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Öğe Analitik Hiyerarşi Prosesi ile Kestane (Castanea sativa Mill.) Ağaçlandırmaları İçin Uygun Alanların Belirlenmesi(2023) Aksoy, HasanArazi kullanımının doğru ve verimli bir şekilde uygulanabilirliği için yer seçimi ve kullanım tipinin belirlenmesi çok önemlidir. En uygun yer seçimi, ülkemizde özellikle kırsal kesimlerde sosyal ve ekonomik yönden daha verimli bir kazanç elde edilmesini sağlayacaktır. Ülkemizde kırsal kalkınma için gelir getirici türlerin ağaçlandırma çalışmalarına yönelik teşvikler yapılmakta, dolayısıyla teşviklerden maksimum fayda ve kazanç sağlamak açısından amaca göre en uygun alanın belirlenmesi gerekmektedir. Bu çalışma, hem doğal yayılış gösteren hem de coğrafi işaret almış kestane ormanlarının bulunduğu Sinop Orman Bölge Müdürlüğü, Ayancık Orman İşletme Müdürlüğünde gerçekleştirildi. Çalışmada Coğrafi Bilgi Sistemleri (CBS) ve Analitik Hiyerarşi Prosesi (AHP) yöntemi kullanılarak uygun kestane (Castanea sativa Mill.) ağaçlandırma alanları belirlendi. AHP için bonitet, arazi kullanımı, yükseklik, eğim ve bakı olmak üzere toplam beş kriter kullanıldı. Bu kriterlere ilişkin uygunluk haritaları ağırlıklarına göre çakıştırılarak uygun kestane ağaçlandırma alanı haritası oluşturuldu. Sonuçlar kestane ağaçlandırması için çalışma alanının %0,42 (340,57 ha)’sinin çok uygun, %2,38 (1.906,25 ha)’inin uygun, %22,96 (18.410,75 ha)’sının orta uygun, %63,54 (50.952,75 ha)’ünün uygun olmayan ve %10,70 (8.584,00 ha)’inin ise hiç uygun olmayan alanlardan oluştuğu tespit edildi. Çalışma alanının %25,76 (22.904,39 ha)’lık kısmında yapılacak kestane ağaçlandırması için yüksek verim ve kazanç sağlanması beklenmektedir.Öğe Determination of landslide susceptibility with Analytic Hierarchy Process (AHP) and the role of forest ecosystem services on landslide susceptibility(Springer, 2023) Aksoy, HasanThe analysis of landslide susceptibility is a crucial tool in the mitigation and management of ecological and economic hazards. The number of studies examining how the form and durability of forest areas affect landslide susceptibility is very limited. This study was conducted in the Marmara region of northwestern Turkiye, where forested areas and industrial zones are intertwined and dense. The landslide susceptibility map was produced by Analytic Hierarchy Process (AHP) method. In the context of AHP, a total of 12 different variables were employed, namely lithology, slope, curvatures, precipitations, aspect, distance to fault lines, distance to streams, distance to roads, land use, soil, elevation, and Normalized Difference Vegetation Index (NDVI). The performance analysis of the landslide susceptibility map was conducted using the Receiver Operating Characteristics (ROC) curve method. The AUC value was computed (0.809) for the landslide susceptibility map generated by using the AHP technique. Forest type maps were used to analyze the impact of forests on landslide susceptibility. In terms of forest structure, 4 main criteria were determined: stand structure, development stage, crown closure, and stand age. Each criterion was analyzed with Geographic Information Systems (GIS) by overlaying it with the landslide susceptibility map of the study area. The results showed that the risk of landslides was lowest in forests with more than one tree species, mature, development stage and of (e) > 52 cm, and crown closure of 41%-70% (2).Öğe Different approaches to estimating soil properties for digital soil map integrated with machine learning and remote sensing techniques in a sub-humid ecosystem(Springer, 2023) Saygin, Fikret; Aksoy, Hasan; Alaboz, Pelin; Dengiz, OrhanToday, data mining has become a relevant topic in digital soil mapping. In this current study, prediction of some soil properties and their spatial distribution were examined by machine learning algorithms (Support Vector Machine, Artificial Neural Network) using reflectance values of Triplesat satellite image bands in Vezirkopru district of Samsun province. The band data obtained from different wavelengths revealed positive correlations between the electrical conductivity and calcium carbonate equivalent contents of the soils. The support vector machine algorithm was the most successful to estimate the textural fractions, organic matter, electrical conductivity, and calcium carbonate equivalent contents of the soils using the bands obtained from satellite images. The mean absolute error for estimating sand, silt, and clay contents by support vector machine was 4.05%, 3.05%, and 3.66%, respectively. Texture classes were determined with an accuracy of 82% with support vector machine and 60% with artificial