thematic Mapper (TM) reflectance values (TM 1- TM 5 and TM 7) and six vegetation indices (SR, DVI, SAVI, NDVI, TVI and NLI). In this study, multiple regression analysis was used to estimate the relationships between the crown closure and tree density (number of tree stems per hectare) using reflectance values and vegetation indices (VIs). The results demonstrated that the model that used SR and DVI had the best performances in terms of crown closure (R2 = 0.674) and the model that used the DVI and SAVI had the best performances in terms of tree density (R2 = 0.702). The regression model that used TM 1, TM 3 together with TM 4 showed the performances of the crown closure (R2 = 0.610) and the regression model that used TM 1 showed the performances of the tree density (0.613). Results obtained from this research show that vegetation indices (VIs) were a better predictor of crown closure and tree density than other TM bands.

Prediction of Some Stand Parameters using Pan-Sharpened IKONOS Satellite Image

Abstract 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 R2=0.55; a root mean square error (RMSE)=153.53 m3 ha-1 and adjusted R2=0.59; RMSE=12.37 m2 ha-1), respectively, and a linear combination of SAVI, EVI and DVI for dominant height (adjusted R2=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 (R2=0.41; RMSE=181.01 m3 ha-1, R2=0.43; RMSE=14.84 m2 ha-1 and R2=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.

Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey

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.

Identifying priority areas for reforestation using remote sensing and geographical information systems: a case study from Turkey

This study uses Landsat 7 ETM and IKONOS satellite data to estimate the priority areas for reforestation in a forest management planning unit of Turkey. Three important parameters, crown closure, slope and soil moisture, were used to identify potential reforestation areas. The existing crown closure classes were obtained from the forest stand type map of the study area. Slope map of the study area was created using a digital elevation model (DEM) digitised from digital topographic maps. Supervised classification method was used to classify soil moistures from the images. 12 priority areas for reforestation were estimated with the help of combined maps for crown closure (degraded and treeless areas), slope and soil moisture classes. The results showed that approximately 47% of the study area, which is suitable for reforestation, was included in the category of degraded and treeless forest areas.

Comparing methods for determining forest sites: a case study in Gümüşhane-Karanlıkdere forest

Forest site classification has long been a problem for managers of Turkish forests. Forest management decisions and land use planning involving afforestation activities and silvicultural prescriptions are based on sound site information to formulate appropriate actions on the ground. In Turkey, two methods of forest site productivity are used; direct and indirect method. Indirect methods are usually reserved for practical applications as they are relatively simple, yet provide less accurate estimation of the real productivity. In this study direct, indirect and remote sensing (RS) methods were used to distinguish and map forest sites of Karanlikdere Forest District in Gumushane, Turkey. One hundred and twenty-two sample plots were established with 300 m × 300 m grids in summer of 2003. In each sample plot, soil samples and the classical timber inventory measurements were taken. According to direct method, water-air economy method is preferred due to a water deficiency in the study area. Four different forest site classes; very dry, dry, moderate fresh and fresh were determined and mapped with geographic information system (GIS). In indirect method, the guiding curve was used to generate anamorphic site indexes (SI) of three classes; good (SI = I and II), medium (SI = III) and poor (SI = IV and V). Furthermore, forest sites were estimated with Landsat 7 ETM (2000) data using supervised classification with a 0.843 kappa statistic value and 88% accuracy assessments. Some important differences between the methods were discovered. The indirect method indicate that site indices I and II are 298.5 ha, III 254.3 ha and IV and V 347.7 ha. In contrast, direct method related to very dry site of 107.7 ha, dry site of 484.6 ha, moderate fresh site of 304.7 ha and fresh site of 246.3 ha. Satellite image indicate that very dry site covers 291.2 ha, dry site 239.2 ha, moderate fresh site 287.4 and fresh site 325.5 ha. Approximately 242.8 ha area (open and degraded areas) were not determined by indirect method but were captured with RS method. The statistical analyses (ANOVA) showed no statistically significant (F = 0.720, p = 0.543) relationship between indirect method and direct method-RS method indicating clearly that indirect method is not an adequate measurement of forest site productivity. Forest sites, particularly in open and degraded areas should be determined with direct method.

RELATIONSHIPS BETWEEN SITE INDEX AND SOME EDAPHIC AND PHYSOGRAPHIC FACTORS OF PURE ORIENTAL SPRUCE (Picea orientalis Link.) IN ARTVİN GENYA MOUNTAIN

Bu arastirma Artvin Orman Bolge Mudurlugu, Artvin Orman Isletme Mudurlugu, Merkez Isletme Şefligi sinirlari icerisindeki Genya Dagi bolgesinde yayilis gosteren saf Dogu Ladini mescerelerinde bonitet endeksi ile bazi edafik ve fizyografik ozellikler arasindaki iliskilerin saptanabilmesi amaciyla gerceklestirilmistir. Bu amacla Genya Dagi bolgesinde saf olarak yayilis gosteren Dogu Ladini mescerelerinden 50 tane deneme alani secilmis, her bir deneme alanina iliskin fizyografik ve edafik ozellikler belirlenmistir. Topraga iliskin ozelliklerin belirlenebilmesi icin toprak profilleri acilmis ve ornekler alinmistir. Ayrica her bir deneme alaninda mescerelerin bonitet endeksi (100 yasindaki ust boy) belirlenmistir. Mescere bonitet endeksi ile edafik ve fizyografik faktorler arasindaki iliskiler korelasyon analizi ile sorgulanmistir. Bu ekolojik etmenlerden egim, fizyolojik toprak derinligi, mutlak toprak derinligi, Ah ve B horizonundaki kil ve kum miktarilari (%) ile bonitet endeksi arasinda onemli ve anlamli iliskiler bulunmustur. Anahtar Kelimeler: Dogu Ladini, Bonitet Endeksi, Edafik Faktor, Fizyografik Faktor, Artvin.

Artificial neural network models by ALOS PALSAR data for aboveground stand carbon predictions of pure beech stands: a case study from northern of Turkey

Abstract The goal of this study was to estimate aboveground stand carbon (AGSC) of pure beech stands in Turkey with ground measurements as well as topographic information and remote sensing data. For this purpose, 153 sample plots were collected from pure beech stands in study area. The AGSC of each sample plot was computed. Eight texture images (variance, dissimilarity, homogeneity, entropy, contrast, mean, second moment and correlation) with five window sizes (3 × 3, 5 × 5, 7 × 7, 9 × 9 and 11 × 11) generated from ALOS PALSAR L-band satellite image. The AGSC models predicting the relationships between ALOS PALSAR texture values and topographic information, and sample plot AGSC were developed by using multiple linear regressions (MLR). Also, artificial neural networks (ANNs) architectures were trained by comparing various numbers of neurons and activation functions in its network types. Our results revealed the ability of ANNs was better than MLR models to predict AGSC values.