neural network. In all estimations, the highest percentage of error was for calcium carbonate equivalent content with very low estimation reliability. The mean absolute percentage of error values for this property are 101.13% and 51.61% for artificial neural network and support vector machine, respectively. Also, in both algorithms, the most successfully estimated soil property was clay fraction of soils. It was also investigated the spatial distribution of actual and estimated values using various interpolation methods (Kriging, inverse distance weighting-, radial basis function). Considering the spatial distributions, it was determined that the most successful method was kriging for sand, silt, and clay contents and inverse distance weighting for electrical conductivity, calcium carbonate equivalent, and organic matter contents. According to our findings, it is concluded that successful estimations and spatial distributions can be made by the support vector machine algorithm using band data from different wavelengths.Öğe Estimation of uneven-aged forest stand parameters, crown closure and land use/cover using the Landsat 8 OLI satellite image(Taylor & Francis Ltd, 2022) Kaptan, Sinan; Aksoy, Hasan; Durkaya, BirsenThis study used the Landsat 8 OLI satellite image and the supervised classification method to estimate uneven-aged forest stand parameters and land use/cover. The spatial success of classification was also investigated. The overall success rates and Kappa values of the classification were, respectively, 74.7% and 0.75 for actual structural type, 84.6% and 0.80 for crown closure, and 88.35% and 0.81 for land use class, whereas the spatial success of classification on the forest cover type map was 36.91% for actual structural type, 64.74% for crown closure, and 41.78% for land use/cover class. The results revealed that the Landsat 8 OLI image can be used to identify stand parameters and land use/cover class. However, because the spatial success rates were below 50% for the actual structural type and land use/cover class of the stand types, it is not suitable for use in spatial classification determination for these classes.Öğe Estimation Stand Volume, Basal Area and Quadratic Mean Diameter Using Landsat 8 OLI and Sentinel-2 Satellite Image With Different Machine Learning Techniques(Wiley, 2024) Aksoy, HasanThe data required for sustainable forest planning is provided by traditional forest inventories, which are labor, time, and cost-intensive. Providing this data quickly, reliably, and accurately is crucial for planners and researchers. The objective of this study was to predict stand basal area (BA), stand volume (V), and quadratic mean diameter (dq) by leveraging vegetation indices (VIs) and reflectance (R) derived from Landsat 8 OLI and Sentinel 2 satellite images, along with topographic (T) data obtained from ALOS-PALSAR satellite imagery. Forest inventory data for a total of 250 sample plots were used for modeling in the study. Stand parameters were estimated using support vector machines (SVM), multiple linear regression (MLR), decision tree (DT), and random forest (RF) algorithms. In modeling V, BA, and dq, both individual and combinations of R, VIs, and T values obtained from satellite imagery were used as independent variables. Using the generated datasets, each of the stand parameters was modeled separately with MLR, SVM, RF, and DT algorithms, and the success of the models was compared to determine the modeling technique and dataset with the highest success for the relevant parameter. The results showed that for each stand parameter, the highest model success was achieved in the combined dataset, which was created by combining all datasets. However, in terms of modeling techniques, the highest success for each stand parameter was achieved with different modeling techniques. The highest success for V is obtained in the model using the SVM method (R-2 = 0.78; RMSE = 0.28 m(3)/ha), the RF method yielded the highest model performance for BA (R-2 = 0.70; RMSE = 2.53 m(2)/ha), and finally, the highest success for dq was obtained in the DT method (R-2 = 0.74; RMSE = 0.02 cm). In general, the datasets obtained from Sentinel 2 images showed higher model success than the datasets obtained from Landsat 8 OLI images.Öğe Evaluation of forest areas and land use/cover (LULC) changes with a combination of remote sensing, intensity analysis and CA-Markov modelling(Scion, 2024) Aksoy, HasanBackground: Land use and land cover change (LULC) is crucial for maintaining the integrity of ecosystems' structure and function, and thus regular measurement and monitoring of LULC are necessary. Methods: In this study, the temporal and spatial changes in forest areas and land cover in the province of Sinop, located in the north of Turkey, were analysed by intensity analysis for two 10-year periods from 2002-2012 to 2022, and 2032 and 2042 forecast LULC maps were generated using the cellular automata CA-Markov model. In the study, datasets were prepared using forest type maps and Landsat images, and the images were classified using various classification techniques. Results: The results indicated that forest areas increased by 23% (37,823.38 ha) from 2002 to 2022, with the mixed forest category showing a decrease of 22% (12,245.43 ha) within this. In non-forest areas, a significant increase of 72% was observed in the settlement category, while a decrease of 63% was noted in the agricultural category. According to the intensity analysis, the rate of change in LULC is faster from 2002 to 2012 than from 2012 to 2022. In both periods, the settlement and agricultural categories have predominantly targeted each other's losses. According to the simulation results of land use/cover from 2022 to 2042, a 0.50% increase in total forest area, a 2.87% increase in settlements, and a decrease of 2.65% and 0.71% in agriculture and water classes, respectively, are anticipated. Conclusions: The overall results suggest that it can contribute to setting an appropriate development goal, especially for forest planners and policymakers, to regulate land use changes to achieve higher carbon stocks and maintain balance in global climate scenarios.Öğe Evaluation of the relationship between indices obtained from different satellite data and soil erosion parameters(Ege Universitesi, 2023) Saygin, Fikret; Alaboz, Pelin; Aksoy, Hasan; Dengiz, Orhan; Imamoǧlu, Ali; Çaǧlar, Aykut; Koç, YusufObjective: The relationship between the indices and reflectances obtained by using different satellite images (Triplesat, Landsat 8), and soil erosion parameters (erosion rate, dispersion rate, structure stability, clay ratio, aggregate stability, and soil crust index) within the boundaries of Vezirköprü district of Samsun province. is to be revealed. Material and Methods: It was carried out in three stages: analyzing soil erosion sensitivity on a total of 32 soil samples taken at 100 x 100 m grid intervals from the study area, obtaining indices and reflectances of Triplesat and Landsat satellite images, and comparing the analysis results with the indices of satellite images." Results: The correlations between the reflectance values obtained from the Red, Green and Blue bands of the Landsat satellite data and the erosion rate were determined to be higher than the reflectances of the Triplesat satellite. No significant correlations were obtained between the aggregate stability and crust ratio properties of the soils and the indices. Higher positive correlations were determined between erosion rate and dispersion rate and indices. Conclusion: It has been seen that the aggregate stability (AS) contents of the soils are in a higher relationship with the indices obtained from Triplesat satellite images, and the erosion rate is higher with the indices obtained from Landsat satellite images. © 2023 The Author(s).Öğe Fiber Morphology and Chemical Composition of Heartwood and Sapwood of Red Gum, Black Willow, and Oriental Beech(2021) Gülsoy, Sezgin Koray; Aksoy, Hasan; Türkmen, Hülya Gül; Çanakcı, GülcanIn this study, the differences in terms of the fiber morphology and the chemical composition between the heartwood and sapwood of red gum (Eucalyptus camaldulensis Dehnh.), black willow (Salix nigra Marsh.), and oriental beech (Fagus orientalis Lipsky) were investigated. The results showed that the heartwood samples had shorter fibers and lower slenderness ratios than those of the sapwood samples. The differences in the vessel element length of the heartwood and sapwood of sampled tree species were statistically insignificant. The heartwood samples had less holocellulose and more klason lignin content. In addition, the ethanol, hot water, and cold water solubility values in heartwood samples were higher. The other morphological and chemical properties of the heartwood and sapwood depended on the tree species.Öğe Flood Risk Analysis with AHP and the Role of Forests in Natural Flood Management: A Case Study from the North of Türkiye(Kastamonu Univ, 2023) Aksoy, HasanAim of studty: The aim of this study is to determine the flood risk map of the study area where floods and flood events are frequently encountered by AHP method.Study area: The study was carried out within the boundaries of the Sinop Regional Directorate of Forestry, Ayancik Forest Management Directorate. Material and method: The flood risk map of the study area was produced by Analytical Hierarchy Process (AHP) method. For AHP, 6 different criteria were used: slope, precipitations, aspect, stream distance, land use, and soil. Forest type maps of the study area were used to analyze the impact of forests on flood risk. In terms of forest structure, the stand structure was divided into 3 classes as coniferous, broadleaf, and mixed forest.Main results: The results showed that flood risk varies with forest structure. Coniferous forest class was determined as the class with the lowest flood risk and mixed forest as the class with the highest flood risk.Research highlights: It was determined that the flood risk changed according to the forest structure. Coniferous forest class was determined as the class with the least flood risk, and mixed forest was determined as the class with the highest flood risk.Öğe Integration of field measurements with unmanned aerial vehicle to predict forest inventory metrics at tree and stand scales in natural pure Crimean pine forests(Taylor & Francis Ltd, 2024) Bulut, Sinan; Gunlu, Alkan; Aksoy, Hasan; Bolat, Ferhat; Soenmez, Muecahit YilmazInventorying forest ecosystems is an essential part of forest management planning. However, it is quite costly and time-consuming, particularly for larger areas. Recently, significant developments have been made in unmanned aerial vehicle (UAV) technology to improve the cost and time efficiency in forest inventory. Therefore, UAV images have become one of the inventory tools that produces data with high spatial resolution in determining forest resources. This study aims to investigate the contribution of UAV data to forest inventory in a case study area with a total of 30 sample plots located in pure and natural Crimean pine (Pinus nigra J.F. Arnold ssp. pallasiana (Lamb.) Holmboe) stands in the Black Sea backward region of T & uuml;rkiye. Total tree height (h) and stem volume (v) were recorded at individual tree level (n = 367), and the number of trees (N), mean height (h(mean)), top height (h(top)), stand basal area (BA) and stand volume (V) were calculated at sample plot level (n = 30) from both the field and UAV-based data. Pearson's correlation coefficients (r) for h and v were 0.96 and 0.72, respectively, the highest correlation at the sample plot level was observed for the h(mean) - h(top) (r = 0.96), while the lowest correlation was found for BA (r = 0.54). The suitability of the observation and prediction values was assessed using a t-test at both individual tree and sample plot levels. According to the t-test results, the observation and prediction values for h, v, h(mean), h(top), BA and V metrics were found to be compatible (p > 0.05), but not for N (p < 0.05). Overall results indicated that UAV technology has a potential to be used in forest inventory and can contribute to the determination of individual tree and stand metrics. Thereby, it saves cost and time in forest inventory studies and helps monitoring the dynamic structure of the forest ecosystem with an effective approach in forest inventory.Öğe Investigations of the spatial and climate characteristics of natural pure chestnut (Castanea sativa Mill.) forests: A case of Zonguldak Regional Directorate of Forestry(2024) Aksoy, HasanChestnuts with high economic and socio-cultural value (Castanea sativa Mill.) must determine forests' spatial and climatic characteristics to improve them, increase fruit yield, and effectively combat diseases and pests. This study aimed to determine the spatial and climatic attributes of pure chestnut forests spreading within the borders of the Zonguldak Forestry Regional Directorate. A total of six criteria were used for spatial and climatic analyses: total precipitation (mm/year), annual mean temperature (C°), wind speed (m/s), altitude (m), slope (%), and aspect (°). Climate values were calculated as maximum, minimum, and average values, respectively; 1 619.25, 866.95, and 1 024.07 mm/year for total precipitation (mm/year), 13, 6 and 11.76 C° for annual mean temperature (C°), 6.62, 0.22 and 2.46 m/s for wind speed (m/s). The spatial characteristics were calculated as 1 221.68, 33.24, and 300.23 m for the height (m), 32.43%, 7.34%, and 18.63% for the slope (%) and finally, 338.63°, 18.18° and 184.18° for the angle (°), respectively, in terms of maximum, minimum and average values. Bartın Forest Management Directorate (FMD) ranks first with 57.18% of the natural chestnut forests spread the most in the study area, while Zonguldak FMD ranks second with 21.45% and Ulus FMD ranks third with 17.13%. The results obtained from the study based on the location and climate will contribute to the selection of the most suitable place for the new chestnut forest afforestation studies to be established and increase the percentage of success.Öğe Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: a study in Northern Turkey(Springer, 2021) Aksoy, Hasan; Kaptan, SinanThe purpose of this study, covering the northern Ulus district of Turkey, was to analyze the forest and land use/land cover (LULC) changes in the past period from 2000 to 2020, and to predict the possible changes in 2030 and 2040, using remote sensing (RS) and geographic information systems (GIS) together with the CA-Markov model. The maximum likelihood classified (MLC) technique was used to produce LULC maps, using 2000 and 2010 Landsat (ETM +) and 2020 Landsat (OLI) images based on existing stand-type maps as reference. Using the historical data from the generated LULC maps, the LULC changes for 2030-2040 were predicted via the CA-Markov hybrid model. The reliability of the model was verified by overlapping the 2020 LULC map with the 2020 LULC model (predicted) map. The overall accuracy was found to be 80.90%, with a Kappa coefficient of 0.74. The total forest area (coniferous + broad-leaved + mixed forest) grew by 10,656.4 ha (15.4%) in the 2000-2020 period. Examination of the types within the Forest Class revealed that the coniferous forest area had grown by 5.9% in the period 2000-2010, whereas it had decreased by 4.7% in the period 2010-2020. The broad-leaved forest area had grown by 1.2% and 3.1%, respectively, between 2000 and 2010 and 2010 and 2020. The mixed forest area had been reduced by 7.1% in the period 2000-2010 but had grown by 1.7% in the 2010-2020 period. In the Non-Forest Class, although the water area had increased in the 2000-2020 period, agricultural land and settlement areas had decreased by 11,553.9 ha (32.3%) and 34.6 ha (0.5%), respectively. According to the 2020-2040 LULC simulation results, it was predicted that there would be 3.8% and 26.4% growth in the total forest and water surface areas and 13.9% and 5.3% reduction in the agricultural and settlement areas, respectively. Using the LULC simulation to separate the Forest Class into coniferous, broad-leaved, and mixed forest categories and subsequently examining the individual changes can be of great help to forest planners and managers in decision-making and strategy development.Öğe Simulation of future forest and land use/cover changes (2019-2039) using the cellular automata-Markov model(Taylor & Francis Ltd, 2022) Aksoy, Hasan; Kaptan, SinanThis study aimed to simulate and assess forest cover and land use/land cover (LULC) changes between 2019 and 2039 using the cellular automata-Markov model. The performance of the model was evaluated by comparing the 2019 simulation map with the 2019 supervised classified map, and it was found to be reliable, with a similarity rate of 85.43%. The LULC analysis and estimates were carried out for a total of six classes: coniferous, broad-leaf, mixed forest, settlement, water and agriculture. Between 1999 and 2019, the areas of total forest increased by 17.4%, settlement by 84.6% and water by 20.1%, whereas the agriculture area decreased by 33.2%. According to 2019-2039 land use/cover simulation results, there were decreases of 2.4% in total forest area and 3.7% in residential and water surface areas, but a 6.9% decrease in the agriculture class. Tracking these changes will contribute to decision making and strategy development efforts of forest planners and managers.Öğe UAV and satellite-based prediction of aboveground biomass in scots pine stands: a comparative analysis of regression and neural network approaches(Springer Heidelberg, 2025) Aksoy, Hasan; Gunlu, AlkanForest ecosystems play a vital role in balancing the global climate through functions such as regulating carbon emissions, carbon sequestration, and energy and water cycles. Aboveground biomass (AGB) is a critical component in forest management to understand better and predict the global carbon cycle. However, traditional methods used in AGB measurement involve time-consuming, costly, and labor-intensive processes. Sentinel-1 (active), Sentinel-2, and Landsat (passive) satellite imagery, which is freely accessible and offers global coverage with frequent updates, and recently developed remote sensing platforms such as Unmanned Aerial Vehicle (UAV) serve as a valuable data source for consistent and continuous monitoring of aboveground biomass. This research focuses on modeling the relationships between AGB and data obtained from various remote sensing sources, including Sentinel-1, Sentinel-2, Landsat 8, and UAV imagery, within pure Scots pine stands in northern T & uuml;rkiye. The study employs multiple linear regression (MLR) and artificial neural networks (ANNs) to establish these relationships. AGB values for each sample plot were calculated using an allometric equation. Backscatter coefficients and band brightness values were extracted from Sentinel-1 imagery, while reflectance values and vegetation indices were generated from Sentinel-2, Landsat 8 OLI, and UAV imagery. Additionally, texture features were computed for varying window sizes (3 x 3, 5 x 5, 7 x 7, 9 x 9, 11 x 11, 13 x 13, and 15 x 15) and orientations (0 degrees, 45 degrees, 90 degrees, and 135 degrees) based on data from Sentinel-2 and Landsat 8 OLI images for each sample plot. The relationships between remote sensing data and AGB were modeled using both MLR and ANN techniques. The findings revealed that the most accurate AGB estimation (R-2=0.82; RMSE = 0.35 ton ha(-)(1)) was achieved using the texture variables derived from the 9 x 9 window size of Sentinel-2 imagery via the ANNs modeling approach, outperforming other image sources and MLR analysis.Öğe Unveiling two decades of forest transition in Anamur, Türkiye: a remote sensing and GIS-driven intensity analysis (2000-2020)(Frontiers Media Sa, 2024) Aksoy, Hasan; Kaptan, Sinan; Dagli, Pelin Kececioglu; Atar, DavutIntroduction Monitoring LULC changes is crucial for developing strategies for natural resource management, assessing the current potential of a region, and addressing global environmental issues. In this context, this study examines land use and land cover (LULC) changes in forest and non-forest areas of Anamur district, located in the Mediterranean Region of T & uuml;rkiye, between 2000 and 2020.Methods Using the intensity analysis method, which offers a detailed and efficient approach to understanding LULC changes, the study analyzes transitions at interval, category, and transition levels. LULC maps were generated through supervised classification of Landsat satellite images, focusing on seven classes: Coniferous, Broad-Leaved, Mixed, Treeless Gap, Settlement, Agriculture, and Water. The analysis evaluated changes within and between these categories, interpreting the results through graphical outputs. The driving forces behind these changes were also explored, and their underlying causes were discussed.Results and Discussion Results at the interval level revealed that the most significant changes occurred during the 2000-2010 period. At the category level, the Coniferous category exhibited the highest degree of change in both intervals. During 2000-2010, Coniferous gains predominantly replaced non-forest areas (Agriculture, Settlement, and Water), while this pattern was less evident in 2010-2020. In contrast, Treeless Gap gains primarily replaced Coniferous areas during 2010-2020, while no significant losses in Treeless Gap were targeted by other categories. Broad-Leaved species were found to heavily target Water losses, likely due to their higher water demands compared to Coniferous species, as supported by prior studies. This research highlights the advantages of intensity analysis in LULC studies, offering insights into spatial changes and their intensity across categories. It aims to promote its adoption and underscores the importance of targeted conservation and land management strategies to mitigate the impacts of forest loss, land use changes, and water resource pressures.Öğe Yabani Kizilcik Odununun (Cornus australis L.) Bazi Kimyasal Özellikleri(Tayfun UYGUNOGLU, 2018) Keskin, Hasan; Aksoy, Hasan; Gençer, Ayhan; Tümen, IbrahimBu çalismada, yabani kizilcikodunun (Cornus Australis L.) bazikimyasal özelliklerinin belirlenmesi hedeflenmistir. Bu amaçla, yabani kizilcikodununun kagit üretimine uygunlugu açisindan temel bazi kimyasal özellikleri,çözünürlükleri ve kül miktari belirlenmistir. Ayrica, yüksek performansli sivi kromatografisi(HPLC) cihazi ile karbonhidrat analizi yapilmistir. Yapilan kimyasalanalizlerde; yabani kizilcik odununda ekstraktif madde (%4.52±0.21), soguk suçözünürlügü (%4.42±0.25), sicak su çözünürlügü (%6.40±0.27), holoselüloz(%72.27±0.27), a-selüloz (%43.24±0.17), lignin (%16.32±0.09), %1 NaOH çözünürlügü(%18.30±0.35) ve kül miktari (%0.56±0.05) tespit edilmistir. HPLC analizsonuçlarina göre kizilcik odununun karbonhidrat içeriginde sakkaroz, glukoz,galaktoz, arabinoz ve fruktoz seker gruplari tespit edilmistir. Bunlardan;sakkaroz %25.53 (30.50 mg/l), fruktoz %17.10 (44.16 mg/l), glukoz %16.63 (22.58mg/l), galaktoz %1.21 ve arabinoz %0.61 olarak bulunmustur. Bu çalismanin amaciülkemizde de yetisen yabani kizilcik odunundanfaydalanma olanaklarinin arastirilmasi ve elde edilen sonuçlarin literatürekazandirilmasidir.
